Search Results (2,038)

The maritime sector is undergoing a profound transformation driven by disruptive technologies and global decarbonization objectives, placing new demands on Maritime Education and Training (MET) systems. Equipping maritime professionals with competencies for low-carbon shipping is now as critical as technological advancement itself. This study examines how disruptive technologies can be effectively integrated into MET frameworks to support environmental sustainability, using Panama as a representative case study of a major flag and maritime service state. A mixed-methods approach was adopted, combining a structured literature review, expert surveys, and a multi-criteria decision-making analysis based on the Analytic Hierarchy Process (AHP). The findings reveal a significant misalignment between existing MET curricula and the competencies required for decarbonized maritime operations. Key gaps include limited training in alternative fuels, emissions measurement and reporting, energy-efficient technologies, digital analytics, and regulatory compliance. Stakeholders also reported fragmented training provision, uneven access to emerging technologies, and weak coordination between academia, industry, and regulators, particularly in developing contexts. The results highlight the urgent need for curriculum reform and stronger cross-sector collaboration to align MET with evolving technological and regulatory demands. The study provides an applied, evidence-based framework for MET reform, with insights transferable to other systems facing similar decarbonization challenges. Full article

Crisis events generate massive information flows from diverse sources, which need to be consolidated into public opinion reports to enable timely response by governments and enterprises. Current LLMs, despite strong generation capabilities, fail to achieve perspective diversity, maintain factual consistency, and perform coherent high-level planning. To address these gaps, we propose MPSR: a multi-perspective self-reflection framework. Our framework first assigns diverse stakeholder personas to agents who independently generate initial writing plans from complementary viewpoints. Subsequently, a three-stage debate mechanism refines these plans by identifying conflicts, formulates resolution strategies, and produces a consensus plan, thereby enhancing factual consistency. Finally, we introduce a Report Fusion mechanism to synthesize reports across temporal batches, ensuring comprehensive event coverage. Extensive experiments demonstrate that MPSR significantly outperforms baselines, achieving Date F1 of 0.67, G-Eval of 4.54, and MiniCheck score of 79.43, which represent improvements of 17.5%, 70.0%, and 25.8% over the strongest baseline, respectively. Full article

Dual Carbon Goals are driving transformation in China’s power system, where increased renewable energy penetration is accompanied by heightened fluctuations on the generation and load sides. Energy storage and microgrid coordination have emerged as key solutions. However, existing research faces the challenge of balancing microgrid operations, energy storage services, and the alignment of user demand with stakeholder interests. This paper establishes a tripartite collaborative optimization framework to balance multi-stakeholder interests and enhance system efficiency, assuming fixed energy storage capacity. Centering on a principal-agent game between microgrid operators and consumer aggregators, energy storage service providers are integrated into this dynamic. Microgrid operators set 24-h electricity and heat pricing while adhering to tariff constraints, prompting consumer aggregators to adjust energy consumption and storage strategies accordingly. The KKT conditional method is employed to solve the model, deriving optimal user energy consumption strategies at the lower level while solving marginal pricing equilibrium relationships at the upper level, balancing accuracy with information privacy. The creative contribution of this article lies in the first construction of a tripartite collaborative optimization architecture in which energy storage service providers are embedded in a game of ownership and subordination. It proposes a dynamic coupling mechanism between pricing power, energy consumption decision-making, and energy storage configuration under fixed energy storage capacity constraints, achieving a balance of interests among multiple parties. By building a case study using MATLAB (R2022b), we compare operation costs, benefits, and absorption rates across different scenarios to validate the framework’s effectiveness and provide a reference for engineering applications. Full article

Public Opinion Reports are essential tools for crisis management, yet their evaluation remains a critical bottleneck that often delays response actions. Recently, dominant Large Language Model (LLM)-based evaluators often overlook a critical challenge: highly open-ended dimensions such as “innovation” and “feasibility” require synthesizing diverse stakeholder perspectives, as different groups judge these qualities from fundamentally different perspectives. Motivated by this, we propose the Role-based Adaptive Evaluation (RAE) framework. This framework employs an adaptive mechanism leveraging multi-perspective evaluation insights through role-based analysis, and further introduces dynamically generated roles tailored to specific contexts for these dimensions. RAE further incorporates multi-role reasoning aggregation to minimize individual biases and enhance evaluation robustness. Extensive experiments demonstrate that RAE significantly improves alignment with human expert judgments, especially on challenging highly open-ended dimensions. Full article

