Environmental noise generated by motorcycle traffic constitutes a critical challenge for intermediate cities of the Global South, where high motorization rates intensify exposure to stressful acoustic environments. In Florencia (Caquetá, Colombia)—a city in which motorcycles represent 96.6% of the vehicle fleet—noise functions not only as an environmental pollutant but also as a psychosocial trigger associated with irritability, stress, and aggressive driving behaviors among young riders. This study evaluates urban motorcyclists’ perceived effectiveness of regulatory measures aimed at noise and traffic control, considering how education level and driving experience shape normative perceptions. Using a non-experimental, cross-sectional design, data were collected from 502 motorcyclists. Kruskal–Wallis tests and Spearman correlations revealed a significant positive association between higher education and favorable perceptions of regulatory effectiveness, while no association was observed for driving experience. An exploratory factor analysis (EFA) confirmed a two-factor structure (54.3% variance), differentiating structural/collective measures from individual/educational ones. Overall, structural and educational interventions were perceived as more effective than coercive approaches. These findings highlight the need for context-sensitive regulatory frameworks that integrate social legitimacy, cultural adaptation, and psychological determinants of behavior. The study contributes empirical evidence for designing participatory and education-centered strategies for noise management and mobility governance in structurally informal urban contexts such as Florencia.

This study develops an integrated planning and operational framework for a next-generation electric bus with high level of service (Electric-BHLS) corridor along the Najaf–Al-Manathira–Al-Meshkhab axis in Iraq. The corridor represents a strategically important urban–rural mobility corridor characterized by rapid demographic growth, fragmented public transport services, congestion, environmental degradation, and increasing dependence on informal low-capacity vehicles. Unlike conventional electric bus operations, the proposed Electric-BHLS model combines high-service operational characteristics—including adaptive service frequency, intelligent transport systems (ITS)-based fleet control, hybrid priority lanes, opportunity charging systems, and real-time operational management—with full electric propulsion and regional accessibility planning. The methodological framework integrates engineering analysis, Geographic Information System (GIS)-based spatial accessibility assessment, operational modeling, and generalized cost optimization. Empirical calibration is based on 2024 field surveys, passenger interviews, Global Positioning System (GPS) based travel-time measurements, institutional datasets, and corridor infrastructure assessments. The proposed system includes articulated electric buses, pantograph opportunity-charging infrastructure, centralized Operations Control Center (OCC) management, smart passenger information systems, and a hierarchical station structure designed to improve operational reliability and multimodal integration. The results demonstrate substantial operational, environmental, and spatial improvements compared with the existing transport system. The optimized Electric-BHLS configuration reduces generalized transport cost by 27%, decreases average passenger waiting time by 61%, and lowers carbon dioxide (CO$_2$) emissions by approximately 29%. Corridor passenger capacity increases from approximately 15,000 to 36,000 passengers per day, while average operating speed improves from 22 km/h to 35 km/h through ITS-supported operational control and selective priority measures. GIS analysis further indicates accessibility gains of 24% in urban areas and 38% in rural catchment zones, improving access to employment, education, healthcare, and regional services. Beyond technical performance, the study evaluates governance, financial feasibility, operational risk, and long-term implementation constraints within the Iraqi context. A phased Design–Build–Operate–Maintain (DBOM) Public–Private Partnership (PPP) framework and a unified corridor governance authority are proposed to support institutional coordination and long-term operational sustainability. The study concludes that Electric-BHLS represents a scalable and economically viable mobility model capable of supporting sustainable regional development and transport modernization in Iraq and comparable developing-country contexts.

High operating temperatures are a major limitation for photovoltaic (PV) systems, as they reduce electrical efficiency and long-term reliability. Effective thermal regulation is therefore essential to maintain stable performance under strong solar irradiation. In this study, a numerical investigation is conducted to examine the thermal performance of a PV panel integrated with a paraffin-based cooling system positioned beneath the module. To improve the low thermal conductivity of paraffin, ternary nanoparticles together with metal foam are introduced into the phase change material (PCM). This hybrid enhancement significantly improves heat transfer, increases thermal diffusion, and accelerates the melting process. The transient melting behavior is modeled using the Galerkin finite element method, which ensures accurate prediction of temperature variation and phase change dynamics. The liquid fraction (LF) is increased by about 68.93%, indicating faster melting and improved energy absorption. In addition, the temperature distribution inside the PCM is enhanced by approximately 5.71%. Compared with a conventional uncooled PV system, the proposed configuration reduces the PV panel temperature ($T_{\mathrm{PV}}$) by 8.53%, while increasing electrical efficiency by 17.16%. Overall, the study demonstrates that combining ternary nanoparticles with metal foam inside PCM provides a strong synergistic cooling effect. This integrated approach offers a more effective thermal management strategy than traditional single-enhancement methods, leading to improved PV performance, higher efficiency, and better thermal stability under real operating conditions.

