This paper presents a reference architecture known as Virtual Physical Space which can be adapted to build Internet of Things applications in various domains. The close relationship under Cyber-Physical Spaces, Internet of Things and Cyber-Physical-Social Spaces are also discussed. Furthermore, the basic building components of the proposed architecture are presented in the paper.

This paper presents the functionality and overall architecture of an eTesting system designed to operate as an integrated part of a virtual eLearning space. The eTesting system provides distant eTesting facilities available to all registered users (e.g. students, educators, test content authors and all others involved in the testing process). An overview of the IoT paradigm is made. The authors' vision of an IoT eLearning ecosystem is described. The possibilities for integrating the presented eTesting system as a proactive unit in an IoT eLearining ecosystem are analyzed and examined. The conceptual approaches adopted by the eTesting system are analyzed in the context of the goal IoT integration. An architecture specifying the integration of RESTful web services with an assessment BDI agent system managing the eTesting processes are explained. Finally, an overview of the proposed eTesting system is provided in the aspect of its compatibility and integration within the virtual eLearning space.

Plovdiv faces significant air pollution challenges due to geographic, climatic, and industrial factors, making accurate air quality assessment critical. This study presents a hybrid multi-agent platform that integrates symbolic and sub-symbolic artificial intelligence to improve the reliability of air quality monitoring. The platform features a BDI agent, developed using JaCaMo, for processing real-time sensor measurements and a ReAct agent, implemented with LangChain, to incorporate external data sources and perform advanced analytics. By combining these AI approaches, the platform enhances data integration, detects anomalies, and resolves discrepancies between conflicting air quality reports. Furthermore, its scalable and adaptable architecture lays the foundation for future advancements in environmental monitoring. This research represents the first stage in developing an AI-powered system that supports more objective and data-driven decision-making for air quality management in Plovdiv.

This paper presents the design and implementation of an integrated software platform that connects air quality monitoring systems with smart agriculture tools, with a focus on secondary air quality standards. The study investigates the correlation between air pollution and the development of tomato crops in the Plovdiv region of Bulgaria. The system integrates two intelligent agents: Air Agent, responsible for analysing real-time pollutant data, and Farmer Agent, which monitors the vegetation dynamics of tomato plants. Experimental results from the 2023 growing season indicate that elevated concentrations of PM2.5 and PM10 during critical growth stages significantly inhibited both plant height and stem development. The proposed platform utilises ontologies, asynchronous communication protocols, and data analytics to provide actionable insights for farmers and to support the practical application of environmental standards in agriculture. These findings underscore the importance of incorporating secondary air quality considerations into agricultural decision-making and environmental policy.