Search Results (2,353)

Transport is among the fastest-growing contributors to carbon dioxide (CO₂) emissions in the Gulf Cooperation Council (GCC) region, where rapid urbanization, population growth, and high mobility demand continue to shape energy use. This study aims to quantify the extent to which economic growth and urbanization drive transport-related CO₂ emissions across Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates between 2012 and 2022. Using sector-specific data from the International Energy Agency and World Bank, we apply panel and country-level log–log regression models to estimate long-run and short-run elasticities of transport CO₂ emissions with respect to GDP and urban population. The analysis also includes robustness checks excluding the COVID-19 pandemic year to isolate structural effects from temporary shocks. Results show that transport emissions remain strongly correlated with GDP in most countries, indicating emissions-intensive growth, while the influence of urbanization varies: positive in Kuwait and Saudi Arabia, where expansion is car-dependent, and negative in Oman and Qatar, where compact urban forms and transit investments mitigate emissions. The findings highlight the importance of differentiated policy responses—fuel-pricing reform, vehicle efficiency standards, electrification, and transit-oriented planning—to advance low-carbon mobility. By integrating elasticity-based diagnostics with decoupling analysis, this study provides the first harmonized empirical framework for the GCC to assess progress toward transport-sector decarbonization. Full article

Fairness reputation refers to the perception of others’ adherence to fair norms based on their behaviors. However, previous studies often rely on simple correlation and regression analyses without comparing cognition across roles in the ultimatum game (UG) and the dictator game (DG). Our study measured the categorical and two-dimensional cognitions (warmth-competence) of participants with different social value orientations toward proposers, responders, and dictators with varying fairness reputations. We found that proposers and dictators with fairness reputations were perceived more positively, and individualists could better distinguish between them. Regarding responders with fairness reputations, they were perceived as more fair, trustworthy, and competent, but less altruistic, cooperative, and warm. The social cognitive network of responders differed from those of proposers and dictators, with warmth cognition being central to three roles, supporting the warmth–competence model. This study highlighted the differential impact of fairness reputation in shaping social cognitions, providing insights into understanding social interactions. Full article

The United Kingdom (UK) government continues exploring ways to externalise its border controls to deter people from travelling to its shores. States, including the UK, use externalised border controls to reduce responsibility and avoid legal obligations through distance-creation in a manifestation of ‘irresponsibilisation’. They argue that extraterritorial border controls do not trigger their obligations under international refugee and human rights law, which are primarily territorial in scope. Were such claims true, they would create accountability gaps, allowing states to evade responsibility through cooperation and offshoring their legal duties. This paper challenges this view. It introduces a ‘responsibility spectrum’ applicable to the UK and other states’ actions involving externalised border controls, especially offshore processing of asylum claims or returns. The argument demonstrates that responsibility can arise for breaches of negative obligations, aiding and assisting a state, or violations of positive obligations. It emphasises that, despite the difficulties posed by ‘irresponsibilisation’, international law will ensure the UK is held accountable for any breaches it facilitates through any future externalisation policies. Full article

Contemporary medical education is shifting from traditional, teacher-centred anatomy instruction toward interactive, student-centred, and clinically integrated approaches. The Jigsaw Method aligns with this shift by fostering collective competence, which is vital for effective clinical practice. This study aimed to introduce the jigsaw model to medical students and explore its perceived effectiveness in teaching anatomy. A phenomenological qualitative design was employed to explore the experiences of second-year medical students (n = 120) at the Faculty of Health-Care Sciences, Eastern University, Sri Lanka. Open-ended questions were used to elicit students’ reflections on the effectiveness of jigsaw learning. Thematic analysis was conducted using NVivo software (ver.14). Students reported generally favourable perceptions of the jigsaw method. Four major themes emerged: two described the positive impact of the approach—enhanced understanding through peer learning and improved interpersonal and communication skills; the remaining themes addressed challenges in implementation and suggestions for refinement. Participants appreciated the structured collaboration and positive interdependence fostered by the method. Moreover, students viewed the jigsaw technique as well-aligned with student-centred learning principles. The jigsaw method was perceived as an effective cooperative learning strategy that enhanced engagement, promoted active participation, and fostered teamwork in anatomy education. These findings support the integration of structured peer-based approaches into medical curricula to enrich students’ learning experiences. Full article

