212 10 3494 https://omeka.ibu.edu.ba/files/original/10fd7497b99cc2fbb04523c422a0dc08.pdf 9c0e572f27870833c0be10e5cacf8493 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 2 – Perceptions category averages. Table 3 – Perceived obstacles for optimisation of the impact measurement process LIST OF FIGURES Figure 1 - Size of MCOs according to portfolio size and number of active loans. Web Technologies In Education GünayKarli1, Miljković Adnan2 1International Burch University, Sarajevo – B&H 2Bosna Sema – Educational Institutions, Sarajevo – B&H Abstract Web technologies are rapidly taking over the traditionally used desktop applications. Depending on the purpose of the use, web technologies can provide more flexible and scalable solutions. In this paper we describe the specific use of web technologies in B&H. IT in the educational field in B&H is still under rise, and several projects have been developed.This paper describes a project called Smart School that has a rise as an alternative to the current solutions available on the market. Smart School meets the requirement set for a stable, scalable and secure application. 1.INTRODUCTION In order to develop and deliver an application in the educational field few aspects should be taken into consideration. A modern application in education would allow usage by several different groups of users, including teacher/professors, students, administrative workers, head of department and other decision making groups. Web applications provide the ability to build a solution, which can be used from any location and from any device in world. In this paper we describe the information systems used in education for management of student’s information, such as marks, attendances, comments and any other resource that can be utilized to track the achievement/progress of a student. In this paper we focus only on solutions used in primary and secondary schools. For specific examples and comparison, an application already used in education is evaluated, and as it do now satisfy the current needs, a detailed work is presented on an alternative 168 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo solution called “Smart School”, which has been developed and is currently used by 8 schools in 4 cantons of B&H. “Smart school” is a web application that manages all school resources, providing access to school employees such as teacher to populate data about a student, and on the other side provides feedback to students and parents. “Smart School” differentiates itself from the other solutions, by having a much easier and friendly user interface allowing simple and fast access. A special commitment has been made in the field of security, where all data is encrypted and passed through secure layers. The security level in “Smart School” is similar to the one used in the banking industry. “Smart School” for the first time in the educational system in B&H introduces the 2-step verification process for the user authentication by utilizing security tokens for generating one time password. 2.CURRENT STATE OF IT IN B&H Information technologies are constantly emerging in all sectors of businesses in B&H. Many businesses use now the latest technologies and software available on the global market to perform everyday tasks easier, faster and with more quality. This is mainly due to the fact that businesses have to evolve and keep on innovating in order to stay competitive over the competition. Unfortunately this is not the case in the educational sector in B&H. The primary and secondary schools are back behind the use of IT technologies in education. The primary and secondary education on a large base still depends on the traditional way of teaching with the conventional methods. During the school year 2005, the Sarajevo Canton Ministry of Education, introduced for the first time in B&H, an information system for schools in Canton Sarajevo CS called Education Management Information System EMIS. The purpose of EMIS is to collect data from schools, and stores it on centralized databases. The collected data is used for statistical purposes by the Ministry(UTIC, 2012). EMIS collects all data about a school including the students, staff, premises and school inventory. EMIS offers the possibility to print student transcripts at the end of the school year. In order to achieve the following, schools are entering student marks after the end of each term. Today after more than 6 years EMIS has retain its functionality in Canton Sarajevo. The main drawbacks of EMIS are its limited functionality, where it is just a statistical application, which has the ability to collect data twice a year. This is mainly due to the complicated use of the application and the lack of knowledge by the staff in schools. The Ministry started with the campaign of basic education of teachers in CS just last October 2011(Logosoft, 2011). This campaign included basic education of teachers with the use of Microsoft line of products such as Windows 7 and Office 2010. Bosna Sema as a private educational institution has recognized the need to elevate the current educational system in B&H to a higher and more quality level. Bosna Sema has 8 primary and secondary educational schools in four cantons in B&H(Bosna Sema, 2011). Following the fact that the primary and secondary schools are mandatory in B&H for all students, the 169 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo need to involve even more the parents in the everyday aspect of a school arose. Bosna Sema recognized the need to constantly provide feedback of student’s performances and receive feedback from the parents. This private institution has 3 schools in the Canton Sarajevo where it is using and supporting EMIS for those schools. Unfortunately EMIS does not satisfy the needs of this private institution, toprovide an educational system that is transparent and able to involve the parents in the process of education. Following these requirements Bosna Sema has started a joint venture with an IT company to development an application that will meet all of the requirements set for the new and better tracking of educational system in B&H. The project started in 2010 under the name Smart School. 3.SMART SCHOOL Smart school is a set of applications that manage all school resources, providing access to school employees such as teacher to populate data about a student, and on the opposite side provides feedback to students and parents. On top of those groups of users, additional decision making users are involved as well. Smart School provides detailed reports about school wide data to school principals, and executive boards. The implementation of Smart School in schools in different cantons of B&H, allows this project to be compatible and satisfy each canton’s possible specific need. Smart School closely follows to cover and give access to each parent and student. Following this guideline, parents and students can receive feedback from Smart School with different mechanisms, including access by internet and mobile phones. 3.1.Smart School Architecture Smart school is built as three-tier architecture, being composed of a data, application and presentation tier. In order to cope with the scalability, performance and efficiency, each of those reside on separate hardware with high speed interconnection in between. The bottom layer which represents the data tier is where the databases reside. The data tier acts independently from the above tiers. Smart School uses MySQL for the databases. MySQL is the world's most popular open source database software, used by hundred thousands of companies all around the world(MySQL). This database software covers the current needs for Smart School. The next tier is the application tier, which covers all of the business logic in Smart School. The application tier communicates with the database in the data tier. The application tier exposes different methods for the above tier though web services. This tier always authenticates each remote request and if accessed by an authorized user, presents the transformed data from the data to the presentation tier. This tier handles request from different presentation applications, and offers different type of data representation. Depending on the type of request the web services can return the data in either Extensible 170 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Markup Language XML or JavaScript Object Notation JSON. The traditional XML allows the representation of data to be both human and machine readable, but often contains repeated tags that describe data. XML is acceptable when exchanging data between high speed connections, but has downsides when it comes to transfer between slow connections, as the case when using mobile internet over mobile providers. In this kind of transfer it is very important to transfer the data as lightweight as possible. In order to cope with this kind of speed and bandwidth issues, JSON is used to return data in Smart School. JSON is very similar to an array or vector in major programming language. It is language independent and many languages include default implementation to read and parse this format of data. In Smart School, JSON is primarily used for data exchange between the mobile apps and this tier. Smart School has several implementation of the presentation tier. This tier first authenticates with the application tier, and then communicates with different requests and replies. The following components make up the presentation tier: Administration Web and Parent/Student Web. The administration web component is a web application that represents the user interface for the entire Smart School. This component allows the authorized users to manage the entire system from a web app. The application exchanged the information with the web services. As the school users have different privileges and overview of the system from the one of parents and students, two separate components have been created. The Parent/Student component has only modules that are directly related to a single student overview. This separation is primarily for security reason and potential bugs in the system, but as well to offer a simplified overview of modules just for parents and students. 3.2.Smart School Feedback Modules In order to cover the targeted user groups of Smart School, the following main feedback modules have been introduced: Student performance/tracking Module Reporting Module Parent/Student Module 3.2.1.Student performance/tracking module This module is used by the user groups that are consisted of subject teachers. The teachers are assigned to subject from the administrative module, and can only manage students enrolled in the teacher’s subjects. From this module the teacher can send and receive messages from parent and students. The teacher can easily and quickly get and overview of the subject average, and get details about the students above and beyond the given thresholds in average. All information from this module can be exported as spreadsheet and managed in locally on 171 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo computer from any spreadsheet tools such as Microsoft Excel or LibreOffice Calc. If a teacher is assigned the role of a class teacher (class manager), additional functionalities are available. Those functionalities enable the class teacher to manage task such as getting collective reports about an entire class, preparing data for parent meeting and scheduling parents meetings by sending bulk emails and short messages to parent mobile phones. Additionally the class teacher is responsible for the behavior tracking of each student in the assigned class. 3.2.2.Reporting Module In order to get overall feedbacks from any application, a reporting module is necessary. The reporting module in Smart School is a collection of predefined reports that given detailed insights about data in the system. The reporting module is used by different groups of users, with the ability to access different types and levels of reports. Apart the predefined reports, a dynamic form allows to create reports on the fly with custom joined data. At the first level, users with teacher privileges can query reports with data about their teaching subjects, and enrolled students. The next level allows the school principals to get school wide report. In those reports the school principal has detailed overview of all teachers, subjects and students. The reports are always collecting direct data from the databases, so they always show the accurate image of the data. In order to provide a higher level of reports over multiple schools, additional two more levels were added. Bosna Sema, which has several schools in several cantons use those levels, one for the head of departments, and one for the executive boards. For the purpose of making decision on the top level, the executive board has to have detailed insight reports for any board meeting. For this purpose, a higher level in the reporting module has been introduced. This level has access to all schools combined, offering information from a single students, and teacher to the entire school performance. This allows the executive board to have accurate and up to date information, upon which they can make crucial decisions. The Cantonal Ministries of Education in B&H could benefit from this level of reporting. Although each school is sending collective data about schools to the Offices of Statistics in B&H, the information on the official Canton web sites are more than outdated. An example can be given for the Canton of Tuzla, where the information about high schools is presented from 01.12.2003, which is outdated for 9 years(Vlada TK, 2003). A similar case is with the ZE-DO Canton, where on the 1st October 2010, a detailed document about the high schools in canton has been presented on the official web site of canton from May 2006(ZE-DO Canton, 2010). Smart School can allow the cantons to have these varieties of reports instantly on any given date. Only with accurate and up to date reports can suitable decisions be made. 172 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 3.2.3.Parents/Student Module Smart School provides an in-depth feedback to parents and students. During the initial design many aspects have been taken into consideration about the traditional way on how the parents and students get the performance information from their schools. Following these practices one of the initial requirements has been to provide first a quick and easy access to information for the preparation of a parent meeting. Smart School here allows the class teacher to collect instantly up to date data for a meeting. Also the class teacher can schedule a meeting through the application and send instant SMS and emails to parents with time and place of the meeting. In order to provide constant access to parents about the performance of their child additional methods have been introduced. One method involved a web application access for parents over the internet, and the second one allows the parent to get information on mobile phone via short messages. 4.Security in Smart School Smart School uses the latest technologies in order to provide secure access to sensitive data. The database contains privileged information about all students’ achievements as well as personal information about the employees from the institutions who utilize Smart School. To achieve a maximum level of security, all communication is exchanged through secure channels over Secure Sockets LayerSSL. Each time a user accesses the web application, a secure connection is established from the client browser to the server, meaning all information passed from the client to the server is encrypted and cannot be seen anywhere in between those two. Continuing with our security layer, with the exchange of data through SSL all communication from the client to the server is secured, but we still have one more possible point which can be vulnerable, and through which unauthorized access can be gained. By design Smart School as a web application can be accessed from any place in the world through any Internet Service Provider ISP. This is primary allowed for the purpose that teachers and professors are not limited with the location from where they can access data. Following this design it is possible, with the use of a username and password to access the web application anytime from anywhere. In case a malicious user gets the real user access details, the malicious user could logon to the web application and perform some unauthorized actions in the application. There are many possible scenarios on getting access details from an authorized user; one includes the fact that many users use similar username and password with multiple services on the internet, such as for email address, social platforms, bulletin boards, forums, chats, online shopping cards and many other. If one of those services gets compromised, the malicious user could try the same password for some other services, and then possibly get access to our application. 173 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Another possible security scenario is that the client computer is infected with a Trojan virus, or some other malicious application such as key logger, which would send access details to a malicious user, where again a possible treat against the system could be used. In order to prevent unauthorized access even when knowing the username and password, Smart School implements an additional layer of security by introducing two-factor authentication. Two-factor authentication requires the user to authenticate with more than just a username and password. There are many variations of the multifactor authentication that includes even more that two factors. Smart School uses a two-factor authentication system. The first factor in the authentication is the well-known username and private password by the user and the second factor is the process of generating an additional verification code called One Time Password OTP, which is generated by a security token. Upon each logging the security token will generate an OTP which will be valid just for once. The security token is based on the Time-based One-time Password Algorithm TOTP, where each token based on its internal serial number on the given time generates a number(TOTP: Time-Based One-Time Password Algorithm). At the time of the logging process the server performs the same algorithm and then compares the entered OTP with the one generated on the server side. The server has a match table between each token and user, so the server ‘knows’ on which serial to perform the algorithm. This security layer prevents possible unauthorized access to the web application, even if the user gets access to the username and password of the account. This method of authentication is widely used by banks for online banking.OTP is becoming more popular among everyday used web services. Google has introduced the so called 2-step verification process for accessing any Google protected resource(Google, 2012). Similarly Amazon implemented AWS multi-factor authentication when accessing the Amazon Web Service infrastructure(AWS, 2012). 5.CONCLUSION Although the majorities of components have been developed and are already in use, Smart School still undergoes under changes and copes to adapt with the new functionalities. All users of the system including the parents and students are constantly providing feedback which guides to a more quality application. The main goal behind this project wasto offer a unique application that will covers and satisfy all of the user’s requests, following the educational legislations in B&H. This goal has been meet. Currently the project is only implemented in Bosna Sema educational institutions, however future plans includes offering this application to all primary and secondary schools in B&H. The future expansion also includes the development of rising mobile applications for two major smartphone mobile platforms used in B&H that consist of iOS and Android. 174 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo BIBLIOGRAPHY AWS. (2012, 04 07). AWS Multi-Factor Authentication. Retrieved from AWS Multi-Factor Authentication: https://aws.amazon.com/mfa/ Bosna Sema. (2011). About Bosna Sema - Educational Institutions. Retrieved from http://bosnasema.ba/en/about-us/d-9hd4f5zm Google. (2012). 2-step verification. Retrieved from http://support.google.com/a/bin/answer.py?hl=en&answer=175197 2-step verification: Logosoft. (2011, 10 27). Logosoft započeo edukaciju nastavnika informatike u KS. Retrieved from http://www.logosoft.ba/prodaja.aspx?docid=49 MySQL. (n.d.). About MySQL. Retrieved from http://www.mysql.com/about/ TOTP: Time-Based One-Time http://tools.ietf.org/html/rfc6238 Password Algorithm. (n.d.). Retrieved from UTIC. (2012, 03 14). About EMIS. Retrieved from http://www.emis.edu.ba/page/Opcenito-osistemu-EMIS.aspx UTIC. (n.d.). Education Management Information Sistem. Retrieved 01 29, 2012, from http://www.emis.edu.ba/page/Opcenito-o-sistemu-EMIS.aspx Vlada TK. (2003, 12 1). PODACI O UČENICIMA - SREDNJE ŠKOLE TK. Retrieved from http://www.vladatk.kim.ba/Ministarstva/MONKS/nastavnici/UceniciSS.htm ZE-DO Canton. (2010, 10 1). Informacija o nastavnicima i stručnim saradnicima u srednjim školama. Retrieved from http://www.zdk.ba/index.php?option=com_k2&view=item&task=download&id=62 175 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1166 Title A name given to the resource Web Technologies In Education Author Author Gunay, Karli Abstract A summary of the resource. Web technologies are rapidly taking over the traditionally used desktop applications. Depending on the purpose of the use, web technologies can provide more flexible and scalable solutions. In this paper we describe the specific use of web technologies in B&H. IT in the educational field in B&H is still under rise, and several projects have been developed.This paper describes a project called Smart School that has a rise as an alternative to the current solutions available on the market. Smart School meets the requirement set for a stable, scalable and secure application. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/9c182a63d08cee3f9752eafc6c688555.pdf 52f1610764d957fa0ba92cf97afec066 PDF Text Text M.Jenks and R.Burgess , Sustainable Urban Forms for Developing Countries, Spon Press, London. Cliff Moughtin , Peter Shirley ,(2005)Urban design: Green dimensions. 2nd edition, Amsterdam Simon Guy, Steven A. Moore,(2005) Sustainable Architectures. Cultures and Natures in Europe and North America, New York Polese, M. and Stren, R. (2000) The social sustainability of cities: diversity and management of change, University of Toronto Press, Toronto Mike Jenks , Colin Jones (2010), Dimensions of the Sustainable City (Future City) ,2nd edition, New York Matthew E. Kahn,( 2006) Green cities : Urban Growth and the Environment, Washington Adam S. Weinberg, Allan Schnaiberg, David N. Pello , ( 2000 ) Urban Recycling and the Search for Sustainable Community Development , Princeton University Press Jon Lang, ( 2005 ) Urban design: a typology of procedures and products John Eade ,Christopher Mele ( 2002 ) Understanding the city. Contemporary and future perspectives Sallie Westwood ,John Williams ( 1997 ) Imagining cities: scripts, signs, memory, London Tio2 Reinforced Al2o3 Composites Gunhan Bayrak1, Ferit Ilgar2, Ediz Ercenk3, Senol Yilmaz3, Uğur Sen3, Volkan Gunay4 1Sakarya University, Arifiye Vocational School, 54580 Arifiye, Sakarya 2Alpha Foundry and Machine Industry Co., Organized Industrial Zone, Avar Street, No: 1 06935 Sincan / Ankara 3Sakarya University, Engineering Faculty, Department of Metallurgical and Materials Engineering, Esentepe Campus, 54187 Sakarya, Turkey, 4TUBITAK-MAM, Material Institute, 41470 Gebze, Kocaeli, Turkey Abstract In this study, the effect of TiO2 addition on properties of alumina ceramics was investigated. The prepared commercial Alcoa alumina reinforced 0-15 % TiO2 were ground in ball mill for 2 h by wet milling and then powders were shaped dry pressing. After shaping operations, the 277 �samples were sintered 1500-1650 °C for 2 h. Firing shrinkage, relative density, flexural strength and hardness tests were performed and also for characterization x-ray diffraction (XRD) analysis and scanning electron microscopy (SEM) were utilized. It was seen that the TiO2 addition to alumina has effected on properties of alumina, significantly. Keywords: Alumina, TiO2, Ceramic Composites. 1. INTRODUCTION Alumina is a consider material for refractory application which has high melting point as 2000 ±30 °C. Also this material was resisted to acids, bases and many liquid metals and glass. Moreover its heat and electrical conducting was very low. Due to have this insulating properties, alumina firstly use in automotive industry as sparking plug. Today alumina is using as cutting tools for machining operation and as a resistant material to corrosion in the chemical industry and as a high temperature materials for heating systems. Furthermore another usage of alumina is special purpose in optic and medical technique application (Ilgar 2008, Rao 2000). Alumina is a ceramic oxide material. Although there are various modifications of Alumina, α–Al2O3 has commercial use (Toy 1994). As a thin film of TiO2 have many application areas because of superior properties of electrical, chemical and optical. Due to Titanium dioxide has very high melting point, it has many optical applications and optic circuit as coating material. Moreover, titanium dioxide can be used as bio-material and implant due to have high corrosion resistance and biocompatibility (Bardakci 2007). At the present study, the effect of TiO2 addition on properties of alumina was investigated Mixtures of alumina-TiO2 were shaped by dry pressing and were sintered at 1500-1650 °C temperatures. After sintering, some physical tests were applied to TiO2 reinforced alumina ceramics and characterized by XRD and SEM. 2. EXPERIMENTAL PROCEDURE In this study, the raw materials were alumina powder (0.4 µm) produced by the Aluminum Company of America (Alcoa A16-SG, USA) and high purity TiO2 (0.1 µm) ceramic powders. TiO2 was added to commercial Alcoa alumina powder in different proportions (0, 5, 10, 15, in wt %). The ratios of alumina-TiO2 compositions and the marking system are shown in Table 1. Table 1. Alumina-basalt sample codes Sample code Alumina (wt. %) TiO2 (wt. %) A0 278 100 - �A5 95 5 A10 90 10 A15 85 15 In order to ensure a homogeneous mixture, each composition was ball-milled in rubber-lined ceramic jars for 2 h using alumina balls and distilled water as the milling media and then sieved to pass through 38 µm. After drying in an oven at 110 °C for 24 hours, the mixtures were granulated in moist conditions and then semidry pressed at 100 MPa to prepare rectangular shaped specimens with the size of 5X8X40 mm. After shaping process, all samples were dried in an oven at 110 °C for 24 hours and were sintered in an electric furnace with a heating rate of 5 oC/min at 1500, 1550, 1600 and 1650 oC for 2 h. Then, the sintered samples were cooled to room temperature in the furnace. The flow chart of experimental procedure and the macro images of the sintered specimens were given in Fig. 1 and Fig. 2, respectively. Figure1. The flow chart of the experimental procedure After sintering, the sintered samples were subjected to physical tests such as firing shrinkage, relative density, flexural strength test by 3-point bending method and hardness. The crystalline phases of the sintered samples were identified by X-ray diffraction analysis (XRD, JEOL MDI/JADE6) with Cu Kα (λ = 1.54056 A˚) radiation. The micro structural characterization of fracture surfaces of sintered samples were examined using a JEOL JSM 6600 scanning electron microscopy (SEM). 