Search Results (154)

Information and communication technologies (ICTs) are becoming increasingly important for sustainable rural development through the smart village concept. This study aims to model ICT’s potential for public services management in European rural areas. It identifies ICT applications across rural service domains, analyzes how these technologies address specific rural challenges, and evaluates their benefits, implementation barriers, and future prospects for sustainable rural development. A qualitative content analysis method was applied using purposive sampling to analyze 79 peer-reviewed articles from EBSCO and Elsevier databases (2000–2024). A deductive approach employed predefined categories to systematically classify ICT applications across rural public service domains, with data coded according to technology scope, problems addressed, and implementation challenges. The analysis identified 15 ICT application domains (agriculture, healthcare, education, governance, energy, transport, etc.) and 42 key technology categories (Internet of Things, artificial intelligence, blockchain, cloud computing, digital platforms, mobile applications, etc.). These technologies address four fundamental rural challenges: limited service accessibility, inefficient resource management, demographic pressures, and social exclusion. This study provides the first comprehensive systematic categorization of ICT applications in smart villages, establishing a theoretical framework connecting technology deployment with sustainable development dimensions. Findings demonstrate that successful ICT implementation requires integrated urban–rural cooperation, community-centered approaches, and balanced attention to economic, social, and environmental sustainability. The research identifies persistent challenges, including inadequate infrastructure, limited digital competencies, and high implementation costs, providing actionable insights for policymakers and practitioners developing ICT-enabled rural development strategies. Full article

The present study focuses on investigating the contribution of puppetry as a pedagogical and psychosocial tool in special education, addressing the literature gap in the systematic documentation of the experiences of special education teachers, concerning its use in daily teaching practice. The main objective is to capture the way in which puppetry enhances the learning, social and therapeutic support of students with complex educational and psychosocial needs. The study employs a qualitative phenomenological approach, conducting semi-structured interviews with eleven special education teachers who integrate puppetry into their teaching. Qualitative data were analyzed using thematic analysis. The findings highlight that puppetry significantly enhances cognitive function, concentration, memory and language development, while promoting the active participation, cooperation, social inclusion and self-expression of students. In addition, the use of the puppet acts as a means of psycho-emotional empowerment, supporting positive behavior and helping students cope with stress and behavioral difficulties. Participants identified peer support, material adequacy and training as key factors for effective implementation, while conversely, a lack of resources and time is cited as a key obstacle. The integration of puppetry in everyday school life seems to ameliorate a more personalized, supportive and experiential learning environment, responding to the diverse and complex profiles of students attending special schools. Continuous training for teachers, along with strengthening the collaboration between the arts and special education, is essential for the effective use of puppetry in the classroom. Full article

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Federated learning (FL) has emerged as a powerful approach for privacy-preserving model training in autonomous vehicle networks, where real-world deployments rely on multiple roadside units (RSUs) serving heterogeneous clients with intermittent connectivity. While most research focuses on single-server or hierarchical cloud-based FL, multi-server FL can alleviate the communication bottlenecks of traditional setups. To this end, we propose an edge-based, multi-server FL (MS-FL) framework that combines performance-driven aggregation at each server—including statistical weighting of peer updates and outlier mitigation—with an application layer handover protocol that preserves model updates when vehicles move between RSU coverage areas. We evaluate MS-FL on both MNIST and GTSRB benchmarks under shard- and Dirichlet-based non-IID splits, comparing it against single-server FL and a two-layer edge-plus-cloud baseline. Over multiple communication rounds, MS-FL with the Statistical Performance-Aware Aggregation method and Dynamic Weighted Averaging Aggregation achieved up to a 20-percentage-point improvement in accuracy and consistent gains in precision, recall, and F1-score (95% confidence), while matching the low latency of edge-only schemes and avoiding the extra model transfer delays of cloud-based aggregation. These results demonstrate that coordinated cooperation among servers based on model quality and seamless handovers can accelerate convergence, mitigate data heterogeneity, and deliver robust, privacy-aware learning in connected vehicle environments. Full article