Driver drowsiness is one of the major reasons behind road accidents, emphasizing the need for accurate and efficient fatigue detection systems that can help monitor practical in-vehicle environments. While significant progress has been made in visual fatigue detection based on deep learning, many previous studies have been performed using a single dataset for training or controlled environments for testing. In this paper, we examine the reliability of lightweight driver-monitoring architectures for vision-based driver drowsiness detection based on three heterogeneous public datasets, i.e., Yawning Detection Dataset (YawDD), Driver Drowsiness Dataset (DDD), and National Tsing Hua University Drowsy Driving Dataset (NTHU-DDD), which cover different lighting conditions, facial characteristics, and head poses as encountered in driving scenarios. Among the considered architectures, Single Shot Detector (SSD)-MobileNetV2 was the most consistent, yielding an accuracy of 92%, precision of 93%, recall of 92%, and F1-score of 92% while also being computationally lighter than the other considered architectures. Reliability of the proposed architecture was statistically validated using the McNemar Test and 95% Confidence Intervals (CI). Our results show that SSD-MobileNetV2 could be a promising baseline for future lightweight driver-monitoring systems for heterogeneous driving environments.

Despite increasing attention to inclusive education, sustainability transitions under the Industry 5.0 paradigm remain constrained by the limited integration of socio-cultural barriers affecting marginalised groups. This study examines how harmful cultural practices (HCPs) and socio-economic conditions are associated with education access among 467 ethnic minority youth engaged in agri-startups, contributing theoretically by linking social norms and human capital perspectives within sustainability transition frameworks. A mixed-methods approach combining regression analysis and the Analytic Hierarchy Process (AHP) is employed to capture both statistical relationships and priority-setting mechanisms. The regression results indicate that early marriage (-0.171), gender bias (-0.199), and restrictive religious norms (-0.127) are associated with lower education access, while household income shows a significant positive association (0.722); the model is statistically significant with acceptable explanatory power (R² = 0.315; Adjusted R² = 0.280). The AHP results, based on expert evaluation, demonstrate consistent judgments (CR = 8.120%) and identify personal factors (0.341), particularly HCP awareness, as the highest priority, followed by socio-economic conditions (0.310), education and skills (0.212) and cultural–community factors (0.141). These findings suggest that although economic capacity is a dominant enabling factor, individual agency and behavioural change are critical for reducing harmful practices and improving access to education. However, education access reflects both actual and perceived opportunities due to infrastructural constraints in remote areas. Although the study is limited to data from marginalised youth in agri-startups, the findings highlight that education functions as a key enabling condition linking cultural transformation and livelihood improvement, offering policy-relevant insights for designing inclusive, human-centred education systems to support future-oriented Industry 5.0 transitions.

Renewable Energy Communities (RECs) are increasingly recognized as decentralized energy systems capable of improving renewable energy integration, enhancing local self-consumption, and supporting the transition toward low-carbon energy infrastructures. However, the effective deployment of RECs still faces significant challenges related to the integration of spatial analysis, energy modelling, operational optimization, and socio-economic assessment within a unified framework. This study investigates an integrated multi-objective framework for the design, evaluation, and operational support of RECs through the combination of geospatial analysis, energy performance modelling, and digital decision-support tools developed within the ENEA Smart Energy Communities (SEC) platform. The proposed methodology was developed by integrating spatially explicit territorial datasets, renewable resource assessments, electricity demand profiles, and multidimensional key performance indicators (KPIs) within a coordinated analytical framework. Three complementary tools were implemented and evaluated: the geoCER geoportal for territorial-scale renewable energy planning and REC scenario modelling, the DHOMUS platform for residential load monitoring and self-consumption optimization, and the Local Token Economy (LTE) system for token-based user engagement and energy-aware behavioral incentives. The results showed that the integrated framework effectively supported the assessment of REC configurations under different territorial and operational conditions. In the Anguillara Sabazia case study, the REC configuration increased the Self-Consumption Index (SCI) from 30% to 65% and the Self-Sufficiency Index from 36% to 79%, while reducing the Energy Surplus Index from 70% to 35%. In the Sardinia case study, the scenario-based analysis demonstrated that renewable energy integration and coordinated energy sharing significantly improved territorial self-sufficiency under optimized REC configurations. The geospatial modelling approach also enabled the identification of suitable renewable deployment scenarios while considering environmental and territorial constraints. The results indicate that the integration of energy modelling, digital monitoring systems, and spatially explicit planning tools provides an effective pathway for improving the operational performance, flexibility, and scalability of RECs. The proposed framework offers practical support for decentralized energy planning, distributed renewable energy management, and data-driven decision-making processes in future community-based energy systems.