279 �Figure 2. Macro images of test specimens 3. RESULT AND DISCUSSION The firing shrinkage values of TiO2 reinforced alumina ceramics depend on sintering temperatures and TiO2 addition is shown in Figure 3. The firing shrinkage was increasing in all specimens with increasing sintering temperature due to sintering effect by dimensional reductions. The firing shrinkage of A0 is less than TiO2 doped samples. Al2TiO5 phase is observed in TiO2 reinforced alumina ceramics as given in the literature Soo et al (2003). Al2TiO5 phase was also detected in our studies given below. Al2TiO5 are generally spherical or angular particles. They were along the grain boundary and triple junction points as expressed in literature (Sathiyakumar 2008). This effect can be seen in SEM microstructures given below. It is probably that the firing shrinkage of TiO2 reinforced alumina ceramics was prevented by Al2TiO5 phase. 20 A0 A5 A10 A15 Firing Shrinkage, % 18 16 14 12 10 1500 1550 1600 1650 Sintering Temperature, °C Figure 3. The firing shrinkage test results versus sintering temperature 280 �The relative density values of TiO2 reinforced alumina ceramics depend on sintering temperatures and TiO2 addition is shown in Figure 4. When the sintering temperature increases, the porosities remain into the grains of alumina ceramics as a result of rapid grain growth. This is obstacle for reaching to theoretical density of alumina ceramic and causes decreasing of densities (Barsoum 1997,Kalpakjian 1997). Since the density of Al2TiO5 phase is lower compared to densities of alumina and TiO2, densities increases with increasing TiO2 addition Soo et al.(2003). 100 Relative Density 95 90 85 80 A0 A5 A10 A15 75 70 1500 1550 1600 1650 Sintering Temperature, °C Figure 4. The relative densities depending on sintering temperature The flexural strength of composites is given in Figure 5. With the increase in sintering temperature, the highest strength value was obtained in the TiO2 free alumina ceramic sintered at 1550 °C. The decreasing of strength was observed via increasing of temperature depending on the grain growth and the pores remaining into grains (Barsoum 1997,Kalpakjian 1997). The flexural strength values of the samples including TiO2 are lower than the samples not including TiO2. Investigation of the effect of TiO2 addition on flexural strength is compressive process in literature, the flexural strength value increases up to the addition of TiO2 2 % and it decreases above TiO2 4 % addition (Sathiyakumar 2002). The flexural strength values of the samples including TiO2 are in agree with the literature values and it is lower compared to the samples not including TiO2. 281 �400 A0 A5 A10 A15 Flexural Strength, MPa 350 300 250 200 150 100 50 1500 1550 1600 1650 Sintering Temperature, °C Figure 5. The flexural strength values versus sintering temperature As seen in Figure 6, high hardness results were observed in high densification conditions. The highest hardness was determined in the 96.05 relative density value of alumina ceramic sintered at 1550 °C. It is correlated with the literature [Yildirim 2002]. The hardness of the samples including TiO2 is not high as much as TiO2 free alumina ceramics. It is reported that Al2TiO5 and TiO2 phases in alumina matrix cause increasing of hardness Soo et al. (2003),Anerisis et al. (2007). Small amount of TiO2 addition has positive role on sintering in alumina composites and the presence of this second phase in matrix provides better mechanical properties. The hardness increases with increasing TiO2 content in alumina matrix composites, it is correlated with the literature Soo et al. (2003). 20 A0 A5 A10 A15 Hardness, GPa 18 16 14 12 10 1500 1550 1600 1650 Sintering Temperature, °C Figure 6. The hardness measurements depending on sintering temperature -Al2O3 phase was determined in the all samples coded A0. In the samples including TiO2, TiO2 (rutile) and Al2TiO5 phases are other phases determined by XRD. As seen in Figure 7, Al2TiO5 phase formed via the reaction between Al2O3 and TiO2 at 1280 °C was seen as lighter zones in grain boundaries and intersection points of the grains. Abnormal grain growth was not observed in the alumina ceramic including TiO2 compared to 282 �alumina ceramics. Finer grain structure was determined in these samples due to presence of Al2TiO5 phase as obstacle against grain growth. (a) (b) (c) (d) een as lighter zones in grain boundaries and intersection points of the grains in SEM microstructure. Finer grain structure was determined in composites due to presence of Al2TiO5 phase as obstacle against grain growth. REFERENCES Ilgar F. (2008) ―The Investigation of The Effect of TiO2 Addition on Sintering Behavoiur of Alumina‖, MSc Thesis, Sakarya University, SAKARYA. Rao, P.,Iwasa M. And Isao K. (2000) ―Properties of Low-temperature-sintered High Purity -alumina Ceramics Scrathing Properties of Alumina Based Ceramics‖, Journal of Material Science Letters, 19, 543-545. Toy, Ç. and Baykara, T. (1994) ―Ceramic As a Material of 21.th Century‖, The Journal of Science and Technique, TUBITAK, 317, ANKARA. 283 �Bardakçı, S. (2007) ―The Determination of Optical Properties of TiO2 Thin Film Prepared with Sol-Jel Method ‖, MSc Thesis, Sakarya University, SAKARYA. Soo, W.L. Carlos, M. Joaquin L.O. Seung, H.K. Tohru, S. Koichi N. and Bernard, J.H. (2003) ―Tribological and Microstructural Analysis of Al2O3/TiO2 Nano composites to Use in the Femoral Head of Hip Replacement‖, Wear, 255, 1040-1044. Sathiyakumar, M. and Gnanam, F.D. (2002) ―Influence of MnO and TiO2 Additives on Density, Microstructure and Mechanical Properties of Al2O3‖, Ceramics international, 28, 195-200. Barsoum, M.W. (1997) ―Fundamentals of Ceramics‖, Mc Graw Hill, New York, USA. Kalpakjian, S. (1997) ―Manufacturing Processes for Engineering Materials‖, Prentice Hall 4. Edition, USA. Yıldırım, İ. (2002) ―The effect of Production Condition and Composition on The Strength Properties of Al2O3 and SiC‖, PhD Thesis, Istanbul Technical University, ISTANBUL Aneziris, C.G. Scharfl, W. and Ullridch, B.(2007) ―Microstructure Evaluation of Al2O3 Ceramics with Mg-PSZ and TiO2 Additions‖, Journal of the European Ceramic Society, 27, 3191-3199. Glass Foams Containing Fly Ash And Sheet Glass By Adding Calcite As Foaming Agent Ediz Ercenk1, Gunhan Bayrak2, Senol Yilmaz1, Volkan Gunay3 1Sakarya University, Engineering Faculty, Department of Metallurgical and Materials Engineering, Esentepe Campus, 54187 Sakarya, Turkey, 2Sakarya University, Arifiye Vocational School, 54580 Arifiye, Sakarya 3TUBITAK-MAM, Material Institute, 41470 Gebze, Kocaeli, Turkey Abstract Glass foam is a porous isolation material used for heat isolation. In this study, the possibilities of glass foam production using calcite as a foaming agent was investigated. The mixture was prepared 10% wt. of waste window glass and 90% wt. Seyitömer thermal power plant fly ash. 2.5 to 10% wt. calcite was added to mixture and pressed under 75 MPa pressure by uniaxial cold pressing to obtain cylindrical specimens. Pressed samples sintered at 750-950 °C temperature range for 1 hour according to differential thermal analysis (DTA) results. The effect of calcite addition and sintering temperature on the porosity, density, compressive strength, microstructure and crystalline phases were investigated. It was determined that the 284 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1257 Title A name given to the resource Tio2 Reinforced Al2o3 Composites Author Author Gunhan , Bayrak Abstract A summary of the resource. In this study, the effect of TiO2 addition on properties of alumina ceramics was investigated. The prepared commercial Alcoa alumina reinforced 0-15 % TiO2 were ground in ball mill for 2 h by wet milling and then powders were shaped dry pressing. After shaping operations, the samples were sintered 1500-1650 °C for 2 h. Firing shrinkage, relative density, flexural strength and hardness tests were performed and also for characterization x-ray diffraction (XRD) analysis and scanning electron microscopy (SEM) were utilized. It was seen that the TiO2 addition to alumina has effected on properties of alumina, significantly. Keywords: Alumina, TiO2, Ceramic Composites. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),Q Science (General) https://omeka.ibu.edu.ba/files/original/50d87109a54e1cef4c3c3c2427a51ffa.pdf e6fe43ecbc349e94ebbc8e326bf0b7ad PDF Text Text Stock market movement direction prediction using tree algorithms Gunter Senyurt, Abdulhamit Subasi E-mail : gsenyurt@ibu.edu.ba, asubasi@ibu.edu.ba Abstract One of the highly challenging businesses today is the task of forecasting the market movements by examining the financial time series data as correctly as possible in order to hedge against the almost incalculable risk involved and to yield better profits for investors. If there was a highly credible estimation technique available giving better results than the traditional statistical tools for financial markets, it would be a great asset for trading decision makers of all kinds such as speculators, arbitrageurs, portfolio fund managers and even individual investors. In this study CART, C4.5 and Random Forest algorithms were used to predict the movement direction of a 10 year Istanbul Stock Exchange index (XU-100). Ten technical market indicators such as momentum, MACD and RSI were used in this study as the feature set. Keywords: Price movement direction, CART, C4.5, Random Forest, forecasting, stock market. 1. INTRODUCTION The complex dynamism of the markets is characterized by the nonlinearity and nonparametric nature of the variables influencing the index movement directions including human psychology and political events. The unpredictable volatility of the market index makes it a highly challenging task to accurately forecast its path of movement. On the other hand, it is crucial for investors to estimate the trend of the markets as precisely as possible in order to reach the best trading decisions for their investments, so in this context it is in the investor's best interest to use the most accurate time series forecasting model to maximize the profit or to minimize the risk. By means of this study, it is aimed at contributing to the demonstration and verification of the XU-100 index movement path predictability through some tree algorithms. The stochastic performance parameter is accuracy and it is defined as the ratio of the correctly classified instances divided by the number of all instances. The remaining part of this study is organized into four sections. The next section presents an overview of the theoretical literature while in section 3 the research data and the structures of tree algorithms CART, C4.5, Random Forest is described. In section 4, the reports and results of empirical findings from the comparative WEKA analysis are given. Finally, the last section contains the concluding remarks. 374 �2. Literature Review CART review The classification tree analysis CART (classification and regression trees) is suggested first by Breiman (1984) and uses the predictor variables splitting rule to build a binary decision tree (Denison, Mallick and Smith, 1998). The CART method is experimented in the credit scoring area, retail lending and evaluation of insurance risks in workers’ compensation showing better results than logistic regression and discriminant analysis (Friedman 1991, Devaney 1994, Lee 2006, Kolyshkina 2002). C4.5 review The C4.5 method is high in efficiency when used for inductive inference. Recent research has shown that this algorithm produces high accuracy in image segmentation (Polat and Gunes, 2009; Mazid, Ali and Tickle, 2010). In another work a hybrid approach including C4.5 is suggested with potentially high outcomes (Jiang and Yu, 2009; Mazid, Ali and Tickle, 2010). It is also used for classification of remote sensing data (Yu and Ai, 2009; Mazid, Ali and Tickle, 2010). Another variant of C4.5 successfully trimmed down the leaf node number and improved accuracy (Yang, 2009; Mazid, Ali and Tickle, 2010). Random Forest review High-dimensional classification and regression problems can be approached by using random forest algorithm that is extensively researched by Breiman (2001). Among the machine learning techniques used to predict markets random forest is quite successful (Dietterich, 2000). Though the practicality of random forest is excellent it is hard to interpret and clarify mathematically (Breiman, 2002; Lin and Jeon, 2006; Biau et al., 2008, Biau and Devroye, 2008). 3. Materials and Methods CART method CART constructs a tree where the data is separated into two parts by binary variable splits. The best divider variable and the best point to split is determined by variance minimization. The CART algorithm can be viewed as a classification procedure consisting of four distinct parts: Part 1: a variance criterion, Part 2: the criterion how good it is split, Part 3: the terminal node class assignments and estimates of resubstitution, Part 4: determining the right tree complexity (Buyukbebeci, 2009). 375 �The root node, internal nodes and leaf (terminal) nodes constitute the CART tree. Two child nodes follow each root and internal node. Each node contains and is defined by the subset of the original learning sample. The splitting of each node into child nodes is characterized by a certain rule depending on the chosen feature. The child nodes inherit subsamples with minimum variance that measures their heterogeneity from parent nodes (Iscanoglu, 2005). The goodness of the splitting procedure is defined by an impurity function that is derived from a a variance function which is applied to each split point indicating the best point for splitting (Iscanoglu, 2005). Gini, Entropy and Twoing are the main rules for binary recursive splitting that are derived from the impurity function (Breiman, Frydman, Olshen and Stone, 1984) C4.5 Method In doing classification with C4.5, the concepts of entropy and correlation coefficient need to be explained in brief. Entropy is a measure of uncertainty among random variables in a collection of data or in other words entropy provides information about the behavior of random processes used in data analysis. Correlation coefficient has its uses as a chief statistical tool in data analysis finding the relationship between variable sets. Different ways of calculations have been introduced to boost the efficiency of the correlation coefficient among which are Kendall, Pearson’s and Spearman’s correlation coefficients.There are several test options with WEKA providing data classification such as training set, supplied test set, percentage split and cross validation. In this paper, cross validation is chosen as the test option (Mazid M., Ali S. and Tickle K. (2010). Random Forest method Random forests are based on conjoining lots of binary regression trees. In the process of growing these large number of regression trees independent subsets of variables are used. Random forests randomly choose variables to split and a bootstrapped sample of the dataset builds the decision trees (Efron and Tibshirani, 1993). When K trees are aggregated the predicted decision is gained as the average value over these K trees. Marking each single tree predictors by , the final outcome is: (x) Research Data In this study, all experiments were conducted on WEKA software using its tree classifiers built-in tool to make comparisons of prediction performances based on the chosen dataset. The dataset is comprised of 10 input variables with 2733 instances in total. These 10 input attributes are technical market indicators as used by Kara, Boyacioglu and Baykan (2010) which are 10-day moving average, 10-day weighted moving average, momentum, stochastic %K, stochastic %D, RSI (Relative Strength Index), MACD (moving average convergence divergence), Larry William's %R, A/D (Accumulation/Distribution) Oscillator and CCI 376 �(Commodity Channel Index). The total number of cases or 2733 trading days have 1440 days with increasing direction (advances), while 1293 days show decreasing direction (declines). In the analysis, 10-fold cross-validation was used as the test option in WEKA. 4. Results and Discussion The relevance and quality of the data, usually, has a big impact on the performance of the model used. Thus, the choice of data becomes the most important part in forecasting the markets. In this study, all series are real-valued and the input data spans from 02/01/1997 to 31/12/2007. For WEKA testing, the accuracy or correctly classified instances metric is utilized, showing the ability of the model to capture the data. The dataset with 10 features is tested using CART, C4.5 and Random Forest classifiers in order to see which tree algorithm has better predictive power over the others. The results of the tests can be seen in the Table 1 where CART and Random Forest classifiers have almost identical prediction power, whereas C4.5 has a little less prediction power compared to the other two tree algorithms. Table 1. Tree Classifiers Test Results % Accuracy (correctly classified instances) CART 78.05 C4.5 77.29 Random Forest 78.23 5. CONCLUSION The issue of accurately predicting the stock market price movement direction is highly important for formulating the best market trading solutions. It is fundamentally affecting buy and sell decisions of an instrument that can be lucrative for investors. This study focuses on predicting the ISE National 100 closing price movement directions using tree algorithms based on the daily data from 1997 to 2007. Even though the prediction performance of tree classifiers such as CART, random forest and C4.5 do not really outperform studies alike in literature, it is still likely that the forecasting performance of the models can still be improved by doing the followings: Either the model parameters should be adjusted by thorough experimentation or the input variable sets need to be modified by selecting those input attributes that are more realistic in reflecting the market workings. (Kara, Boyacioglu, and Baykan, 2010) had already proved the significance of using ten particular technical market indicators which gave also about %78 accuracy in this study, as well. More appropriate 377 �variables has to be found that may improve the forecasting performance of the models employed that can be a further subject of study for interested readers. Acknowledgement : We sincerely deliver our special thanks to Assist. Prof. Melek Acar Boyacioglu for her graciousness in sharing her knowledge with us. REFERENCES Biau G., Devroye L., and Lugosi G. (2008), Consistency of random forests and other averaging classifiers, Journal of Machine Learning Research, 9, 2015-2033. Biau G., Devroye L., and Lugosi G. (2008), On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification, Technical report, Universite Paris 6. Breiman, L., Frydman, H., Olshen, R.A., and Stone, C.J. (1984), Classification and Regression Trees, Chapman and Hall, New York, London. Breiman, L. (2001), Random forests, Machine Learning, Kluwer Academic Publishers, 45, 532. Breiman, L. (2002), Manual on setting up, using, and understanding Random Forests v3.1, Technical Report, http://oz.berkeley.edu/users/breiman. Buyukbebeci, E. (2009), Comparison of MARS, CMARS and CART in predicting default probabilities for emerging markets, Master Thesis, METU, Ankara. David G.T. Denison, Bani K. Mallick, Adrian F.M. Smith, Biometrika, Vol.85, No.2 (1998), 363-377. Devaney, S. (1994), The Usefulness of Financial Ratios as Predictors of Household Insolvency: Two Perspectives, Financial Counseling and Planning, 5, 15-24. Dietterich T.G. (2000), Ensemble methods in machine learning, Lecture Notes in Computer Science, Springer-Verlag, 1-15, 2000. Efron B. and Tibshirani R. J. (1993), An Introduction to the Bootstrap, New York, Chapman and Hall. Friedman, J.H. (1991), Multivariate adaptive regression splines, The Annals of Statistics, 19, 1 ,1-141. Frydman, H., Olshen, R.A., and Stone, C.J., Classification and Regression Trees, Chapman and Hall, New York, London, 1984. Iscanoglu, A. (2005), Credit Scoring Methods and Accuracy Ratio, Master Thesis, METU, Ankara. Jiang S. and Yu W. (2009), A Combination Classification Algorithm Based on Outlier Detection and C4.5, Springer Publications. 378 �Kara Y., Boyacioglu M.A., Baykan O.K., (2010). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications 38, 5311–5319. Kolyshkina I. and Brookes R. (2002), Data Mining Approaches to Modeling Insurance Risk, Report, Price Waterhouse Coopers. Lee T., Chiu C., Chou Y., and Lu C. (2006), Mining the customer credit using classification and regression tree and multivariate adaptive regression splines, Computational Statistics & Data Analysis, 50, 1113-1130. Lin Y. and Jeon Y. (2006), Random forests and adaptive nearest neighbours, Journal of American Statistical Association, 101, 578-590. Mazid M., Ali S. and Tickle K. (2010), Improved C4.5 Algorithm for rule based classification, Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases, Australia. Polat K. and Gune S. (2009), A novel hybrid intelligent method based on C4.5 decision tree classifier and one against-all approach for multi-class classification problems, Expert Systems with Applications, vol.36, 1587-1592. Yang X.Y. (2009), Decision tree induction with constrained number of leaf node, Master Thesis, National Central University, Taiwan. Yu M. and Ai T.H. (2009), Study of RS data classification based on rough sets and C4.5 algorithm, In Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series. Weka (1999-2010), Waikato Environment for Knowledge Analysis, Version 3.7.3, The University of Waikato Hamilton, New Zealand. 379 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1187 Title A name given to the resource Stock market movement direction prediction using tree algorithms Author Author Gunter , Senyurt Abstract A summary of the resource. One of the highly challenging businesses today is the task of forecasting the market movements by examining the financial time series data as correctly as possible in order to hedge against the almost incalculable risk involved and to yield better profits for investors. If there was a highly credible estimation technique available giving better results than the traditional statistical tools for financial markets, it would be a great asset for trading decision makers of all kinds such as speculators, arbitrageurs, portfolio fund managers and even individual investors. In this study CART, C4.5 and Random Forest algorithms were used to predict the movement direction of a 10 year Istanbul Stock Exchange index (XU-100). Ten technical market indicators such as momentum, MACD and RSI were used in this study as the feature set. Keywords: Price movement direction, CART, C4.5, Random Forest, forecasting, stock market. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/59c63e4f9184a29658e37da3591c302d.pdf 6f9c3b15771ba2925356753867ae9a1e PDF Text Text Stock market price index return forecasting using ANN Gunter Senyurt, Abdulhamit Subasi E-mail : gsenyurt@ibu.edu.ba, asubasi@ibu.edu.ba Abstract Even though many new data mining techniques have been introduced in prediction estimation, there is still no single best solution to all financial problems. In this study, an artificial neural network (ANN) model is utilized for predicting price index returns through regression. Ten technical market indicators, seven macroeconomic variables, a couple of other international market indices and a sliding window of ten inputs make up the 30 attributes used in this study. Different combinations of attribute sets is experimented with different ANN model parameter values to find the highest forecasting accuracy. Keywords: Price index return, ANN, Forecasting, Data Mining Techniques. 1. INTRODUCTION While there are certain techniques to forecast in which direction the market would be moving or what price levels would be expected, empirical evidence shows that some models work better than the others in different cases (Satchell, 2005). It is of utmost importance for investors to estimate the trend of the markets as precisely as possible in order to reach the best trading decisions for their investments, so in this context it is in the investor's best interest to use the most accurate time series forecasting model to maximize the profit or to minimize the risk. All in all, it is a quite challenging job to make accurate predictions of stock market index movements and model the time series data, especially in highly volatile markets such as the Turkish stock market. That is due to the fact that stock markets are in general chaotic and complex mechanisms with dynamic, nonlinear and nonparametric variables (Abu-Mostafa and Atiya, 1996). Moreover, markets are influenced by numerous macroeconomic factors, institutional investor choices, human psychology, political events, company policies, other stock market movements and economic affairs (Tan, Quek, and See, 2007). In this study, the ISE National 100 Index (XU-100) has been chosen for data analysis, since the Turkish stock market is a relatively young emerging market and it has presented an outstanding growth rate since its establishment in the late 80's. There is lots of empirical work available in literature on well established and developed markets such as Dow Jones (USA) or DAX (Germany), whereas little research material is available on new emerging markets such as ISE (Kara, Boyacioglu, and Baykan, 2010). The Istanbul Stock Exchange is highly volatile in terms of market returns, a feature which is attracting many local and 367 �international investors worldwide seeking for high return possibilities (Armano, Marchesi, and Murru, 2005). By means of this study, it is aimed at contributing to the demonstration and verification of the XU-100 index price level predictability through ANN. The related predicting performances are compared based on statistical criteria such as relative absolute error (RAE), root relative squared error (RRSE) and the squared value of the correlation coefficient The remaining part of this study is organized into four sections. The next section presents an overview of the theoretical literature while in section 3 the research data and the structure of ANN is described. In section 4, the reports and results of empirical findings from the comparative analysis are given. Finally, the last section contains the concluding remarks. 2. Literature Review There are various ANN methods that can be used in predicting stock price returns and a great deal of research has been conducted on using ANN to forecast financial time series data outputs suggesting ANN as a powerful tool in predicting stock market return (Avci, 2007; Karaatli, 2005). Chen, Leung and Daouk (2003) used the probabilistic neural network (PNN) which showed strong predictive power over other models such as the GMM-Kalman filter and random walk. Diler (2003) who trained back propagation neural networks, based the input attributes on some technical market indicators like momentum, moving average, moving average convergence divergence (MACD), RSI and stochastic %K and forecasted the ISE 100 index direction with % 60.81 accuracy while Altay and Satman (2005) also used ISE-30 and ISE-ALL indices to see the performances of several neural network models. Cao, Leggio, and Schniederjans (2005) effectively proved that multivariate neural networks could outperform the linear models for stock price movement predictions of Shanghai Stock Exchange listed companies. 