Search Results (4,989)

Innovation in university education has become a key pillar for improving learning quality and ensuring faculty adaptation to the challenges of the 21st century. This study aims to analyze the innovative competence profile of university faculty, exploring their disposition toward innovation, the use of advanced pedagogical methodologies, and their integration of information and communication technologies (ICT). A quantitative, non-experimental, cross-sectional design was employed, using a validated questionnaire administered to a sample of 136 faculty members at the University of Murcia. Findings indicate that educational innovation in higher education is influenced by both individual and institutional factors. Female faculty members demonstrate greater openness to innovation, particularly in development and training, while those with intermediate teaching experience (11–20 years) report higher implementation of innovative methodologies compared to those with less than 10 years or more than 20 years of experience. Additionally, the Faculty of Education stands out for its integration of innovative strategies, in contrast to other faculties where adoption is more limited. Despite a generally positive attitude toward innovation, shortcomings were identified in the evaluation and dissemination of these methodologies, which hinder their consolidation within the academic community. The results highlight the need for institutional strategies that enhance teacher training, promote effective evaluation, and foster interfaculty collaboration to share experiences and best practices. Full article

Environmental literacy is essential for preparing students with the knowledge, skills, and dispositions to address pressing environmental challenges. This systematic literature review examines how pedagogical approaches used in secondary education foster students’ environmental literacy. The review enriches the current literature by shifting attention away from the predominant focus on higher education and providing new empirically grounded insights into the effectiveness of classroom practices in enhancing students’ environmental literacy at the secondary education level. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 22 peer-reviewed studies published between 2010 and 2024 were identified through Web of Science, Scopus and ERIC. The analysis is guided by Joyce and Calhoun’s taxonomy of teaching models and the conceptualization of environmental literacy developed by the North American Association for Environmental Education (NAAEE). Findings show that strategies grounded in the social family and information-processing models of teaching were most frequently used, reflecting a pedagogical shift toward collaboration, critical thinking, and active engagement, yet a significant gap remains in cultivating environmentally responsible behavior (ERB). The review highlights the need for pedagogical designs that support the integration of every dimension of environmental literacy. It further emphasizes culturally responsive approaches and systematic investment in teacher professional development as critical conditions for pedagogical success. Full article

This study presents a comprehensive bibliometric analysis of 217 publications on nanomaterials for soil and groundwater remediation, sourced from the Scopus database, covering the period from 2010 to 2024. The findings highlight significant contributions from various countries, with India identified as the leading contributor, followed by China and the United States. This reflects robust international collaboration in addressing environmental contamination. The analysis also identifies influential journals in this field, particularly “Science of the Total Environment” and “Environmental Science and Technology”, which are recognized for their high citation impact and play a crucial role in disseminating research findings and advancing knowledge in nanomaterials for environmental remediation. A keyword co-occurrence analysis reveals six distinct clusters that emphasize critical research themes. The first cluster focuses on environmental toxicity, underscoring the risks posed by contaminants, particularly heavy metals and emerging pollutants such as PFAS, highlighting the need for advanced monitoring strategies. The second cluster showcases innovative nanoremediation technologies, particularly zero-valent iron (nZVI) and carbon nanotubes (CNTs), which are noted for their effectiveness in pollutant removal despite challenges like surface passivation and high production costs. The third cluster addresses heavy metals and phytoremediation, advocating integrated strategies that enhance crop resilience while managing soil contamination. The fourth cluster explores photocatalysis and advanced oxidation processes, demonstrating how nanomaterials can enhance pollutant degradation through light-activated catalytic methods. The fifth cluster emphasizes adsorption mechanisms for specific contaminants, such as arsenic and pharmaceuticals, suggesting targeted remediation strategies. Finally, the sixth cluster highlights the potential of nanomaterials in agriculture, focusing on their role in improving soil fertility and supporting plant growth. Overall, while nanomaterials demonstrate significant potential for effective environmental remediation, they also pose risks that necessitate careful consideration and further research. Future studies should prioritize optimizing these materials for practical applications, addressing both environmental health and agricultural productivity. Full article

Green hydrogen has the potential to contribute to the decarbonization of the fossil fuel industry, and its development is expected to increase in the coming years. The social dynamics among the various actors in the green hydrogen sector are studied to understand their public perception. Using the technological innovation system research approach for the stakeholder analysis and the qualitative thematic analysis method for the interviews with experts, this study presents an overview of the actors in the green hydrogen sector and their relations in Luxembourg. As a central European country with strategic political and geographic relevance, Luxembourg offers a timely case for analyzing public perception before the large-scale implementation of green hydrogen. Observing this early stage allows for future comparative insights as the national hydrogen strategy progresses. Results show high expectations for green hydrogen in mobility and industry, but concerns persist over infrastructure costs, safety, and public awareness. Regional stakeholders demonstrate a strong willingness to collaborate, recognizing that local public acceptance still requires effort, particularly in areas such as clear and inclusive communication, sharing knowledge, and fostering trust. These findings provide practical insights for stakeholder engagement strategies and theoretical contributions to the study of social dynamics in sustainability transitions. Full article