Global climate change has emerged as a critical challenge for human society in the 21st century. As hubs of population and economic activity, urban and rural areas are increasingly exposed to complex and compounded disaster risks. To systematically evaluate the role of educational intervention in climate adaptability capacity building, this study employs a case study approach, focusing on the “Climate Change Adaptation Education and Teaching Alliance Program” launched in Taiwan in 2014. Through a comprehensive analysis of its institutional structure, curriculum, alliance network, and practical activities, the study explores the effectiveness of educational innovation in cultivating climate resilience talent. The study found that the program, through interdisciplinary collaboration and a practice-oriented teaching model, successfully integrated climate adaptability content into 57 courses, training a total of 2487 students. Project-based learning (PBL) and workshops significantly improved students’ systems thinking and practical abilities, and many of its findings were adopted by local governments. Based on these empirical results, the study proposes that urban and rural planning education should be promoted in the following ways: first, updating teaching materials to reflect regional climate characteristics and local needs; second, enhancing curriculum design by introducing core courses such as climate-resilient planning and promoting interdisciplinary collaboration; third, enriching hands-on learning through real project cases and participatory workshops; and fourth, deepening integration between education and practice by establishing multi-stakeholder partnerships supported by dedicated funding and digital platforms. Through such an innovative educational framework, we can prepare a new generation of professionals capable of supporting global sustainable development in the face of climate change. This study provides a replicable model of practice for education policymakers worldwide, particularly in promoting the integration of climate resilience education in developing countries, which can help accelerate the achievement of UN Sustainable Development Goals (SDG11) and foster interdisciplinary collaboration to address the global climate crisis. Full article

Background: The objective of this work was to support the implementation of the European Health Technology Assessment Regulation (EU HTAR) and optimize performance of the evolving EU HTA system. Therefore, an inclusive multi-stakeholder framework of key performance indicators (KPI) for success measurement was developed. Methods: A modified Delphi-procedure was applied as follows: (1) development of a generic KPI pool at the Fall Convention 2024 of the European Access Academy (EAA); (2) review of initial pool and identification of additional KPIs; (3) development of prioritized KPIs covering patient, clinician, Health Technology Developer (HTD), and System/Member State (MS) perspectives, and (4) consolidation of the stakeholder-centric KPIs after EAA’s Spring Convention 2025. Results: Steps 1 and 2 of the Delphi procedure revealed 14 generic KPI domains. Steps 3 and 4 resulted in four prioritized KPIs for patients (patient input; utilization of patient-centric outcome measures; time to access; equity); six for clinicians (population/intervention/comparator/outcomes (PICO); addressing uncertainty; clinician involvement; transparency; equity and time to access); four for HTDs (PICO; joint scientific consultation (JSC) process; joint clinical assessment (JCA) process; time to national decision making); five from a system/MS perspective (PICO; learning and training the health system; reducing duplication; equity and time to access). The scope of, e.g., the PICO-related KPI, differed between stakeholder groups. Also, several KPIs intentionally reached beyond the remit of EU HTA as they are also dependent on MS-specific factors including national health systems and budgets. Discussion and Conclusions: The KPI framework developed here presents a step towards the generation of systematic multi-stakeholder evidence to support a successful implementation of the EU HTAR. The relevance of the identified stakeholder-centric KPIs is confirmed by their alignment with the Health System Goals suggested in the context of “Performance measurement for health improvement” by the World Health Organisation. Implementation of the framework, i.e., measurement of KPIs, is envisioned to provide evidence to inform the 2028 revision of the EU HTAR. Full article