Mapping marine habitats is fundamental for biodiversity conservation and ecosystem-based management in oceanic regions under increasing anthropogenic and climatic pressures. In the context of global initiatives—such as marine protected area expansion and international agreements—habitat mapping has become mandatory for regional and global conservation policies. It provides spatial data to delineate essential habitats, support connectivity analyses, and assess pressures, enabling ecosystem-based marine spatial planning aligned with EU directives (2008/56/EC; 2014/89/EU). Beyond biodiversity, macrophytes, rhodolith beds, and coral reefs deliver key ecosystem services—carbon sequestration, coastal protection, nursery functions, and fisheries support—essential to local socioeconomies. This systematic review (PRISMA guidelines) examined 69 peer-reviewed studies across Central-Eastern Atlantic archipelagos (Macaronesia: the Azores, Madeira, the Canaries, and Cabo Verde) and the Mid-Atlantic Ridge. We identified knowledge gaps, methodological trends, and key challenges, emphasizing the integration of cartographic, ecological, and technological approaches. Although methodologies diversified over time, the lack of survey standardization, limited ground truthing, and heterogeneous datasets constrained the production of high-resolution bionomic maps. Regional disparities persist in technology access and habitat coverage. The Azores showed the highest species richness (393), dominated by acoustic mapping in corals. Madeira was most advanced in the remote mapping of rhodoliths; the Canaries focused on shallow macrophytes with direct mapping; and Cabo Verde remains underrepresented. Harmonized protocols and regional cooperation are needed to improve data interoperability and predictive modeling. Full article

Background: The increasing complexity of global supply chains has intensified the demand for transparency, traceability, security, and sustainability in logistics and operations. Blockchain technology enables decentralized, immutable frameworks that improve data integrity, automate transactions via smart contracts, and integrate seamlessly with the IoT and AI. Methods: This bibliometric review analyzes 559 peer-reviewed publications retrieved from Scopus and Web of Science using a PRISMA-guided protocol. Data were processed with Bibliometrix and Biblioshiny to examine scientific production, contributing institutions, author countries, collaboration patterns, thematic clusters, and keyword evolution. Results: The analysis reveals a 400% increase in publications after 2020, with China, India, and the USA leading in output but with limited international collaboration. Keyword co-occurrence and thematic mapping reveal dominant topics, including smart contracts, food supply chain traceability, and sustainability, as well as emerging themes such as decentralization, privacy, and the circular economy. Conclusions: The field is marked by interdisciplinary growth, yet it remains thematically and geographically fragmented. This review maps the intellectual structure of blockchain-enabled sustainable supply chains, offering insights for policymakers, developers, and industry leaders and outlining future research avenues centered on global cooperation, platform efficiency, and ethical and regulatory dimensions. Full article

The reliability and robustness of global electrical networks are being impacted by the reciprocal effects of climate change and severe weather events. This article assesses research and collaborative trends to further these concerns. This study attempts to identify trends, principal contributors, and emerging fields of research by a comprehensive bibliometric analysis of articles relevant to power system resilience during extreme weather events. A comprehensive search was conducted in the Web of Science and Scopus databases to acquire appropriate papers from 2014 to 2025. The implementation criteria for eligibility requirements comprised peer-reviewed publications, including reviews and conference papers. The Bibliometrix and Biblioshiny tools were used to conduct data analysis, evaluating keyword co-occurrences, citation networks, and cooperation networks. The study selection process and reporting adhered to the PRISMA 2020 framework. A dataset of 1178 documents from 535 sources indicated an annual growth rate of 13.06%. China was the most producing country, while the USA, China, UK, and Iran became the most cited countries. Keyword analysis identified common topics including resilience, power outages, and extreme weather, alongside an increasing focus on AI-driven modeling, distributed energy resources, and optimization algorithms. This systematic review emphasizes the growing research field addressing power system resilience, focusing on improvements in modeling strategies, optimization approaches, and risk management applications. Future research must concentrate on the integration of AI, evaluations of regional vulnerabilities, and the development of predictive frameworks to tackle rising climate concerns. Full article

