Search Results (2,460)

This study investigates the role of subcontracting partnerships in enhancing collaboration and sustainability in urban rail transit system design, addressing the challenges posed by fragmented practices and environmental goals under China’s “Dual Carbon” policy. Using a mixed-methods approach, we integrate structural equation modeling (SEM) and factor analysis to identify critical success factors (CSFs) and their impacts on design performance. SEM, a statistical technique capable of analyzing complex relationships between unobservable “latent variables” (e.g., trust, innovation) and measurable outcomes, was employed to validate the hypothesized relationships among five key factors: Excellence in Quality, Interactive Collaboration, Collaborative Vision, Risk Strategy, and Strategic Innovation. Factor analysis consolidated 19 CSFs from the literature into these five constructs, explaining 69.09% of the variance. The SEM results revealed that Excellence in Quality, Interactive Collaboration, Risk Strategy, and Strategic Innovation directly improve design performance, while Collaborative Vision indirectly influences outcomes through mediating effects on risk management and innovation. These findings provide actionable strategies for leveraging BIM/blockchain tools and institutional frameworks to enhance sustainability in urban transportation projects. By contextualizing partnership dynamics within China’s state-led infrastructure ecosystem, this research enriches the theoretical understanding of partnership mechanisms. Full article

Nowadays, evidence-based management (EBM) plays an important role in bringing sustainability into the decision-making process in the healthcare industry. The present study examines how evidence-based management affects the strategic decision criteria for the cost efficiency, equity, and accessibility of medical services in Saudi Arabia. A mixed-methods approach used hybrid surveys, interactive focus groups, digital ethnography, and experience sampling methods to collect data from healthcare managers, policymakers, and stakeholders. Structural equation modeling (SEM), latent semantic analysis (LSA), XGBoost models, and dynamic network analysis (DNA), among others, were used to provide robust insights about the system. The results showed a 25 percent increase in cost efficiency, a 20 percent improvement in inequitable resource allocation, and a 15 percent improvement in accessibility with evidence-based management adoption. According to the XGBoost models, streamlined resource management explains 30% of the variation in cost efficiency, and data-driven decision-making practices explain 35% of the variance in equity performance. After EBM implementation, collaborative efforts among stakeholders increased by 40%, as determined by DNA analysis. In addition, time-series analysis revealed a 22% reduction in operational delays, improving service delivery. These results suggest that evidence-based management is an important opportunity to ‘bend the curve’ of patient care, driving healthcare sustainability by optimizing resource use, equity, and accessibility. The contributions of this research to the broader discourse on sustainable healthcare management lie in its proven actionable insights and scalable framework for evidence-based management practices. The integration of advanced analytics underlines its relevance for global healthcare systems to attain long-term efficiency and sustainability. Full article

Ethiopian pastoralist communities are facing a recurrent drought crisis that significantly affects the availability of water and pasture resources for communities dependent on livestock. The increasing intensity, duration and frequency of droughts in the pastoral community in Ethiopia have drawn the attention of multiple stakeholders and increased stakeholder debates on the role of early warning systems (EWSs) for anticipatory action to build climate resilience in the pastoral community. The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), in collaboration with various partners, has developed an interactive web-based digital EWS to provide near real-time information on water and pasture conditions in pastoral and agro-pastoral regions of Ethiopia. In this study, a stakeholder analysis was conducted to identify key stakeholders, understand stakeholder needs, and facilitate collaboration towards sustaining the EWS. The stakeholder analysis revealed the roles and information needs of key actors engaged in livestock water and pasture monitoring and early warning systems aimed at improving the pastoral communities’ resilience. The analysis showed a pressing need for access to real-time information on water and pasture availability and seasonal climate forecasts by local communities for effective and optimal resources management. Local and national governments need similar data for evidence-based decision-making in resource allocation and policy development. International and non-governmental organizations (INGOs) require the same information for efficient humanitarian responses and targeted development interventions. The private sector seeks insights into market dynamics to better align production strategies with community needs. An EWS serves as a vital tool for development partners, facilitating improved planning, coordination, and impact assessment. It also emphasizes the importance of proactive collaboration among stakeholders, including local communities, government bodies, INGOs, and academic and research institutions. Enhanced communication strategies, such as partnerships with local media, are essential for timely information dissemination. Ultimately, sustained collaboration and adaptive strategies are crucial for optimizing the impact of an EWS towards improving the livelihoods and resilience of pastoral communities amid climate variability. Full article