3. Materials and Methods 3.1 Research Data In this study, all experiments were conducted on WEKA software using its MLP built-in tool to make comparisons of prediction performances based on the chosen dataset. The full dataset is comprised of 30 input variables in total. The first 10 input attributes are technical market indicators as used by Kara, Boyacioglu and Baykan (2010) which are 10-day moving average, 10-day weighted moving average, momentum, stochastic %K, stochastic %D, RSI (Relative Strength Index), MACD (moving average convergence divergence), Larry William's %R, A/D (Accumulation/Distribution) Oscillator and CCI (Commodity Channel Index). Another 10 inputs are mainly chosen from macroeconomic variables, consisting of USD(sell)-Turkish Lira exchange rate, gold price (close), monthly interest rate, CPI (consumer price index), WPI (wholesale price index), PPI (producer price index), Industrial 368 �Production Index, DJI (Dow Jones) closing price, DAX (Germany) closing price and BOVESPA (Brazil) closing price. These variables are slightly differently chosen than Boyacioglu and Avci (2010)'s input variables. The final 10 inputs are a sliding window of the last 10 elements of XU-100 closing price index. In Yumlu and Gurgen (2005) an input window size of seven was used but it is preferred to use the last 10 elements in this study. For the regression analysis, 10-fold cross-validation was used as the test option in WEKA. 3.2 Artificial Neural Network (ANN) Model Artificial neural networks are capable estimation models for financial modeling and prediction (Kara, Boyacioglu, and Baykan, 2010). In this study, a three layered feed-forward ANN structure (a multilayer perceptron) is used to forecast stock market index movements. Multilayer perceptrons (MLP) have one or more layers between input and output layers, called hidden layers, that can approximate any nonlinear relation to any accuracy given sufficiently large number of neurons. The nonlinearity used in the nodes provides MLP with a universal approximation power. “It has been scientifically proved that a three-layered MLP using sigmoidal activation function can approximate well any continuous multivariate function to any accuracy.” (Du and Swamy, 2006). MLP shows high efficiency in function approximation for high-dimensional spaces. It has clear advantage over linear regression methods in that the input dimensionality does not affect the error convergence rate, while conventional linear regression methods suffer from the size of dimensionality. The most popular learning rule in supervised learning is the back propagation learning algorithm which is used to train the neural network. In order to minimize a cost function that is equivalent to MSE (mean squared error) between the desired and actual network outputs, a gradient search method is utilized. An input pattern is introduced to the system and the resulting computed output is compared with the actual given output (target output). The error of each calculated output is propagated backward that establishes a closed-loop control system which adjusts weights by a gradient-descend based algorithm (Du and Swamy, 2006). 4. Results and Discussion The relevance and quality of the data, usually, has a big impact on the performance of the model used. Thus, the choice of data becomes the most important part in forecasting the markets. In this study, all series are real-valued and the input data spans from 02/01/1997 to 31/12/2007. For WEKA testing, the statistical model adequacy metrics relative absolute error (RAE), root relative squared error (RRSE), and the square of the correlation coefficient are utilized, showing the ability of the model to capture the data. A dataset of 10, 20 and 30 inputs are tested in order to see which attribute set have better predictive power over the others. Table 1 and 2 prove the effectiveness of the sliding window when used together with technical indicator inputs creating much lower error values. 369 �Table 1. MLP regression results (% relative absolute error values - % RAE). # of neurons in the hidden layer(n) Input Feature Sets 4 7 10 20 40 50 70 90 1 0.87 1.06 1.15 1.13 1.24 0.94 1.33 1.80 1.61 1.70 1.76 1.88 1.90 1.78 1.83 technical indicators 1.71 1.63 1.74 2 2.32 2 2 2.1 technical indicators + last 10 0.39 0.42 0.42 0.6 0.73 0.75 1.84 1.63 macroeconomic variables + last 10 3.46 3.35 3.33 3.41 3.55 3.60 3.41 8.9 technical indicators + macro economic variables + last 10 technical indicators + macro economic variables Table 2. MLP regression results (% root relative squared error - %RRSE). # of neurons in the hidden layer(n) Input Feature Sets 4 7 10 20 40 50 70 90 1.05 0.95 1.20 1.29 1.24 1.35 1.04 1.53 1.73 1.91 1.79 1.87 1.95 1.98 1.87 1.90 technical indicators 1.86 1.80 1.91 2.22 2.46 2.1 2.13 2.24 technical indicators + last 10 0.47 0.49 0.49 0.69 0.83 0.87 3.1 1.94 macroeconomic variables + last 10 3.81 3.70 3.70 3.79 3.96 4 3.91 18.9 technical indicators + macro economic variables + last 10 technical indicators + macro economic variables 370 �Figure 1. MLP regression result for n=4 (4 neurons in the hidden layer) and 30 features (technical indicators+macroecon. variables+last 10 slid. window). Figure 2. MLP regression for n=4 (4 neurons in the hidden layer) and 30 features (technical indicators+macroecon. variables+last 10 sliding window). 5.CONCLUSION 371 �The issue of accurately predicting the stock market price levels is highly important for formulating the best market trading solutions. It is fundamentally affecting buy and sell decisions of an instrument that can be lucrative for investors. This study focuses on predicting the ISE National 100 closing price levels using ANN based on the daily data from 1997 to 2007. The experimental results give us some very important clues. Firstly, ANN shows superior predicting power in forecasting the stock market price level index. MLP presents 0.39 % RAE in its best case, which is a perfectly good outcome. Even though the prediction performance of ANN outperforms studies alike in literature, it is still likely that the forecasting performance of the model can still be improved by doing the followings: Either the model parameters should be adjusted by thorough experimentation or the input variable sets need to be modified by selecting those input attributes that are more realistic in reflecting the market workings. (Kara, Boyacioglu, and Baykan, 2010) had already proved the significance of using ten particular technical market indicators which gave also good results in this study, as well. Besides, the use of a sliding window of the last ten elements of the ISE 100 index proved to be an effective tool in forecasting the market level and direction. However, the seven macroeconomic variables and three other international market indices were not found to be very useful in this study, which means that more appropriate variables has to be found that may improve the forecasting performance of the models employed that can be a further subject of study for interested readers. Acknowledgement : We sincerely deliver our special thanks to Assist. Prof. Melek Acar Boyacioglu for her graciousness in sharing her knowledge with us. REFERENCES Abu-Mostafa, Y. S., & Atiya, A. F. (1996). Introduction to financial forecasting. Applied Intelligence, 6(3), 205–213. Altay, E., & Satman, M. H. (2005). Stock market forecasting: Artificial neural networks and linear regression comparison in an emerging market. Journal of Financial Management and Analysis, 18(2), 18–33. Armano, G., Marchesi, M., & Murru, A. A. (2005). Hybrid genetic-neural architecture for stock indexes forecasting. Information Sciences, 170, 3–33. Avci, E. (2007). Forecasting daily and sessional returns of the ISE-100 index with neural network models. Journal of Dogus University, 8(2), 128–142. Boyacioglu M.A., Avci D., (2010). An Adaptive Network-Based Fuzzy Inference System (ANFIS) for the prediction of stock market return: The case of the Istanbul Stock Exchange. Expert Systems with Applications 37, 7908–7912. 372 �Cao, Q., Leggio, K. B., & Schniederjans, M. J. A. (2005). A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Computers & Operations Research, 32, 2499–2512. Chen, A. S., Leung, M. T., & Daouk, H. (2003). Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Computers & Operations Research, 30(6), 901–923. Colby, Robert W. The Encyclopedia of Technical Market Indicators, McGraw-Hill, 2nd. edition, 2003. Diler, A. I. (2003). Predicting direction of ISE national-100 index with back propagation trained neural network. Journal of Istanbul Stock Exchange, 7(25–26), 65–81. Du K.-L., Swamy M.N.S., (2006). Neural Networks in a Softcomputing Framework, Springer-Verlag. Kara Y., Boyacioglu M.A., Baykan O.K., (2010). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert Systems with Applications 38, 5311–5319. Karaatli, M., Gungor, I., Demir, Y., & Kalayci, S. (2005). Estimating stock market movements with neural network approach. Journal of Balikesir University, 2(1), 22–48. Satchell, C., (2005). Pattern Recognition and Trading Decisions, McGraw-Hill.Tan, T. Z., Quek, C., & See, Ng. G. (2007). Biological brain-inspired genetic complementary learning for stock market and bank failure prediction. Computational Intelligence, 23(2), 236–261. Tan, T. Z., Quek, C., & See, Ng. G. (2007). Biological brain-inspired genetic complementary learning for stock market and bank failure prediction. Computational Intelligence, 23(2), 236–261. Weka, Waikato Environment for Knowledge Analysis, Version 3.7.3, The University of Waikato Hamilton, New Zealand, 1999-2010. Yumlu, S., Gurgen, F., Okay, N., (2005). A comparison of global, recurrent and smoothedpiecewise neural models for Istanbul stock exchange (ISE) prediction. Pattern Recognition Letters 26, 2093–2103. 373 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1186 Title A name given to the resource Stock market price index return forecasting using ANN Author Author Gunter , Senyurt Abstract A summary of the resource. Even though many new data mining techniques have been introduced in prediction estimation, there is still no single best solution to all financial problems. In this study, an artificial neural network (ANN) model is utilized for predicting price index returns through regression. Ten technical market indicators, seven macroeconomic variables, a couple of other international market indices and a sliding window of ten inputs make up the 30 attributes used in this study. Different combinations of attribute sets is experimented with different ANN model parameter values to find the highest forecasting accuracy. Keywords: Price index return, ANN, Forecasting, Data Mining Techniques. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/ac5f944f4ef95a7a3d7fd6232b148164.pdf 54e8d6680158472248051fb551a2bb3a PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Porter, M. E. (2009). The Competitive Advantage of Nations, States, and Regions, Harvard Business School, Advanced Management Program. OECD, (2007). Competitive Regional Clusters: National Policy Approaches, (http://www.oecd.org/document/2/0,3746,en_2649_33735_38174082_1_1_1_1,00.html), (22.04.2012). Singh, A., (1999). Competition Policy, development and developing Countries, Indian Council for research on international economic relations, New Delhi. Vietor, R.H.K. (2006). Strategy, Structure, and Government in the Global Economy, Harvard Business School Press ,Boston, Massachusetts. World Economic Forum, The Global Competitiveness Report, (2008-2009). World Economic Forum, The Global Competitiveness Report, (2009-2010). World Economic Forum, The Global Competitiveness Report, (2010-2011). World Economic Forum, The Global Competitiveness Report, (2011-2012). Implementation Of Critical Path Method And Project Evaluation And Review Technique Ali Göksu, Selma Ćatović International Burch University,Faculty of economics Management and information technologies Sarajevo, Bosnia and Herzegovina Abstract Because of the growing effects of the globalization in various business environments, the manufacturing industry is expected to be effective and yet efficient. According to this, in 205 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo planning, scheduling and controlling a project, which is a combination of various activities, project management techniques (PERT and CPM) are used. Therefore, the research question is How will the implementation of CPM and PERT influence the effectiveness and efficiency of furniture company ''Dallas''? The answer to this question is relevant in oder to point out the importance of those methods in reducing the project completion time and costs. The data are taken from the furniture company ''Dallas'' and it will be combined with literature reviews. The research study is fueled by the following objectives: First is to determine the activities that are involved in the manufacturing proces in selected company. Second is to demonstrate the benefits, as well as the drawbacks that those methods might create in the organization. And third is to demonstrate the influence of CPM and PERT in the entire furniture industry and its competitiveness. Implications of this research paper are evaluation of the project completion time and control of the resources, in oder to see that the project is completed within the planned time and cost by using mentioned methods. At the end of the study, the result is expected to help all the individuals as well as the companies to understand more the concept of CPM and PERT methods in reducing the project completion time and costs. Keywords : CPM, PERT, Furniture Company, Optimization 1.INTRODUCTION Planning, Scheduling (or organising) and Control are considered to be basic Managerial functions, and CPM/PERT has been rightfully accorded due importance in the literature on Operations Research and Quantitative Analysis. Far more than the technical benefits, it was found that PERT/CPM provided a focus around which managers could brain-storm and put their ideas together. Most important, it became a useful tool for evaluating the performance of individuals and teams. The research study is fueled by the following objectives: First is to determine the activities that are involved in the manufacturing proces in selected company. Second is to demonstrate the benefits, as well as the drawbacks that those methods might create in the organization. And third is to demonstrate the influence of CPM and PERT in the entire furniture industry and its competitiveness. This paper comprises the possibility to generate importance of CPM and PERT methods in reducing the project completion time and costs in furniture industry. The study can gain advantages that are helpful in the continuous progress of the investigation. One of those advantages is to cover the literature gaps concerning the past studies related to the same subject. In addition, through the collection of information, the study can emphasize the idea about the methods applied in production process in furniture industry. 1.1.Previous Literature Review In This Field Since the development of CPM and PERT during the 1950s, the techniques have been the subject of hundreds of research papers, but little work has been done in the area of the time-cost problem in furniture industry. Research has generally been focused on PERT, since 206 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo the deterministic CPM presents few problems of interest. Feng, Liu, & Burns, (2000) presented a hybrid approach that combines simulation techniques with a genetic algorithm to solve the time-cost trade-off problem under uncertainty. Lu and Li (2003) suggest that redundant relationships be removed before the backward pass, but they do not provide a procedure as to how to remove them. In response to some shortcomings of the CPM model, Kuchta (2002) proposed a fuzzy method to measure the criticality of project activities and the whole project. In another study, Jassbi (2008) proposed a fuzzy inference system to determine activities’ criticality in deterministic networks. Mota (2009) presented a model for supporting project managers to focus on the main tasks of a project. They used a multiple criteria decision aid (MCDA) approach and considered several points of view in their study. 2.MATHEMATICAL PROGRAMMING Mathematical Programming (MP) is the use of mathematical models, particularly optimizing models, to assist in taking decisions. The term 'Programming' antedates computers and means 'preparing a schedule of activities'. Mathematical Programming is more restrictive in what it can represent than other techniques and it is very suitable for problems involving blending, continuous flow processing, production and distribution, and strategic planning. 2.1.Critical Path Method CPM is for projects that are made up of a number of individual "activities." If some of the activities require other activities to finish before they can start, then the project becomes a complex web of activities. Risk analysis modules can be used as an enhancement of CPM. In such case, the assumption is that deterministic analysis can provide a solid basic schedule and budget unless risk events interfere. Either way, potential risk events, their likelihoods, and their anticipated effects are listed in a risk register. Float (slack) - amount of time that a task can be delayed without causing a delay to: subsequent tasks (free float) project completion date (total float) Critical path is the sequence of activities which add up to the longest overall duration. It is the shortest time possible to complete the project. Any delay of an activity on the critical path directly impacts the planned project completion date (there is no float on the critical path). A project can have several, parallel, near critical paths. An additional parallel path through the network with the total durations shorter than the critical path is called a subcritical or non-critical path. Critical activity – activity with zero float . 207 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 2.2.Project Evaluation and Review Technique Program (Project) Evaluation and Review Technique (PERT) is a project management tool used to schedule, organize, and coordinate tasks within a project. It is basically a method to analyze the tasks involved in completing a given project, especially the time needed to complete each task, and to identify the minimum time needed to complete the total project. PERT assumes a beta probability distribution for the time estimates. For a beta distribution, the expected time for each activity can be approximated using the following weighted average: Expected time = ( Optimistic + 4 x Most likely + Pessimistic ) / 6 This expected time may be displayed on the network diagram. To calculate the variance for each activity completion time, if three standard deviation times were selected for the optimistic and pessimistic times, then there are six standard deviations between them, so the variance is given by: [ ( Pessimistic - Optimistic ) / 6 ] The variance in the project completion time can be calculated by summing the variances in the completion times of the activities in the critical path. Given this variance, one can calculate the probability that the project will be completed by the certain date assuming a normal probability distribution for the critical path. 3.FURNITURE INDUSTRY The furniture industry is essentially an assembling industry, which employs various raw materials to manufacture its products. They range from wood and wood based panels to metals, plastics, textile, leather and glass. The European furniture sector comprises around 150,000 companies, generates a turnover of almost €126 billion and an added value of €38 billion and employs around 1.4 million people (EU27, 2006). From 2005 production volumes increased slightly but in 2008 this positive trend was reversed and production dropped again. Major factors of competitiveness for the sector consist of research and innovation, skills and quality, design and added value, knowledge and know-how, together with better access to third country markets. 3.1. Current trends in Bosnia A major trend is taking a page from the past and giving it a fresh new look. Old furnishings are looking new again, adding features and new tricks of the trade to give them an updated look that is perfect for today's homes. After years of dulled hues and beige, walls are coming alive with colors. Some are even adopting an extremely bright palette to rid their home of the bland colors associated with bland economic times. Others are adding just a splash of color to make a room really pop. Another current trend is adding décor that is influenced by the music scene. Furniture is all about comfort, too. Look for large, overstuffed pieces and ergonomic designs that welcome you to relax and recharge. Modular furniture is 208 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo still popular, since it allows those on a budget to mix and match furnishings. This style is especially popular in urban communities, which tend to also gravitate toward modern and contemporary furniture, which by the way never seems to go out of style. 3.2.Manufacturing Process Before start of manufacturing of any furniture product a desired design is selected. Selection of elegant design is important to ensure attractive finished product. The seasoned wood blocks are cut into desired shape and slices according to the requirement of design. The slices of wood are molded into the desired shape according to the design. Carving means different elegant pattern carved in the wood. Quality of carving depends on the skills of the labor. Once the different pieces are carved & molded than these parts/pieces are assembled or fixed together to give the shape to the final product. Assembled product is grind to make the surface smooth. Once the surface is smooth, finishing material is applied to make the surface ready for paint or polish. After the base is prepared final finishing is applied depending on requirement in term of paint/polish. Upholstery of fabric is carried out according requirement of design. 4.IMPLEMENTATION OF CPM AND PERT (Example) This paper describes the implementation of the traditional PERT/CPM algorithm for finding the critical path in a project network. An example of manufacturing furniture with various activities will be used. The completion time of each activity is not known with certainty; only estimates are available. It is expected that final, quantitative results will point out the importance of implementing those methods in planning, scheduling and controlling a project in terms of providing effectiveness and efficiency of furniture company. 4.1.About the Dallas Company Dallas is a family company with twenty years tradition in producing upholstery and with more than 1000 employees. The company works in two countries, Serbia and Federation of Bosnia and Herzegovina. In its rapid progression, the Company has developed its own retail network with department-store chain in all bigger towns of Serbia, Bosnia and Hercegovina, and Montenegro. Dallas strategy is to expand in European and overseas markets on long terms, to win them with its quality, design and popular prices. The company management is turned towards future, because Dallas is the name that inspires confidence and security, whose business background is based on customers’ satisfaction. For more, visit http://www.dalas.rs/ . 209 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 4.2.Collecting and processing data The applied method in the study is the processing of primary data as a result of direct interview with an expert team in furniture company. In primary data collection, qualitative and quantitative methods can be used, as primarily quantitative study may uses qualitative results to help interpret or explain the quantitative findings. The key point here is that the collected data are unique to this research and, until it is publish, no one else has access to it. It is important to know in advance what questions to ask/areas to cover – they are ‘imposed’. When all needed data are provided, then the steps of CPM and PERT can be followed. 4.3. Reasearch Question The following is the basic question of research paper: How will the implementation of CPM and PERT influence the effectiveness and efficiency of furniture company ''Dallas''? If there is a single critical path, there is still only a 50% probability of the project being completed by the target date, since mean activity times are used to calculate the completion time of the project. If there are numerous possible critical paths the probability may be much less than 50%. This may be costly if there are penalties for late completion of the project. The complete distribution of project completion time needs to be considered when crashing. Since there may be numerous possible critical paths, crashing a given activity by one time period will not necessarily reduce the completion time of the project by one time period. The expected reduction in project completion time must be considered in addition to the time/cost slope when selecting an activity to be crashed. 4.4.Defining Problem To answer to research question, six products from different product lines have been selected. In order to define ‘critical path’, project duration and cumulative project costs for all of this products, time and cost of all activities in production process need to be presented in table. The obtained findings are expected to show that these techniques considerably reduce the project completion time. All findings will be compared with previous data of the company (project duration and cumulative project costs of selected products). This paper will help to all managers in furniture industry to implement CPM and PERT to their projects, and by doing that, they will improve effectivness and efficiency of their organizations. Futhermore, it may challenge other researchers to fullfil gaps in literature reviews related to this topic. 4.5.Study Restriction PERT is a probabilistic tool used with three and its basically a tool for planning while CPM is a deterministic tool and also allows and explicit estimate of and control of time. PERT is more suitable for R&D related while CPM is best suited for routine and those projects where the project is performed for projects where time and cost estimates can the 210 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo first time and the estimate of duration be accurately calculated. The probability factor is major in PERT while in CPM the deterministic factor is more so values outcomes may not be exact. 5.RESULT CPM and PERT considerably reduce the project completion time in furniture comapany Dallas. At the end of the study, the result is expected to help all the individuals as well as the companies to understand more the concept of CPM and PERT methods in reducing the project completion time and costs. Apparently, as it is expected the final, quantitative results point out the importance of implementing those methods in planning, scheduling and controlling a project in terms of providing effectiveness and efficiency of furniture company. The same routine have been done on six selected products and obtained findings showed that these techniques considerably reduce the project completion time. All findings are compared with previous data of the company (project duration and cumulative project costs of selected products). Because it confirms the hypothesis, this paper will help to all managers in furniture industry to implement CPM and PERT to their projects, and by doing that, they will improve effectivness and efficiency of their organizations. 