Background: NHS England has an ageing workforce. Approximately 30 percent of the NHS England workforce are aged 50 years and over, and the British Medical Association has argued that it is important that employers meet the needs of their ageing workforce and retain their skills and expertise. Objective: This sought to explore how NHS England Trusts support employees with fluctuating long-term health conditions, investigating systemic workforce challenges to providing adequate support and identifying opportunities for more inclusive and sustainable employment practices. Methods: Qualitative interviews were conducted with staff working in human resources, occupational health staff and clinical line managers involved in the support and management of staff with fluctuating long-term health conditions (n = 17). Results: The research found a misalignment between clinical managerial practices, human resource procedures, and the overarching NHS human resource policy framework, which was often seen as rigid and poorly suited to the fluctuating nature of some long-term conditions. These tensions were exacerbated by high staff turnover and limited organisational capacity. Nonetheless, instances of effective, person-centred support were also reported, typically occurring where cross-departmental collaboration and flexible, locally adapted approaches were in place. Conclusions: Findings suggest that targeted, flexible interventions for NHS employees with fluctuating long-term health conditions could enhance staff retention, reduce absenteeism, and promote more resilient workforce strategies. Identifying and scaling examples of good practice may be key to fostering a more inclusive and adaptive NHS employment model. Full article

Large language model (LLM)-based agents are rapidly emerging as transformative tools across biomedical research and clinical applications. By integrating reasoning, planning, memory, and tool use capabilities, these agents go beyond static language models to operate autonomously or collaboratively within complex healthcare settings. This review provides a comprehensive survey of biomedical LLM agents, spanning their core system architectures, enabling methodologies, and real-world use cases such as clinical decision making, biomedical research automation, and patient simulation. We further examine emerging benchmarks designed to evaluate agent performance under dynamic, interactive, and multimodal conditions. In addition, we systematically analyze key challenges, including hallucinations, interpretability, tool reliability, data bias, and regulatory gaps, and discuss corresponding mitigation strategies. Finally, we outline future directions in areas such as continual learning, federated adaptation, robust multi-agent coordination, and human AI collaboration. This review aims to establish a foundational understanding of biomedical LLM agents and provide a forward-looking roadmap for building trustworthy, reliable, and clinically deployable intelligent systems. Full article

The surge in electric vehicles (EVs) and renewable energy has made lithium-ion batteries (LIBs) critical to the global energy transition. This review examines how LIBs contribute to a circular economy, focusing on China and the United States as key actors shaping the battery value chain. We analyze technological advancements, market growth, supply chain dynamics, ESG risks, and strategies for recycling, reuse, and next-generation chemistries. China’s approach centers on vertical integration and scale, while the U.S. emphasizes innovation, policy incentives, and diversification. Despite progress, gaps remain in closed-loop systems, ethical sourcing, and supply chain resilience. Realizing sustainable battery growth will require coordinated efforts in technology, governance, and international collaboration to align resource efficiency with long-term environmental and economic goals. Full article

Against the backdrop of a high proportion of distributed renewable energy sources being integrated into the power grid, distribution networks are confronted with issues of grid-wide and decentralized harmonic pollution and voltage deviation, rendering traditional point-to-point governance methods inadequate for meeting collaborative governance requirements. To address this problem, this paper proposes a source-tracing-oriented harmonic mitigation and optimization control strategy for distribution networks. Firstly, it identifies regional dominant harmonic source mitigation nodes based on harmonic and reactive power sensitivity indices as well as comprehensive voltage sensitivity indices. Subsequently, with the optimization objectives of reducing harmonic power loss and suppressing voltage fluctuation in the distribution network, it configures the quantity and capacity of voltage-detection-based active power filters (VDAPFs) and Static Var Generators (SVGs) and solves the model using an improved Spider Jump algorithm (SJA). Finally, the effectiveness and feasibility of the proposed method are validated through testing on an improved IEEE-33 standard node test system. Through analysis, the proposed method can reduce the voltage fluctuation rate and total harmonic distortion (THD) by 2.3% and 2.6%, respectively, achieving nearly 90% equipment utilization efficiency with the minimum investment cost. Full article