This study explores the use of a theatre project to enhance reading competencies among students with special educational needs (SENs) in inclusive classrooms. The project, titled “Stop Bullying! A Theatre Project”, aimed to improve students’ reading skills through dramatised engagement with texts, with a particular focus on promoting literacy and social interaction. Employing a Design-Based Research (DBR) methodology, the study involved iterative cycles of implementation and data collection. Participants, including students with varying reading abilities, engaged in theatrical activities that incorporated reading strategies such as reading aloud, paired reading, and choral reading—each designed to support comprehension, fluency, and reading confidence. Findings from multiple cycles indicated improvements in students’ social dynamics, including stronger peer interactions and increased group cohesion. While quantitative reading assessment data showed only modest gains in reading performance, qualitative observations revealed significant improvements in reading skills and social interactions during collaborative performances. The study concludes that a theatre-based approach can effectively support reading development while fostering a more inclusive and supportive classroom environment. Full article

This study explores how employee–AI collaboration can promote employees’ proactive behavior by reducing their workload, and examines the mediating role of workload and the moderating effect of AI literacy. Based on a survey of employees across multiple industries, the study finds that employee–AI collaboration significantly reduces employees’ workload, which in turn encourages more proactive behavior. In this process, workload serves as a central mediating mechanism, as it helps alleviate task pressure and frees up cognitive resources, enabling employees to take on additional responsibilities and put forward innovative suggestions. Furthermore, with increasing levels of employee–AI collaboration, employees with higher AI literacy tend to experience greater workload relief, while those with lower literacy demonstrate a stronger and more consistent proactive behavioral response. These findings offer theoretical insight into employee–AI interaction and practical implications for enhancing initiative and innovation through effective AI integration. Full article

Driver distraction can have severe safety consequences, particularly in public transportation. This paper presents a novel approach for detecting bus driver actions, such as mobile phone usage and interactions with passengers, using Kolmogorov–Arnold networks (KANs). The adversarial FGSM attack method was applied to assess the robustness of KANs in extreme driving conditions, like adverse weather, high-traffic situations, and bad visibility conditions. In this research, a custom dataset was used in collaboration with a partner company in the field of public transportation. This allows the efficiency of Kolmogorov–Arnold network solutions to be verified using real data. The results suggest that KANs can enhance driver distraction detection under challenging conditions, with improved resilience against adversarial attacks, particularly in low-complexity networks. Full article

This paper provides a comprehensive review of the current state of research on robotic technologies in sports, with a specific emphasis on skiing. Using a systematic review methodology, I conducted an extensive search for relevant academic articles and conference papers across several databases, including Scopus, Web of Science, and IEEE. A predefined set of keywords guided the search process, leading to an initial collection of 327 papers. After applying specific selection criteria, 24 studies were identified as most relevant to the topic. These selected works were analyzed in detail, covering various key aspects of robotics in skiing, from biomechanical modeling to robotic ski systems. The findings underscore the growing interest in this interdisciplinary field while also highlighting significant research gaps, particularly in stability control, real-time terrain perception, and snow–surface interaction. Overall, the review concludes that while promising developments exist, the field is still in its early stages and would benefit greatly from interdisciplinary collaboration and more robust real-world experimentation. Full article

As building information model technologies become more complex and interconnected, the validation of building information models remains critical to ensure their reliability and effectiveness in practical applications. However, most of the existing research focuses on the application of building information modeling in a single domain and lacks the collaborative validation of the overall behavior of complex dynamic systems. Therefore, how to ensure the correctness and reliability of complex building systems has become a challenging issue. To solve this problem, this paper proposes a symmetry-aware hybrid validation framework that combines Timed Automata (TA), Unified Modeling Language (UML), and AnyLogic simulation to enhance the logical correctness and practical reliability of complex building information systems; the framework inherently preserves structural and temporal symmetry between formal models and dynamic simulations, ensuring consistent validation across virtual–physical interactions. Taking the Building Information Physical Model (BIPM) as an example, the method first solves the defects of traditional methods in logical consistency and reliability validation by firstly modeling the structural model and behavioral logic of the BIPM through UML normalization, transforming the behavioral logic of the BIPM into a network of TA, and realizing the formal validation of its dynamic interaction mechanism to enhance the logical correctness and practical reliability of the complex building information system. Secondly, AnyLogic is used to map the BIPM structural model into a visual simulation model, which supports the real-time dynamic display of building system behavior and performance analysis, enhances the interpretability of the model, and provides an intuitive decision-making platform for stakeholders. Finally, an empirical study of an air conditioning system as a case study shows that the method can effectively integrate formal verification and dynamic visualization techniques, providing a scalable solution for the collaborative verification of complex building systems. Full article

