In the early decades of the twenty-first century, the application of artificial intelligence has been expanding across all sectors of society, including industrial energy systems. This paper emphasizes the significance of integrating artificial neural networks into boilers with automatic firing, as part of a research project currently in its fifth year of experimental validation. The implementation of neural networks in such systems has demonstrated promising results in the domain of risk management, particularly through the prediction of system malfunctions and their proactive elimination via software interventions. The application of AI-based solutions in boiler control not only contributes to the reduction of environmental impact but also enhances operational safety by preventing accidents that may endanger human health and cause material losses.

The rapid development of artificial intelligence over the past few years has significantly impacted all levels of education. Despite the widespread adoption of generative AI in education, empirical studies simultaneously analyzing the attitudes of teachers and students particularly in primary and secondary education remain relatively scarce. The emergence of generative AI systems, such as ChatGPT, Gemini, Copilot, and other tools based on large language models, has created new opportunities for personalizing instruction, enhancing the learning process, and supporting teachers in the preparation and implementation of teaching activities. The aim of this research is to analyze the attitudes of primary and secondary school teachers and students regarding the use of generative artificial intelligence in teaching, identify the key benefits and risks of its implementation, and assess the educational system’s readiness for its integration. The study was conducted in schools falling under the jurisdiction of the two largest school administrations in the Republic of Serbia and involved 1042 teachers and 2587 students. The study was guided by a multidimensional conceptual framework integrating AI literacy, perceived benefits, perceived risks, ethical considerations, adoption readiness, and perceived learning outcomes. Data were collected using structured questionnaires and analyzed using descriptive statistics, Mann–Whitney U tests, and Spearman’s rank correlation. The internal consistency of the research dimensions was assessed using Cronbach’s alpha, with coefficients ranging from 0.82 to 0.91 and an aggregate value of α = 0.92. The analysis involved comparing the attitudes of teachers and students, as well as identifying statistically significant differences between the groups studied. The results indicate that 93.4% of students and 72.6% of teachers reported having used AI-based tools. Students reported higher AI literacy (M = 4.21 vs. 3.89) and perceived benefits (M = 4.31 vs. 4.12), whereas teachers reported higher perceived risks (M = 4.32 vs. 3.83) and stronger ethical concerns (M = 4.46 vs. 4.03); these group differences were statistically significant (p < 0.001). In addition, frequency of AI use was positively associated with perceived benefits (Spearman’s ρ = 0.56, p < 0.001). At the same time, both groups identified academic integrity, inaccurate information, over-reliance on AI systems, and the need for clear guidelines as important concerns. The obtained results provide a foundation for formulating policies to integrate artificial intelligence into the education system, developing teacher professional development programs, and defining national guidelines for the safe, ethical, and pedagogically sound application of AI technologies in education. This study provides large-scale empirical evidence on the adoption of generative AI in primary and secondary education in the Republic of Serbia and offers practically relevant insights for educational institutions, policymakers, and curriculum developers. The novelty of this study lies in the simultaneous, multidimensional examination and direct comparison of teachers’ and students’ perspectives on generative AI within the same K–12 educational context, integrating AI literacy, perceived benefits, perceived risks, ethical considerations, adoption readiness, and perceived learning outcomes within a common conceptual framework.

Urban drought is a climate-related challenge that threatens environmental sustainability, public health, and socio-economic stability in urban areas. With increasing climate variability, sustainable smart city planning requires reliable forecasting methodologies to facilitate adaptive water resource management and long-term climate resilience plans. This study proposes and evaluates a time series forecasting methodology for the climatic drivers of urban drought, using standard statistical approaches—Seasonal Autoregressive Integrated Moving Average ((S)ARIMA) and Holt–Winters exponential smoothing. The methodology includes systematic preprocessing of meteorological data, univariate time series modeling, and performance evaluation using recognized accuracy metrics (RMSE, MAE, and MAPE). Air temperature, precipitation, soil moisture, and wind speed are analyzed as key climatic variables affecting urban drought dynamics. The results indicate that forecast performance varies based on the statistical characteristics of each variable: (S)ARIMA models provide superior predictive accuracy for series with significant seasonality or stochastic fluctuations, whereas the Holt–Winters method is more appropriate for variables displaying sustained downward trends, particularly soil moisture. The forecasts provide a methodological foundation for calculating drought indices and classifying severity, enhancing early warning capabilities and supporting sustainable smart city planning under increasing climate uncertainty.