6.CONCLUSION One of the most challenging jobs that any manager in furniture companies can take on is the management of a large-scale project that requires coordinating numerous activities throughout the process of making the final product. A myriad of details must be considered in planning how to coordinate all these activities, in developing a realistic schedule, and then in monitoring the progress of the project. Fortunately, two closely related operations research techniques, PERT (program evaluation and review technique) and CPM (critical path method), are available to assist the project manager in carrying out these responsibilities. These techniques make heavy use of networks to help plan and display the coordination of all the activities. They also normally use a software package to deal with all the data needed to develop schedule information and then to monitor the progress of the project. Project management software, such as MS Project in your OR Courseware, now is widely available for these purposes. REFERENCES [1] Dan Trietsch , Kenneth R. Baker, PERT 21: Fitting PERT/CPM for use in the 21st century, International Journal of Project Management 30 (2012) 490–502, Available online at www.sciencedirect.com 211 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo [2] Ghaleb Y. Abbasi, Adnan M. MukattashCrashing PERT networks using mathematical programming, International Journal of Project Management 19 (2001) 181±188, Available online at www.sciencedirect.com [3] Nasser Eddine Mouhoub, Abdelhamid Benhocine, Hocine Belouadah, A new method for constructing a minimal PERT network, Applied Mathematical Modelling 35 (2011) 4575–4588, Available online at www.sciencedirect.com [4] Pierpaolo Pontrandolfo, Project duration in stochastic networks by the PERT-path technique, International Journal of Project Management 18 (2000) 215±222, Available online at www.sciencedirect.com [5] Rafael Herrer&'as Pleguezuelo, Jos&e Garc&'a P&erez, Salvador Cruz Rambaud, A note on the reasonableness of PERT hypotheses, Operations Research Letters 31 (2003) 60 – 62, Available online at www.sciencedirect.com [6] S.M.T. Fatemi Ghomi, E. Teimouri, Path critical index and activity critical index in PERT networks, European Journal of Operational Research 141 (2002) 147–152, Available online at www.sciencedirect.com [7] U.,A., Bakshi and A.,V.,Bakshi, (2010) Network Analysis, Technical Publications, Pune [8] http://www.eudoxus.com/lp-training/1-what-is-mathematical-programming [9] http://www.mindtools.com/critpath.html [10] http://www.netmba.com/operations/project/pert/ [11] http://people.brunel.ac.uk/~mastjjb/jeb/or/netanal.html [12] http://www.stanford.edu/class/cee320/CEE320B/CPM.pdf 212 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1114 Title A name given to the resource Implementation Of Critical Path Method And Project Evaluation And Review Technique Author Author GÖKSU, Ali Abstract A summary of the resource. Because of the growing effects of the globalization in various business environments, the manufacturing industry is expected to be effective and yet efficient. According to this, in planning, scheduling and controlling a project, which is a combination of various activities, project management techniques (PERT and CPM) are used. Therefore, the research question is How will the implementation of CPM and PERT influence the effectiveness and efficiency of furniture company ''Dallas''? The answer to this question is relevant in oder to point out the importance of those methods in reducing the project completion time and costs. The data are taken from the furniture company ''Dallas'' and it will be combined with literature reviews. The research study is fueled by the following objectives: First is to determine the activities that are involved in the manufacturing proces in selected company. Second is to demonstrate the benefits, as well as the drawbacks that those methods might create in the organization. And third is to demonstrate the influence of CPM and PERT in the entire furniture industry and its competitiveness. Implications of this research paper are evaluation of the project completion time and control of the resources, in oder to see that the project is completed within the planned time and cost by using mentioned methods. At the end of the study, the result is expected to help all the individuals as well as the companies to understand more the concept of CPM and PERT methods in reducing the project completion time and costs. Keywords : CPM, PERT, Furniture Company, Optimization Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/ad4f0662348df5c08101a9ffdd5e40f1.pdf c83030735a35d4737eef2dbd69bd04b4 PDF Text Text Importance Of Training Aimed At Production In Businesses: Educational Studies Carried Out By The Turkish Private Sector Gökhan Ofluoğlu1, Sibel Buzkan2, Sadık Kiliç1 1Zonguldak Karaelmas University Faculty of Economics and Administrative Sciences Department of Labor Economics and Industrial Relations İncivez – Zonguldak 2Zonguldak Karaelmas University Faculty of Economics and Administrative Sciences Department of Business Administration İncivez – Zonguldak Emails: gofluoglu@yahoo.com, sibelbuzkan@yahoo.com, kilicsadik80@gmail.com Abstract There are basically three kinds of educational activities in the World and in Turkey. These are formal/organized education, non-formal education, and informal education. In this age the importance of informal education is increasing. This is due to the inadequacy of formal education and its complementary, non-formal education, for the postfordist contexture of production. In Turkey, businesses do not play a part in any of these three kinds of educational activities. However, their active output are getting larger and larger from formal to informal education. While there is an indirect participation of the businesses on this issue in formal and non-formal education, in informal education there is direct participation. Actually, the core of informal education constitutes the learnings experienced in the work place. In this study, within the training aimed at production in the businesses, apprenticeship workshops which are implemented within the frame of formal education and vocational training which is the extension of formal education, as well as various educational activities and informal education which are implemented within the formal education are discussed. The necessity of informal education and its further connection to productivity is emphasized. Keywords: Productivity, Formal Education, Non-Formal Education, Informal Education. 1.INTRODUCTION There have been studies on the issue of the importance of education beyond measure. Most of these studies emphasized the importance and necessity of education. Indeed, when education is handled efficiently a lot of important development, including productivity follow. In this study, educational activities in Turkey are argued. Not the full extent of the educational 547 �activities, but only the activities which the businesses participate in in the process of education are dealt with. However, educational issues, as they pose an integrated lot, have occasionally been excluded. In the study, it is assumed that there is a closer relation between informal education and productivity and with a presupposition the relation between education and productivity is often assumed to be positive. Assessment and evaluation is one of the leading issues of economic requirements of our time. Above all, assessment provides focusing and scrutinizing in case there is a problem. Various productivity assessments are made. The assessment applied is shaped according to the goal pursued and data provided. In this manner, classification related to productivity assessment can be made according to its extent, its field of application, and according to the scientific discipline. (Akçay, 2011:37-44). Productivity assessments according to their extent are divided into two as follows: with a single factor and with multiple factors. In single factor measurements, not only labor productivity but also capital productivity is assessed. In productivity assessments with multiple factors, on the other hand, input is evaluated as a whole. At this point, labor and capital are evaluated simultaneously and sometimes inputs such as energy and supplies are also attached(OECD, 2001:12). Speaking of productivity, in essence, we comprehend the input-output ratio. Labour productivity analysis at macro level is calculated either as amount of productivity or value of yield per laborer or work done per hour. Instead of productivity value, sometimes wage level can be applied in productivity analysis. However, research has been done indicating that sometimes the wage level and productivity level are not the same(Dearden, Reed, Reenen, 2005:22). In Turkey, since 2005, apart from the first quarter of 2009, labour productivity has displayed a steady increase (General Directorate For Productivity, 2012:1). Table 1: Productivity Charts Country/Disctrict Name Work Done Per Hour Annual Labour Productivity Rate of GDP Per capita Increase (USA=100) 2005 2006 2007 2008 2009 2010 Turkey 44,7 5 4.6 4.7 -0.9 -4.4 3.1 Germany 90,7 1.2 3.6 1.7 -0.1 -2.5 1.4 France 97,7 1.5 2.8 0.3 -1.7 -0.2 1.7 England 78,3 1.2 2.2 1.8 -0.5 -2.1 0.9 USA 100 1.5 0.8 1.2 0.7 2.1 3 OECD Total 75,3 1.5 1.7 1.6 -0.1 -0.3 2.2 G7 Countries 87,6 1.6 1.4 1.2 0.1 0.2 2.4 Source: (online), http://stats.oecd.org/Index.aspx?DatasetCode=LEVEL, and (Online), http://stats.oecd.org/Index.aspx?DatasetCode=PDYGTH, 2012. 548 �In reference to Table 1, Turkey is considerably behind the other countries and regions with regard to productivity of workforce per working hour while sharp increases and decreases are perceived at productivity growth. This calculation is favourable when we consider labour productivity as input-output ratio . Yet, when we try to focus on reasons of increase and decrease of labour productivity we do not have much data because there are a lot of elements effecting labour productivity. Although some of these may be dependent on other factor productivities such as capital and provisions they may also be independent of them. A business or an economy are each an open system. Labour productivity both at macro economic level and management level are effected by environmental factors extensively. Thereby, the fluctuation of labour productivity takes form by the influence of intermediary factors. At that point it becomes difficult to clarify labour productivity. Multi-factor productivity analyses are developed to overcome this drawback. Moreover calculations are made to indicate which factor is higher (Triplett, Bosworth, 2003:27). However, these are also far from taking environmental factors into consideration. A lot of internal and external environmental factors such as the structure of industrial relations, competition, the international openness of the market and Research and Development activities effects productivity (Dawkins, Rogers, 1998:196). “Micro economic reforms” implemented in Australia since 1980 are leading sample cases on this issue. For, these reforms are an effective insidence of environmental factors. Privatizations, repealing or reducing the protective taxes against international trade, labour market deregulation, lifting the impediments in getting into the markets are some of these reforms (Borland, 2012). It is argued that these reforms have positive effects on productivity in many researches made in Australia (Mckenzie, 2005). The allegations in these researches are also supported by ampirical data. Consequently, there are tens of factors that affect labour productivity. Yet, there is such a fact that uneducated society is unskilled at the same time. In this respect, the impact of education on productivity, though it is not possible to prove empirically, has a positive effect. Education in the World and in Turkey can basically be divided into three. These are formal, non-formal and informal education. Formal education (with diploma); is the term given to the kind of education classified traditionally as pre-school, primary education, secondary education and higher education. Non-formal education (certificated) is qualified as the supplementary of formal education and apprenticeship and vocational training in Turkey can be evaluated in this context. As for informal education, it encompasses the education beyond the two denoted education types above and rather related to educational activities performed by private sector. These educational activities are set up to make up some shortcomings. There is usually no diploma or certificate; even if there is a certificate it does not have much formal value. The core of informal education constitute the kind of training, commonly, denoted by the expression “uncertificated” which refers to on-the-job-training. Nonetheless, educational activities arranged to supply with the interests and requirements of the workers of a business are in the range of informal education (İSO, 2012). Besides, an educational activity sometimes goes under more than one category. Particularly, non-formal and informal education may be confused. Hence, certificate is awarded at some kinds of informal education. Human Capital Theory also seperates general (formal) and private (informal) education from one another. Formal education is not an education studied solely for a particular employer or work place or work. Throughout an individual’s life formal education has the quality of being 549 �used in various jobs. However, non-formal education comprises some special gains and in general these gains cannot be transfered from one workplace to another(Viele, 2010:584). Formal education which is agreeable with the Fordist production structure and its complementary non-formal education cannot be satisfying enough for today’s markets. For, postfordist production structure requires keeping the current workforce appropriate to the volatile market conditions. Similarly, replacement of centralized planning by the market oriented economic system emerged in 1980s corroborated this course. In this context, the sample case presented by Lechener (1999:74) for East Germany is basically feasible to a large extent for the other countries as well. That is; in place of formal and non-formal education funded by state, informal education funding of which is undertaken by those who need the education and private sector organization becomes widespread. There is a common belief that there is a linear and positive connection between education and productivity. Moreover, economists perceive the widespreading of education as the crucial element of economic growth(Vinovskis, 1970:550). A lot of writers, such as Schultz, declare that productivity will rise with the rise of the qualities of workforce(Arrow, 1962:172). As educational level increases, possibility of easy adaptation to the changes that occur and structure that is more suitable to technological developments is constituted. Hence it is a known fact that education also generates a lot of positive externality(Nelson, Phels, 1965:75). The common belief that relation between formal education and productivity is positive is one of the fundamental hypothesis of Human Capital Theory. Arrow considers the method of learning named “learning by doing”, substituted by the concept of experience, important in many aspects. In view of Arrow, it is not possible not to observe the importance of experimentation in the growth of productivity (1965:156). Romer (1986:1002) articulated that, in long term growth, instead of falling marginal productivity of the classical theory, rising marginal productivity should be debated on the issue of “knowledge”. According to Romer, when a company’s or an individual’s knowledge increases this situation cannot be restored by the company or the individual and the knowledge spreads. On the issue of decreasing productivities law, which creates indecision, asserting that laborcapital correlation can be positively sloped, there is emphasis on the importance of education, knowledge and learning on the basis of approaches which weakens the basic assumptions of the classical theory. The internal development model elements consist of surge of knowledge, public expenditures and impact of human capital, constitute the models that are developed as an alternative to classical theory. In this respect, Kar and Ağır (2006) who examined the years between 1926 – 1994 reached the finding that spending on education increased growth. At another study researched between the years 1969 – 2001, it is observed that the impact of human capital on growth is more explicitly highlighed (Taban, Kar, 2006:175). In the same manner, yet in another study researched between the years 1960 – 2004, it is observed that growth and education influence one another mutually(Şimşek, Kadılar, 2010:115). Similar findings are reached in the studies researched between 1950 - 2000(Serel, Masatçı, 2005) and between 1923 – 2005 (Özsoy, 2009). The impact of formal education on productivity is realized in an adjournment. The return of today’s formal education investments are gained after quite a long time. The situation on non-formal and informal education is a little different. In these educational types, as there is the question of supplying certain necessities, the impact on productivity is expected at a much earlier time. We must also take into consideration that 550 �education that is not befitting for the necessities, may lessen productivity instead of enhancing it. In essence there are two kinds of education aimed at productivity: the first one is standard (knowledge) and the second one is flexible (aimed at outcome). Today’s educational activities tends rather towards the second one. Some of the causes of this are: structural changes such as: privatization, deregulation, decentralization and authorization; for the companies becoming more international; labour market becoming more flexible, quality and productivity becoming a strategic instrument in engendering a new market; strategic management becoming more valuable than hierarchical management; human resources and human skills being conceived as the most important productivity factor gradually and others (Prokopenko, North, 1997:A-3). In education – productivity corrolation, without doubt, the gainings of education related to work ( the informal education) emerges in a shorter time and it has more direct effect (Dearden vd, 2005:23). But, the acquisition provided after any educational investment and its impact on the productivity is quite difficult to assess. Moreover, assessing solely the production encompasses crucial complications. Labour productivity, on the whole, denotes output coinciding employment per hour. To find this output in service sector is even more difficult(Bolino, 1981:5). Service industry is the sector which has the broadest area of the present day. The business evaluations in this sector are more compelling and evaluation outputs less precise.The difficulties endured at performance evaluation in this sector are also valid at productivity evaluation. In this context, Lee’s categoric division between businesses can be taken into account. Lee divides businesses into two as those that can be evaluated definitely and those that cannot be evaluated definitely(1985:324). It is possible to make precise and trustworthy evaluations with works that we can come to a conclusion and count substantially. The second one is the works that we are trying to evaluate the transformation process between input and output or means-end relations. The target is whether the organization is progressing or not and whether it is effective and efficient or not for which the process requires a variety of behaviours to reach the means-end. The expansion of the service sector has increased the number of businesses the evaluation of which is difficult to make. In a research it is found out that education given to industrial sector provided a rate of increase of productivity more than the education given in services sectors(Maglen, Hopkins, Burke, 2001). Undoubtedly, every country has some educational problems. However, when we compare Turkey with countries such as Germany, France and England, we observe that the problems in Turkey is at a larger dimension. Table 2: Principal Indicators In Education Name Country Educational Participation Expenditures As Of Age GDP Percentage Range 18-24 of In Education Rate of Number of Participation Of students per Age Range 25-64 teacher (2009) In Education(Lifelong Learning) Turkey 2,82 (2006) 26,4 2,5 21,1 England 5,40 (2008) 45,4 19,4 15,8 551 �Germany 4,55 (2008) 55,9 7,7 16,6 France 5,58 (2008) 55,3 5 14,6 Europe 15 4,97 (2008) 53,4 10,4 - Source: data compiled from EUROstat http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/themes, 2012. . (Online) In the context of both formal and non-formal education, it can be observed that with compare to other European countries Turkey has quite unfavourable indicators. Particularly with indicators related to participation to education and educational expenditures there is a distinct difference. When the fact that the population of Turkey is comperatively young is considered, the importance of these negative indicators are doubled. Further additions can be made to Table 2. The rate of illiterate population, the rate of schooling, ranged from preschool to higher education, financial troubles, crowded classes despite excess supply of teaching staff applicants are some of them. To sum up, Turkey’s educational problems are structural. EU membership is a favourable aim at overcoming these problems. Whether full membership to EU is actualized or not it is essential for Turkey to take measures on the issue of education(Gediklioğlu, 2005:70). 2. Participation To Educational Activities In Businesses Turkish private sector administrations participate in educational activities within the frame of some exceptional applications of formal education, the workplace applications of non-formal education and informal education. Contribution to formal and non-formal education shows up in indirect ways whereas informal education is a matter of direct contribution. 3. Formal Education Businesses participate in formal educational activities in connection with apprenticeship applications within the scope of vocational high schools and vocational colleges of higher education. In this respect, it will be more precise to confine the subject matter to vocational high schools and vocational colleges of higher education. However, not only apprenticeship applications are taken into consideration about this issue, but also the common problems of vocational education is dealth with. For, these issues are interlocking issues and their solution requires an integrated point of view. There are a lot of vocational highschools in service. These are basically divided into two; vocational schools like : industrial vocational highschools, trade vocational high schools, islamic vocational high schools, vocational schools for girls, vocational schools of justice and vocational schools of health constitute the first division and the second division constitutes the technical high schools. There is a relatively negative structure in question from the scope of vocational education when compared to economically developed countries according to the percentage of vocational and technical highschools in secondary education. Table 3: The Percentage of Vocational and Technical Highschools Within Secondary Education in Turkey 552 �199697 Percentage of Vocational %45,8 and Technical Highschools Within Secondary Education 199900 200203 200506 200809 200910 201011 %39,56 %32,59 %36,2 %40,7 %42,9 %44 Source : TÜİK, “Educational Statistics”, (Online), www.tuik.gov.tr, 2012. As seen on Table 3, the percentage of vocational and technical highschools from 1996-1999 school years of 45,8% continuously decreased until school year 2002-03 reaching the bottom line of 32,59 % . The percentages that are already behind a lot of western countries diminished lesser and lesser, until it turned to rising trend from 2004 on, coming closer to the level of 1996. During this process vocational schools have lost a lot of respectability in the public eye. Without doubt, the changes on the coefficient applied at university entrance exams is the main reason why this process is endured (Şahin, Fındık, 2008:79). The circumstances gave rise to qualitative losses so much so that it overshadowed the quantitative losses that arose in this respect. Vocational highschools, became the primary educational institutions preferred by the students who fell below the level of avarage. Therefore, even if the coefficient comes up to balance today, it will not be able to solve the problem automatically. German Vocational Educational System could be an agreeable target to enhance the labour productivity of vocational highschools because Germany holds one of the most successful educational systems in the World. In this system named The Dual System, predominantly, the age groups of 15 – 22 get education. 3 – 4 days a week is spent in workplace and 1-2 days a week at school. Two-thirds (2/3) of the time spent at school is filled by vocational subjects (BMBF, 2003:34). Those who graduate from this system may also carry on with the university education. Approximately 2/3 of the age group is involved in The Dual System (ibid:7). Companies contribute in financing directly, and completely set an example of good corporatism. A large majority of students who graduate take place in working life, gaining the status of being “skilled”. German dual system virtually displays non-formal educational characteristic. But, majority of vocational education -leaving apprenticeship education aside- materializes within the scope of formal education. When German system is targeted, 49 we can easily affirm that we are far behind this aim. German dual system is also presented as a leading example model by The World Bank. Hence, this model is even suggested for those countries which are specified as “developed” . It is frequently disclosed that this model also provides a significant amount of cost advantage. Herein, Bennell ve Segerstrom (1998:280)’s comments should be given heed. In their view, German dual system depicts a unique characteristic. Between employers and their uppermost organizations, workers and labour unions and the government an intrinsic corporatism is in question and the roots of this characteristic is extended even to middle ages. 49 See Esin Özdemir, “The Role of German Vocational Education System and Inferences on Vocational Education in Our Country and Chamber System” for further information on determining the German system as an aim and why this system is determined, TOBB European Cooperation Board, (online). www.tobb.org.tr 2012 553 �It is, in essence, absurd to determine corporatism as a target because corporatism (the democratic corporatism) is a social structure that occurs spontaneously and is generally related to culture. However, some of the technical characteristics of German dual system may be determined as target. The application side of apprenticeship of formal education in Turkish vocational educational system remains quite primitive with compare to the German dual system. Training period, summer applications of vocational highschools of health the duration is as long as it is determined in their programs and it is stipulated 300 hours in other vocational highschools and between 30 work-days (240 hours) and 60 work-days in vocational colleges in universities. (Vocational Education Regulations, art.59). One can easily reach to the conclusion that on the whole the implementation applied as two days of school and three days of workshop at workplace is important and necessary in the evaluation with regard to productivity in the final year of vocational schools. Hence, by this means the students have a chance to get to know the work environment. But with regard to the length of time it can easily be suggested that it is not sufficient. At the same time, there is grave distrust on the way this short period is put to use. Principally, the best method of learning is learning by practice (Karcı, 2009:101). Turkey must absolutely move vocational education to workplaces in steady paces. At workplace also, it must definitely be operative and forceful. To attain this, both students’ and employers’ awareness must be raised. Broad responsibilities are conferred upon universities, chambers of industry and commerce and local authorities on this issue. The concerns of vocational education is not solely confined to secondary education. In higher education (tertiary education) similar problems exist. It is a known fact that two years spent in higher education is insufficient, besides, this period is spent with theoretical subjects. Again if we take the implementations in Germany into consiredation, in Germany at higher education institutions equivalent to vocational colleges that take 7 semesters, minimum 2 semesters of apprenticeship training is stipulated. Before training starts, minimum 12 weeks workplace apprenticeship is stipulated as well. Besides, the students who succeed in graduating are entitled a diploma as engineers (Karcı, 2009:104). 