Large language models (LLMs) have rapidly advanced natural language processing, showcasing remarkable effectiveness as automated annotators across various applications. Despite their potential to significantly reduce annotation costs and expedite workflows, annotations produced solely by LLMs can suffer from inaccuracies and inherent biases, highlighting the necessity of maintaining human oversight. In this article, we present a synergistic human–LLM collaboration approach for data annotation enhancement (SYNCode). This framework is designed explicitly to facilitate collaboration between humans and LLMs for annotating complex, code-centric datasets such as Stack Overflow. The proposed approach involves an integrated pipeline that initially employs TF-IDF analysis for quick identification of relevant textual elements. Subsequently, we leverage advanced transformer-based models, specifically NLP Transformer and UniXcoder, to capture nuanced semantic contexts and code structures, generating more accurate preliminary annotations. Human annotators then engage in iterative refinement, validating and adjusting annotations to enhance accuracy and mitigate biases introduced during automated labeling. To operationalize this synergistic workflow, we developed the SYNCode prototype, featuring an interactive graphical interface that supports real-time collaborative annotation between humans and LLMs. This enables annotators to iteratively refine and validate automated suggestions effectively. Our integrated human–LLM collaborative methodology demonstrates considerable promise in achieving high-quality, reliable annotations, particularly for domain-specific and technically demanding datasets, thereby enhancing downstream tasks in software engineering and natural language processing. Full article

Background/Objectives: Quality by Design (QbD) has revolutionized pharmaceutical development by transitioning from reactive quality testing to proactive, science-driven methodologies. Rooted in ICH Q8–Q11 guidelines, QbD emphasizes defining Critical Quality Attributes (CQAs), establishing design spaces, and integrating risk management to enhance product robustness and regulatory flexibility. This review critically examines QbD’s theoretical frameworks, implementation workflows, and industrial applications, aiming to bridge academic research and commercial practices while addressing emerging challenges in biologics, advanced therapies, and personalized medicine. Methods: The review synthesizes regulatory guidelines, case studies, and multidisciplinary tools, including Design of Experiments (DoE), Failure Mode Effects Analysis (FMEA), Process Analytical Technology (PAT), and multivariate modeling. It evaluates QbD workflows—from Quality Target Product Profile (QTPP) definition to control strategies—and explores advanced technologies like AI-driven predictive modeling, digital twins, and continuous manufacturing. Results: QbD implementation reduces batch failures by 40%, optimizes dissolution profiles, and enhances process robustness through real-time monitoring (PAT) and adaptive control. However, technical barriers, such as nonlinear parameter interactions in complex systems, and regulatory disparities between agencies hinder broader adoption. Conclusions: QbD significantly advances pharmaceutical quality and efficiency, yet requires harmonized regulatory standards, lifecycle validation protocols, and cultural shifts toward interdisciplinary collaboration. Emerging trends, including AI-integrated design space exploration and 3D-printed personalized medicines, promise to address scalability and patient-centric needs. By fostering innovation and compliance, QbD remains pivotal in achieving sustainable, patient-focused drug development. Full article

Within the multidisciplinary field of Sustainable Mobility and Transport(ation) (SMT), there are few review studies that analyze the vast and complex literature in a comprehensive manner, often paying limited attention to the key structural and interpretive elements and their interrelationships. Aiming to fill this research gap, the present study offers a thorough review of the literature from the past thirty years (1992–2020), analyzing and organizing it to ultimately provide a unified synthesis. Bibliometric network visualization of the SMT literature (2084 peer-reviewed journal articles) and content analysis of its most influential subset (220 articles) are combined using a mixed-methods approach. Based on this synthesis, three main bibliographic clusters are identified: “technology”, “behavior change”, and “policy–governance”, each addressing twenty-one bibliographic themes. These structural elements (clusters and themes) are then interpreted through three main narratives and twelve sub-narratives, revealing their dynamic interactions. The entire set of clusters, themes, narratives, and sub-narratives, along with their interconnections, constitutes a conceptual framework of the SMT literature. This study highlights the importance of fostering interdisciplinarity through deeper collaboration between researchers from applied sciences, social sciences, and the humanities, and identifies key thematic research areas and topics for future exploration. Full article

