Search Results (404)

Small and medium-sized enterprises (SMEs) face growing challenges in optimizing their sustainable supply chains because of fragmented logistics data and changing regulatory requirements. In particular, globally operating manufacturing SMEs often lack suitable tools, resulting in manual data collection and making reliable accounting and benchmarking of transport emissions in lifecycle assessments (LCAs) time-consuming and difficult to scale. This paper introduces a novel hybrid AI-supported knowledge graph (KG) which combines large language models (LLMs) with graph-based optimization to automate industrial supply chain route enrichment, completion, and emissions analysis. The proposed solution automatically resolves transportation gaps through generative AI and programming interfaces to create optimal routes for cost, time, and emission determination. The application merges separate routes into a single multi-modal network which allows users to evaluate sustainability against operational performance. A case study shows the capabilities in simplifying data collection for emissions reporting, therefore reducing manual effort and empowering SMEs to align logistics decisions with Industry 5.0 sustainability goals. Full article

Proton Exchange Membrane Fuel Cells (PEMFCs) are seen as an alternative for heavy-duty transportation electrification. Powered by a green hydrogen source, they can provide high efficiency and low carbon emissions compared to traditional fuels. However, to be competitive, these systems require high reliability when operated in real-life conditions, as well as safe and efficient operating management. In order to achieve these goals, the X-in-the-loop (also called model-based design) methodology is well suited. It has been largely adopted for PEMFC system development and optimisation, as they are complex multi-component systems. In this paper, a systematic analysis of the scientific literature is conducted to review the methodology implementation for the design and improvement of the PEMFC systems. It exposes a precise definition of each development step in the methodology. The analysis shows that it can be employed in different ways, depending on the subsystems considered and the objectives sought. Finally, gaps in the literature and technical challenges for fuel cell systems that should be addressed are identified. Full article

Background: Developing countries face significant challenges in accessing high-quality pharmaceutical products due to resource constraints, limited regulatory capacity, and market dynamics that often prioritize cost over quality. This review addresses the critical gap in regulatory frameworks that fail to ensure pharmaceutical quality equity between developed and developing nations. Objective: This comprehensive review examines a novel dual-pathway regulatory framework that leverages stringent regulatory authority (SRA) approvals, artificial intelligence-based evaluation systems, and harmonized pricing mechanisms to ensure pharmaceutical quality equity across global markets. Methods: A comprehensive systematic analysis of current regulatory challenges, proposed solutions, and implementation strategies was conducted through an extensive literature review (202 sources, 2019–2025), expert consultation on regulatory science, AI implementation in healthcare, and pharmaceutical policy development. The methodology included an analysis of regulatory precedents, an economic impact assessment, and a feasibility evaluation based on existing technological implementations. Results: The proposed framework addresses key regulatory capacity gaps through two complementary pathways: Pathway 1 enables same-batch distribution from SRA-approved products with pricing parity mechanisms. At the same time, Pathway 2 provides independent evaluation using AI-enhanced systems for differentiated products. Key components include indigenous AI development, which requires systematic implementation over 4–6 years across three distinct stages, outsourced auditing frameworks that reduce costs by 40–50%, and quality-first principles that categorically reject cost-based quality compromises. Implementation analysis demonstrates a potential for achieving a 90–95% quality standardization, accompanied by a 200–300% increase in regulatory evaluation capability. Conclusions: This framework has the potential to significantly improve pharmaceutical quality and access in developing countries while maintaining rigorous safety and efficacy standards through innovative regulatory approaches. The evidence demonstrates substantial public health benefits with projected improvements in population access (85–95% coverage), treatment success rates (90–95% efficacy), and economic benefits (USD 15–30 billion in system efficiencies), providing a compelling case for implementation that aligns with global scientific consensus and Sustainable Development Goal 3.8. Full article