This paper examines the application of artificial intelligence in automated boiler systems with the aim of improving environmental risk management and reducing emissions generated during combustion processes. The study focuses on the use of neural network models as intelligent monitoring and predictive control tools in industrial heating systems. The main objective of the research is to evaluate how artificial intelligence can support safer and more efficient operation of automated boilers while contributing to lower fuel consumption and reduced environmental impact. The research methodology is based on experimental data collected from an automated boiler system of the OZON 55 type equipped with sensor-based monitoring devices. Operational parameters such as temperature, air supply, fuel characteristics, and gas emissions were recorded and analyzed using recurrent neural network models designed to predict deviations in combustion behavior. The obtained results indicate that neural network–based predictive monitoring can detect anomalies in operational parameters at an early stage and enable timely adjustments of combustion conditions. Such improvements contribute to increased operational safety, improved fuel efficiency, and lower emissions of harmful gases. The findings suggest that the integration of artificial intelligence into automated boiler systems represents an effective technological approach for enhancing environmental protection, improving risk management, and supporting more sustainable energy use in industrial heating systems.

Artificial Intelligence (AI) and computer vision technologies are increasingly integrated into educational environments through intelligent tutoring systems, gesture-based learning, facial expression analysis, and automated evaluation tools. However, selecting the most appropriate image recognition algorithms for educational applications remains a challenge due to varying requirements regarding speed, accuracy, hardware compatibility, and usability in dynamic classroom conditions.This paper proposes a hybrid multi-criteria decision-making (MCDM) model based on the Step-wise Weight Assessment Ratio Analysis (SWARA) and Net Worth Analysis (NWA) methods to evaluate and rank nine widely used AI-based visual recognition algorithms. The evaluation is conducted using five education-relevant criteria: processing speed, recognition accuracy, robustness to classroom noise, compatibility with low-end devices, and energy efficiency. Expert assessments from the field of educational technology were used to derive weight coefficients and evaluate algorithm performance.The results show that Fast R-CNN achieved the highest overall score (1.141), followed by U-Net (1.077) and DeepLab (1.062), indicating their suitability for real-time and resource-constrained EdTech environments. Algorithms such as MobileNet (1.057) and YOLO (1.037) also demonstrated balanced performance, making them viable for mobile or moderately demanding educational scenarios. The proposed model offers a structured and transparent decision-support framework that can assist researchers and practitioners in selecting optimal AI algorithms for diverse educational applications.

Smart water management is turning into one of the key elements for a sustainable water infrastructure in the Balkans. Systems based on collected data can solve major problems, such as leakage, pollution and inefficient water distribution. This paper explores the path from data to action using artificial intelligence and machine learning in regional smart water management projects. In the paper, a comparative analysis of the implementation of smart systems in Bosnia and Herzegovina, Croatia, Serbia and Montenegro was carried out, with a focus on predictive analytics, the application of edge AI technology and real-time decision support systems.This study introduces hybrid technologies that combine IoT with machine learning algorithms such as Random Forest,XGBoost, and Deep Q-Learning with the goal of adaptive water distribution.Results indicate that localized AI models can reduce network losses by up to 30 % and improve anomaly detection accuracy by 25 %. Despite financial and infrastructural limitations, the adoption of AI-powered smart water systems demonstrates a measurable impact on sustainability and operational efficiency. The paper concludes with a roadmap for regional collaboration and the standardization of AI-driven water management practices in the Balkans.