The nuclear membrane has emerged as a dynamic regulatory platform coordinating genome organization, mechanotransduction, and regulated cell death (RCD). Beyond its barrier function, the nuclear skeleton—comprising lamins, actin–myosin isoforms, nuclear matrix proteins, and the LINC complex—supports nuclear integrity and gene regulation. Recent evidence shows that type II membrane-bound bZIP transcription factors such as cAMP-responsive element-binding protein 3 (CREB3) and CREB3L1 localize to the inner nuclear membrane (INM), linking chromatin tethering with stress signaling. Their stress-induced cleavage by S1P/S2P disrupts chromatin anchoring and, in some contexts, triggers karyoptosis, a novel form of RCD defined by nuclear rupture. These findings position the nuclear envelope (NE) as a mechanosensitive signaling hub with direct implications for disease and therapy. In this review, we provide a comprehensive discussion on how type II membrane-bound bZIP transcription factors and chromatin acting as a nucleoskeleton cooperate to regulate nuclear membrane integrity. Full article

Students with autism spectrum disorder (ASD) often lack the social skills required for interpersonal interactions, highlighting the urgent need for evidence-based intervention programs. STEM activities that emphasize collaboration and communication offer a new pathway for social skill development. This study developed an adaptive STEM project-based learning instructional framework teaching model and employed a multiple-probe across-participant design to evaluate the participants’ social skills achievement rates and frequency of emotional and behavioral incidents. The results indicated that STEM activities exerted positive intervention effects; they effectively improved social skills (including cooperation, empathy, engagement, and communication) in students with ASD and reduced the occurrence of emotional and behavioral problems. Feedback from teachers, parents, and students further confirmed the social validity of STEM activities. Finally, recommendations for implementing STEM education among students with ASD are proposed from three perspectives: constructing interdisciplinary collaboration mechanisms, developing adaptive STEM curricula, and implementing dynamic teaching support strategies. Full article

Underground coal mine conveying systems operate in unstructured environments. Influenced by geological and operational factors, coal conveyors are frequently contaminated by foreign objects such as coal gangue and anchor bolts. These contaminants disrupt conveying stability and pose challenges to safe mining operations, making their effective removal critical. Given the significant heterogeneity and unpredictability of these objects in shape, size, and orientation, precise manipulation requires dual-arm cooperative control. Traditional control algorithms rely on precise dynamic models and fixed parameters, lacking robustness in such unstructured environments. To address these challenges, this paper proposes a cooperative grasping method tailored for heterogeneous objects in unstructured environments. The MATD3 algorithm is employed to cooperatively perform dual-arm trajectory planning and grasping tasks. A multi-factor reward function is designed to accelerate convergence in continuous action spaces, optimize real-time grasping trajectories for foreign objects, and ensure stable robotic arm positioning. Furthermore, priority experience replay (PER) is integrated into the MATD3 framework to enhance experience utilization and accelerate convergence toward optimal policies. For slender objects, a sequential cooperative optimization strategy is developed to improve the stability and reliability of grasping and placement. Experimental results demonstrate that the P-MATD3 algorithm significantly improves grasping success rates and efficiency in unstructured environments. In single-arm tasks, compared to MATD3 and MADDPG, P-MATD3 increases grasping success rates by 7.1% and 9.94%, respectively, while reducing the number of steps required to reach the pre-grasping point by 11.44% and 12.77%. In dual-arm tasks, success rates increased by 5.58% and 9.84%, respectively, while step counts decreased by 11.6% and 18.92%. Robustness testing under Gaussian noise demonstrated that P-MATD3 maintains high stability even with varying noise intensities. Finally, ablation and comparative experiments comprehensively validated the proposed method’s effectiveness in simulated environments. Full article

This paper addresses the challenge of cooperatively escorting a moving underwater target, such as a human-occupied vehicle (HOV), using a multi-AUV formation in complex ocean environments. We propose a comprehensive framework, RG-SAPF scheme, that integrates a rigidity graph (RG)-based reconfigurable formation control strategy with a safe artificial potential field (SAPF) motion planning method. The RG-based controller enables the AUVs to form and dynamically reconfigure a 3D escort formation around the target using only relative position information, ensuring the target remains within the formation’s convex hull. Meanwhile, the SAPF algorithm, enhanced with an adaptive Widrow–Hoff rule, enables real-time and collision-free path planning in obstacle-rich environments. Simulation and experimental results demonstrate that the proposed method effectively maintains formation integrity, supports flexible obstacle avoidance, and provides continuous target escort under dynamic conditions, validating its potential for practical underwater escort applications. Full article

In space missions, particularly in on-orbit servicing (OOS) missions, many tasks involve non-cooperative targets. To ensure the safety and precision of such missions, complete identification of the target’s inertia parameters is essential. This paper proposes a novel method for identifying the inertia parameters of a non-cooperative target, introducing an innovative approach to position and velocity estimation based on a time-of-flight (TOF) camera. The paper first describes the physical configuration of the system, followed by the overall identification process of the target. Subsequently, all inertia parameters are reviewed, and the associated data processing procedures are presented. The (angular) momentum of both the satellite and the manipulator is calculated to make preparations for subsequent identification steps. The motion parameters of the target are estimated using the Kalman filter (KF) and extended Kalman filter (EKF), with newly designed models for position and velocity. Furthermore, a novel full-parameter identification method is proposed, building upon the preceding motion estimation process. Simulations show that the identification errors of all inertia parameters are less than 0.3%, which validates the correctness and effectiveness of the proposed methods. Full article