In the era of low-carbon economy, green mergers and acquisitions (green M&As) have emerged as a pivotal strategic pathway for heavily polluting enterprises to not only carve out a competitive edge in the market but also contribute significantly to the achievement of Sustainable Development Goal 12 (SDG 12)—Responsible Consumption and Production. Based on the data of China’s heavily polluting enterprises listed on the Shanghai and Shenzhen A-share markets from 2010 to 2022, this study applies the multi-temporal difference-in-differences method to analyze the impact of green M&As on the market power of heavily polluting enterprises. The findings suggest that the adoption of green M&As by heavily polluting enterprises in China can enhance market power, and this conclusion remains valid after a series of robustness tests. The mediation effect analysis shows that green M&As promote the market power of heavily polluting enterprises by increasing green total factor productivity, optimizing human capital structure and enhancing brand capital. Meanwhile, according to the heterogeneity study conducted, the implementation of green M&As by non-state-owned heavily polluting enterprises and heavily polluting enterprises within the growth period has a more pronounced effect on market power promotion. In addition, domestic green M&As have a stronger effect on the market power of heavily polluting enterprises. By bridging the theoretical gap between green M&As and the driving mechanisms of market power, this study not only enriches the academic literature but also provides actionable insights for heavily polluting enterprises seeking to enhance their market competitiveness while adhering to sustainable development principles. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, most existing land use models emphasize quantity forecasting and spatial allocation, while overlooking the third critical dimension—structural complexity, which is essential for understanding the nonlinear, fragmented evolution of urban systems, thus limiting their ability to fully capture the evolutionary characteristics of urban land systems. To address this gap, this study proposes an integrated SD-PLUS-FD model, which combines System Dynamics, Patch-based Land Use Simulation, and Fractal Dimension analysis to construct a comprehensive three-dimensional framework for simulating and evaluating land use patterns in terms of quantity, spatial distribution, and structural complexity. Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). Fractal dimension analysis further reveals that since 2000, the spatial boundary complexity of all land use types—except forest land—has generally shown an upward trend across multiple scenarios, highlighting the increasingly nonlinear and fragmented nature of urban expansion. The FD values for construction land and cultivated land declined to their historical low in 2005, then gradually increased, reaching their peak under the SSP1-2.6 scenario. Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. Its structural complexity remained generally stable under all scenarios except SSP5-8.5, reflecting higher structural resilience and boundary adaptability under diverse socioclimatic conditions. The SD-PLUS-FD model effectively reveals how land systems respond to different socioclimatic drivers in both spatial and structural dimensions. This three-dimensional framework reveals how land systems respond to socioclimatic drivers across temporal, spatial, and structural scales, offering strategic insights for climate-resilient planning and optimized land resource management in rapidly urbanizing regions. Full article

Blending child-led exploration with purposeful teacher guidance and clearly defined learning goals, playful learning has been promoted as a promising approach for introducing computational thinking (CT) in early childhood education (ECE). However, there is a lack of practical guidance for teachers on how to design and implement playful CT learning effectively. To address this gap, we conducted a collaborative action research project and asked these two questions: (1) How can teachers effectively prepare and design a playful learning CT program using tangible CT toys? (2) How do teachers facilitate playful learning in the CT program? Through iterative cycles of planning, acting, observing, and reflecting, the first and second authors (teacher-researchers) designed and implemented their CT programs in a preschool classroom and an afterschool program respectively, and collected data including video recordings of sessions, participant-generated artifacts, program documentation, and anecdotal reflection notes. Based on our thematic analysis of the data, we identified practical principles for selecting CT toys, three key themes for CT program design and preparation—interest, ownership, and application, and two forms of teacher scaffolding during implementation: embodied approach and storytelling as scaffolding and assessment. The findings highlight practical ways that teachers can enhance children’s engagement, problem-solving skills, and conceptual understanding of CT, while also promoting autonomy and creativity through coding and storytelling. Full article

