The effect of one of the most popular 3D visualization and modelling technologies with haptic and touch feedback possibilities-augmented reality (AR)-is analysed herein. That includes a specific solution, incorporating augmented reality. A case study for delivering STEM (science, technology, engineering, and mathematics) content using this tool at one secondary school in Sofia is presented. The experience gained in one school year of using facilities for a STEM enrichment program has been examined.

In the ecological future of the planet, intelligent agriculture relies on CPS and IoT to free up human resources and increase production efficiency. Due to the growing number of connected IoT devices, the maximum scalability capacity, and available computing power of the existing architectural frameworks will be reached. This necessitates finding a solution that meets the continuously growing demands in smart farming. Cloud-based IoT solutions are achieving increasingly high popularity. The aim of this study was to design a scalable cloud-based architecture for a smart livestock monitoring system following Agile methodology and featuring environmental monitoring, health, growth, behaviour, reproduction, emotional state, and stress levels of animals. The AWS services used, and their specific tasks related to the proposed architecture are explained in detail. A stress test was performed to prove the data ingesting and processing capability of the proposed architecture. Experimental results proved that the proposed architecture using AWS automated scaling mechanisms and IoT devices are fully capable of processing the growing amount of data, which in turn allow for meeting the required needs of the constantly expanding number of CPS systems.

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The article proposes an approach to evaluate human-computer interaction in the collective decision-making model. It is believed that all team members interact with each other through a distributed information system. The approach involves considering, when modelling, the personality characteristics of perception, each member of the team as a set of cognitive styles. Within the scope of the proposed technique, it is believed that information flows are interconnected with the processes of collective decision-making, which makes it possible to model the process of collective decision-making, monitor and analyse the effectiveness of the collective's activities. Experimental studies accomplished with statistical data processing were carried out and discussed.

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Smart farms, as a part of high-tech agriculture, collect a huge amount of data from IoT devices about the conditions of animals, plants, and the environment. These data are most often stored locally and are not used in intelligent monitoring systems to provide opportunities for extracting meaningful knowledge for the farmers. This often leads to a sense of missed transparency, fairness, and accountability, and a lack of motivation for the majority of farmers to invest in sensor-based intelligent systems to support and improve the technological development of their farm and the decision-making process. In this paper, a data-driven intelligent monitoring system in a cloud environment is proposed. The designed architecture enables a comprehensive solution for interaction between data extraction from IoT devices, preprocessing, storage, feature engineering, modelling, and visualization. Streaming data from IoT devices to interactive live reports along with built machine learning (ML) models are included. As a result of the proposed intelligent monitoring system, the collected data and ML modelling outcomes are visualized using a powerful dynamic dashboard. The dashboard allows users to monitor various parameters across the farm and provides an accessible way to view trends, deviations, and patterns in the data. ML models are trained on the collected data and are updated periodically. The data-driven visualization enables farmers to examine, organize, and represent collected farm’s data with the goal of better serving their needs. Performance and durability tests of the system are provided. The proposed solution is a technological bridge with which farmers can easily, affordably, and understandably monitor and track the progress of their farms with easy integration into an existing IoT system.