Urban agriculture presents unique challenges, particularly in the context of microclimate monitoring, which is increasingly important in food production. This paper explores the application of convolutional neural networks (CNNs) to forecast key sensor measurements from thermal images within this context. This research focuses on using thermal images to forecast sensor measurements of relative air humidity, soil moisture, and light intensity, which are integral to plant health and productivity in urban farming environments. The results indicate a higher accuracy in forecasting relative air humidity and soil moisture levels, with Mean Absolute Percentage Errors (MAPEs) within the range of 10–12%. These findings correlate with the strong dependency of these parameters on thermal patterns, which are effectively extracted by the CNNs. In contrast, the forecasting of light intensity proved to be more challenging, yielding lower accuracy. The reduced performance is likely due to the more complex and variable factors that affect light in urban environments. The insights gained from the higher predictive accuracy for relative air humidity and soil moisture may inform targeted interventions for urban farming practices, while the lower accuracy in light intensity forecasting highlights the need for further research into the integration of additional data sources or hybrid modeling approaches. The conclusion suggests that the integration of these technologies can significantly enhance the predictive maintenance of plant health, leading to more sustainable and efficient urban farming practices. However, the study also acknowledges the challenges in implementing these technologies in urban agricultural models.

This article represents and compares two control systems for a vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV): a sliding proportional–integral–derivative (PID) controller and an adaptive L1 controller. The goal is to design a high-performing and stable control system for a specific VTOL drone. The mathematical model of the unique VTOL drone is presented as a control object. The sliding PID and adaptive L1 controllers are then developed and simulated, and their performance is compared. Simulation results demonstrate that both control systems achieve stable and accurate flight of the VTOL drone, but the adaptive L1 controller outperforms the sliding PID controller in terms of robustness and adaptation to changing conditions. This research contributes to ongoing work on adaptive control systems for VTOL UAVs and highlights the potential benefits of using L1 adaptive control for this application.

The integration of Sustainable Development Goals (SDGs) into the educational process of engineers and electrical engineers is pivotal for fostering a sustainable future. This paper discusses the significance of embedding SDGs in engineering education and presents methodologies for incorporating these goals into various study subjects. The study explores current trends, proposes innovative pedagogical approaches, and provides practical recommendations for educators and institutions. The outcomes suggest that incorporating SDGs not only enhances the relevance of engineering education but also equips future engineers with the necessary skills and mindset to address global sustainability challenges.

This study presents a novel methodology for evaluating the quality of life in urban areas of Kazakhstan through the development of tailored sustainability indicators. While urban livability and sustainability are global concerns, few studies have focused on developing comprehensive, region-specific metrics for assessing these factors in Kazakhstan, a country with diverse urban environments and newly established administrative divisions. This research fills a critical gap by creating aggregated indicators that address economic, social, environmental, and infrastructural dimensions in a uniquely Kazakhstan context. The study goes beyond the existing literature by integrating local dynamics and challenges, offering a nuanced understanding of urban sustainability that reflects the country’s regional characteristics. The findings provide policymakers and urban planners with actionable insights to improve quality of life and make informed decisions. Moreover, the methodology is designed for ongoing application, allowing for continuous monitoring and adaptation to evolving urban development strategies across Kazakhstan. This research advances the field by offering a replicable framework that can be adapted to other regions with similar complexities.

This paper presents a new approach to the creation of innovative roads having sustainable energy efficient road pavement as their basis. It is a new type of intelligent roads that is able to service itself and provide power, i.e. it is also a renewable source of electricity. It is planned to use the studies on the PV panels in sustainable energy efficient road pavement to determine their load parameters. The work used the methods of mathematical analysis and theoretical electrophysics to carry out the studies on load characteristics of various types of silicon PV panels in order to define the most effective panels from the point of view of generated electrical energy load resistance values. The analysis of the obtained results of the experimental research has shown that in order to make the operation of PV panels of series FS-100M and FS-110P most efficient, their load must be maintained within 3–3.5 Ω range. If load resistance exceeds the specified limits, the work of PV panels of this series will be ineffective. The road having a sustainable energy efficient road pavement is able to track road conditions, traffic, weather conditions and react quickly to their changes. It is shown how road markings can change dependance on road conditions.

The article discusses vulnerability of wireless sensors networks to weather-based disruptions considering the opinions of different experts published in a range of scientific materials. The introduction provides a brief overview of wireless signals in real world conditions focusing on how weather affects signals (rain, fog and clouds, snow, hail, lightning, wind, bodies of water, trees and physical obstruction). Information about the effects of weather on wireless sensor networks using Free Space Optical / Radio Frequency (FSO/RF) communication is then provided. Finally, the impact of weather conditions on MANET routing protocols is considered theoretically, and experimental simulations are performed by comparing the sustainability of different protocols to different weather conditions. After analysis of experiment results, ideas on how to decrease vulnerability of wireless networks to weather-based disruptions are discussed.