The Sustainable Development Goals commit states to Universal Health Coverage (UHC) for all; yet displaced populations—including asylum seekers, refugees, internally displaced persons (IDPs), and undocumented migrants—remain systematically excluded from national health systems across southern and eastern Africa. This paper applies a whole-of-route, rights-based framework to examine how legal status, policy implementation, and structural governance shape healthcare access for displaced populations across South Africa, Kenya, Somalia, and the Democratic Republic of Congo (DRC). Drawing on 70 key informant interviews and policy analysis conducted between 2020 and 2025, the study finds that despite formal commitments to health equity, access remains constrained by restrictive legal regimes, administrative discretion, and fragmented service delivery models. Critical gaps persist in migration-sensitive planning, gender-responsive care, and mental health integration. The findings highlight the limitations of rights-based rhetoric in the absence of legal coherence, intersectoral coordination, and political will. To realise UHC in displacement contexts, health systems must move beyond citizen-centric models and embed migration-aware, inclusive, and sustainable approaches across all stages of displacement. Without such structural reforms, displaced populations will remain at the margins of national health agendas—and the promise of health for all will remain unmet. Full article

The dynamic vision sensor (DVS), or event camera, is emerging as a successful sensing solution for many application fields. While state-of-the-art datasets for event-based vision are well-structured and suitable for the designed goals, they often rely on simulated data or are recorded in loosely controlled conditions, thereby making it challenging to understand the sensor response to varying camera parameters and illumination conditions. To address this knowledge gap, this work introduces the JRC INVISIONS Neuromorphic Sensors Parametric Tests dataset, an extensive collection of event-based data specifically acquired in controlled scenarios that systematically vary bias settings and environmental factors, enabling rigorous evaluation of sensor performance, robustness, and artifacts under realistic conditions that existing datasets lack. The dataset is composed of 2156 scenes recorded with two different off-the-shelf event cameras, eventually paired with a frame camera across three different controlled scenarios: moving targets, mechanical vibrations, and rotation speed estimation; the inclusion of ground truth enables the evaluation of standard computer vision tasks. The proposed manuscript is complemented by an experimental analysis of sensor performance under varying speeds and illumination, event statistics, and acquisition artifacts such as event loss and motion-induced distortions due to line-based readout. The dataset is publicly available and, to the best of our knowledge, represents the first dataset of its kind in the literature, providing a valuable resource for the research community to advance the development of event-based vision systems and applications. Full article

Non-conformities—deviations from established standards or procedures—can significantly impact product quality and process performance. Although various tools and methodologies exist, current research lacks an integrated, deferred, and corrective approach to non-conformance management that bridges day-to-day operations with systematic quality control. The proposed tool aims to address this gap by providing a practical framework that combines batch data processing using the “Daily Challenge” tool with structured problem solving and corrective strategies. It serves as a comprehensive decision-making tool for systematically managing deviations. The methodology begins with identifying non-conformities through data collection and direct observation, followed by focused reporting and active discussion during departmental meetings. Issues are then categorized based on their frequency, operational impact, and resource requirements to determine the appropriate resolution path—whether through immediate correction or detailed analysis using structured tools such as the “Daily Challenge” sheet. It integrates well-established methodologies such as 5M and PDCA into a structured, daily workflow for resolving non-conformities. Implemented solutions are evaluated for effectiveness with ongoing monitoring to ensure continuous improvement. A key feature of this system is the use of the “Daily Challenge” form, which facilitates documentation, accountability, and knowledge retention—helping to reduce the recurrence of similar situations. The case studies illustrate the methodology through two examples: a labeling issue involving the omission of quantity information on product labels due to operator oversight and the management of production downtime caused by equipment and sensor failures. Although a standard existed, the errors revealed the need for reinforced procedures. Corrective actions included revising procedures, retraining personnel, repairing and recalibrating equipment, enhancing maintenance protocols, and using visual documentation to enhance process understanding. The “Daily Challenge” tool provides a replicable framework for managing non-conformities across various industries, aligning operational practices with quality assurance goals. By integrating structured analysis, clear documentation, and corrective strategies, it fosters a culture of continuous improvement and compliance. Full article