We examined the effects of cooperative learning on learning satisfaction and learning effectiveness of elementary school students on the Quizlet online platform using an experimental design approach. Third and fourth-grade students from an elementary school in Taichung City were recruited in this study. A total of 55 students participated in this study and were grouped into the experimental group (28) and the control group (27). The experimental group engaged in game-based cooperative learning activities, while the control group participated in individual learning sessions. After eight weeks of the intervention in which one 30 min session was conducted every week, data on learning satisfaction and learning effectiveness were collected using a questionnaire. The collected data were analyzed to obtain descriptive statistics, independent sample t-tests, Pearson correlation analysis, and regression analysis. The participants in the experimental group reported improved learning satisfaction, learning environments, learning outcomes, and peer interactions compared with the control group. The experimental group also scored higher in learning effectiveness including skills. Significant differences were observed in learning satisfaction and effectiveness across genders and grade levels. A strong positive correlation indicated that higher learning satisfaction improved learning effectiveness among elementary students. Learning satisfaction was a significant predictor of learning effectiveness, highlighting the importance of cooperative learning for better learning outcomes. The results of this study provide a reference for elementary English teachers to incorporate online platforms in their teaching practices. Full article

Countries are embracing intelligent transportation systems (ITSs), the application of information and communication technologies to transportation, to address growing challenges in urban mobility, congestion, safety, and sustainability. Architecture Reference for Cooperative and Intelligent Transportation (ARC-IT) is a notable ITS framework comprising Enterprise, Functional, Physical, and Communications Views (or layers). This review focuses on the Communications View, examining how publish/subscribe middleware enhances ITS through the communication layer. It identified application areas across ITS infrastructure, transportation modes, and communication technologies, and highlights key challenges. In the infrastructure domain, publish/subscribe middleware enhances responsiveness and real-time processing in systems such as traffic surveillance, VANETs, and road sensor networks, especially when replacing legacy infrastructure is cost-prohibitive. Moreover, the middleware supports scalable, low-latency communication in land, air, and marine modes, enabling public transport coordination, cooperative driving, and UAV integration. At the communications layer, publish/subscribe systems facilitate interoperable, delay-tolerant data dissemination over heterogeneous platforms, including 4G/5G, ICN, and peer-to-peer networks. However, integrating publish/subscribe middleware in ITS has several challenges, including privacy risks, real-time data constraints, fault tolerance, bandwidth limitations, and security vulnerabilities. This paper provides a domain-informed foundation for researchers and practitioners developing resilient, scalable, and interoperable communication systems in next-generation ITSs. Full article

Background: First aid training in secondary education enhances emergency preparedness and supports public health. Despite its inclusion in many school curricula, there is no consensus on the most effective teaching methodologies. This systematic review aims to compare instructional strategies used in first aid training during Physical Education and evaluate their impact on students’ knowledge, practical skills, and confidence. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Six databases (SCOPUS, Web of Science, ERIC, DIALNET, MEDLINE, and PsycINFO) were searched up to December 2024. Eligible studies were quasi-experimental or observational, involved students aged 11–18, and focused on first aid instruction within Physical Education. Methodological quality was assessed using the PEDro scale. Results: Eleven studies with a total of 3069 students aged 11–18 were included. Active and technology-based methodologies outperformed traditional approaches, improving knowledge acquisition (10.2–30.5%) and practical skill development (18.6–42.3%). Long-term retention ranged from 14.2% to 45.8%, with longer interventions yielding better outcomes. Gamification, simulations, and peer learning improved CPR quality and boosted student confidence. However, most studies assessed only short-term outcomes, limiting conclusions about sustained learning. Conclusions: Active methodologies, particularly gamification, simulation, and cooperative learning, enhance knowledge retention, practical skills, and confidence in providing first aid. Although the results were consistently positive, methodological heterogeneity and limited long-term follow-up reduce their generalizability. Further high-quality, longitudinal research is needed to identify the most effective and sustainable strategies. These findings support integrating first aid training into Physical Education as a public health initiative to strengthen emergency preparedness in schools. Full article

As generative AI (GenAI) technologies proliferate, ensuring trust and transparency in digital ecosystems becomes increasingly critical, particularly within democratic frameworks. This article examines decentralized Web3 mechanisms—blockchain, decentralized autonomous organizations (DAOs), and data cooperatives—as foundational tools for enhancing trust in GenAI. These mechanisms are analyzed within the framework of the EU’s AI Act and the Draghi Report, focusing on their potential to support content authenticity, community-driven verification, and data sovereignty. Based on a systematic policy analysis, this article proposes a multi-layered framework to mitigate the risks of AI-generated misinformation. Specifically, as a result of this analysis, it identifies and evaluates seven detection techniques of trust stemming from the action research conducted in the Horizon Europe Lighthouse project called ENFIELD: (i) federated learning for decentralized AI detection, (ii) blockchain-based provenance tracking, (iii) zero-knowledge proofs for content authentication, (iv) DAOs for crowdsourced verification, (v) AI-powered digital watermarking, (vi) explainable AI (XAI) for content detection, and (vii) privacy-preserving machine learning (PPML). By leveraging these approaches, the framework strengthens AI governance through peer-to-peer (P2P) structures while addressing the socio-political challenges of AI-driven misinformation. Ultimately, this research contributes to the development of resilient democratic systems in an era of increasing technopolitical polarization. Full article