3.1. Non-formal Education The non-formal education is constituted by Apprenticeship Training Centers (MEM), Community Colleges (HEM) and other non-formal educational establishments in Turkey. There are totally 392 MEM ( Apprenticeship Training Centers) in Turkey, nearly 300 thousand students take courses in these centers. There are three formal levels which are respectively apprenticeship, journeymanship and workmanship as a result of which students are granted a workmanship certificate and are allowed to open their own workplace. HEM, on the other hand, arranges three kinds of courses. These are: reading and writing courses, vocational technical courses and social cultural courses. Among these only vocational technical courses are directly related to labour market. Yet, it is observed that even these courses are generally aimed at people outside labour or employment (especially the unemployed are considered). Hence within the body of HEM there are 3,4 million trainees (Turkish Statistical Institute : TUİK, 2010:2). Within the category of other elements various schools, centers and institutes exist. Advanced Technical Schools For Girls, Applied School of Art and Craft For Girls, Applied Industrial Apprenticeship Schools, Adults Technical Training Center, Adult Training Center of Hotel Management and Tourism, Open Education Vocational – Technical School, Tourism Training Centers are some of them(Kenar, 2009). 554 �In view of Kenar (2009), the most important component of non-formal education is apprenticeship training. Apprenticeship training is a part of vocational education. There nearly 300 thousand participants receiving apprenticeship education, the number of which corresponds to 10 % of the total vocational education. The programs of apprenticeship education that vary between 2 to 4 years is decided by the boards of “provincial employment and vocational education”. Educational activities done by İş-Kur, The Turkish Employment Organization is within the range of non-formal education. By the establishment of unemployment insurance fund, at the educational activities of the Turkish Employment Organization a huge amount of increase occurred within active employment policies. Hence, in the body of labour training courses, while there was 130 courses and 3868 participants, the amount rose up to 1888 courses and 32 206 participants in 2008 (İş-Kur :The Turkish Employment Organization, 2012). The number of participants reached 224 thousand between January and November in 2011 (General Management Of The Turkish Employment Organization, 2011). Specialized Vocational Course Centers UMEM Skills’10 project and (apprenticeship) or on-the-job training covers quite reformist practices and it is related to our subject more closely. The project of Specialized Vocational Course Centers, consists of educational activities which are set up to overcome the existing structural unemployment. However, as the activities implemented lead to acquiring a more qualified workforce, it should be expected to effect productivity. The project of Specialized Vocational Course Centers: UMEM commenced by signing a protocol between TOBB (Turkish Union of Chamber and Commodity Exchanges, Ministry of Labor And Social Security, Ministry of Education, TOBB (Turkish Union of Chamber and Commodity Exchanges University of Economy and Technology and for the time being it is spread to 81 provinces. In this sense, it displays a good public-private sector cooperation. The components of the project is consist of strenghtening the foundation of education, analysis of labour market requirements, matching/replacing implementation (selection of course trainee, placing to apprenticeship and job replacement of the successful participants) and the application of the newly envisaged courses. The target aimed as a consequence of the project is to employ the course trainee in the particular workplace (UMEM Skills’10 project, 2012). The number of course trainees within the context of Specialized Vocational Course Centers Project rose up to 35 thousand between January – November 2011. 20 TL pocket money is given to the course trainees daily. Another reformist activity by İş-Kur, The Turkish Employment Organization is on-the-job training (apprenticeship). In 2011 5209 participants practiced on this rather new implementation of training, which is very few in number. However, by the objective set by the Ministry Of Labour And Socail Security (MOLSS), deputy under secretary, it is aimed at rising this amount to 400 thousand until 2015 (Tan, 2011:10). There is no doubt that in case this figure is reached, a considerable amount of distance will be covered. Is-Kur provides the participants’ financial support (20 TL daily) for on-the-job training which lasts 6 months. Besides, in the context of “Operation to Promote Young Employment”, encouraging on-thejob training is planned, again, by the support of İş-Kur :The Turkish Employment Organization (İş-Kur :The Turkish Employment Organization, 2011:92). These are pivotal activities because we believe that on-the-job training is the kind of education that has the biggest impact on productivity. This should not come to mean that theoretical education should be totally disregarded. The necessity of certain basic theoretical study is an undeniable reality. Thus, we should avoid making a generalization for all occupational groups because in some occupational groups, intensive theoretical discussions 555 �are necessary. In this generalization rather the professions in the context of the occupational education is emphasized. 3.2. Informal Education The significance of informal education is increasingly better understood in Turkey,like in the World. Informal education comes out with two of its aspects. The first one is a completely informal education (i.e. uncertified), where there is no setup of any kind for education. All sorts of knowledge and skills a worker in any workplace learns on his/her own is in this coverage. The second one is not totally informal. In case of resolving the educational demand of some or all of the workers of a business on any subject, the education acquired by means of purchasing a service is also informal education. This is because in the end of the training, on the whole, either a certificate that may not be transfered to another business is given or else, no certificate of the sort is given at all. For the informal education to have a positive effect on productivity, before all, this education has to be productive, itself. The minimum terms for this kind of education to be productive are as follows: Need-base education analysis: An educational activity done when there is no need to do it may cause the fall of productivity. what is targeted in analysis is to determine the absence of any of the three characteristics of an employee. These are knowledge, skills, and attitute. There is no need for education if there is no inadequacy with any of these characteristics. Instructional design: In line with the specified necessities, first the present situation is examined. At this point, the matter of circumstances concerning what to learn and by whom and the learning medium and its limitations (such as time and money) are important. These are called educational conditions. The present situation as well as other circumstances are related to the motivation of the participants and the desired output. After these conditions are assessed, a method of teaching is selected and practiced (Reigeluth, 1999:9). The evaluation of the results: Undoubtedly the most complicated stage is this one. Business managers and their co-workers wish to know the impact of the educational investment on productivity. An investment has costs. These costs are consist of direct and indirect costs. Educators’ pay, cost of organisation are direct costs. On the other hand, as the worker is away from work for that period of time, this causes a loss of labour. Besides, an opportunity cost also arises at this point. Managers make educational investment with the expectation that these costs will be covered by means of a productivity increase. When faced with the difficulty of calculating the productivity increase, with their intuition, they perceive whether the cost is covered or is not covered. The way to bring this beyond a thought is to activate the process which is known as chain of impact. After an educational investment the following stages must be evaluated respectively (Philips, 1997:5-6): Reaction: Whether the anticipation of participation to an educational program is met or not, is a concern of the satisfaction gained from the program. The level of satisfaction is usually assessed by a post educational survey. However, in the end of this survey whether new knowledge or skill is acquired or not cannot be determined. Learning: It is the study of the evaluation of what the participants gained by the end of the program. The evaluation, although other methods are also used, is assessed by an examination by the end of the program. Yet, the result achieved does not reveal any information about the application of the acquired knowledge about on-the-job practice. 556 �Job applications: The skills learned must be practiced on the job. At this stage, it has to be evaluated whether the acquired skill is applicable on the job or not by various methods. Commonly, this is actualized in a few months period after the program. The outcome of this stage, is a significant assessment that reveals the success of the program. Still, this also does not give a clue whether the job application of the skills contributes to organizational success. Business impact: At this stage, whether the organizational objectives are achieved or not is scrutinized. For instance, customer satisfaction, quality, outputs and costs are some of those. However, these also do not reveal information about the amount of the cost of the program. Return on investment: This is the final stage of the assessment. In this, the financial profit is tried to be calculated. That is, answer to the question: “ Does the program meet the costs?” is searched. The other name for this assessment is cost-benefit analysis. In the book that Philips wrote in 1994 there were only four levels (1994:7). In 1997, by the addition of “business impact” the levels rose up to five. As it is summed up on Table 4, the value of the knowledge acquired at the assessments increase by the rise of the level. Similarly, the power of displaying the actual results and the difficulty of evaluation is growing. But the rate of usage diminishes. Table 4: Chain Of Impact Chain of Impact Value Knowledge Level 1 Frequent of Power Exposing Results of Rate of Usage The The least Valueable Difficulty Evaluation The least powerful of Too Easy (Reaction) Level 2 (Learning) Level 3 (Behavior) Level 4 (Results) Difficult The Most Valuable The Most Powerful Very Rare Source, 1994: 7) Human Resources managers or experts assume significant responsibility in informal education as the unit that determines the educational requirement of the employers are Human Resources Managements. The duty of Human Resources management is to keep the staff in the required number and the qualification available for the business. To this purpose providing workers outside the workplace may be in question as well as the preparation of the present workers to prospective positions by being trained. The latter is more recognized and a more preferred alternative. In respect to this, education is one of the primary duties of the human resources management. 557 �The in-house trainings that are implemented in businesses and educational activities that they materialize by the method of purchasing services from private educational institutions play an important role in informal education in Turkey. But besides this, it is known that they contribute in the process with seminars, conferences and many other educational activities at universities by the collaboration of universities and the industry. Apart from this, it is viewed that institutions such as Small and Medium Industry Development Organization (KOSGEB) also take part in informal education . Hence, KOSGEB (Small and Medium Industry Development Organization) provides financial support for the firms that require education and these educational activities are appraised within informal education. Findings in the research named “Occupational Education In Enterprises Research Results” made by The Turkish Statistical Institute (TUİK) in 2007 have significance from the perspective of our subject. In this research, it is possible to get an idea about the rate of informal education in Turkey. But there are no completely informal, that is, unplanned educations here. Unplanned informal education is continuously effective anytime anywhere. Table 5: Rate of Businesses That Provide Training For Their Employees Among All Businesses, 2007 Case of Producing Occupational Kind Of Occupational Training Training Activity The Size Workplace Group The Rate of The Rate of The Rate of The Rate of Enterprises Enterprises Enterprises Enterprises Providing No Providing Providing Providing Other Of Occupational Occupational Occupational Forms Of Education Education Education Occupational Activities Activities Courses Education Total 68,0 32,0 17,1 23,7 10-49 70,6 29,4 14,7 21,7 50-249 59,7 40,3 23,7 30,0 250+ 53,4 46,6 35,6 34,2 Source: TUİK (The Turkish Statistical Institute). When Table 5 is studied, as the the size of workplace grows, it is seen that the rate of providing occupational education increases. It is a known fact that The Human Resources Management units in larger, more corporate firms are more effective. Yet, it is seen that, including even half of those whose workplace is over 250 employees and 32% of the total businesses organize educational activity. Graph 1: The Rate of Enterprises Providing Occupational Education According to Course Types, 2007 558 �Source: TUİK(The Turkish Statistical Institute). The educational activities of (TUİK) The Turkish Statistical Institute are extended to two divisions, as “courses” and “others”. Courses can be provided internally, organized by the businesses as well as externally, by paying for the services. The other activities are on-the-job guided training, rotation and exchange in offices, employment visits, quality and learning circles, self-directed learning, participating in conferances, workshops, commercial fairs, seminars etc. All these are typical examples of informal education. On Graph 1, the provision of education according to course types and the size ofworkplace is outlined. It is observed that in Turkey providing an external course is preferred rather than the others. As the workplace gets larger, especially when the number of employees it holds becomes more than 250, it is seen providing internal courses is preferred close to 70%. By and large, the rates commonly indicate that informal education in Turkey is yet at the phase of development. Without doubt, informal education compared to other education types has a more effective potential for productivity. This potential has to be used as productively as possible. Educational need analyses are held in informal education in Turkey. Generally, educational activities are initiated according to the analyses results. It is gradually understood better that when the issue of education, which brings out substantial costs for the businesses that operate by the rules of market economy is governed effectively it has a beneficial potential that exceeds the costs. By the information obtained from Chamber of Industry In Istanbul50 , as to whether businesses provide the education in their own organizations or get external education, educational need-base analyses are held. No information has been received on educational planning. However, as an educational activity cannot be implemented without planning, this has to be initially reconciled. That is, no matter what kind of education is talked about at a certain rate a well-designed educational planning is made. The last stage of informal education is evaluation. In Turkey, just like in the world we are confronted with the same chart (Table 4). In the end of the interview with Istanbul Chamber of Industry, a finding of impact assessment after all of the types of education organized is reached. After the impact assessment , the findings show that transition to learning level is fifty percent less. It is concluded that the third level (job application) is rarely a matter and no data are reached in the fifth level application. 50 Istanbul Chamber of Industry Expert Hakan Çoban is interviewed. We are obliged to extend our thanks to Hakan Çoban, Expert and to İSO, the biggest chamber of Industry in Turkey for the invaluable information given on informal education. 559 �In essence, it is natural that chain of impact process works in this manner. Hence, each stage of chain of impact process, although carries on complementing one another, adds some cost. In this respect, the calculation of the return of informal education is a concern of academics rather that firms. To conclude, the calculation of investment return seems to remain as an academic activity-area for some more time to come. By means of some research, it is proved that this return is at quite a high level. (McLinden, Davis, Sheriff, 1994:140). Life-long education in particular is a concept which covers formal, non-formal as well as informal education within its scope. By concept, it is indicated that rather informal education is emphasized. In accordance with the law no. 5544 issued in 2006, Vocational Qualification Authority is established. The aim of this institution is to determine the fundamentals of competence in national technical and vocational areas by taking national and international occupational standarts as a base, and to establish and administer the national competence system necessary to implement assessment and evaluation, and to inform and certify related activities. The professions that require minimum bachelor’s degree are excluded by this law (Law No.5544, article. 1). One of the most important functions of Vocational Qualification Authority(Professional Competency Board) is to award professional competence certificate. Definitions of all existing professions, standards of duty, operation and success, competence related to the instruments used, knowledge and skills requirements, manner and behavior requirements and finally assessment and evaluation criteria are presented in detail. Although completing these impressive studies in a short time is difficult, its progress is known to be rapid. Professional competence certificate is awarded to the labourers who could achieve these standarts. All of these are formed within the framework of national competency. The congruence of National competencies to European Qualifications Framework (AYÇ) still continues. European Qualifications Framework is made up of 8 stages and certification is awarded by these stages. These certifications mean legalizing informal and non-formal education. Via the life-long learning process, a rough calculation is made for an individual who goes through all stages of formal education and is included in the nonformal education regularly every year and is found that the time spent for formal and nonformal education remains 15% and 85% of the time is spent in informal education (Borat, 2009:12). Endeavours for the extention and legalization of this sort of education of no-cost to the public result in noteworthy developments. 4. CONCLUSION Although the interdependence of productivity-education is subject to debate, the impact of education on productivity is an undeniable reality. In Turkey, formal education has multidimentional structural problems. Problems concerning vocational education constitute one of the central problems of formal education. The rate of vocational education is comperatively lower. Apprenticeship application is inadequate quantitavely and is undetermined qualitatively. Within the scope of non-formal education, apprenticeship education and a lot of certificate awarding educational activities are conducted. Apprenticeship education is the most effectively administered area of on-the-job training, which is the best way of learning. Yet, it has been aimed at a comperatively restricted area and a comperatively restricted amount of people. İş-Kur, The Turkish Employment Organization, increased its efficiency by using 560 �unemployment insurance fund, which produced significant outcomes for nonformal education. The project of Specialized Vocational Course Centers, UMEM, and on-the-job training practices are recognized as extremely successful projects. Informal education in the World, as well as in Turkey, is widespread. As the effort to overcome the shortages of knowledge, skills and attitude of employees in informal education outweigh, it has to be emphasized that these kinds of education are more attached to productivity. 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Instructional –Design Theories and Models: A New Paradigm of Instructional Theory, Mahwah: Lawrance Erlbaurn. Romer, P.M. (1986). Increasing Returns and Long-Run Growth, Journal of Political Economy, 94 (5), 1002-1037. Serel, H & Masatçı, K. (2005). Türkiye’de Beşeri Sermaye ve İktisadi Büyüme İlişkisi: KoEntegrasyon Analizi (Human Capital and Financial Growth Relationship In Turkey: CoIntegration Analysis), Atatürk University Faculty of Economy and Administrative Sciences Journal, 19(1). 49-58. Şahin, İ. & Fındık, T. (2008). Türkiye’de Mesleki ve Teknik Eğitim: Mevcut Durum, Sorunlar ve Çözüm Önerileri(Vocational and Technical Education In Turkey: Existing State, Problems and Solution Propositions), TSA, 12(3), 65-86. Şimşek, M. & Kadılar, C. (2010). Türkiye’de Beşeri Sermaye, İhracat ve Ekonomik Büyüme Arasındaki İlişkinin Nedensellik Analizi(Causality Analysis Between Human Capital, Exportation and Economic Growth In Turkey), C.Univ. Journal of Economic and Administrative Sciences , 11(1) 115-140. Taban, S. & Kar, M. (2006). Beşeri Sermaye ve Ekonomik Büyüme: Nedensellik Analizi(Human Capital and Economic Growth:Causality Analysis) 1969-2001, Anadolu University Journal of Social Sciences, 1, 159-181. Tan, N. (2011). İşsizlikle Mücadele Devam Edecek”(Struggle With Unemployment Is Carried On), Employment 3İ, 4. T.C. Bilim, Sanayi ve Teknoloji Bakanlığı Verimlilik Genel Müdürlüğü(Rep.Of Turkey, Ministry of Industry and Technology Productivity Gen.Directorate). (2012). Verimlilik Göstergeleri Bülteni(Productivity Charts Bulletine), Üç Aylık Verimlilik Göstergeleri(Productivity Charts Quarterly): 2011 Üçüncü Dönem(Third Term). Triplett J.E. & Bosworth, B.P. (2003). Productivity Measurement Issues in Services Industries: ‘Baumol’s Disease’ Has Been Cured, FRBNY Economic Policy Review, 23-33. Türkiye İstatistik Kurumu (TÜİK)Turkish Statistics Institution. (2010) Milli Eğitim İstatistikleri(National Education Statistics): Nonformal Education 2008/2009, Ankara: TÜİK. UMEM Skills’10, “What is Skills’10?”, (online), http://www.umem.org.tr/index.php?option=com_content&view=article&id=25&Itemid=28, 2012. 563 �Viele, P.V.D. (2010). The Impact of Training Participation and Training Costs on Firm Productivity in Belgium, The International Journal of Human Resource Management, 21(4), 582-599. Vinovskis, M.A. (1970). Horace Mann on the Economic Productivity of Education, The New England Quarterly, 43(4), 550-571. Comparison Study of Approaches to Measuring Poverty Implementing Fuzzy Set and Classic Set Using The Household Data of Turkey Alparslan Abdurrahman Basaran1,Murat Alper Basaran2 1Hacettepe University, Faculty of Economics and Administrative Sciences, Department of Public Finance, 06800, Ankara, Turkey 2Akdeniz University, Faculty of Engineering, Management Engineering Department, 07425,Alanya, Turkey E-mails: aab@hacettepe.edu.tr, muratalper@yahoo.com Abstract Poverty is one of the issues several industrialized and developing countries encounter in the world. No country is exempt from this problem and its consequences. The top list item of the agendas of both countries and international agencies is related to diminishing poverty. Before taking action against it, countries and agencies need to measure poverty based on collected data. It is a sophisticated issue having several dimensions. So far measuring it with available data has resulted with indicators which show some deficiencies. When poverty is considered, it is a linguistic term and has a vague concept as mentioned in the theory of fuzzy set. Therefore, a new approach is proposed in the literature to examine it in order to overcome those deficiencies mentioned when classic tools are employed. On the other hand, fuzzy set theory is a mathematical tool used for linguistic calculations. For example, when said that income level is low. Actually everybody knows what it means. But what it means changes depending upon the perception of the person. Therefore, measuring low income is a problematic area. Fuzzy set theory enables practitioners to calculate those linguistic terms. In this study, the household data of Turkey of the year 2003 collected annually based on almost 25000 is used to calculate both classic poverty indicator(s) and fuzzy poverty indicator in order to compare those measures. In the end we will show that fuzzy poverty indicator can be comprehensive in some comparisons. Also, it provides more information in terms of understanding the concept of poverty 564 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1213 Title A name given to the resource Importance Of Training Aimed At Production In Businesses: Educational Studies Carried Out By The Turkish Private Sector Author Author Gökhan , Ofluoğlu Abstract A summary of the resource. There are basically three kinds of educational activities in the World and in Turkey. These are formal/organized education, non-formal education, and informal education. In this age the importance of informal education is increasing. This is due to the inadequacy of formal education and its complementary, non-formal education, for the postfordist contexture of production. In Turkey, businesses do not play a part in any of these three kinds of educational activities. However, their active output are getting larger and larger from formal to informal education. While there is an indirect participation of the businesses on this issue in formal and non-formal education, in informal education there is direct participation. Actually, the core of informal education constitutes the learnings experienced in the work place. In this study, within the training aimed at production in the businesses, apprenticeship workshops which are implemented within the frame of formal education and vocational training which is the extension of formal education, as well as various educational activities and informal education which are implemented within the formal education are discussed. The necessity of informal education and its further connection to productivity is emphasized. Keywords: Productivity, Formal Education, Non-Formal Education, Informal Education. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),T Technology (General) https://omeka.ibu.edu.ba/files/original/1327021b5697df833a10afa547321a57.pdf dc82e2123e8376f815988f68ba64e329 PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo The Investigation On Sustainability Of Total Quality Management In Higher Education Through Deming’s Pdca Cycle Göktaş Pinar, Çetinceli Esra Süleyman Demirel Üniversitesi Isparta Meslek Yüksekokulu,Isparta Türkiye E-mails: pinargoktas@sdu.edu.tr,esracetinceli@sdu.edu.tr Abstract Total Quality Management (TQM) is an approach that seeks to improve quality and performance which will meet or exceed customer expectations. This can be achieved by integrating all quality-related functions and processes throughout the organizations. Total Quality Management (TQM) is an integrated organizational effort designed to improve quality at every level. In a TQM effort, participation of all members of an organization is very important about sustainability. The term sustainability has become popular in policyoriented research as an expression of what public policies ought to achieve. According to Brundtland, sustainable development is development that meets the needs of the present without compromising the needs of future generations to meet their own needs. Sustainability management, the ability to direct the course of a company, community, organization, or country in ways that restore and enhance all forms of capital (human, natural, manufactured, and financial) to generate stakeholder value and contribute to the well-being of current and future generations. TQM as a management system could be expanded to include components of sustainability. The methods for implementing this approach come from the teachings of such quality leaders as Philip B. Crosby, W. Edwards Deming, Armand V. Feigenbaum, Kaoru Ishikawa and Joseph M. Juran. For example W. Edwards Deming in the 1950's proposed that business processes should be analyzed and measured to identify sources of variations that cause products to deviate from customer requirements. Deming created a (rather oversimplified) diagram to illustrate this continuous process, commonly known as the PDCA cycle for Plan, Do, Check, Act. In higher education, this study is aimed that offering sustainability of quality education among the most important goals for university strives to achieve. Moreover the purpose of this study is to increase the degree of quality awareness, practice, and appreciation of using PDCA cycle in higher education. As a result, in this study it is mentioned about relationship between sustainability of total quality management in higher education using by PDCA cycle technique. 114 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Keywords: Total Quality Management, PDCA Cycle, Sustainability, Sustainability Management, Higher Education 1.DEFINING OF QUALITY, TOTAL QUALITY MANAGEMENT, DEMING CYCLE AND SUSTAINABILITY The concept of "quality" has been contemplated throughout history and continues to be a topic of intense interest today. Quality presently is addressed in numerous academic and trade publications, by the media, and in training seminars; it is perhaps the most frequently repeated man-tra among managers and executives in contemporary organizations. In a recent survey, executives ranked the improvement of service and product quality as the most critical challenge facing U.S. businesses (Zeithaml, V. A., Parasuraman, A., & Berry, L. L. 1990.). Quality has been described as "the single most important force leading to the economic growth of companies in international markets"( Feigenbaum, A. V. 1982) “TQM is a structured attempt to re-focus the organisation’s behaviour, planning and working practices towards a culture which is employee driven, problem solving, stakeholder oriented, values integrity, and open and fear free. Furthermore, the organisation’s business practices are based on seeking continuous improvement, the devolution of decision making, the removal of functional barriers, the eradication of sources of error, teamwork, honesty, and fact-based decision making”(Ghobadian and Gallear, 1996) TQM is a management system consisting of values, methodologies and tools aimed at satisfying or preferably exceeding the needs and expectations of the customers with a reduced amount of resources. (Bergman & Klefsjö, 2003). The founders of modern quality management and organization excellence - Crosby, Demings and Juran among others - considered ethics, principles and respect for people as key principles. For example, Crosby (1986) stated that: ‘‘the organizations will prosper only when all employees feel the same way and when neither customers nor employees will be hassled’’. Deming’s (1986) 14 points highlighted the ‘‘driving out of fear’’. He advocated an organizational climate where dealings between managers, employees and customers were conducted on an ethical basis. ( Crosby, P. (1986) 1.1.Deming Key Principles 1."Create constancy of purpose towards improvement". Replace short-term reaction with long-term planning. 2."Adopt the new philosophy". The implication is that management should actually adopt his philosophy, rather than merely expect the workforce to do so. 3."Cease dependence on inspection". If variation is reduced, there is no need to inspect manufactured items for defects, because there won't be any. 4."Move towards a single supplier for any one item." Multiple suppliers mean variation 115 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo between feedstocks. 5."Improve constantly and forever". Constantly strive to reduce variation. 6."Institute training on the job". If people are inadequately trained, they will not all work the same way, and this will introduce variation. 7."Institute leadership". Deming makes a distinction between leadership and mere supervision. The latter is quotaand target-based. 8."Drive out fear". Deming sees management by fear as counter- productive in the long term, because it prevents workers from acting in the organisation's best interests. 9."Break down barriers between departments". Another idea central to TQM is the concept of the 'internal customer', that each department serves not the management, but the other departments that use its outputs. 10."Eliminate slogans". Another central TQM idea is that it's not people who make most mistakes - it's the process they are working within. Harassing the workforce without improving the processes they use is counter-productive. 11."Eliminate management by objectives". Deming saw production targets as encouraging the delivery of poor-quality goods. 12."Remove barriers to pride of workmanship". Many of the other problems outlined reduce worker satisfaction. 13."Institute education and self-improvement". 14."The transformation is everyone's job". The Deming Cycle W. Edwards Deming in the 1950's proposed that business processes should be analyzed and measured to identify sources of variations that cause products to deviate from customer requirements. He recommended that business processes be placed in a continuous feedback loop so that managers can identify and change the parts of the process that need improvements. As a teacher, Deming created a (rather oversimplified) diagram to illustrate this continuous process, commonly known as the PDCA cycle for Plan, Do, Check, Act: PLAN: Design or revise business process components to improve results DO: Implement the plan and measure its performance CHECK: Assess the measurements and report the results to decision makers ACT: Decide on changes needed to improve the process ( Tague, 2005) Deming's PDCA cycle can be illustrated as follows: Deming's focus was on industrial production processes, and the level of improvements he sought were on the level of production. In the modern post-industrial company, these kinds of improvements are still needed but the real performance drivers often occur on the level of business strategy. Strategic deployment is another process, but it has relatively longer-term variations because large companies cannot change as rapidly as small business units. Still, strategic initiatives can and should be placed in a feedback loop, complete with 116 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo measurements and planning linked in a PDCA cycle. To illustrate the relationship of business unit processes to strategic processes, we may construct two nested PDCA cycles: Figure 1: PDCA Cycle This 'wheel within a wheel' describes the relationship between strategic management and business unit management in a large company. There are actually several separate business units, of course, each with its own set of metrics, goals, targets and initiatives. But this figure illustrates the idea that the business activities constitute the DO part of the overall strategic effort. ( Tague, 2005) 1.2.Definition of Sustainability According to Brundtland (1987): This is the most commonly quoted definition and it aims to be more comprehensive than most: Sustainable development is development that meets the needs of the present without compromising the needs of future generations to meet their own needs.It contains within it two key concepts: The concepts of needs, in particular the essential needs of the worlds poor, to which overriding priority should be given, and: The idea of limitations imposed by the state of technology and social organization on the environments ability to meet present and future needs. 2.SUSTAINABILITY OF TOTAL QUALITY MANAGEMENT IN HIGHER EDUCATION In this study at first, factors of quality in higher education are determined. These factors are; students, lecturers, management, physical conditions, social life on campus, career planning and shareholders. To improve quality in higher education, it can be used PDCA cycle. In this context when PDCA cycle is analysed, the first stage is plan. In this stage: Plan: At first, quality improving team are created and then sub-quality improving team are created. To improve each factors of quality in higher education, the following are planned.    117 Students: incresasing the success, socialization and motivation Lecturers: increasing job satisfaction and institutional commitment, supporting (Project, study..) Physical Conditions: tracking technology, improving quality life in campus, studies for students with disabilities (on-campus transportation, row, lift...) �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo     Management: supporting projects and studies, increasing motivation of personnel and students Social Life on Campus: increasing sports activities and artistic works (theatre, concert…) increasing the activity of club works. Career Planning: raiseing awareness of students about career planning, leading to students Shareholders: the creation of public and private sector cooperation, meeting of students and industry managers with career days, sectoral promotion, providing internship opportunities. When this study is applied, in the stage of “DO”; the above mentioned plans are carried out, in the stage of “CHECK”; plans and actualized are compared, in the stage of “ACT” as a result of comparison,the necessary arrangements will be made. 3.CONCLUSION In this study, to improve quality in higher education, with Deming’s PDCA cycle, sustainable total quality management in higher education is aimed. Deming’s PDCA cycle has four stages. These are “Plan”, “Do”, “Check” and “Act”. When all phases occurs, their sustainability will be provided by making the necessary changes in future periods. So, students, lecturers, physical conditions, management, social life on campus, career planning and shareholders such factors’ quality will be increased. In this context, university’s quality standard will be increased and it will contribute to positive image of university. This study which is fulfilled the stage of “Plan” (Deming’s PDCA cycle), in the future period will be able to improve in terms of the other stages Deming’ REFERENCES Bergman, B. & Klefsjö, B. (2003), Quality From Customer Needs to Customer Satisfaction, Second edition, Studentlitteratur, Lund. Crosby, P. (1986), Quality Without Tears: The Art of Hassle Free Management, McGrawHill, NY. Deming, W.E. (1986), Out of The Crisis, MIT Press, NY. Ghobadian and Gallear, (1996). Ghobadian, A and Gallear, D. (1996) “Total Quality Management in SMEs”, OMEGA -International Journal of Management Science, Vol. 24. Feigenbaum, A. V. (1982) , Quality and Business Growth Today. Quality Progress. Parasuraman, A., Berry, L.L. and Zeithaml, V.A. (1990), An Empirical Examination of Relationships in an Extended Service Quality Model, Marketing Science Institute, Cambridge, MA. Tague, N.R. (2005), The Quality Toolbox, Second Edition 118 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1104 Title A name given to the resource The Investigation On Sustainability Of Total Quality Management In Higher Education Through Deming’s Pdca Cycle Author Author Göktaş, Pinar Abstract A summary of the resource. Total Quality Management (TQM) is an approach that seeks to improve quality and performance which will meet or exceed customer expectations. This can be achieved by integrating all quality-related functions and processes throughout the organizations. Total Quality Management (TQM) is an integrated organizational effort designed to improve quality at every level. In a TQM effort, participation of all members of an organization is very important about sustainability. The term sustainability has become popular in policyoriented research as an expression of what public policies ought to achieve. According to Brundtland, sustainable development is development that meets the needs of the present without compromising the needs of future generations to meet their own needs. Sustainability management, the ability to direct the course of a company, community, organization, or country in ways that restore and enhance all forms of capital (human, natural, manufactured, and financial) to generate stakeholder value and contribute to the well-being of current and future generations. TQM as a management system could be expanded to include components of sustainability. The methods for implementing this approach come from the teachings of such quality leaders as Philip B. Crosby, W. Edwards Deming, Armand V. Feigenbaum, Kaoru Ishikawa and Joseph M. Juran. For example W. Edwards Deming in the 1950's proposed that business processes should be analyzed and measured to identify sources of variations that cause products to deviate from customer requirements. Deming created a (rather oversimplified) diagram to illustrate this continuous process, commonly known as the PDCA cycle for Plan, Do, Check, Act. In higher education, this study is aimed that offering sustainability of quality education among the most important goals for university strives to achieve. Moreover the purpose of this study is to increase the degree of quality awareness, practice, and appreciation of using PDCA cycle in higher education. As a result, in this study it is mentioned about relationship between sustainability of total quality management in higher education using by PDCA cycle technique.Keywords: Total Quality Management, PDCA Cycle, Sustainability, Sustainability Management, Higher Education Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General) https://omeka.ibu.edu.ba/files/original/5416f38c974462981311f535403f5529.pdf 09cd9cf7cbc55809bffe2e9767231296 PDF Text Text SCHEIN, Edgar H, “Organizational Culture”, American Psychologist, Vol. 45, No. 2, pp. 109-119, 1990. SIMSEK, M. Şerif, H. Serdar Öge, Human Resources Management with Strategic and International Perspectives, Gazi Publishing, Ankara, 2007. SPOKANE, Arnold R., Eric J. Luchetta and Matthew H. Richwine, “Holland’s Theory of Personalities in Work Environments”, Career Choices and Development, Jossey-Bass, San Francisco, pp. 373-427, 2002. SOUTGATE, Nicole, An Exploration of Career Salience, Career Commitment, and Job Involvement, Master Thessis, Masters in Industrial Psychology at University of the Witwatersrand, Supervisor: Dr. Andrew Thatcher, 2005. TUZ, Melek Vergiliel, “Kariyer Planlamasında Yeni Yaklaşımlar”, U.Ü. Journal of Science and Literature Faculty, Vol. 4, No. 4, pp. 169-176, 2003. TYLOR, Edward B., Primitive Culture: Researches Into the Development of Mythology, Philosophy, Religion, Art, and Custom, Volume I, John Murray Albemarle Street, London, 1871 YOUNG, Richard A, Ladislav Valach and Audrey Collin, “A Contextualist Explanation of Career”, Career Choices and Development, Jossey-Bass, San Francisco, pp. 206-255, 2002. The Factors Determined To The Improvement In The Least Developed And Developing Countries: Testing A Model Gözde Ergin, Adil Oğuzhan Trakya University, Department of Econometrics Abstract Finding the different ways of the improvement as a multidimensional process causes different improvement ways in all countries in the world. The economic improvement that cause a structural changing is very important in all economies all over the world and it is necessary for the least developed countries at the same time. These countries have solved the phenomena of poverty, unemployment, low life standards and unimproved. The differentiation in the socio-cultural structures of the least developed and developing countries effect the improvement in a positive way. In the study, the socio-economic factors of improvement and a classification according to the gross national product levels per person in the least developed and developing countries have been done by taking the definition accepted by World Bank into consideration. There are fifteen countries in the classification of the least developed and developing countries. The data of thirty-three factors in the comparison of these countries have been obtained from the data source of World Bank, OECD, EUROSTAT and UN (2000 – 2009). 610 �The Statistical and Casual Models in the kinds of econometric models have been estimated with ‘’Panel Model Method’’. For choosing the suitable model, the test for choosing model ‘’Hausman’’ has been used. As a result, the factors determined to the improvement of the countries in a different improvement levels have been discussed and the comments related to them have been made. 1.INTRODUCTION The concept of economic development could be defined as the process of increasing of material wellbeing, abolishing the poverty, the input in production and the usage of these outputs as a result and besides, as an activity of the protections of the level of the socioeconomical standards of the society in order to use them more actively and with different methods for the production process. The problems of individuals and the world increase gradually because of the increase of the world population and the globalization. In todays world, where the incomes of the individuals raise, the distribution of income gets worse and the poverty increases, the importance of the development problematic dramatically increased. In a world which gets smaller with the expansion of communication tools due to globalization, the level of information acquisition of both countries and individuals has risen. Therefore, solutions are sought through development policies for the alternating lifestyles, economies and the differentiation in countries’ and individuals’ socio-cultural structures. Development, having multiple aspects, has various angles and these bring about different development periods in each country. For that reason, development is defined in different ways by various people and thinkers. Economic Development is very important in every economies and it nearly becomes compulsory for the low-level of development countries. Because, these countries can only find a solution for poverty, low-living standarts and backwardness entity with economic development. But on the other hand when examining for advanced tecnology, development shows necessity for maintaining current growth rate in advanced tecnologies (Jain & Ohri, 2007:2) Development carries meaning of recovering economy and in any case of negativities for underdeveloped countries. As known,it should be tried to be developed and handled with all defects in this issue and origins of these defects for achive successing in a weak issue. The biggest step of developing should be provided the process of developing with handle by looking cause and effects of deficincies in economy. Development not only developt in terms of economy, but also known as social and politically changes and positive contributions of these changes. 611 �2.Development Theories After from 1950s, a lot of development theories suggested to the world.(boyacıoğlu, 200:2728). The major ones of these theories are known as The Development Theory of Rostow, The Balanced Development Theory and Unbalanced Development Theories. According to Rostow’s development theory in 1960, the development countries are the countries surpassed the stage of traditional society,transition,rising,maturity, and the mass consumption. In the countries which is in the stage of traditional society occur an intense agricultural sector and the functions of the limited production and modern scientific-technical practices. Education and infrastructure investment in the transition stage society have a dramatically increasing and bring about new initiatives. .In rising stage the composed profit returns to investment and technology is started to use successfully at all sectors. Anymore, the societies in the maturity stage use their sources in the areas having modern technology. While their production and exportation are increasing, parallelly the requiremenst for new import goods are increasing, as well. As for in the stage of mass consumption, the per capita income arise and the society starts concentrating on consumption rather than production.Impetus between these stages is the expediting economic growth as returning internal and external austerity to enough amount investment. (Dolun ve Atik, 2006: 8). Balanced development aims a condition of equilibrium in the economy.The economic events occured in the underdeveloped society rely on the complementarity link.In terms of thought, complementarity is the important factor of the balanced development and it is not an instrument to realize the balance situation but it is an directive item.The balanced development model rests on the mutual dependence.As a first, it is the mutual dependence in production.On one hand, every economic group have to find income and look for the market for its outcome .As a second, it is that every income growth create an enhancement in demand. The balanced development with balance, food products with clothing, agricultural feedstock with industrial products, public enterprises with other investments and such as production for the export and domestic demand are asked to arised for many other ecomomic situations . According to Rosentein-Roden respected the pioneer of balanced development in order to increase income and demand are needed benefical and healthy investments.Concerted investments are going to increase income and demand.Thus, investment in parts is not enough both increasing demand and income.Overall, coordinated investments in Rosentein-Roden model supplies with the external savings. With the aim of comparing economic development and economic growth, an organized schedule is given below. With the aim of comparing economic development and economic growth, an organized schedule is given below. 612 �Content Economic Development Economic Growth Economic Development refers to either mutations savings and national (mutations betwen institutional and tecnologic) frame or progressive mutations of economic structure. Economic Growth is an increase in capacity of an ecenomy to produce goods and services like investment, savings and revenues. Economic Development refers to benefit from unused resources in underdevelop countries. Use Economic Growth is related with development of low-used sources from developed contries to use in optimum way. Development, equilibrium rate is connected with the raising of high steady state. .Growth is connected with general steady and graduaded raise at the rate of investment and outcome. Economic Development implies the problems of underdevelopment countries. Economic Growth implies the problem of developed countries. Action Creates both qualitative and quantitative mutations in economy. Creates only quantitative mutations economy. Scope Connected with all mutations in economy. Connected with small motations in economy. Boost (büyüme) Definition 3. Panel Data Analysis When T numbered observations of N numbered econometric units are dealed together establish panel data model. Assets belonging to any year establish the cross section of the panel; the assets the economic units take by years establish the time sector. In other words, across every econometric unit there is a time series. Panel data analysis model is the model where economic relations are presumed using time sector cross. (Powel, 2010: 1). 4. A Model Test Regarding Development Factors Affecting Development at Underdeveloped and Developing Countries Taking into consideration the development factors affecting the development of developing and underdeveloped countries with the condition of benefiting from panel data, the Socio-Economic variables of countries taking place at the panel model are defined as below. X 1 : Research- Development Cost GDP % X 2 : GDP Per Capita(Year ) X 3 : Rural Population’s % Among Total Population X 4 : The Rate of Urban Poplation in the Overall Population X 5 : Death Rate ( 1000 Person) 613 X 6 : Tax Revenue GDP % �X 7 : İnfant Death Rate (1000 İnfant) X 8 : Agricultural Rate % in GDP X 9 : Service Sector % in GDP X 10 : Industrial Sector % in GDP X 11 : Import of Good and Service % in GDP % X 27 :FDI %in Net Capital Inflow X 12 : Export of Good and Service % in GDP % X 13 : GDP Rate X 14 : Real Inflation Rate X 15 : Unemployment Rate X 17 : The Number of Scientfic Article X 19 : Expectancy of Life (Year) X 18 : Electrical Consumption Per Capita X 20 : Inflation Rate X 22 : Cultiroted Land (Hek.) X 23 : The Rate of Employed In Industrial Sector X 24 : The Rate of Employed In Service Sector X 25 : Dependency Rate X 28 : Comminication Revenue X 29 : Energy Import % in GDP X 30 : The Rate of Big Urban in Over Population X 31 : Women at the Parliament X 32 : The Rate of Population ( Year) X 33 : GDP per Capita($) 4.1. Approximation Results According to Panel Model of Underdeveloped Countries Under this chapter underdeveloped countries are Uzbekistan; Kyrgyzstan; Ethiopia; Kenya, Nepal, Bangladesh and Afghanistan. These countries are considered as underdeveloped ones according World Bank’s definitions. For these countries different approximation models of social and economic sector will be tested. Table 1: Approximation Results of Underdeveloped Countries Social Sector According to Panel Model Model I Constant Effective Model Variables C X19? X25? X30? X31? Random Effective Model Coefficients Coefficients -6.672591 (0.2953) 3.505694 (0.0047) -1.995913 (0.0007) 1.940735 (0.0044) 0.103846 (0.0518) -1.786856 (0.7641) 2.428435 (0.0139) -1.605449 (0.0037) 1.279782 (0.0045) 0.132759 (0.0077) Random Effects (Cross 0.136976 Fixed Effects (Cross) _UZB--C 0.099859 614 Model II Model III Model IV Model V Pooled Least (LSDV)Model Fit Panel Data Model using GLS, removing Autocorrelation and homoscedasticity Robust Score Coefficients Coefficients Coefficients 3.505694 (0.0047) -1.995913 (0.0007) 1.940735 (0.0044) 0.103846 (0.0518) 8.353807 (0.000) .5925234 (0.000) -1.352025 (0.000) .1943402 (0.000) .05734 (0.000) -1.516349 (0.897) 2.367225 (0.166) -1.583195 (0.014) 1.244267 (0.195) .1343365 (0.015) -6.572731 - - - �_KIR--C _ETOP--C _KEN--C _NEPAL--C _BANG--C _AFG--C -1.402104 0.571702 0.641290 0.470263 -0.696803 0.315794 -0.869378 0.178319 0.633556 0.270047 -0.483477 0.133956 -8.074695 -6.100889 -6.031301 -6.202328 -7.369394 -6.356797 - - R 2 0.847275 0.506778 0.847275 - - R 2 0.821389 0.476426 0.821389 - - Se 0.215075 0.220746 0.215075 - - ∑ e2i 2.729168 3.167381 2.729168 - - 14.23175 - 14.23175 62.08698 - 32.73143 16.69666 32.731 - - 0.000000 0.000000 0.000000 - - -0.092336 - -0.092336 - - 0.260999 - 0.260999 - - 0.048013 - 0.048013 - - 1.167119 - - Log likelihood F-statistic Prob(Fstatistic) Akaike info criterion Schwarz criterion HannanQuinn criter. DurbinWatson stat Wald-ist. LM corr(u_i,Xb) F u_i=0 sigma_u sigma_e Rho 1.167119 1.009085 - 66.79 - 399.78 - -0.8864 25.72 .76597963 .21507476 108.83 0 (assumed) .48702719 .21507476 - - - .92692184 .83680818 - - - Table2: Panel Model of Approximation Results Economic Development Sector of Underdeveloped Countries’ Model I Constant Effective Model Variables C 615 Coefficients -21.94974 (0.0544) Model II Model III Model IV Fit Panel Data Model using GLS,removing Autocorrelation and homoscedasticity Model V Random Effective Model Pooled Least (LSDV)Model Coefficients Coefficients Coefficients Coefficients 8.061894 (0.0137) - 10.48043 (0.000) 8.83934 (0.000) Robust Score �X22? 