The maritime sector is under pressure to increase ship energy efficiency and reduce greenhouse gas (GHG) emissions as a part of global decarbonization goals. Various innovative technologies are being adopted in recent years, raising concerns not only about technological feasibility but also about the economic viability of such technologies in the context of sustainable maritime practices. This study evaluates the operational performance, potential to increase energy efficiency, and economic feasibility of wind-assisted propulsion technologies such as rotor sails across different vessel types and operational profiles. As a contribution to cleaner and more efficient shipping, energy savings produced by rotor thrust were analyzed in relation to vessel dimensions and rotor configuration. The results derived from publicly available industry data including shipowner reports, manufacturer case studies, and classification society publications on 25 confirmed rotor sail installations between 2010 and 2025 indicate that savings typically range between 4% and 15%, with isolated cases reporting up to 25%. A simulation model was developed to assess payback time based on varying fuel consumption, investment cost, CO₂ pricing, and operational parameters. Monte Carlo analysis confirmed that under typical assumptions rotor sail investments can reach payback in three to six years (as the ship is also liable for CO₂ payments). These findings offer practical guidance for shipowners and operators evaluating wind-assisted propulsion under current and emerging environmental regulations and contribute to advancing sustainability in maritime transport. The research contributes to bridging the gap between simulation-based and real-world performance evaluations of rotor sail technologies. Full article

Driven by the global energy transition and the pursuit of “dual carbon” goals, sustainability risks within the coal supply chain have emerged as a central obstacle impeding the low-carbon transformation of high-carbon industries. To address the critical gap in systematic and multidimensional risk assessments for coal supply chains, this study proposes a hybrid framework that integrates the analytic hierarchy process (AHP) with the fuzzy comprehensive evaluation (FCE) method. Utilizing the Delphi method and the coefficient of variation technique, this study develops a risk assessment system encompassing eight primary criteria and forty sub-criteria. These indicators cover economic, operational safety, ecological and environmental, management policy, demand, sustainable supply, information technology, and social risks. An empirical analysis is conducted, using a prominent Chinese coal enterprise as a case study. The findings demonstrate that the overall risk level of the enterprise is “moderate”, with demand risk, information technology risk, and social risk ranking as the top three concerns. This underscores the substantial impact of accelerated energy substitution, digital system vulnerabilities, and stakeholder conflicts on supply chain resilience. Further analysis elucidates the transmission mechanisms of critical risk nodes, including financing constraints, equipment modernization delays, and deficiencies in end-of-pipe governance. Targeted strategies are proposed, such as constructing a diversified financing matrix, developing a blockchain-based data-sharing platform, and establishing a community co-governance mechanism. These measures offer scientific decision-making support for the coal industry’s efforts to balance “ensuring supply” with “reducing carbon emissions”, and provide a replicable risk assessment paradigm for the sustainable transformation of global high-carbon supply chains. Full article

Over the past decade, the Chongqing land ticket model has played a pivotal role in the market-oriented reform of rural land factors and serves as a representative practice of the TDR program in China. This paper constructs a systematic evaluation framework from two perspectives—policy effectiveness and citizen feedback—to comprehensively understand the policy effect of this model. The study employs methods of policy texts bibliometrics and content analysis based on big data. The results indicate that the effectiveness of land ticket policies exhibit significant fluctuations, with peaks aligning with milestones in the model’s development. Policy measures are well-aligned with the goals set forth. However, policymakers in Chongqing have historically focused more on institutional construction within the land ticket model, only recently shifting attention to the protection of farmers’ rights and interests. This imbalance may have led to potential risks regarding the loss of farmers’ property rights. The analysis of citizen feedback from the online space further took into account the impact of policy content on its audience (farmers), revealing that shortening the compensation payment time rather than increasing the compensation amount is the most common and critical demand among farmers. This underscores the urgent need for a policy-related response from the government to meet farmer’s demands for “procedural justice”. Our conclusions address a gap in the existing literature by integrating policy text analysis with public opinion, thereby offering referential insights into understanding the evolutionary process, policy features, and implementation effects of TDR program in China. Full article