The deepening operation of the carbon emission trading market has reshaped the cost–benefit structure of the power generation side. In the process of participating in the market quotation, power suppliers not only need to calculate the conventional power generation cost but also need to coordinate the superimposed impact of carbon quota accounting on operating income, which causes the power suppliers a multi-time-scale decision-making collaborative optimization problem under the interaction of the carbon market, power market, and coal market. This paper focuses on the multi-market-coupling decision optimization problem of thermal power suppliers. It proposes a collaborative bidding decision framework based on a multi-agent deep deterministic policy gradient (MADDPG). Firstly, aiming at the time-scale difference of multi-sided market decision making, a decision-making cycle coordination scheme for the carbon–electricity–coal coupling market is proposed. Secondly, upper and lower optimization models for the bidding decision making of power suppliers are constructed. Then, based on the MADDPG algorithm, the multi-generator bidding scenario is simulated to solve the optimal multi-generator bidding strategy in the carbon–electricity–coal coupling market. Finally, the multi-scenario simulation based on the IEEE-5 node system shows that the model can effectively analyze the differential influence of a multi-market structure on the bidding strategy of power suppliers, verifying the superiority of the algorithm in convergence speed and revenue optimization. Full article

The Impact Monitor Project is a European initiative designed to develop an impact assessment toolbox and framework, targeting the European aviation sector. The proposed framework is not only aimed at the environment, economics, and operations but also the societal impacts of new technologies and aircraft configurations. The toolbox works by setting out the key steps in the impact assessment cycle and presenting guidance, tips, and best practices. Led by DLR, the consortium includes research institutions and universities that have contributed their expertise and tools to develop the collaborative assessment toolbox and framework. The project defines three use cases by considering three assessment levels: aircraft, airport, and air transport system. This article focuses on Use Case 2 on continuous descent operations (CDOs) at the aircraft and airport levels. It describes the workflow proposal, along with the tools involved. The collaborative approach showcases integrating these tools and using collaborative strategies enabled by CPACS (Common Parametric Aircraft Configuration Schema) and RCE (remote component environment). The list of tools includes Scheduler (DLR; flight schedule simulation), AirTOp (NLR; TMA simulation), Dynamo/Farm (UPC; trajectory simulation and assessment), LEAS-iT (NLR; emissions simulation), Tuna (NLR; noise simulation), AECCI (ONERA; emissions simulation), TRIPAC (NLR; third-party risk simulation), and SCBA (TML; social and economic impact assessment). Interactions with other use cases of the project will be demonstrated via new aircraft configurations stemming from the use case at the aircraft level of the project. The results demonstrate the workflow’s feasibility, the cooperation among the tools to obtain and refine the outcomes, as well as the analysis of the operational scenario of a generic airport, CAEPport, which has been extensively used in previous Clean Sky 2 projects. Full article

Staphylococcus aureus is a formidable human pathogen responsible for infections ranging from superficial skin lesions to life-threatening systemic diseases. This review synthesizes current knowledge on its pathogenesis, emphasizing colonization dynamics, virulence mechanisms, biofilm formation, and antibiotic resistance. By analyzing studies from PubMed, Scopus, and Web of Science, we highlight the pathogen’s adaptability, driven by surface adhesins (e.g., ClfB, SasG), secreted toxins (e.g., PVL, TSST-1), and metabolic flexibility in iron acquisition and amino acid utilization. Nasal, skin, and oropharyngeal colonization are reservoirs for invasive infections, with biofilm persistence and horizontal gene transfer exacerbating antimicrobial resistance, particularly in methicillin-resistant S. aureus (MRSA). The review underscores the clinical challenges of multidrug-resistant strains, including vancomycin resistance and decolonization strategies’ failure to target single anatomical sites. Key discussions address host–microbiome interactions, immune evasion tactics, and the limitations of current therapies. Future directions advocate for novel anti-virulence therapies, multi-epitope vaccines, and AI-driven diagnostics to combat evolving resistance. Strengthening global surveillance and interdisciplinary collaboration is critical to mitigating the public health burden of S. aureus. Full article

High levels of stigma among the healthcare workforce impede efforts to increase access to effective substance use disorder (SUD) treatments. Education on SUDs that (1) is tailored to physicians in training and (2) directly addresses and attempts to combat SUD stigma may help produce lasting reductions in SUD stigmatization within the healthcare setting. This study aims to describe the development of a technology-based, interactive SUD stigma intervention for medical students, created in collaboration with medical students, practicing clinicians, and experts in the fields of psychology and addiction medicine. This intervention is unique in its interactive application-based approach and the use of a computerized intervention authorizing system (CIAS) to guide the participant through the training. The final intervention includes four interactive online modules focused on SUD education using a biopsychosocial model, including stigma acknowledgment, an examination of patient perspectives, and the application of skills. Planned future studies will examine the feasibility, acceptability, and preliminary efficacy of the intervention among medical students. This intervention leverages the existing CIAS to provide interactive training that can be used as a part of medical student training and be expanded to other healthcare professionals (e.g., nurses and community health workers). Ultimately, this work will be used to drive a reduction in SUD stigma in medical settings. Full article