1.808341 (0.0157) Fixed Effects (Cross) _UZB--C 0.570444 _KIR—C 2.574761 _ETOP—C -2.399361 _KEN--C 0.266447 _NEPAL— 1.172460 C _BANG--C -0.874519 _AFG--C -1.310231 -0.144044 (0.4889) Random Effects (Cross 0.416194 0.075481 -0.598519 0.407666 1.808341 -.3189611 (0.000) -.1946205 (0.025) -21.37930 -19.37498 -24.34911 -21.68330 - - -0.181637 -20.77728 - - 0.137800 -0.256984 -22.82426 -23.25998 - - R 2 0.684503 0.006429 0.684503 - - R 2 0.648883 -0.008183 0.648883 - - 0.301551 0.316311 0.301551 - - 5.637852 6.803588 5.637852 - - -11.16097 - -11.16097 50.3867 - 19.21651 0.439978 19.21651 - - 0.000000 0.509375 0.00000 - - 0.547456 - 0.547456 - - 0.804427 - 0.804427 - - 0.649528 - 0.649528 - - 0.663113 0.459701 0.663113 - - - 0.44 82.60 - 256.57 - 5.04 - -0.9727 0 (assumed) - - - 16.40 1.6653277 .30155042 .96825253 .39296918 .30155042 .62938698 - - - Se ∑ 2 ei Log likelihood F-statistic Prob(Fstatistic) Akaike info criterion Schwarz criterion HannanQuinn criter. DurbinWatson stat Wald-ist. LM corr(u_i, Xb) F u_i=0 sigma_u sigma_e Rho 4.2. A Model Test According to the Economic Development of Underdeveloped Countries When the economic factors dimension of improvement models of underdeveloped countries is seen as a panel model, Constant Effective Model has been estimated as a first model. In the estimation of this model all economic variables has been added to the model as explanatory variables. 616 �The Hausman Test was applied to understand which model is more coherent at the above approximated Fixed Effect Cross Model and Random Effects. The results are below. Table 3: Hausman Determination Model Test Results Correlated Random Effects - Hausman Test Pool: Untitled Test cross-section random effects Chi-Sq. Statistic Chi-Sq. d.f. Test Summary Cross-section random 7.819711 Prob. 1 0.0052 Because the test result is p<0.05 the hypothesis is denied and FEM is preferred. In addition the  i of the countries statistic meaning test is approximated at LSDV model III. 4.3. Panel Model Approximation Results of Developing Countries Under this chapter as developing countries; Azerbaijan, Argentina, Brazil, Bulgaria, China, Mexico, Turkey and Kazakhstan are taken. These countries are considered as developing ones according World Bank’s definitions. For these countries different approximation models of social and economic sector will be tested. Table 4: Panel Model of Approximation Results Economic Development Sector of Developing Countries Model I Model II Constant Effective Model Variables C X8? X18? Coefficients 1.205352 (0.5830) -1.417998 (0.0000) 1.281596 (0.0000) Fixed Effects (Cross) _AZER— -0.481379 C _ARJ--C 0.479222 _BRE--C 0.072678 _BULG— -0.609218 C _CHN--C 0.386770 617 Model III Model IV Model V Random Effective Model Pooled Least (LSDV)Model Fit Panel Data Model using GLS, removing Autocorrelation and homoscedasticity Robust Score Coefficients Coefficients Coefficients Coefficients -1.417998 (0.0000) 1.281596 (0.0000) 8.461126 (0.000) -1.315091 (0.000) .3027834 (0.010) 2.974038 (0.332) -1.443142 (0.000) 1.059926 (0.004) -0.460849 0.723973 - - 0.464491 0.013988 1.684574 1.278031 - - -0.427305 0.596134 - - 0.265768 1.592122 - - 3.536970 (0.0721) -1.449383 (0.0000) 0.988897 (0.0001) Random Effects (Cross - �_MEK--C _TC--C _KAZ--C 0.019974 1.092678 -0.960724 -0.059541 0.996506 -0.793057 1.225326 2.298030 0.244628 - - R 2 0.868931 0.679488 0.883863 - - R 2 0.260507 0.671163 0.868931 - - 4.750457 0.268295 0.260507 - - -0.563648 5.542612 4.750457 - - 59.19276 - -0.563648 9.971518 0.000000 81.62015 59.19276 - - - 0.000000 0.000000 - - 0.264091 - 0.264091 - - 0.561845 - 0.561845 - - 0.383469 - 0.383469 - - 1.472890 1.202092 1.472890 - - - 163.24 129.86 - 174.48 - 76.11 - -0.5420 0 (assumed) - - - 29.98 .66598321 .26050657 .86729755 .4772026 .26050657 .77040971 - - - Se ∑ 2 ei Log likelihood F-statistic Prob(Fstatistic) Akaike info criterion Schwarz criterion HannanQuinn criter. DurbinWatson stat Wald-ist. LM corr(u_i, Xb) F u_i=0 sigma_u sigma_e Rho Table 5. Hausman Determination Model Test Results Correlated Random Effects - Hausman Test Pool: Untitled Test cross-section random effects Test Summary Cross-section random Chi-Sq. Statistic Chi-Sq. d.f. 6.672753 2 Prob. 0.0356 Because the test result is p<0.05 the hypothesis is denied and FEM is preferred. In addition the  i of the countries statistic meaning test is approximated at LSDV model III. 618 �Table 6: Panel Model of Approximation Results Social Sector of Developing Countries Model I Model II Model III Pooled Least Sabit Etkili Model Tesadüfi Etkili Model (LSDV)Model Katsayılar Katsayılar Katsayılar 11.25996 30.32038 Değişkenler Model IV Model V Fit Panel Data Model using GLS, removing Autocorrelation and homoscedasticity Robust Score Katsayılar Katsayılar 7.424574 31.62741 (0.000) (0.000) C X25? X30? (0.0407) (0.0000) -7.450049 -6.270002 -7.450049 .0323962 -6.833261 (0.0000) (0.0000) (0.0000) (0.868) (0.000) 9.174063 0.856127 9.174063 .2843438 1.173124 (0.0000) (0.0001) (0.0000) (0.000) (0.009) Fixed Effects (Cross) Random Effects (Cross _AZER--C -9.249312 -1.556559 2.010649 - - _ARJ--C -5.165218 0.628508 6.094744 - - _BRE--C 3.671319 0.593179 14.93128 - - _BULG--C -2.847708 -0.949942 8.412254 - - _CHN--C 14.45671 -0.236747 25.71667 - - _MEK--C -1.194810 1.310704 10.06515 - - _TC--C -0.779853 0.497350 10.48011 - - _KAZ--C 1.108873 -0.286492 12.36884 - - R2 0.886835 0.527576 0.886835 - - R2 0.872286 0.515305 0.872286 - - Se 0.257151 0.326793 0.257151 - - 619 �e 4.628865 8.223100 4.628865 - Log-Lik. 0.473513 - 0.473513 -100.2685 42.994 60.95 - - 2 i F -Statistic - Prob(F-statistic) 0.000000 0.000000 0.000000 - - Akaike info criterion 0.238162 - 0.238162 - - Schwarz criterion 0.535916 - 0.535916 - - Hannan-Quinn criter. 0.357540 - 0.357540 - - Durbin-Watson stat 1.493082 0.852996 1.493082 - - Wald-ist. - 85.99 - 311.09 72.20 LM - 58.48 - - - -0.9951 0 (assumed) - - - F u_i=0 71.25 - - - - sigma_u 7.0313029 .43614757 - - - sigma_e .25715142 .25715142 - - - rho .99866425 .74204622 - - - corr(u_i, Xb) 5.CONCLUSION The development of economies is possible trough achieving a better position of the accepted criteria and indicators of development. Societies and countries can be categorized among developed countries when they manage to realize the necessary conditions of development. The variation among development factors and socio-economic levels of countries has led to the establishment of categories of developed, under developed and developing countries. Development is a well-rounded process, thus because of its well rounded face the difference of development processes in each country is dissimilar. Economic development brings also structural change which is very important for every economy but in countries where the development level is rather low, is almost compulsory. Because these countries can bring solution to their poverty, unemployment, low level of living standard and underdevelopment trough economic development. The diversification of socio-cultural structure of underdeveloped countries affects positively the development. In these countries culture has limited effect upon economic actions and brings a slow development process. 620 �In developed countries development is a necessity to prolong existent growth rate. In these countries it is aimed to upgrade the living standards of people trough economic development. In developing countries the first target of development which is the skewness of the economy and inequality brings also poor level of living. In these countries the sociocultural development criteria are in low levels and the existence of a traditional cultural approach hinders development. According to the evaluations of social criteria of underdeveloped countries in this essay, life expectations, the rise number of women at the parliament and the increase of life percentages in metropole together with the decrease of dependence rate, affects positively the development. These factors have shown that they are an important step towards development level of the underdeveloped countries. It is arrived to conclusion that in undeveloped countries the decrease of rural population and exports has positive effects upon development. When we look to the suggestive variations of the social criteria model of developing countries, we see that while the increase of life percentages in metropole increases development, the increase of dependence rate has negative effects upon development. According to economic criteria, the increase of the agricultural sector at GDP affects negatively the development. The increase of per capita electric consumption is an important indicator of development for the developing countries. Therefore the increases in prosperity and positive economic activities are only possible trough economic development. In conclusion via development policies is possible to create more modern societies. REFERENCE Balgati, H. Badi, ‘’Forecasting With Panel Data’’, Journal of Forecasting, Wiley İnterScience, January, 2008, s.155-156 Balgati, H. Badi, Econometric Analysis of Panel Data, Third Edition, John Wiley & Sons Ltd, England, 2005, s.7-99 Balgati, H. Badi; Bresson, Georges; Pirotte Alain, ‘’Joint LM Test for Homoskedasticity in a One-Way Error Component Model’’, Syracuse University, Department of Economics and Center for Policy Rearch, New York, October, 2005, s.4-6 Berber Metin, İktisadi Büyüme ve Kalkınma, 3. Baskı, Derya Kitabevi, Trabzon, 2006, s.2217 Boyacıoğlu, Ebru, ‘’Gelişmiş ve Gelişmekte Olan Ülkelerin Kalkınma Kriterleri Açısından Karşılaştırılması ve Türkiye İçin Öneriler’’, Doktora Tezi, 2007, s.68 Cypher, James M., Dietz, James L., The Process of Economic Development, Published by Routlegde, Oxon, 2009, s.147-148 Dinler, Zeynel, İktisada Giriş, Ekin Dağıtım, Bursa, 2009, s. 587 Dolun, Leyla; Atik, A.Hakan, ‘’Kalkınma Teorileri ve Modern Kalkınma Bankacılığı Uygulamaları’’, Ekonomik ve Sosyal Araştırmalar Müdürlüğü, Ekim, 2006, Ankara, s.6-8 Dülgeroğlu Ercan, Kalkınma Ekonomisi, Uludağ Üniversitesi Basımevi, Bursa, 2000, s.42 Gasper, D., Developments Ethics: An Emergent Field?, eds: R. Prendergast & F. Stewart, St. Martin’s Press, 1994, New York. 621 �Ghatak, Subrata, İntroduction to Development Economics, New York, 2003, s. 113 Hsiao, Cheng, Analysis of Panel Data, Cambrıdge University Press, Second Edition, New York, 2003(8-11-12-14-15-33). Jain, T.R.; Ohri, V.K., Indian Economy, Issues in Economic Development & Planning in India and Sectoral Aspects of Indian Economy, V.K. Publication, New Delhi, 2007, s.2 Jain, T.R. ; Bajaj, Balbir Kaur; Gupta, Ashok; Sandhu, AS, Development Economics, Star Offset, Delhi, 2007, s.160-161 Jain T.R., Malhotra Anil, Development Economics, V.K. Publication, New Delhi, 2009, s.167 Pazarlıoğlu, M.V., Kilen Gürler, Özlem, ‘’Telekominikasyon Yatırımları ve Ekonomik Büyüme: Panel Veri Yaklaşımı’’, Finans Politik & Ekonomik Yorumlar, Cilt:44, Sayı: 508, 2007, s.39 Powel, James L., Panel Data Models, Departmant of Econometrics University of California workingnotes, Berkeley, 2010, s:1 İlkin, Akın, Kalkınma ve Sanayi Ekonomisi, İstanbul Üniversitesi Yayını, İstanbul, 1988, s.13-221 Kızılgöl, Özlem; Üçdoğruk, Şenay, ‘’ 2002-2006 Yılları Arasında Türkiye’de Yaşam Standartları ve Yoksulluğa İlişkin Mikro Ekonometrik Analizler’’ ,2009, s.6 Thomas, Clive Y., The Rise of the Authoritarian State in Peripheral Societies, Monthly Review Press, New York, 1984, s.51-50 Tüylüoğlu, Şevket; Çeştepe, Hamza, Kalkınma Ekonomisi, ‘’Kalkınma Teorilerinin Temelleri ve Gelişimi’’ Editörler: Sami Taban- Muhsin Kar, Ekin kitapevi, Bursa, 2004, s.44-45 Yaffee, A. Robert, ‘’A Primer of Panel Data Analysis’’, Social Sciences, Statistics & Mapping, Connet İnformation Tecnology at New York, September, 2003, s.1-2 Yavilioğlu Cengiz, ‘’Geri Kalmışlık Olgusu ve Ekonomistik Kalkınma Teorileri (Eleştirisel Bir Yaklaşım)’’, C.Ü. İktisadi ve İdari Bilimler Dergisi, Cilt:3, Sayı:2, Sivas, 2002, s.7-60 http://www.diffen.com/difference/Economic_Development_vs_Economic_Growth http://econ.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:2042 0458~isCURL:Y~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html (01.10.2010) http://data.worldbank.org/indicator/SP.POP.GROW (27.09.2010) http://data.worldbank.org/indicator/SL.AGR.EMPL.FE.ZS(27.09.2010) 622 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1219 Title A name given to the resource The Factors Determined To The Improvement In The Least Developed And Developing Countries: Testing A Model Author Author Gözde , Ergin Abstract A summary of the resource. Finding the different ways of the improvement as a multidimensional process causes different improvement ways in all countries in the world. The economic improvement that cause a structural changing is very important in all economies all over the world and it is necessary for the least developed countries at the same time. These countries have solved the phenomena of poverty, unemployment, low life standards and unimproved. The differentiation in the socio-cultural structures of the least developed and developing countries effect the improvement in a positive way. In the study, the socio-economic factors of improvement and a classification according to the gross national product levels per person in the least developed and developing countries have been done by taking the definition accepted by World Bank into consideration. There are fifteen countries in the classification of the least developed and developing countries. The data of thirty-three factors in the comparison of these countries have been obtained from the data source of World Bank, OECD, EUROSTAT and UN (2000 – 2009). The Statistical and Casual Models in the kinds of econometric models have been estimated with ‘’Panel Model Method’’. For choosing the suitable model, the test for choosing model ‘’Hausman’’ has been used. As a result, the factors determined to the improvement of the countries in a different improvement levels have been discussed and the comments related to them have been made. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),T Technology (General) https://omeka.ibu.edu.ba/files/original/e0ae8aa47127c85c854c7f614b985622.pdf e89461f899b582d2641dd49f62b7633f PDF Text Text 3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo c. Managers analyze Financial Statements to capture a 1 2 3 4 5 6 7 company's financial position for a given period. This allows users of financial information to analyze and compare the health of one company to another. d. I am satisfied at how my system is set up at this time. 1 2 3 4 5 6 7 e. Sometimes it happens that accountants make mistakes 1 2 3 4 5 6 7 purposely, the only solution for this is termination. Thank you for your participation. The link between deposit insurance And banks’ risk taking Göçmen Yağcilar Gamze, Demir Yusuf, Kalkan Gürkan Suleyman Demirel University, Isparta, Turkey E-mails: gamzeyagcilar@sdu.edu.tr, yusufdemir@sdu.edu.tr Abstract Deposit insurance is an insurance system that guarantees bank deposits of people in case of bank failure or a run on the bank. The system is first introduced in 1933 for Turkey and taken its final form with regulations in 2004. Deposit insurance in Turkey is handled by Savings Deposit Fund Insurance and according to the latest regulations compensation limit covers a maximum of 50,000 TL per depositor per member institution. Deposit insurance system which is adopted in most countries has various advantages for both individuals and banks. However academic debates commonly focus on whether this system encourages banks to take excessive risk. In this context the purpose of this study is to analyze the link between deposit insurance and bank risk taking. For this purpose, a panel regression analysis is applied to the ratio of deposits under insurance to total deposits and basic risk measures of banks operating in Turkey during 2004-2010. Keywords: Deposit insurance, bank risk taking, panel data regression. 1. INTRODUCTION Banking sector is special with its nature of financing long term investments with relatively short term deposits. This feature makes banks vulnerable to various types of risks both from market and from themselves. One of the threats towards banking system is the sudden withdrawals of large amount of deposits which is known as bank runs. This brings the need of applying some regulatory techniques to maintain “safety and soundness” of banks. Deposit insurance system is used as a regulatory tool in most countries for many years. The aim of such a system is to provide banking sector’s stability preventing banks from being subject to runs. Carapella and DiGiorgio (2004:77) define this system as: 119 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo “…an instrument through which the banking system guarantees that funds deposited by the public in a bank are independent of solvency and liquidity conditions of the bank itself, so that depositors may be sure of being reimbursed at any time”. Deposit guarantees are designed to protect small and usually uninformed depositors (Silva, 2008:28) from losses depending on bank defaults, while protecting banking system’s stability (Aydın, Başar, et al., 2006:246). Thus, it reduces the likelihood of bank panics and protects banks from facing the problem of excessive and unexpected deposit withdrawal (Şıklar: 2004:243). Deposit insurance system has various advantages for both depositors and banks. However academic debates commonly focus on whether this system is a source of moral hazard which reduces incentives of depositors to monitor their banks while encourages banks to take excessive risks (Silva, 2008; Beck, 2008:8; Boyd, De Nicola, 2005:1330; Bartholdy, Boyle et al., 2003:701; Bossone, 2000). In Turkey, the system is first introduced in 1933 and taken its final form with regulations in 2004. Deposit insurance in Turkey is handled by Savings Deposit Fund Insurance and according to the latest regulations compensation limit covers a maximum of 50,000 TL per depositor per member institution. From this point of view, the purpose of this study is to investigate whether the amount of insured deposits affects the risk taking incentives of individual banks. According to our knowledge, this is the first study which analysis the bank level effects of deposit insurance system in Turkey. Originality of the study also depends on the deposit insurance proxy variable used in the analysis. The rest of this paper is organized as follows: Section 2 looks at the related literature. Section 3 describes data and variables and Section 4 introduces the methodology used in our empirical analysis. Section 5 presents the empirical findings. Finally in Section 6 we conclude. 2. Literature Review Demirgüç-Kunt and Detragiache (1999) tested the effect of deposit insurance on bank stability. Using the data of 61 countries during 1980-1997, the study found that explicit deposit insurance tends to be detrimental to bank stability. Ninimaki (2000) analyzed the joint effect of competition and deposit insurance on banks’ risk taking when the riskiness of banks can not be observed by depositors. According to the results, if the bank is monopoly or banks compete only in the loan market, deposit insurance has no effect on risk taking. But introduction of deposit insurance triggers risk taking if there is competition in deposit market. In a similar study, Wu and Chi (2006) aimed to find out the relationship between competition and risk taking. They found that this relationship depends on the interactions of market structure between loan and deposit markets, deposit insurance and depositors’ risk aversion. Focusing on the effects of deposit insurance, the results suggest that with full deposit insurance coverage an increase in competition for deposit will trigger moral hazard problem while an inverse impact occurs under competition for loan. If the deposit insurance system is not introduced, then the risk taking behaviors of banks depend on depositor’s risk internalization. Bartholdy, Boyle et al. (2003) used data from 13 countries to investigate the relationship between deposit insurance and deposit risk premiums. Results suggest that insured deposits have a lower risk premium compared to the uninsured deposits. Another result of the study is that relationship between the risk premium and the maximum dollar value of insurance 120 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo coverage is non-linear that means moral hazard incentives are recognized and priced by investors. Gropp and Vesala (2004) investigated the impact of deposit insurance on EU banks’ risk taking during 1990s. The results suggest that the introduction of explicit deposit insurance system may significantly reduce risk taking. The authors also found some evidence that explicit deposit insurance might be a useful way to limit the safety net, increase market monitoring of banks and reduce moral hazard. Leaven and Levine (2008) assessed the relationships among risk taking of banks, their ownership structures and national bank regulations including deposit insurance system. Their results suggest that the impact of deposit insurance on banks’ risk taking varies depending on the ownership structure of banks. If the bank is widely-held, deposit insurance has not have a significant impact on risk taking. On the other hand if bank has a majority owner, bank risk increases significantly with an increase in deposit insurance. Silva (2008) has introduced deposit insurance in a model of information based bank runs. Results show that the net effect of deposit insurance on the equilibrium demand deposit contract is to raise its value and also the risk of runs. So deposit insurance induces moral hazard. Ioannidou and Penas (2010) analyzed the effect of deposit insurance on the risk taking behavior of banks. Using the case of Bolivia, the authors compared the risk taking behavior of banks before and after the introduction of deposit insurance system in December 2001. Their main findings indicate that the introduction of deposit insurance system led to an increase in the probability of a bank originating a subprime loan. The results also suggest that banks do not increase collateral requirements or decrease loan maturity to compensate for the extra risk. Cross sectional analysis confirm the consequence that banks take more risk after the deposit insurance system is introduced. Angkinand and Wihlborg (2010) analyzed whether deposit insurance systems and ownership structures of banks affect the degree of market discipline on banks’ risk taking. They found that total effect of explicit deposit insurance coverage on risk taking is shown as a U-shaped curve. This indicates that risk taking is minimized at a positive and partial insurance coverage level where market discipline is at its strongest. Ng, Lim et al. (2010) searched the relation between explicit deposit insurance and risk taking of banks in Malaysia during 2004-2007. The authors found that explicit deposit insurance had different effects on various risk factors. After the introduction of deposit insurance scheme, only two risks, interest rate risk and risk-weighted capital ratio deteriorated. Deposit rate, credit risk, liquidity risk and core capital ratio were not significantly changed for the postintroduction period. 3. Data and Variables To analyze the effects of deposit insurance on banks’ risk taking, we used annual bank level data of 27 banks continually operated in Turkey during 2004-2010 (from the beginning of implementation of the latest deposit insurance regulation to the present). Following Ng, Lim, et al. (2010), we aimed to understand the effects of the insured deposits/total deposits ratio (as the proxy of explicit deposit insurance) on banks’ risk taking and activities. For investigating banks’ risk taking behaviors, following variables are selected: 121 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Table 1: Definition of Variables Variables Acronyms Definition Deposit insurance DI Insured deposits/total deposits Credit Risk NPL Non-performing loans/total loans Liquidity Risk LIQ Liquid assets/short term liabilities Interest Rate Risk Exposure INT Interest rate sensitivity of balance sheet and * off-balance sheet position/total capital Capital Adequacy Ratio CAR Capital as a fraction of risk-weighted assets 4. Methodology Following the existing literature, the main hypothesis of this study is that banks tend to take more excessive risks if their ratio of insured deposits to total deposits is higher. In order to investigate this assumption, we applied regression analysis to our panel data set of 189 observations including 27 banks and 7 years. Effects of insured-deposit-rates on several risk factors are analyzed separately. So our key independed variable is deposit insurance (DI). An Ordinary Least Squares technique is used; because it is suitable to use for the econometrics of panel data because of the double individual dimensions of the data (Batisse, 2001). Random effects technique is selected in estimations according to the data structure. Definitely, insured-deposits-rate is not the only variable determining the risk levels of banks; but the others wouldn’t be considered in the context of this study. In our empirical analysis, we just add three control variables to improve the explanatory power of DI. These variables are;    Crises Dummy: The global financial crisis quite likely has affected the level of risk variables. The crisis has begun in 2007 but its effects are experienced in Turkey especially in 2009. We can understand this looking at the negative growth rates of the economic indicators (mainly Gross Domestic Product-GDP growth) in 2009. So the variable takes “1” for 2009 and “0” for other years. Real GDP: Gross Domestic Product is expected to have an impact on the risk levels of banks affecting their borrowers’ solvency as well as risk appetite of banks. Data is obtained from www.dpt.gov.tr. Inflation: Proxied by producer price index. Basic impact of inflation is expected to be on interest rates. Inflation also increases the uncertainty of the future for borrowers, depositors and for banks. So the variable is expected to have a positive effect on risk levels. Data is obtained from www.dpt.gov.tr. * Ratio of the difference between the liabilities subject to repricing within one year and the assets subject to repricing within one year plus off-balance sheet position to total capital 122 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo 5. Empirical Results Applying OLS technique to our panel data set, we have found that the effect of deposit insurance is significant only for two variables. These variables are NPL (non performing loan ratio) and LIQ (liquidity ratio). The directions of these effects are consistent with literature and with our expectations. Results suggest that the amount of insured deposits over total deposits has a positive and significant effect on NPL supporting the “moral hazard” argument. According to this argument, deposit insurance makes banks less sensitive in screening and monitoring of loans and this attitude increases the level of NPL. Another significant effect of DI is on LIQ and the direction of this effect is negative as expected. This result suggests that when the insured deposit rate is higher, i.e. bank’s responsibility is undertaken by government, bank’s incentive to invest in liquid assets in order to meet its obligation is destroyed. Interest rate sensitivity (INT) is affected positively by DI. It means that banks become less careful in matching assets and liabilities according to the time remaining to repricing. But this effect is not statistically significant. Deposit insurance affects Capital Adequacy Ratio (CAR) negatively. Banks consider deposit insurance as compensation towards their potential losses but if their obligations are insured by the government, they don’t consider equity necessary. However, this effect is not statistically significant. In determination of LOAN variable, DI gets negative coefficient but it is not significant. In the equation of DEP, DI gets positive coefficient but this effect is insignificant either. Table 2- Empirical Results Depended Variable Coefficient of DI Probability R2 NPL 0.090385 0.0940* 0.019856 LIQ -0.303803 0.0454** 0.094406 INT 0.003329 0.4712 0.021026 CAR -0.081813 0.2556 0.075056 LOAN -0.904939 0.5806 0.024697 DEP 0.056876 0.2336 0.021429 * Significant at %10 significance level * Significant at %5 significance level 6. Conclusion Deposit insurance is a system which guarantees repayments of deposits to depositors and in this way protects financial system’s stability preventing bank runs. However, there is a common suspicion in academic literature on whether this system leads banks to behave less 123 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo prudently and encourages them to take excessive risks. This question is widely investigated in academic researches and common view is that the system is a source of moral hazard. In Turkey, deposit insurance system is being held for many years under various politic attitudes. For example during 1990s, deposit insurance covered 100% of deposits in each bank. Since 2004, coverage limit is discounted to 50,000 TL for each depositor in each bank. In this context, the aim of this study is to determine what kind of results occurs at bank level by implementing this new system. To analyze the possible effects of deposit insurance system, we applied Ordinary Least Squares method to our bank level data including a panel of 27 banks operated in Turkey during 2004-2010. The key independed variable of this study is the amount of insured deposits over total deposits (DI). The aim of the study is to determine the effects of DI on various kinds of risk factors and activities. We considered three risk factors which are credit risk (NPL), interest rate risk and liquidity risk. Other depended variable are Capital Adequacy standard ratio (CAR), loans/deposits ratio (LOAN) and deposits/total assets ratio (DEP). Supporting the moral hazard argument, results suggest that deposit insurance raises credit risk which is proxied by NPL ratio. Insured deposit rate also has a deteriorating effect on banks’ liquidity. On the other hand, the variable isn’t found related with interest rate risk, capital ratio, loan ratio and deposits ratio. Basing on these findings, we suggest that regulatory institutions should focus on the moral hazard of banks to eliminate the adverse effects of the system. In order to explore whether the impact of deposit insurance on banks changed after the latest regulation, a further analysis is necessary. REFERENCES Angkinand, Apanard, C., Wihlborg, (2010), “Deposit Insurance Coverage, Ownership and Banks’ Risk Taking in Emerging Markets”, Journal of International Money and Finance, Vol. 29, 252-274. Aydin, N., Başar M., Coşkun, M., (2006), Bankacılık Uygulamaları, Anadolu Üniversitesi Ya. No. 1711, Eskişehir. Bartholdy, Jan, Glenn W. Boyle, R.D. Stover, (2003), “Deposit Insurance and the Risk Premium in Bank Deposit Rates”, Journal of Banking and Finance, Vol. 27, 699-717. Batisse, C., (2001), “Externalities and Local Growth: A Panel Data Analysis Applied to Chinese Provinces”, International Conference of the Chinese Economy, Has China Become a Market Economy?, May 17-18 2001, France. Beck, T., (2008), “Bank Competition and Financial Stability: Friends or Foes?”, World Bank Policy Reseach Working Paper, No. 4656, pp. 1-30. Bossone, B., (2000), “What Makes Banks Special? A Study of Banking, Finance and Economic Development”, World Bank Working Papers, No. 2408, pp.1-66. Boyd, J.H, de Nicola, G, (2008), “The Theory of Bank Risk Taking and Competition Revisited”, the Journal of Finance, Vol. 60, No. 3, 1329-1343. Carapella, F., G. Di Giorgio, (2004), “Deposit Insurance, Institutions and Bank Interest Rates”, Transition Studies Review, Vol. 11, no. 3, 77-92. Demirgüç-Kunt, A., E. Detragiache, (1999), “Does Deposit Insurance Increase Banking System Stability? An Empirical Investigation”, World Bank Policy Research Working Paper, No. 2247. 