There is a significant portion of the South African population with unknown HIV status, which slows down epidemic control despite the progress made in HIV testing. Machine learning (ML) has been effective in identifying individuals at higher risk of HIV infection, for whom testing is strongly recommended. However, there are insufficient predictive models to inform targeted HIV testing interventions in South Africa. By harnessing the power of supervised ML (SML) algorithms, this study aimed to identify the most consistent predictors of HIV testing in repeated adult population-based surveys in South Africa. The study employed four SML algorithms, namely, decision trees, random forest, support vector machines (SVM), and logistic regression, across the five cross-sectional cycles of the South African National HIV Prevalence, Incidence, and Behavior and Communication Survey (SABSSM) datasets. The Human Science Research Council (HSRC) conducted the SABSSM surveys and made the datasets available for this study. Each dataset was split into 80% training and 20% testing sets with a 5-fold cross-validation technique. The random forest outperformed the other models across all five datasets with the highest accuracy (80.98%), precision (81.51%), F₁-score (80.30%), area under the curve (AUC) (88.31%), and cross-validation average (79.10%) in the 2002 data. Random forest achieved the highest classification performance across all the dates, especially in the 2017 survey. SVM had a high recall (89.12% in 2005, 86.28% in 2008) but lower precision, leading to a suboptimal F₁-score in the initial analysis. We applied a soft margin to the SVM to improve its classification robustness and generalization, but the accuracy and precision were still low in most surveys, increasing the chances of misclassifying individuals who tested for HIV. Logistic regression performed well in terms of accuracy = 72.75, precision = 73.64, and AUC = 81.41 in 2002, and the F₁-score = 73.83 in 2017, but its performance was somewhat lower than that of the random forest. Decision trees demonstrated moderate accuracy (73.80% in 2002) but were prone to overfitting. The topmost consistent predictors of HIV testing are knowledge of HIV testing sites, being a female, being a younger adult, having high socioeconomic status, and being well-informed about HIV through digital platforms. Random forest’s ability to analyze complex datasets makes it a valuable tool for informing data-driven policy initiatives, such as raising awareness, engaging the media, improving employment outcomes, enhancing accessibility, and targeting high-risk individuals. By addressing the identified gaps in the existing healthcare framework, South Africa can enhance the efficacy of HIV testing and progress towards achieving the UNAIDS 2030 goal of eradicating AIDS. Full article

With the rapid proliferation of Generative Artificial Intelligence (AIGC) technologies in higher education, identifying effective integration pathways into design curricula has become a pressing issue in the field of educational technology. This study employs a mixed-methods approach grounded in the Technology Acceptance Model (TAM) and the Technological Pedagogical Content Knowledge (TPACK) framework, incorporating course analysis, questionnaire surveys, structural path modeling, and interview analysis. Focusing on both instructors and students, this research systematically investigates the acceptance, integration mechanisms, and sustainable development potential of AIGC in university-level design education. The findings indicate that students generally acknowledge the value of AIGC in enhancing creativity and improving efficiency, although gaps persist in their understanding of ethical considerations and original expression. On the teachers’ side, technological knowledge exerts a significant positive influence on the integration of content knowledge, while the impact of pedagogical knowledge remains underutilized. Interview data further reveal a structural tension within current teaching practices, characterized by the rapid adoption of technological tools contrasted with the slower evolution of pedagogical systems. Based on these insights, this study offers five strategic recommendations for sustainable teaching, including the development of teacher training systems, curriculum module design, student media literacy enhancement, and pedagogical reconstruction aligned with the Sustainable Development Goals (SDGs). These findings provide both theoretical and practical contributions to the effective and sustainable integration of AIGC into higher design education. Full article