In the age of digitization and the ever-present use of artificial intelligence (AI), it is essential to develop methodologies that enable the systematic evaluation and ranking of different AI algorithms. This paper investigated the application of the PIPRECIA-S model as a methodological framework for the multi-criteria ranking of AI algorithms. Analyzing relevant criteria such as efficiency, flexibility, ease of implementation, stability and scalability, the paper provided a comprehensive overview of existing algorithms and identified their strengths and weaknesses. The research results showed that the PIPRECIA-S model enabled a structured and objective assessment, which facilitated decision-making in selecting the most suitable algorithms for specific applications. This approach not only advances the understanding of AI algorithms but also contributes to the development of strategies for their implementation in various industries.

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 aim of this paper is to introduce the standard principles of operation of educational institutions, as well as to emphasize the need for the introduction of internal audit in pre-university institutions. A short research on the educational institutions' management opinion on internal auditing is presented in the text. There is the great need to introduce internal auditing function in these institutions according to modern standards. If they decided to make changes, this process would also encounter the difficulties.

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.

Cilj istraživanja je ispitivanje odnosa između emocionalne inteligencije zaposlenih i njihovih percepcija ekonomskih performansi organizacije. Rezultati ukazuju na statistički značajnu, ali umerenu povezanost emocionalne inteligencije i ekonomskih performansi, uz izražene razlike u obrascima povezanosti između poduzoraka formiranih prema nivou emocionalnih kompetencija. Nalazi potvrđuju da emocionalna inteligencija u organizacijama u Srbiji ima ograničen, ali relevantan doprinos u objašnjenju ekonomskih procena zaposlenih, pri čemu njen značaj zavisi od individualnih i organizacionih uslova.

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.