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics. Full article

Electric vehicles (EVs) are pivotal for enhancing the flexibility of power systems, with vehicle-grid integration (VGI) constituting the fundamental mechanism for their participation in grid regulation. VGI relies on multi-source information from EVs, charging infrastructure, traffic network, power grid, and meteorology. However, ineffective data integration mechanisms have resulted in data silos, which impede the realization of synergistic value from multi-source data fusion. To address these issues, this paper develops a quadripartite evolutionary game model that incorporates data providers, data users, government, and data service platforms, overcoming the limitation of traditional tripartite models in fully capturing the complete data value chain. The model systematically examines the cost–benefit dynamics and strategy evolution among stakeholders throughout the data-sharing process. Leveraging evolutionary game theory and Lyapunov stability criteria, sensitivity analyses were conducted on key parameters, including data costs and government subsidies, on the MATLAB platform. Results indicate that multi-source data integration accelerates system convergence and facilitates a multi-party equilibrium. Government subsidies as well as reward and punishment mechanisms emerge as critical drivers of sharing, with an identified subsidy threshold of ε_S = 0.02 for triggering multi-source integration. These key factors can also accelerate system convergence by up to 79% through enhanced subsidies (e.g., reducing stabilization time from 0.29 to 0.06). Importantly, VGI data sharing represents a non-zero-sum game. Well-designed institutional frameworks can achieve mutually beneficial outcomes for all parties, providing quantitatively supported strategies for constructing incentive-compatible mechanisms. Full article

The broader aim of this research is to examine how Learning Analytics (LA) can become ethically sound, pedagogically actionable, and realistically adopted in educational practice. To address this overarching challenge, the study investigates three interrelated research questions: ethics by design, learning impact, and adoption conditions. Methodologically, the research follows an exploratory sequential multi-method design. First, a meta-synthesis of 53 studies is conducted to identify key ethical challenges in LA and to derive an ethics-by-design framework. Second, a quasi-experimental study examines the impact of interface-based LA guidance (strong versus minimal) on students’ self-regulated learning skills and academic performance. Third, a mixed-methods adoption study, combining surveys, focus groups, and ethnographic observations, investigates the factors that encourage or hinder teachers’ adoption of LA in K–12 education. The findings indicate that strong LA-based guidance leads to statistically significant improvements in students’ self-regulated learning skills and academic performance compared to minimal guidance. Furthermore, the adoption analysis reveals that performance expectancy, social influence, human-centred design, and positive emotions facilitate LA adoption, whereas effort expectancy, limited facilitating conditions, ethical concerns, and cultural resistance inhibit it. Overall, the study demonstrates that ethics by design, effective pedagogical guidance, and adoption conditions are mutually reinforcing dimensions. It argues that LA can support intelligent, responsive, and human-centred learning environments when ethical safeguards, instructional design, and stakeholder involvement are systematically aligned. Full article

This paper develops a new conceptual and operational understanding of cultural heritage transformation, interpreting it as a systemic and dynamic process rather than a static state. It explores the realities and opportunities for action when cultural heritage is understood and managed as a complex, adaptive system. The study builds on a critical review of contemporary literature to identify the multi-scalar challenges currently facing urban heritage systems, such as climate change, disaster risks, social fragmentation, and unsustainable urban development. To respond to these challenges, the paper introduces a metamodel for heritage-based urban transformation, designed to apply systems thinking to heritage management that was developed based on cases from the Western European context. This metamodel integrates key variables—actors, resources, tools, and processes—and is used to test the hypothesis that a systems-oriented approach to cultural heritage can enhance the capacity of stakeholders to connect, adapt, use, and safeguard heritage in the face of complex urban transitions. The hypothesis is operationalized through scenario-based applications in the fields of disaster risk management (DRM), circular economy, and broader sustainability transitions, demonstrating how the metamodel supports the design of cross-over resilience strategies. These strategies not only preserve heritage but activate it as a resource for innovation, cohesion, identity, and adaptive reuse. Thus, cultural heritage is reframed as a strategic investment—generating spillover benefits such as improved quality of life, economic opportunities, environmental mitigation, and enhanced social capital. In light of the transition toward a greener and more resilient society, this paper argues for embracing heritage as a driver of transformation—capable of engaging with well-being, behavior change, innovation, and education through cultural crossovers. Heritage is thus positioned not merely as something to be protected, but as a catalyst for systemic change and future-oriented urban regeneration. Full article

