This paper presents a video recognition-based solution for an innovative beekeeping system, which we have developed and have evaluated in a prototype. We have designed this solution to improve the monitoring and management of beekeeping operations, a critical aspect for ensuring efficiency and sustainability in modern apiculture. The proposed system enables continuous monitoring of bee colonies, providing real-time insights into environmental changes and bee behavior. In addition, the system's analytical tools can identify and analyze correlations between following factors affecting bee productivity, such as weather conditions, geographical location of the apiary, and other environmental influences.The introduction of this stand-alone beekeeping solution offers beekeepers the ability to remotely track key hive conditions. For instance, it can detect whether the temperature inside the hive is nearing critical levels, or whether the bees are facing food shortages. This timely information allows beekeepers to take swift action to prevent potential harm to the colonies, thereby improving overall colony health and productivity. Such a system must be particularly beneficial during winter, helping to preserve hibernating bee colonies, while also saving valuable time and resources typically spent on manual hive inspections.The autonomous beekeeping system aligns with the broader goals of promoting economically sustainable agriculture, supporting the conservation of agricultural and forestry resources. The introduction of advanced monitoring and control features ensures that beekeepers can maintain optimal hive conditions with minimal manual intervention. Moreover, as the project progresses, we are planning further enhancements, such as incorporating artificial intelligence (AI) techniques, including neural networks, to enhance image processing capabilities. This would significantly expand the system's functionality, providing even more comprehensive data analysis and decision-making tools for beekeepers.

The autonomous beekeeping solution provides the possibility to monitor the bee hives from a distance. The distance monitoring opens a floor for new business opportunities, like production of mono-floral honey, as well as more intensive manufacture of honey products. The solution is based on new generation of IoT resources, and deployment of IoT concept in agricultural sector [3; 23; 24; 25]. It is worth noting that physical survey disturbs the bees and causes them stress, which can negatively affect the productivity of the bee colony, as well as taking beekeeper time. In addition, distance does not always allow the beekeeper to react quickly to crisis situations in the apiary. The technology developed in the project allows to use distance monitoring of the beehive, which significantly facilitates the daily life of the beekeeper, allows quick decision-making in critical situations and does not reduce bee productivity, as it does not affect the daily life of the beehive.The beekeeper can observe the apiary remotely and will be able to decide to visit the apiary only if necessary. A great helper is the hive video surveillance system, which will help to monitor the flight activity of bee colonies during the day, as well as to visually assess the condition of the apiary. The new system uses alternative energy sources (solar, wind energy) for the power supply of the system, which increases the possibilities of using the system in distance monitoring on fields.

This work explores whether a low-resolution thermal camera can estimate three discrete sensor measurements on a resource-constrained IoT node. Correlation analysis showed that individual thermal pixels correlate strongly with air temperature, negatively with relative humidity and positively with light intensity. Three lightweight regressors VGG CNN, ViT-Tiny and CvT-Tiny were trained from 1 053 single channel 120 x 160-pixel thermal frames to estimate sensor measurements. Experimental tests confirmed the CNN superiority as it achieved RMSE of 2.29 °C and R² of 0.978 (estimating air temperature), RMSE of 0.075 %RH and R² 0.897 (estimating relative air humidity) and RMSE of 0.059 lux and R² of 0.924 (estimating light intensity), outperforming ViT-Tiny and CvT-Tiny on humidity and light intensity estimation. The findings demonstrate that convolutional models remain critical for lightweight and accurate environmental measurement estimation in edge deployments.

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.

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.

The advances in computer vision methods in agriculture continue to gain attention due to safe non-invasive approach to estimate plant well-being in agricultural food production environments. This paper reviews some of the latest advances and methods in this field, particularly when estimating various plant characteristics and overall plant well-being from an image or set of plant images on the IoT Edge devices. The Vision Transformers (ViT) and Convolutional Neural Network (CNN) models currently are widely adopted and compared in this field. The most recent of the two types of model architectures – ViT is being researched in the context of IoT networks and its potential for deployment on Edge is being evaluated. However, more research is needed in this field to overcome the transformer model limitations and drawbacks. The current research shows that for transformer model deployment on the Edge there exists some optimization and compression techniques that would allow it to be adopted more widely across different computer vision tasks in agriculture on Edge. The hybrid model approach is also emerging where the advantages of both CNN and ViT are combined to reach higher accuracy and better performance.