Energy communities constitute the main collective form for energy consumers to participate in the current energy transition. The purpose of this research paper is to present a tool that assists energy communities to achieve fair and sustainable daily operation. In this context, the proposed algorithm (i) assesses the day-ahead operation cost (or profit) of energy communities, taking into consideration photovoltaic (PV) production, battery energy storage system (BESS), and flexible loads, as well as the potential profit from selling energy to the power system, under the net billing scheme, and (ii) compares the derived cost for each member with the cost for non-cooperative operation, as single prosumers. Taking the aforementioned costs or profits into consideration, the developed algorithm then proposes three cost-sharing options for the members, peer-to-peer (P2P), so that their participation in the community is more beneficial than individual operation. The algorithm is tested on a hypothetical energy community in Greece, highlighting the importance of the cooperation amongst the members of the community for their mutual benefit; for the simulated case of different PV shares, the cooperation can result in a 24.5% cost decrease, while having a BESS can reduce the cost by 25.0%. Full article

The shift towards decentralized energy systems demands innovative strategies to manage renewable energy integration, optimize resource allocation, and ensure grid stability. This review investigates the application of game theory and robust predictive control as essential tools for decentralized and peer-to-peer energy management. Game theory facilitates strategic decision-making and cooperation among prosumers, distributors, and consumers, enabling efficient energy trading and dynamic resource distribution. Robust predictive control complements this by addressing uncertainties in renewable energy generation and demand, ensuring system stability through adaptive and real-time optimization. By examining recent advancements, this study highlights key methodologies, challenges, and emerging technologies such as blockchain, artificial intelligence, and digital twins, which enhance these approaches. The review also explores their alignment with global sustainability objectives, emphasizing their role in promoting affordable clean energy, reducing emissions, and fostering resilient urban energy infrastructures. A systematic review methodology was employed, analyzing 153 selected articles published in the last five years, filtered from an initial dataset of over 200 results retrieved from ScienceDirect and IEEE Xplore. Practical insights and future directions are provided to guide the implementation of these innovative methodologies in decentralized energy networks. Full article

Women are the fastest-growing segment of the incarcerated population and experience high rates of cumulative trauma exposure, mental illness, and PTSD. The aim of this study is to assess the implementation of a peer-led Seeking Safety (an evidence-based intervention for addressing trauma and addiction) pilot program for women in jail. Guided by principles from community-based participatory research and cooperative inquiry, participant surveys were analyzed (secondary data) using descriptive methods (n = 60), and qualitative interviews with program facilitators were conducted and analyzed using a general inductive approach (n = 7). Peer-led Seeking Safety is feasible, acceptable, and appropriate for women in jail, with high levels of participant satisfaction. We describe several “lessons learned” related to the jail context, including structure and security processes and vicarious and retraumatization experiences among facilitators. Preventing facilitator burnout is necessary for the sustainability of the program. Future implementations of Seeking Safety in jails should consider the lessons learned in this study. Full article

This paper analyzes the possibility of creating worker cooperatives in which members are paid not through wages but through dividends calculated on the organization’s residual income, as stipulated by the economic theory of the labor-managed firm. It is shown how dividends paid to members can be linked to the value of their financial participation in the capital of the cooperative. In the presence of a financial market, cooperative shares would be issued and allocated to both members and non-member outside investors, thus addressing the problem of the under-capitalization of worker cooperatives. It is hypothesized that the strong financial incentives of this type of capital structure, together with involvement in the democratic governance of the cooperative, peer pressure, and other horizontal monitoring mechanisms, would support members’ intrinsic motivation to work and help overcome the problem of free-riding in the labor process. Flexible economic and financial structure in the absence of fixed wages would promote job stability, as already observed in existing worker cooperatives. Full article