We recast cooperative localization and scheduling in heterogeneous multi-agent systems through the lens of symmetry and symmetry breaking. On the geometric side, the Fisher Information Matrix (FIM) objective is invariant to rigid Euclidean transformations of the global frame, while its maximization admits symmetric optimal sensor formations; on the algorithmic side, heterogeneity and task constraints break permutation symmetry across agents, requiring policies that are sensitive to role asymmetries. We model communication as a random graph and quantify structural symmetry via topology metrics (average path length, clustering, betweenness) and graph automorphism-related indices, connecting these to estimation uncertainty. We then design a hybrid reward for reinforcement learning (RL) that is equivariant to agent relabeling within roles yet intentionally introduces asymmetry through distance/FIM terms to avoid degenerate symmetric configurations with poor observability. Simulations show that (i) symmetry-aware, FIM-optimized path planning reduces localization error versus symmetric but non-informative placements; and (ii) controlled symmetry breaking in policy learning improves robustness and data rate–reward trade-offs over baselines. Our results position symmetry/asymmetry as first-class design principles that unify estimation-theoretic invariances with learning-based coordination in complex heterogeneous networks. Under DDPG training, the total data rate (SDR) reaches $6.63\pm 0.97$ and the average reward per step (ARPS) is $-80.70\pm 6.94$, representing improvements of approximately $11.8\%$ over the baseline $(5.93\pm 3.51)$ and $11.1\%$ over SAC $(5.97\pm 2.66)$, respectively. The network’s mean shortest-path length is $L=1.721$, and the average betweenness centrality of the coordination nodes is ≈0.098. Moreover, the FIM-optimized path-planning strategy achieves the lowest localization error among all evaluated policies. Full article

Existing research has made significant strides in UAV formation control, particularly in active localization and certain passive methods. However, these approaches face substantial limitations in electromagnetically silent environments, often relying on strong assumptions such as fully known and stationary emitter positions. To overcome these challenges, this paper proposes a comprehensive framework for bearing-only passive localization and adjustment of UAV formations under strict electromagnetic silence constraints. We systematically develop three core models: (1) a geometric triangulation model for scenarios with three known emitters, enabling unique target positioning; (2) a hierarchical identification mechanism leveraging an angle database to resolve label ambiguity when some emitters are unknown; and (3) a cyclic cooperative strategy, Perceive-Explore-Judge-Execute (PEJE), optimized via an improved genetic algorithm with adaptive discrete neighborhood search (GA-IADNS), for dynamic formation adjustment. Extensive simulations demonstrate that our proposed methods exhibit strong robustness, rapid convergence, and high adjustment accuracy across varying initial deviations. Specifically, after adjustment, the maximum radial deviation of all UAVs from the desired position is less than 0.0001 m, and the maximum angular deviation is within 0.00013°; even for the $30\%R$ initial deviation scenario, the final positional error remains negligible. Furthermore, comparative experiments with a standard Genetic Algorithm (GA) confirm that GA-IADNS achieves superior performance: it reaches stable peak average fitness at the 6th generation (vs. no obvious convergence of GA even after 20 generations), reduces the convergence time by over $70\%$, and improves the final adjustment accuracy by more than $95\%$ relative to GA. These results significantly enhance the autonomous collaborative control capability of UAV formations in challenging electromagnetic conditions. Full article

This study introduces a dual-proxy framework for household-adaptive, non-invasive shower detection using standard water-heater logs. The framework leverages proxy at two complementary levels: a feature-level proxy (washing_seconds) that captures washing duration, and a scheme-level proxy (proxy-driven training) that enables learning in periods without direct shower labels. The proxy feature (washing_seconds) serves as an indirect descriptor of washing behavior, enabling effective inference even under label scarcity. We investigated three research questions: (RQ1) the effectiveness of proxy features in improving shower detection, (RQ2) how proxy-driven evaluation identifies compact yet reliable feature subsets, and (RQ3) the robustness of these subsets in long-term, real-world scenarios. Experiments on two households showed that washing_seconds consistently improved discrimination (raising summer PR-AUC, lowering non-summer false alarms), and that compact subsets of only two or three features, anchored by the proxy feature, achieved stable performance across households. The evaluation represents an illustrative example based on two cooperating households, providing practical evidence of the framework’s real-world applicability. Evaluation in real-world conditions confirmed robustness: representative subsets maintained micro PR-AUC 0.724–0.728, micro F1 0.66–0.69 (macro F1 0.55–0.58), and summer PR-AUC near 0.87, with generalization gaps within ±0.01 for discrimination and small positive shifts for F1 (+0.02–+0.05). These results demonstrate that proxy can function both as a feature and as a methodological principle, and that the proposed framework is model-agnostic and transferable to other learning architectures. It provides a foundation for adaptive, privacy-preserving smart home applications that can scale to broader household and healthcare contexts. Full article