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 research aims to develop the evaluation framework for assessing the efficiency of collaborations between universities and enterprises. Recognizing the diverse interests and strategies of stakeholders, the framework incorporates 15 essential criteria, including alignment of objectives, communication, resource allocation, research output, knowledge transfer, and student involvement. Utilizing Microsoft Excel as a semi-automated evaluation support tool, the study analyzes collaboration performance and impact. The results reveal areas of strength and improvement, highlighting the significance of communication, resource optimization, and mutual trust. The findings contribute to optimizing university-enterprise collaborations, fostering innovative research, and enhancing industrial impact. The evaluation framework facilitates data-driven decision-making, supporting stakeholders in driving successful collaborative ventures.

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 aim of current paper is to analyze air quality monitoring possibilities in public transport vehicles. Using IoT, to offer the prototype for air quality monitoring in public transport vehicles and provide recommendations to ensure its sustainability also in unsafe epidemiological situation.

This study evaluates university cooperation and collaboration using the Weighted Sum Model (WSM). The research aims to provide a comprehensive assessment by considering key criteria, including research output, funding, mobility programs, joint degrees, collaborative projects, societal impact, and more. WSM allows assigning relative weights to each criterion, reflecting their importance. The evaluation identifies strengths and areas for improvement in collaborations, enabling stakeholders to make informed decisions. The study’s significance lies in optimizing partnerships, fostering research excellence, and promoting knowledge exchange. The research offers a transparent and flexible evaluation framework for enhancing collaboration initiatives among universities worldwide.

This paper considers various models for forecasting environmental sensor data values. The aim is to evaluate and compare the performance of forecasting methods, such as machine learning and neural networks when forecasting CO 2 , Temperature and Humidity sensor data. The research methodology entails finding and employing widely used algorithms to conduct experiments aimed at forecasting humidity, temperature, and CO 2 sensor data. The models Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Vector Autoregressive (VAR) model were implemented and used in the experiments. The findings reveal that the LSTM model demonstrates the lengthiest training duration but has consistent performance across all evaluation metrics. In contrast, the VAR model excels in temperature forecasting with reduced training times but exhibits inferior performance in forecasting humidity and CO2 levels. The CNN model, however, consistently underperforms in comparison to the other two models, particularly in humidity and CO2 forecasting. Results show that model selection is contingent upon the specific problem and data characteristics, with LSTMs being more for scenarios with long-range dependencies, and VAR models being advantageous for linear and stable relationships between variables.

According to the COVID-19 pandemic, educational institutions around the world were forced to urgently switch to distance learning. A particularly difficult situation has arisen in higher educational institutions when teaching special technical disciplines, where practical and laboratory exercises, which were conducted in laboratories and special classes before the COVID-19 pandemic, play an important role. Therefore, the paper presents a method for conducting practical and laboratory classes during on-line training on the example of the Department of Automotive Electronics of the Kharkiv National Automobile and Highway University (Ukraine). It is shown that the use of information technologies makes it possible to largely compensate for the lack of full-time classes. For special (subject) disciplines, the perception of information is achieved by using films demonstrating methods for the implementation of subject tasks. The main task of the teacher when conducting practical exercises in the conditions of distance learning is the competent selection of the source video material and its optimal fragmentation. In this regard, a classification of educational video content is proposed, and examples of its use by teachers during online classes are considered. The results of this work are intended to help educators improve the level of engineering education during distance learning.

The goal of this paper is to show development and research as well as building and testing a of novel UV-C disinfection device prototype for small surfaces in public transportation. Device prototype has been successfully developed and tested on S. aureus and E. coli bacteria and could be modified to suite various other applications in public spaces. After testing, there is still room for improvement and authors have given their fair thoughts of how to achieve better results.

In connection with the COVID-19 pandemic, there is an urgent need for disinfecting devices that can be used both indoors and in transport. Currently, the most common of these devices are ultraviolet (UV) germicidal lamps. However, they have significant disadvantages, such as short service life, presence of mercury, lack of flexible control, large dimensions, etc. The paper analyzes the sources of UV radiation to find an alternative to UV lamps. Although these elements currently have low efficiency and high cost, etc., it is proposed to use UVC LEDs as a UV source. Due to the COVID-19 pandemic and the general interest in the fight against viruses, as well as the ban on the use of mercury, investments have been attracted in the development of UVC LEDs, which will make them competitive in the future compared to germicidal lamps both in cost and efficiency. The paper presents a disinfection device developed on the basis of UVC LEDs. The principle of operation is described; the control system, the drawing, and the design of the UVC LED-based disinfection device are presented. Due to the described limitations of UVC LEDs, this design can be used for disinfection of small surface areas where frequent on/off switching is required and high power is not required.