Many nations have been investing in hydrogen energy in the most recent wave of development and numerous projects have been proposed, yet a substantial share of these projects remain at the conceptual or feasibility stage and have not progressed to final investment decision or operation. There is a need to identify initial potential sites for green hydrogen production from renewable energy on an objective basis with minimal upfront cost to the investor. This study develops a decision support system (DSS) for identifying optimal locations for green hydrogen production using solar and wind resources that integrate economic, environmental, technical, social, and risk and safety factors through advanced Multi-Criteria Decision Making (MCDM) techniques. The study evaluates alternative weighting scenarios using (a) occurrence-based, (b) PageRank-based, and (c) equal weighting approaches to minimize human bias and enhance decision transparency. In the occurrence-based approach (a), renewable resource potential receives the highest weighting (≈34% total weighting). By comparison, approach (b) redistributes importance toward infrastructure and social indicators, yielding a more balanced representation of technical and economic priorities and highlighting the practical value of capturing interdependencies among indicators for resource-efficient site selection. The research also contrasts the empirical and operational efficiencies of various weighting methods and processing stages, highlighting strengths and weaknesses in supporting sustainable and economically viable site selection. Ultimately, this research contributes significantly to both academic and practical implementations in the green hydrogen sector, providing a strategic, data-driven approach to support sustainable energy transitions. Full article

This paper presents the Living Lab Assessment Method (LLAM), a context-sensitive framework for assessing impact and value creation in Living Labs (LLs). While LLs have become established instruments for Open and Urban Innovation, systematic and transferable approaches to evaluate their impact remain scarce and still show theoretical and practical barriers. This study proposes a new methodological approach that aims to address these challenges through the development of the LLAM, the Living Lab Assessment Method. This study reports a five-year iterative development process embedded in Ghent’s urban and social innovation ecosystem through the combination of three complementary methodological pillars: (1) co-creation and co-design with lead users, ensuring alignment with practitioner needs and real-world conditions; (2) multiple case study research, enabling iterative refinement across diverse Living Lab projects, and (3) participatory action research, integrating reflexive and iterative cycles of observation, implementation, and adjustment. The LLAM was empirically developed and validated across four use cases, each contributing to the method’s operational robustness and contextual adaptability. Results show that LLAM captures multi-level value creation, ranging from individual learning and network strengthening to systemic transformation, by linking participatory processes to outcomes across stakeholder, project, and ecosystem levels. The paper concludes that LLAM advances both theoretical understanding and practical evaluation of Living Labs by providing a structured, adaptable, and empirically grounded methodology for assessing their contribution to sustainable and inclusive urban innovation. Full article

The incorporation of digital music into the reform of education has become one of the primary methods to improve educational outcomes, increase creativity, and innovate the practices in the classroom. This combination, together with the close industry–academia cooperation, presents the possibilities to integrate educational strategies in accordance with the technological and creative demands of the contemporary world. Nevertheless, uncertainty, reluctance, symmetry, and subjectivity in expert ratings are significant problems to cope with when considering multi-criteria decision-making (MCDM). To resolve them, this paper suggests a Picture Fuzzy Faire Un Choix Adequat (PF-FUCA) decision support model, where fifteen options will be rated by seven criteria, depending on the contribution of four professional decision-makers. These findings indicate that the PF-FUCA framework is effective and superior to the current PF-MCDM models, as illustrated by sensitivity and comparison analysis. The identified best strategies based on the framework are blockchain-based music copyright education and integrated music–STEM platforms that, when combined, offer a viable policy instrument to policymakers, educators, and industry stakeholders. Full article

The complexity of modern cyber–physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as model-based systems engineering (MBSE). By creating system models, MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through artificial intelligence (AI), especially generative AI, has so far focused mainly on easing the introduction phase, for example by using large language models (LLMs) to assist in creating system models. However, generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph Retrieval-Augmented Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers. Full article

Mobility-as-a-Service (MaaS) has emerged as a sustainable solution that integrates multiple transport services through digital platforms. Across different cities, MaaS development exhibits variation in terms of economic support, infrastructure capacity, service integration level, and long-term sustainability orientation. The complexity of multistakeholder interactions and functional components in MaaS ecosystems calls for a more comprehensive performance evaluation framework. To address this, this study proposes a holistic four-dimensional indicator system covering economic, infrastructure, integration and sustainability aspects. To address the hybrid uncertainties arising from the heterogeneous information aggregated by the proposed framework, encompassing both quantitative statistics and qualitative expert judgements, a novel rough–fuzzy best–worst method (BWM) and rough–fuzzy data envelopment analysis (DEA) approach is developed. The empirical application to six representative core cities in China reveals that high performance in “Integration” and “Economic” dimensions plays a pivotal role in determining overall MaaS performance, and coordinated enhancement across dimensions is also important. Comparative and sensitivity analyses validate the framework’s robustness, offering policymakers a reliable tool for benchmarking MaaS maturity. Full article