The industrialization of using crop straw as energy is currently hindered by systemic bottlenecks, including high collection and storage costs, a poorly coordinated industrial chain, and underdeveloped market mechanism. This study takes Guangdong province as a case study to construct a tripartite evolutionary game model on the transition of straw to energy among the government, enterprises, and farmers. Different from previous studies that focused on the strategy of penalizing the open burning of straw by farmers, this work investigated the cooperation of farmers for straw removal from field, the operational strategies of enterprises for straw utilization as energy, and the selection of government-guided incentive policies. It analyzes the behavioral evolution of these stakeholders under various incentive policies and cooperative scenarios. Numerical simulations were performed to identify the system’s evolutionary stable strategies and assess the potential of expanding straw for energy utilization. It indicated that mild government intervention could lead to a stable equilibrium through facilitating the removal of straw from fields and the utilization of straw as energy by enterprises. Farmers were sensitive to the fluctuation of acquisition price, and their willingness to cooperate would be negatively impacted by a large-scale price reduction. Enterprise expansion was exposed to significant risk under intensive policy intervention. The feasible pathway to increase the proportion of straw utilization as energy in Guangdong began at a small scale. Under mild incentive policies, a scenario targeting a 20% increase was more likely to achieve a market equilibrium for large-scale production than that targeting a 55% increase. The government should draw up positive incentive policies to promote the utilization of straw as energy. By guiding farmers in straw removal from the field and improving the energy enterprises’ competitiveness, the government should curb irrational industry expansion and corporate speculation, and shift from investment support to incentive policies. Meanwhile, the ecological construction of industry and supply chains should be enhanced, and the scale should be used to reduce the high supply-side costs of the straw. It would overcome the central barrier to the commercialization of straw utilization as energy. This work sets an example for conducting dynamic analysis of multi-stakeholder interactions for straw utilization. Full article

This study explored key predictors of teachers’ readiness for inclusive education, focusing on training, perceived success (self-efficacy), experience with students with special educational needs (SEN), and years of service. A total of 319 teachers completed questionnaires assessing professional preparation and four readiness dimensions identified through exploratory factor analysis: (F1) Teaching Adaptation and Collaborative Practices, (F2) Classroom Management and Behavioral Skills, (F3) Positive Attitudes toward Inclusion and Diversity, and (F4) Willingness to Cooperate and Comply. Multiple linear regression revealed that self-efficacy consistently predicted all four dimensions, underscoring its central motivational role in inclusive teaching. Training was positively associated with F1, while its effect on F2 was not significant. Experience with SEN predicted F2 and F4, suggesting that direct classroom exposure enhances behavioral management and collaborative engagement. Years of service predicted only F3, indicating that professional experience primarily fosters positive attitudes toward inclusion. Overall, the findings highlight that effective inclusive practices require transformative professional learning and a synergistic combination of strong self-efficacy, structured training, and experiential engagement, with each factor contributing differentially to specific aspects of teacher readiness. Full article

This bibliometric analysis aims to map the evolution, disciplinary structure, and collaboration dynamics of green hydrogen (GH) research in North Africa from 2019 to 2025. Drawing on a corpus of ~39,000 global publications, indexed in Scopus and analysed through SciVal, we isolate and examine the contributions of Egypt, Morocco, Algeria, Tunisia, and Libya. Egypt leads the region with 842 publications and a field-weighted citation impact of 2.42, followed by Morocco (232 Pubs., FWCI 2.30) and Algeria (184 Pubs., FWCI 1.65). Notably, Tunisia exhibits the highest growth factor (41 times since 2019), while Libya remains marginal with only 18 publications in the GH field. The region is well represented in Energy and Environmental fields but is underrepresented in trendy areas such as Materials and Chemical Engineering, highlighting critical gaps in consistency, sophistication, and technical infrastructure. While international collaboration exceeds 69% for most countries, it rarely translates into a high impact compared to the global average. Conversely, the limited industrial collaboration shows the highest citation impact (e.g., Tunisia: 68 citations/publications). A thematic analysis reveals shared strengths in electrolytic hydrogen production and renewable energy integration, with Egypt showing diversification into microalgae and nanocomposites and Morocco excelling in techno-economic assessments and ammonia-based systems. By revealing patterns in research quality, collaboration, and thematic positioning, this study offers evidence-based insights to inform national science strategies, enhance regional cooperation, and position North Africa more strategically in the emerging global green hydrogen economy. Full article