Architectural design stands as a highly knowledge-intensive field, with designers serving as the linchpin for its premium development. China’s construction industry is now navigating a transitional phase of slower growth, where a misalignment in designer capabilities significantly obstructs the nation’s shift from being a mere “construction giant” to becoming a true “construction powerhouse”. Based on the spatial mismatch model and Geodetector, this study empirically analyzes the mismatch relationship among designers and its influencing factors using panel data from 31 provinces in China from 2013 to 2023, and proposes strategies for cultivating architectural design talents. Findings reveal that China’s architectural designers exhibit spatial supply imbalance, and complex trends in designer allocation-simultaneous growth and decline coexist. China exhibits diverse types of architect mismatch: 22.58% of regions are in a state of Positive Mismatch, and 12.90% experience Negative Mismatch. In over one-third of regions, the architectural design talent market can no longer self-correct architect mismatch through market mechanisms, urgently requiring collaborative intervention policies from governments, design associations, and enterprises to address architect supply–demand governance. For a smooth transition during the transformation and upgrading of the construction and design industries, the architectural design talent market should accommodate frictional designer mismatch. The contribution of designer mismatch varies significantly, with factors such as innovation, industrial structure, and fiscal self-sufficiency exerting more direct influence, while other factors play indirect roles through dual-factor enhancement effects and nonlinear enhancement effects. The insights from the analysis results and conclusions for future designer cultivation include fostering an interdisciplinary teaching model for designers through university–enterprise collaboration, enhancing education in AI and intelligent construction literacy, and establishing an intelligent service platform for designer supply–demand matching to promptly build a new differentiated and precise designer supply system. Full article

This work presents a task-oriented framework for optimizing robotic surface finishing to improve efficiency and ensure feasibility under realistic kinematic and geometric constraints. The approach combines surface subdivision, optimal placement of the workpiece, and region-based toolpath planning to adapt machining strategies to local surface characteristics. A novel time evaluation criterion is introduced that improves our previous kinematic approach by incorporating dynamic aspects. This advancement enables a more realistic estimation of machining time, providing a more reliable basis for optimization and path planning. The framework determines both the optimal position of the workpiece and the subdivision of its surface into regions systematically, enabling machining directions and speeds to be adapted to the geometry of each region. The methodology was validated on several semi-complex surfaces through simulation and experimental trials with collaborative robotic manipulators. The results demonstrate that improved region-based optimization leads to machining time reductions of 9–26% compared to conventional single-direction machining strategies. The most significant improvements were achieved for larger, more complex geometries and denser machining paths, confirming the method’s industrial relevance. These findings establish the framework as a practical solution for reducing cycle time in specific robotic surface finishing tasks. Full article

In a global industrial landscape where the digital economy accounts for over 40% of total output, cloud computing technology is reshaping business models at a compound annual growth rate of 19%. This trend has led to an increasing number of cloud computing tasks requiring timely processing. However, most computational tasks are latency-sensitive and cannot tolerate significant delays. This has led to the urgent need for researchers to address the challenge of effectively scheduling cloud computing tasks. This paper proposes a hybrid SAO and RIME optimizer (HSAO) for global optimization and cloud task scheduling problems. First, population initialization based on ecological niche differentiation is proposed to enhance the initial population quality of SAO, enabling it to better explore the solution space. Then, the introduction of the soft frost search strategy and hard frost piercing mechanism from the RIME optimization algorithm enables the algorithm to better escape local optima and accelerate its convergence. Additionally, a population-based collaborative boundary control method is proposed to handle outlier individuals, preventing them from clustering at the boundary and enabling more effective exploration of the solution space. To evaluate the effectiveness of the proposed algorithm, we compared it with 11 other algorithms using the IEEE CEC2017 test set and assessed the differences through statistical analysis. Experimental data demonstrate that the HSAO algorithm exhibits significant advantages. Furthermore, to validate its practical applicability, we applied HSAO to real-world cloud computing task scheduling problems, achieving excellent results and successfully completing the scheduling planning of cloud computing tasks. Full article

This study explores and models the use of MATLAB technology in multimodal learning interactions to address the challenges of teaching and learning statistics in a multinational postgraduate classroom. The term multimodal refers to the deliberate integration of multiple representational and interaction modes, i.e., visual, textual, symbolic, and interactive computational modelling, within a coherent instructional design. MATLAB is utilised as it is a comprehensive tool for enhancing students’ understanding of statistical skills, practical applications, and data analysis—areas where traditional methods often fall short. International postgraduate students were chosen for this study because their diverse educational backgrounds present unique learning challenges. A qualitative case study design was employed, and data collection methods included classroom observations, interviews, and student work analysis. The collected data were analysed and modelled by conceptualising key elements and themes using thematic analysis, with findings verified through data triangulation and expert review. Emerging themes were structured into models that illustrate multimodal teaching and learning interactions. The novelty of this research lies in its contribution to multimodal teaching and learning strategies for multinational students in statistics education. The findings highlight significant challenges international students face, including language and technical barriers, limited prior content knowledge, time constraints, technical difficulties, and a lack of independent thinking. To address these challenges, MATLAB promotes collaborative learning, increases student engagement and discussion, boosts motivation, and develops essential skills. This study suggests that educators integrate multimodal interactions in their teaching strategies to better support multinational students in statistical learning environments. Full article