The aim of this paper is to show the way – how to develop LED UV-C prototype of disinfection devices with safety solution to user, using UV-C light emitting diode for different small surfaces disinfection in public transport system. In order to achieve the aim of the paper, the prototype for disinfecting small surfaces in public transport using UV-C spectrum irradiation have been developed. The designs of prototype cases or parts thereof are created in Autodesk Fusion 360, while the designs of the printed electronic plates have been developed in the program Autodesk Eagle. The ranges of irradiation sources used in the prototypes were tested on a spectrometer at Laboratory of High-Resolution Spectroscopy and Light Source Technology, Institute of Atomic Physics and Spectroscopy, University of Latvia, while the efficiency of disinfection was determined in the Water Research and Environmental Biotechnology Laboratory, Riga Technical University. Two different bacterial cultures were used for microbiology tests: E.coli and S.aureus. In order for UV irradiation not to affect passengers of public transport, the prototypes were equipped with safety systems that prevent exposure of humans to UV-C irradiation. In order to determine the energy efficiency of equipment, only power measurements have been made before the supply and control elements of prototypes over a single cycle and power measurements on irradiation sources over a single cycle. UV-C irradiation is effective disinfection method and we should continue to search for ways to implement it even more, meanwhile looking for ways to protect living things from unwanted harm via irradiation.

Conversion of traditional cars with internal combustion engines into electric vehicles (EV) is quite relevant for companies that deliver or transport mail, small-sized and lightweight cargo, food delivery from restaurants, for courier services, etc. EVs have significant advantages, namely, they do not require frequent maintenance, are much more reliable than traditional cars with internal combustion engines, more economical and environmentally friendly. Therefore, studies were carried out on the rationality of converting a car from an internal combustion engine to an EV with the calculation of the payback period of the converted car. Considered the main components of the EV configuration. The calculation of the power of the electric motor for the selected electric vehicle has been carried out. The calculation of the economic indicators of the conversion of the ZAZ Lanos-pickup car into EV has been carried out. The calculation results show that in the 3rd year of operation of the converted vehicle, a payback of the costs incurred will be obtained. The graphical dependence of the traction motor power on the EV speed is built. Calculations are carried out and the dependence of the traction motor power on the road slope when the EV is moving at a certain speed is built. Re-equipment of cars with internal combustion engines into EVs is expedient and cost-effective to implement for companies engaged in a variety of services for the delivery of goods within the city. But here one should take into account the fact that charging infrastructure should be developed within the city or night charging should be provided at the parking place.

The article presents the European evaluation within the scope of the project SMARTCITY:Innovative Approach Towards a Master Program on Smart Cities Technologies. The evaluation covers assessment of teachers from PC universities (SSTU, NSTU, NUM, MUST.KAZNU, ENU), who have been selected to participate in the project-related mobilities to increase knowledge in the field of SCT. In the project is planned developing of 18 teaching materials published with e-ISBN by PC teachers involved in the project.The article also covers quality assessment of teaching materials developed within the project by PC universities and assessment of students’ knowledge which is done at the beginning of their training in the EU Universities and during the Master theses defense.

Within food production in agriculture, different sensor networks are used to manage and monitor different processes, ranging from the presence of certain plant nutrients in soil to the presence of pests to determine plant health and to increase yields. Some limitations exist on how it is possible to use these sensor devices to monitor plant environments, for example it is not possible to attach or use one sensor for each plant. These monitoring tools and methods need to be scalable and affordable so they can be expedient in future farms. The potential exists to use already gathered data to combine or fuse different data and make sensors that are used in food production more effective and reduce costs in the process and help farmers to make more informed decisions. This literature review study investigates machine learning and computer vision based research that can be used within monitoring in IoT sensor network architectures with an aim to reduce the physical sensor devices needed in data gathering processes and to reduce the overall cost of IoT network implementation in agricultural farms which are growing food. Different machine learning and deep learning architectures in this context already have been implemented successfully to achieve a high accuracy in data forecast and image processing tasks.