124 �3rd International Symposium on Sustainable Development, May 31 - June 01 2012, Sarajevo Gropp, Reint, Jukka Vesela, (2004), “Deposit Insurance, Moral Hazard and Market Monitoring”, European Central Bank, Working Paper Series, No. 302. Ioannidou, Vasso, P., M.F. Penas (2010), “Deposit Insurance and Bank Risk Taking: Evidence from Internal Loan Ratings”, Journal of Financial Intermediation, Vol. 19, 95-115. Keeley, M.C., (1990), “Deposit Insurance, Risk and Market Power in Banking”, The American Economic Review, Vol. 80, No. 5, 1183-1200. Leaven, L., R. Levine, (2008), “Bank Governance, Regulation and Risk Taking”, NBER Working Paper Series, No: 14113. Ng, Tuan Hock, Lim, Y.S., Tan N. L., (2010), “Deposit Insurance and Bank Risks: The Case of Malaysia”, European Journal of Economics, Finance and Administrative Sciences, Issue 18, 19-27. Ninimaki, J-P., (2000), “The Effects of Competition on Banks’ Risk Taking with and without Deposit Insurance”, Bank of Finland Discussion Papers, No. 21. Şıklar, İ, (2004), Finansal Ekonomi, Anadolu Üniversitesi Ya., No. 1588, Eskişehir. Silva, Nancy (2008), “Deposit Insurance, Moral Hazard and The Risk of Runs”, Central Bank of Chile Working Papers No. 478. Wu, R-J, C-P, Chi, (2006), “Competition, Deposit Insurance and Bank Risk Taking”, http://centerforpbbefr.rutgers.edu/2006/Paper%202006/16AS02-056-Chien-Ping%20Chi.pdf. www.dpt.gov.tr www.tbb.org.tr Control of a chaotic finance system with passive control Selçuk Emiroğlu, Yılmaz Uyaroğlu, Etem Köklükaya Sakarya University, Electrical Electronics Engineering Department, Turkey E-mails: selcukemiroglu@sakaryaedu.tr, uyaroglu@sakarya.edu.tr, ekaya@sakarya.edu.tr Abstract In this paper, complicated dynamical behavior of a finance system is investigated. The change in behavior of finance system from stable behavior to chaotic behavior is shown with varying some system parameters. In addition, chaotic finance system with passive control is considered and the stability of the controlled system is investigated. In order to control the chaos in finance system, the controller is designed based on passive control technique. Designed controller is applied to the chaotic finance system for stabilization of system. After controller is added to the system, the change in behavior of finance system from chaotic behavior to stable behavior is shown with passive control. Keywords: Chaotic finance system, chaos control, passive control 125 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1286 Title A name given to the resource The link between deposit insurance And banks’ risk taking Author Author Göçmen , Yağcilar Gamze Abstract A summary of the resource. Deposit insurance is an insurance system that guarantees bank deposits of people in case of bank failure or a run on the bank. The system is first introduced in 1933 for Turkey and taken its final form with regulations in 2004. Deposit insurance in Turkey is handled by Savings Deposit Fund Insurance and according to the latest regulations compensation limit covers a maximum of 50,000 TL per depositor per member institution. Deposit insurance system which is adopted in most countries has various advantages for both individuals and banks. However academic debates commonly focus on whether this system encourages banks to take excessive risk. In this context the purpose of this study is to analyze the link between deposit insurance and bank risk taking. For this purpose, a panel regression analysis is applied to the ratio of deposits under insurance to total deposits and basic risk measures of banks operating in Turkey during 2004-2010. Keywords: Deposit insurance, bank risk taking, panel data regression. Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),HB Economic Theory,HG Finance,HJ Public Finance https://omeka.ibu.edu.ba/files/original/41ee66983f5f7f59a45bee6afe90e705.pdf de17892933bb4c47a125ffc865c741a8 PDF Text Text Structural Engineering Applications of Artificial Neural Networks Hakan Başaran, Muhiddin Bağcı Celal Bayar University, 45140, Manisa, Turkey E-mails: hakan.basaran@bayar.edu.tr,muhiddin.bagci@bayar.edu.tr Abstract In this study, infilled planar frames and confined reinforced concrete section have been analysed using Artificial Neural Network (ANN). ANN architecture was chosen in which multi layer, feed forward, and back propagation algorithm was used. The training data of infill frame used were provided by a finite element model in which non-linearity of materials and the structural interface were taken into account under increasing lateral load. Using the proposed analytical model (layered model) were generated the training data for confined reinforced concrete section. Analytical technique uses realistic material models for confined and unconfined concrete. After completing the training phase, verification of the performance of the network was carried out using old (included in training phase) and new (not included in training phase) patterns. The controls conducted in the test phase. The findings of this exercise show that the ANN algorithm can be successfully and easily used within reasonable accuracy in order to decrease computational time in finding infill frame and the momentcurvature relationships of reinforced concrete sections. Keywords:. Artificial Neural Network, Finite Elements Method, Infilled Frame, Confined Reinforced Concrete Section, Moment-Curvature 1. INTRODUCTION The mathematical models have been widely applied for the analysis of infilled frame. Holmes M (1961) modelled the infill effect occurring in an infilled frame without considering the effects on the interface between frame and infill. In studies conducted by Smith BS (1962), the approach of diagonal compression strut was dealt with in a more detailed way. Using a finite element model, Mallick DV and Severn RT (1967) attained the results without considering the shear effect on the infill frame interface. With a program they prepared. Infilled planar frames have been analysed using artificial neural network by Bağcı and Altintaş (2006). The layered model for confined reinforced sections was first used by Pavriz et al (1991). Moment-curvature relationships of confined concrete sections were investigated by Ersoy U and Özcebe G (1997). For some other examples of ANN applications, the reader 433 �is referred to (Jadid MN and Fairbairn DR (1996), Lee et al (1992), Avdelas et al (1995), Karlık et al (1998). In this study, the stiffness, moment and shear force values on frame for five different height of infill wall are calculated using finite elements method (FEM). The behavior values of confined reinforced concrete sections subjected to flexure and axial load are obtained by using analytical solution (layered model). The calculated key values are used in training a multi-layer, feed forward, back propagation artificial neural network (ANN). The outcomes of training phase were then tested using the data set reserved for this the network purpose. The findings of this exercise have shown that the ANN algorithm can be successfully and easily used within reasonable accuracy in order to decrease computational time in infilled frame and confined section problems. 2. PARAMETRIC STUDIES Dimensions of infilled frame given by Fiorato AC and Sözen M (1973) in Fig. 1 are shown, and the materials properties are listed in Tab. 1. The lateral load (P) was applied at the top left hand corner of the frame in Fig. 1a in 20 increments of 10 kN each. Figure 1a. Frame-infill wall 1b. Mesh model of with full infill wall 434 �Table 1. Properties of material Modulus of elasticity Compression Strength 2 Tension Strength Poisson (kN/m2) Ratio 2 (kN/m ) (kN/m ) Frame 2.85x107 3.1x104 3x103 0.2 Infill 1.7x107 3.1x104 2.8x103 0.2 The wall was modelled mesh of quadrilateral-shaped isoparametric plane stress elements as shown in Figure 1b. The results of a numerical study are given in Tab. 2, with respect to whether the infill fills the space among the frame. Infill height is h with  being ranging from 0 and 1 (=1, =0.8, =0.6, =0.4, =0.2 and bare). Table 2. Results of FEM Infill Loadheight P Stiffness (infill / no infill) (kN) Left column shear force Left column moment Infill Loadheight P Stiffness (infill / no infill) (kN) Left column Left column Moment shear force /lateral load (Infill / no infill) (Infill / no infill) / lateral load h 435 10 5,65700 0,19000 0,19000 20 5,65700 0,19000 30 5,65700 40 0,4h 10 1,3140 0,51400 0,87900 0,19000 20 1,2570 0,54200 0,91900 0,19000 0,19000 30 1,2170 0,55000 0,93800 5,65700 0,19000 0,19000 40 1,2050 0,55200 0,94700 50 5,57100 0,19000 0,19000 50 1,2000 0,56000 0,94700 . . . . . . . . . . . . . . . . �It has been seen that the effect of infill gets clear only when it reaches at 0.4 for the value at the initial step of loading, though the stiffness of, infilled frame reaches at 5.7 fold, a rather high number, local failures occurring in the infill as a result of increasing dimensionless load, leads to a decrease in the overall stiffness of the system. Shear strength of the column increases with the height of infill. When the height of infill reached at the value of 0.8h, it was seen that the shear force of the column was 50 % higher than the shear force it carries when it was a bare frame. In this case, “short column” verifies its effect. When the height of infill was organised at the height of storey, it was seen that it was useful in term of shear strength of column. Input parameters are lateral load (P) and height of infill (h) values. Output parameters are stiffness (infill /no infill), shear force / lateral load and moment (infill / no infill) at the loaded column. As it is known, in neural network applications, the input values and output values can be reduced to the values between 0-1. That is the normalization process, which is done in this work dividing P’s by 220 and dividing h’s by 1.1h. The output values were also divided by 5.7 stiffness ratio, 0.7 shear force ratio and 1.1 moment ratio, which were the highest values that we used in our application. Training was performed for the heights of wall h, 0.8h, 0.6h, 0.4h, 0.2h and bare and for loads of frame 10, 40, 70, 110, 150,190. As known, the general aim in the training process is to teach the relations between input and output values to the program and to obtain good answers to different input values with the possible lowest error rates. Values obtained from the numerical procedure (FEM) are used in the network training. A special code was used for ANN exercise by Karlık, B et al (17). It is adapted and fitted to our application with some changes. ANN architecture with multilayered, forward feeding and backward propagation algorithm was chosen for the training. The ANN architecture used is a 2:9:9:3 multi-layer architecture as shown in Fig. 2. Exact and ANN values of output are compared in Tab. 3 for various h and P values. For these training  FEM  ANN / ANN number of output values, the ANN algorithm produced results with average error less than 0.2 %. The maximum value for FEM / ANN is about 1.0351 in 0.4h infill height and 70 kN load value. 436 �Figure 2. Network Architecture for infilled frame Table 3. The results of ANN and FEM in training Left column Infill Height Lateral load kN Solution Method Stiffness ratio (Infill / no infill) FEM/ ANN Left column Shear force/lateral load FEM/ ANN Moment ratio FEM/ ANN (Infill / bare) FEM 5.65699 10 0.18999 0.19000 0.9977 ANN 5.67016 FEM 5.65699 40 0.9984 0.19030 0.18999 0.19000 1.0136 ANN 5.58108 FEM 4.77100 70 1.0004 0.18992 4.79018 FEM 3.97100 0.9650 0.19688 0.21499 0.25199 0.9960 ANN 1.0246 0.18543 0.9963 0.21580 1.01.91 0.24728 h 110 0.28000 0.34500 1.0105 ANN 3.92982 FEM 3.66800 150 0.9961 0.28110 0.31999 0.39299 0.9976 ANN 3.67685 FEM 3.51399 190 0.9859 0.32458 1.0051 0.39100 0.35999 0.42000 1.0034 ANN 0.9858 0.34998 0.9830 3.50218 0.36621 0.9942 0.42245 In Fig. 3, the mean square errors (MSE) in training versus iteration numbers are shown for problem. After 1600 iterations, the mean square errors dropped drastically. For more than 15000 iterations, our architecture 2:9:9:3 used in the analysis possesses the lowest total error values. 25,00 % MSE 20,00 15,00 10,00 5,00 437 0,00 0 200 400 600 800 1.000 Number of ıteration 1.200 1.400 �Figure 3. Mean Square Errors (MSE) based on iteration numbers for infilled frame Different input values were applied to the program for testing the neural network and the results were obtained in milliseconds. Testing was performed for height of wall h and for load values of frame 20, 60, 100, 140, 180. In Tab. 4, we compare the test phase results of ANN and FEM. Table 4. Test Phase Results for infilled frame Load of Infill P wall kN Stiffness ratio Height 20 60 h 100 140 180 438 Method (Infilled / no infill) FEM 5,65700 ANN 5,64500 FEM 5,18800 ANN 5,22100 FEM 4,1140 ANN 4,2550 FEM 3,73100 ANN 3,68000 FEM 3,54200 ANN 3,53400 Left Column FEM Left column / shear force / Moment ratio ANN /lateral load (infilled/ 1.0021 0.9937 0.9669 1.0138 1.0022 0,19000 0,18600 0,19500 0,19700 0,27000 0,27130 0,31000 0,3088 0,35000 0.3485 FEM ANN FEM / ANN no infill) 1.0215 0.9898 0.9953 1.0038 1.0043 0,19000 0,19120 0,21400 0,2134 0,32600 0,31600 0,38000 0,37290 0,41400 0,41485 0.9938 1.0028 1.0316 1.0190 0.9998 � FEM  ANN / ANN number of output ) obtained is obviously about 0.269. The The average % error ( maximum value for FEM / ANN is about 1.0316 in 100 kN load value. From an engineering point of view, these errors are considerably low. The other parametric study has been conducted to observe the effect of different variables on behavior of confined reinforced section shown in Fig. 4. Figure 4. The cross-section considered in analyses. Variables selected to incorporate in the expression of moment-curvature are compressive strength of concrete (fck), the ratio of the axial load to the axial load capacity (N/No), yield strength in transverse reinforcement (fsh), space of transverse reinforcement (s), diameter of transverse reinforcement (Ø), ratio of longitudinal steel (), yield strength of longitudinal steel (fyk) as shown in Tab. 5. Where TY, TH, CvC, CoC , , M are yield in tension, hardening of reinforcing in tension , cover crushing, core crushing, strain at maximum moment, and maximum moment, respectively. The results obtained from Tab.5 demonstrates no very significant effect on Moment capacity from compressive strength (fck) in case of pure bending (N=0). The compressive strength becomes effective with increasing axial load. Maximum moment capacity shows changes of ±25% due to ±25% compressive strength variation. The increasing compressive strength causes the decrease in ductility. As level of the axial load (N/No) on the cross-section increases, ductility decreases. Increase in ductility with decreasing axial load is very significant. It is interesting to note that, although the section considered is well confined, the behavior becomes very brittle under high levels of axial load. The upper limits imposed on axial loads in seismic codes results from such considerations. Table 5. The results according to different variables of confined concrete section 439 �It is seen that yield strength in transverse reinforcement (fsh) has no effect on behavior for all levels of axial load. The spacing of the lateral reinforcement (s) in the confined section is ineffective on behavior at low level of axial load. The maximum moment capacity and ductility increase when spacing of the lateral reinforcement is reduced with increasing axial load. As ductility increases with diameter of transverse reinforcement (Ø), it has no very effect on moment capacity. The crushing of core concrete delays with increasing diameter of transverse reinforcement. The diameter of transverse reinforcement becomes effective with the increasing axial load. The quantity of longitudinal reinforcement (ρ) has an important effect on behavior of the confined section. Maximum moment capacity causes increasing 10% due to a the quantity of longitudinal reinforcement variation 30%. The quantity of longitudinal reinforcement has very significant effect on behavior at low level axial load. The moment capacity decreases with the higher axial load . The quantity of longitudinal reinforcement is ineffective on ductility. The yield strength of longitudinal bar (fyk) is effective parameter in case of pure bending. Maximum moment capacity causes changing ±10% due to a yield strength of longitudinal reinforcement variation ±30%. In this study , a neural network program which was written by Karlık et al. (1998) in PASCAL was used . Seven variables for input and six variables for output values were considered in the application. As it is known, in neural network applications, the input values and output values can be normalized to the values between 0-1. It is seen that the best results were obtained with learning rate  of 0.7, and momentum value µ of 0.9. The number of nodes in the hidden layer was changed for new trials. 1000 iterations were performed for each 440 �node number between 1 and 0, and the errors were obtained from the program per 100 iterations. The chances in % error values of 1000 iterations due to the number of hidden layer nodes are shown in Fig 5. Finally, the lowest errors were obtained in the order of 7:12:13:6 which means 7 input values, 12 and 13 nodes in hidden layers and 6 output value. Thus, the network architecture would be as in Fig 6 Figure 5. The error changes due to the number of nodes in the hidden layer 1000 iterations. The training iterations were increased to 5000. So, we obtained as low as 0.07% average errors, which is reasonably good for ANN applications. The change in errors can be seen in Fig. 7.. Figure 6. ANN architecture for confined sections 441 �% error 1,2 1,1 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Number of iterations Figure 7. The error change at ANN architecture (7:12:13:6) for confined sections ANN values of output are compared in Tab. 6. The average error between analytical and Analytical ANN / ANN numberof solution ANN ( ) is produced less than 0.2 %. The maximum difference (Analytical / ANN) for TY, TH, CvC, CoC, and M is about 0.965, 0.978, 1.039, 0.961 , 0.962 , and 0.976 , respectively. From an engineering point of view, these errors are considered low. Table 6. Training process and results for confined sections A comparison of test and analytical values is given in Tab. 7. The average error Analytical ANN / ANN numberof solution ( ) obtained is obviously about 0.33%. The maximum difference (Analytical / ANN) for TY, TH, CvC, CoC,  and M is about 0.967, 0.966, 0.972, 0.968 , 0.991 , and 0.992 , respectively. From an engineering point of view, these errors are considered low. 442 �Table 7. Testing process and results for confined sections 4. CONCLUSION In this paper, an alternative numerical and analytical technique, an ANN algorithm is used in the analysis of infilled frame and confined reinforced section. Neural simulation of numerical and analytical procedure is given in this study. To reduce the calculation time of the microprocessor of system, a new computer program is used by the ANN method, which gives answer in milliseconds. ANN architecture was chosen in which multi layer, feed forward, and back propagation algorithm is used. The training data of infill frame are provided by a finite element model in which non-linearity of materials and the structural interface were taken into account under increasing lateral load. For the inelastic static analysis, an incremental iterative procedure is adopted. Using the proposed analytical model (layered model) are generated the training data for confined reinforced concrete section. Developed model is using layered modeling technique and capable of taking into account; crushing of cover and core concrete, strain hardening of steel and effect of confinement on core concrete. After completing the training phase, verification of the performance of the network was carried out using old (included in training phase) and new (not included in training phase) patterns. The controls conducted in the test phase. ANN algorithms can not of course replace totally the conventional numerical and analytical techniques, since they need some key values for training. However, in the analysis infilled frame and confined reinforced sections, they can be implemented as an efficient supplementary tool reducing drastically the computational cost. Modeling process in neural network is more direct, since there is no necessity to specify a mathematical relationship between input and output variables. The trained ANN is able to produce quick results in the analysis of infilled frame and confined reinforced section with the same degree of accuracy as numerical and analytical model. Therefore, the trained ANN may be used in practice for the design of infilled frame and confined cross section as on alternative to the time consuming numerical and analytical procedure. 443 �REFERENCES Holmes M.1961. Steel Frames with Brick Work and Concrete Infilling. Proc. Instn. Civ. Engrs. 19: 473-498 Smith BS.1962. Lateral Stiffness of Infilled Frames. Journal of Struct. Div. ASCE. 8, 183-99 Mallick DV and Severn RT. 1967. The behaviour of infilled frames under static loading. Proc. Instn. Civ. Engrs. 38, 639-656. Bağcı M., Altıntaş G.2006. Artificial Neural Network Analysis of Infilled Planar Frames, Proceedings Of ICE, Structures & Buildings 159(1), 37-44. Parviz S, Jongsung S, and Jer-Wen H. 1991. Axial / Flexural Behavior of Reinforced Concrete Sections / Effects of Design Variables. ACI, 88, 17-21. Ersoy U.and Özcebe G.1997. Moment-Curvature Relationship of Confined Concrete Sections. First Japan-Turkey Workshop On Earthquake Engineering, Ankara, Turkey, 10-21. Jadid MN and Fairbairn DR.1996, Neural-network Applications in Predicting Momentcurvature Parameters from Experimental Data. Engineering Applications of Artificial Intelligence, 9, 309-319. Lee Y, Oh SH., Hong HK., and Kim MW.1992. Design Rules of Multi-Layer Perceptron. Science of Artificial Neutral Nets in Structural Mechanics. Structural Optimisation, 1710: 329-339. Avdelas AV, Panagiotopoulos PD, and Kortesis S.1995. Neutral Networks for Computing in the Elastoplastic Analysis of Structures. Meccanica, 30: 1-15. Karlık B, Özkaya E, Aydın S, and Pakdemirli M.1998. Vibration of beam-mass system using artificial neural networks. Computers & Structures, 1998, 69: 339-347. Fiorato A. C., Sözen M. A.1973. An investigation of the interaction of reinforced concrete frames with masonry filler walls. Structural research series report No. 370, University of Illinois, Urbana. 444 � Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Extent The size or duration of the resource. 1201 Title A name given to the resource Structural Engineering Applications of Artificial Neural Networks Author Author Hakan , Başaran Abstract A summary of the resource. In this study, infilled planar frames and confined reinforced concrete section have been analysed using Artificial Neural Network (ANN). ANN architecture was chosen in which multi layer, feed forward, and back propagation algorithm was used. The training data of infill frame used were provided by a finite element model in which non-linearity of materials and the structural interface were taken into account under increasing lateral load. Using the proposed analytical model (layered model) were generated the training data for confined reinforced concrete section. Analytical technique uses realistic material models for confined and unconfined concrete. After completing the training phase, verification of the performance of the network was carried out using old (included in training phase) and new (not included in training phase) patterns. The controls conducted in the test phase. The findings of this exercise show that the ANN algorithm can be successfully and easily used within reasonable accuracy in order to decrease computational time in finding infill frame and the momentcurvature relationships of reinforced concrete sections. Keywords:. Artificial Neural Network, Finite Elements Method, Infilled Frame, Confined Reinforced Concrete Section, Moment-Curvature Date A point or period of time associated with an event in the lifecycle of the resource 2012-05-31 Keywords Keywords. Conference or Workshop Item PeerReviewed H Social Sciences (General),T Technology (General)