Artificial intelligence is applied in smart grids to improve efficiency, reliability, and the integration of conventional and renewable energy sources. A state of the art review of artificial intelligence methods in smart grids is presented. A methodology is used for resource identification and systematic review. A taxonomy is proposed to classify machine learning models by method and application domain. Models are compared based on accuracy and computational efficiency. Key applications such as demand response, energy forecasting, fault detection, and grid optimization are analyzed. Artificial neural networks, decision trees, long short term memory networks, support vector machines, convolutional neural networks, and random forest models are identified as the most used approaches. The best performance is reported for convolutional neural network based and random forest based models. Load forecasting and energy management are identified as the most common application areas.
This study presents a real-time hand-sign recognition system to support communication for persons with hearing and speech impairments, with a focus on one-handed finger-spelling (the “dactyl alphabet”). The method performs on-device, frame-based detection and classification using RetinaNet with a ResNet-50 backbone, chosen for its favourable speed–accuracy trade-off and stable training on modest, class-imbalanced datasets typical of sign recognition. To ensure robustness beyond laboratory conditions, we curated a purpose-built dataset of more than 5600 annotated images captured across varied indoor and outdoor scenes, lighting conditions, backgrounds, camera distances, skin tones, and hand shapes. In a user study with 30 participants, the system achieved an average accuracy exceeding 93
The rapid development of innovative learning technologies in recent years has served as a basis for the transition from traditional teaching methods to interactive, learner-centered approaches. Among such methods, gamification is recognized as a promising pedagogical tool aimed at increasing students’ interest, motivation and engagement in learning. This article proposes a systematic literature review (SLR) of research on the use of gamification in school geography teaching. This review followed the PRISMA protocol and PICOS criteria to ensure transparency and reproducibility. The purpose of the study-Evaluating the effectiveness and directions of gamification in geography lessons and identifying problems arising in the process of its implementation in practice. The research was based on the PRISMA methodology. The search in the international databases Scopus and Google Scholar initially resulted in 22,712 documents. After refining the search, duplicates and irrelevant papers were excluded and 40 scientific articles published between 2019 and 2024 were analyzed in depth. VOSviewer software was used to visualize bibliometric connections and thematic clusters. The results showed that gamification has a positive effect on students’ cognitive development, motivation and spatial reasoning. However, the use of subject-specific digital tools, adapting content to match students’ learning styles, and assessing long-term changes in motivation have not been sufficiently explored. Experience with interactive maps, mobile applications and VR/AR technologies, especially in geography, is rare. Infrastructure, teacher training and challenges of adaptation in rural school settings also require attention. The results show that gamification has a positive effect on students’ motivation and cognitive development, but the limitations are related to the small number of empirical studies and insufficient representation of developing countries. Future research should evaluate the long-term effects of gamification and the possibilities of its wider application in geography teaching.
This study examined the motivating factors behind Bangladesh’s July 2024 uprising, which initially began as a protest against the civil service quota system and subsequently evolved into a nationwide revolutionary movement. Employing Reflexive Thematic Analysis, the research draws on protest narratives, media reports, and activist statements to identify the key dynamics that shaped the uprising’s trajectory. Findings revealed six interrelated themes: structural grievances, rooted in corruption, inequality, and authoritarian governance; the emergence of youth identity as a generational force for change; the role of emotions, including anger, hope, and fear, in sustaining mobilization; the strategic framing processes that expanded sectoral demands into systemic challenges; aspirations for democracy and justice; and online-offline mobilization cycle which gave the movement future-oriented legitimacy. The study contributed to scholarship on contentious politics by demonstrating how grievances, emotions, identity, and framing interact to transform protests into revolutionary moments. It also highlighted the methodological value of Reflexive Thematic Analysis in capturing the meaning-making processes of social movements. While limited by reliance on textual data and focus on immediate events, the research underscores broader implications for theory, policy, and activism. It shows that ignoring youth grievances risks fueling systemic unrest, whereas youth-driven mobilizations can catalyze democratic renewal in semi-authoritarian contexts.
This study examines the interrelated challenges of climate change and energy poverty across two distinct industrial regions: Borsod-Abaúj-Zemplén in Hungary and Zarqa in Jordan. Both areas face unemployment and low-income levels, as well as environmental legacies of industrial activity; however, they differ significantly in their energy policies and infrastructure development. Using 2025 survey data, we develop indices of energy poverty, financial poverty, and climate perceptions, aligned with OECD guidelines. Regression analysis indicates that the model accounts for approximately 40% of the variance in energy poverty. Notably, heightened perceptions of climate change are associated with increased reports of energy hardship, suggesting that economically deprived households possess greater climate risk awareness. Resilience capacities, including adaptive skills, income stability, and community support, are found to substantially mitigate energy poverty. Income and employment status also play protective roles, underscoring the importance of economic resources. The impact of financial poverty varies markedly, being negligible in Hungary but severe in Jordan due to structural and infrastructural constraints. Our findings underscore the need for tailored, inclusive policy interventions that emphasize energy efficiency and retrofitting in Hungary and promote financial support and the adoption of renewable energy in Jordan. Integrating principles of energy justice into climate resilience strategies is crucial for promoting equitable and sustainable energy transitions, mitigating local vulnerabilities, and enhancing overall household resilience.
