This article presents a unified benchmark of energy consumption per synaptic event across five classes of neuro-computational substrates: graphics processing units, neural processing units, field-programmable gate arrays, digital spiking processors, and in-memory/memristive devices, with biological synapses used as reference bands. The main contribution is methodological as well as empirical. Methodologically, we introduce a common event-level metric that makes heterogeneous systems directly comparable despite major differences in architecture, coding scheme, and learning dynamics. Empirically, we combine measurements obtained by the authors with carefully normalized literature data to map the present energy landscape of artificial and biological neural computation. The results reveal three robust regimes. Dense von Neumann ANN implementations on GPUs, NPUs, and FPGA operate mainly in the $10^{5}$ to $10^{8}$ fJ range per synaptic event. Digital spiking processors reduce this requirement to about $10^{3}$ to $10^{4}$ fJ per event. Memristive and transistor-based artificial synapses span a much broader interval, from array-level values near $10^{6}$ fJ down to a few femtojoules in the most efficient device-level realizations. In particular, the best organic PANI memristive samples approach ~2 fJ per synaptic event, entering the biological vicinity and, in some cases, surpassing the rat reference range. Taken together, the benchmarked landscape spans up to ten orders of magnitude across the full set of systems considered in this study. This result clarifies where current AI hardware stands relative to biological efficiency, identifies event-driven and in-memory computation as the most promising routes toward sustainable AI, and provides a quantitatively grounded reference framework for future neuromorphic benchmarking. INDEX TERMS Artificial neural networks, energy consumption, energy efficiency, memristive devices, neuromorphic computing, spiking neural networks, sustainable AI.

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.

This research presents a minimal computational market model, i.e., a model of a trading venue, with sequential order matching, in a declarative style, and proceeds to demonstrate how some fundamental properties can be formally proved. It is a challenging task to formally certify the properties of a fundamental system in any realm of human endeavor, especially of systems with infinite state space. With the recent development of theoretical frameworks based on formal logic, it is now possible (albeit very difficult) to both formalize and reason about an object system in the same language. This research derives from the previous research presented in [1], and represents a simplification to obtain a minimal model. The computational model of a minimal market, presented here in a declarative style, is important from the perspective of both market design and verification.