machine learning]]> load capabilities we will develop a collection of machine learning methods for detecting buildings
with best cooling load capabilities. This collection will study the influence of 8 input variables (relative
compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area
distribution) on one output parameter, that is cooling load of buildings. The results of this study
support the practicability of using machine-learning software to estimate building parameters as a
convenient and accurate approach, as long as the methods chosen are well suited for the type of data
in question.]]> The front end is built with React and Leaflet (Leaflet.draw over OpenStreetMap tiles), allowing workers to define one or more work areas by drawing circles, rectangles, and polygons. On the server, a NestJS, TypeORM, PostgreSQL/PostGIS stack normalizes incoming GeoJSON to SRID 4326 (WGS84) and consolidates all shapes into a canonical MultiPolygon with GiST indexing for efficient spatial queries [1].
Security is managed through a production-grade authentication subsystem, including OTP email verification, Google OAuth, and short-lived JWT access tokens (RS256/ES256) carrying a kid header mapped to a database Keystore and Signing Key tables. Refresh tokens are device-scoped, hashed, and rotated on use, while a public JWKS route exposes verification keys for third-party consumers. Role-scoped access is enforced via modular guards, with session-based authentication planned for first-party web sessions.
The matching system blends relevance and fairness by combining distance containment, price fit, category/competency match, listing freshness, and Bayesian worker rating into a configurable score. The query builder pre-filters by geometry and competencies before ranking, targeting sub-500 ms feed latency under spatial

indexing. A focused test suite with Jest unit tests validates token/key rotation, keystore lifecycle, query-builder filters, and geospatial utilities.
Operational deployment is packaged for Dokploy (API, database, secrets) with optional Tailscale access, while OpenAPI/Swagger documentation ensures discoverability.
Radesso contributes a cohesive blueprint—and a working core—for a localized job-seeking platform where workers proactively seek orders. The near-term roadmap includes reviews and appeals, enhanced admin moderation, and telemetry, positioning Radesso to evolve into a trusted, geospatially precise hub for local work.
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