To address the flood risks driven by climate change and urbanization, this study proposes the DRIRA model (Driving Force, Resistance, Influence, Recoverability, Adaptability). Distinct from BRIC (Baseline Resilience Indicators for Communities) and PEOPLES (Population, Environmental/Ecosystem, Organized Governmental Services, Physical Infrastructure, Lifestyle, Economic Development, Social–Cultural Capital), the model emphasizes dynamic interactions across the entire disaster lifecycle, introduces the “Influence” dimension, and integrates SNA (Social Network Analysis) with a modified gravity model to reveal cascading effects and resilience linkages among cities. Based on an empirical study of 30 cities in the Central Plains Urban Agglomeration, and using a combination of entropy weighting, a modified spatial gravity model, and social network analysis, the study finds that: (1) Urban flood resilience increased by 35.5% from 2012 to 2021, but spatial polarization intensified, with Zhengzhou emerging as the dominant core and peripheral cities falling behind; (2) Economic development, infrastructure investment, and intersectoral governance coordination are the primary factors driving resilience differentiation; (3) Intercity resilience connectivity has strengthened, yet administrative fragmentation continues to undermine collaborative effectiveness. In response, three strategic pathways are proposed: coordinated development of sponge and resilient infrastructure, activation of flood insurance market mechanisms, and intelligent cross-regional dispatch of emergency resources. These strategies offer a scientifically grounded framework for balancing physical flood defenses with institutional resilience in high-risk urban regions. Full article

The flexible job shop scheduling problem (FJSP) with transportation resources such as automated guided vehicles (AGVs) is prevalent in manufacturing enterprises. Multi-type AGVs are widely adopted to transfer jobs and realize the collaboration of different machines, but are often ignored in current research. Therefore, this paper addresses the FJSP with multi-type AGVs (FJSP-MTA). Considering the difficulties caused by the introduction of transportation and the NP-hard nature, the artificial bee colony (ABC) algorithm is adopted as a fundamental solution approach. Accordingly, a Q-learning hybrid multi-objective ABC (Q-HMOABC) algorithm is proposed to deal with the FJSP-MTA. First, to minimize both the makespan and total energy consumption (TEC), this paper proposes a novel mixed-integer linear programming (MILP) model. In Q-HMOABC, a three-layer encoding strategy based on operation sequence, machine assignment, and AGV dispatching with type selection is used. Moreover, during the employed bee phase, Q-learning is employed to update all individuals; during the onlooker bee phase, variable neighborhood search (VNS) is used to update nondominated solutions; and during the scout bee phase, a restart strategy is adopted. Experimental results demonstrate the effectiveness and superiority of Q-HMOABC. Full article

Research on unmanned aerial vehicle (UAV) path planning technology in urban operation scenarios faces the challenge of multi-objective collaborative optimization. Currently, mainstream path planning algorithms, including the multi-objective particle swarm optimization (MOPSO) algorithm, generally suffer from premature convergence to local optima and insufficient stability. This paper proposes a Zaslavskii chaotic multi-objective particle swarm optimization (ZAMOPSO) algorithm to address these issues. First, three-dimensional urban environment models with asymmetric layouts, symmetric layouts, and no-fly zones were constructed, and a multi-objective model was established with path length, flight altitude variation, and safety margin as optimization objectives. Second, the Zaslavskii chaotic sequence perturbation mechanism is introduced to improve the algorithm’s global search capability, convergence speed, and solution diversity. Third, nonlinear decreasing inertia weights and asymmetric learning factors are employed to balance global and local search abilities, preventing the algorithm from being trapped in local optima. Additionally, a guidance particle selection strategy based on congestion distance is introduced to enhance the diversity of the solution set. Experimental results demonstrate that ZAMOPSO significantly outperforms other multi-objective optimization algorithms in terms of convergence, diversity, and stability, generating Pareto solution sets with broader coverage and more uniform distribution. Finally, ablation experiments verified the effectiveness of the proposed algorithmic mechanisms. This study provides a promising solution for urban UAV path planning problems, while also providing theoretical support for the application of swarm intelligence algorithms in complex environments. Full article