Search Results (5,633)

Problem: Entrepreneurship education continues to expand, yet it remains fragmented across disciplines and loosely connected to the knowledge, innovation, and venture ecosystems that shape entrepreneurial success. At the same time, AI is transforming research, collaboration, and venture development, but its use in education is typically limited to narrow, task-specific applications rather than ecosystem-level integration. Objective: This paper seeks to develop a comprehensive conceptual model for integrating AI into entrepreneurship education by positioning AI as a connective infrastructure that links and activates the knowledge, innovation, and venture ecosystems. Methods: The model is derived through an integrative synthesis of literature, programs, and activities on entrepreneurship education, ecosystem-based learning, and AI-enabled research and innovation practices, combined with an analysis of gaps in current educational approaches. Key Findings: The proposed model defines a progressive learning pathway consisting of (1) AI competency training that builds foundational capacities in critical judgment, responsible application, and creative adaptation; (2) AI praxis labs that use AI-curated ecosystem data to support iterative, project-based learning; and (3) venture studios where students scale outputs into innovations and ventures through structured ecosystem engagement. This pathway demonstrates how AI can function as a structural mediator of problem definition, research design, experimentation, analysis, and narrative translation. Contributions: This paper reframes entrepreneurship education as an iterative, inclusive, and ecosystem-connected process enabled by AI infrastructure. It offers a new theoretical lens for understanding AI’s educational role and provides actionable implications for curriculum design, institutional readiness, and policy development while identifying avenues for future research on competency development and ecosystem impacts. Full article

Intelligent recommender systems are essential for identifying at-risk students and personalizing learning through tailored resources. Accurate prediction of student performance enables these systems to deliver timely interventions and data-driven support. This paper presents the application of machine learning models to predict final exam grades in a university-level programming course, leveraging multi-modal student data to improve prediction accuracy. In particular, a recent raw dataset of students enrolled in a programming course across 36 class sections from the Fall 2024 and Winter 2025 terms was initially processed. The data was collected up to one month before the final exam. From this data, a comprehensive set of features was engineered, including the student’s background, assessment grades and completion times, digital learning interactions, and engagement metrics. Building on this feature set, six machine learning prediction models were initially developed using data from the Fall 2024 term. Both training and testing were conducted on this dataset using cross-validation combined with hyperparameter tuning. The XGBoost model demonstrated strong performance, achieving an accuracy exceeding 91%. To assess the generalizability of the considered models, all models were retrained on the complete Fall 2024 dataset. They were then evaluated on an independent dataset from Winter 2025, with XGBoost achieving the highest accuracy, exceeding 84%. Feature importance analysis has revealed that the midterm grade and the average completion duration of lab assessments are the most influential predictors. This data-driven approach empowers instructors to proactively identify and support at-risk students, enabling adaptive learning environments that deliver personalized learning and timely interventions. Full article

Mega-event-led urban waterfront regeneration has played a key role in shaping public open spaces, particularly in newly developed areas within the Chinese context. However, public perceptions and their influence on the use of newly built open spaces created through mega-event-led regeneration have not been examined in existing research. To address this gap, this study establishes an integrated assessment framework to evaluate the quality of urban waterfront open spaces. A mixed methods approach was adopted, including direct observations and 770 online questionnaires collected between July and October 2024 at the South Bank of the Qiantang River (SBQR) in Hangzhou, China. Spatial analysis and Importance–Performance Analysis (IPA) were employed to determine priority improvement areas that should inform future waterfront regeneration strategies. The results indicate that inclusiveness emerged as the most important factor for enhancing waterfront open space quality, while spatial aesthetics ranked the lowest. Among the sub-sub factors, elements related to improving water accessibility, enhancing natural surveillance, providing artificial shelters and diverse seating options, introducing distinctive water features, and shaping collective memory through digital technologies are the key priorities for improvement in the future urban waterfront regeneration policies. Finally, the study highlights that the intangible legacies of the Asian Games and the adaptive reuse of informal built heritage have the potential to reshape a distinctive new city image and collective memory, even in the absence of tangible and formally recognised heritage buildings. Full article

Background/Objectives: Adaptive sports engagement has been strongly studied for physical and social gains for athletes with disabilities, with much less investigation into adaptive sports encompassing holistic health (i.e., reaching domains of physical, social, cognitive, emotional, and spiritual). Therefore, the purpose of this study is to explore adaptive sport participants’ perspectives on their engagement in sport as a complementary and holistic intervention to improve resiliency, promote health, and live in recovery. Methods: This study employed a qualitative, phenomenological, and participatory action research design to explore how individuals with disabilities perceive their engagement in adaptive sports. Data were collected from eligible participants across the United States, aged 12 years and older, who provided open-ended responses via survey detailing their adaptive sport experiences. Results: Adaptive sport participants (n = 47), primarily male (n = 26), and White (n = 37) with a range of ages 12–75, provided qualitative findings that formed three deductive themes with further inductive subthemes: (1) Improving Resiliency highlighting promotive and protective factors supporting resilience development, (2) Promoting Health defined by World Health Organization’s holistic health definition, and (3) Living in Recovery framed by the Health Protection/Health Promotion Model. Conclusions: For this sample of adaptive sport participants across the United States, engagement in adaptive sports is seen as a complementary and holistic health intervention that achieves outcomes beyond just physical and social. Key aspects of adaptive sports were shown to be vital for building resiliency through the disability community environment, improving holistic health, and providing a recovery mindset through new life opportunities. Full article

The COVID-19 pandemic accelerated vaccine innovation but also exposed weaknesses in global access and manufacturing. Yeast-based platforms, particularly Saccharomyces cerevisiae and Pichia pastoris, also known as Komagataella phaffii, offer a practical complement to vector systems. These eukaryotic microorganisms combine safety, scalability, and cost-effectiveness with the ability to express complex antigens and assemble virus-like particles. Building on the success of the recombinant hepatitis B vaccine, recent advances in glycoengineering, CRISPR-based host optimization, and surface display technologies have expanded the utility of yeast-based platforms for the rapid development of vaccines. Yeast-derived SARS-CoV-2 receptor-binding domain (RBD) subunit vaccines, such as Corbevax and Abdala (CIGB-66), demonstrate that affordable, immunogenic, and thermostable products are feasible at scale. Emerging innovations in glycan humanization, thermostable formulations, and oral or mucosal delivery highlight the potential of yeast-based vaccines for decentralized manufacturing and equitable pandemic preparedness. This review summarizes recent technical and clinical progress in yeast-based vaccine research, positioning these platforms as accessible and adaptable tools for future outbreak responses and global immunization strategies. Full article

Pronunciation assessment is central to computer-assisted pronunciation training (CAPT) and speaking tests, yet most systems still adopt a native norm, treating deviations from canonical L1 pronunciations as errors. In contrast, rating rubrics and psycholinguistic evidence emphasize intelligibility for a target listener population and show that listeners rapidly adapt their phonetic categories to new accents. We argue that automatic assessment should likewise be referenced to the target learner group. We build a Transformer-based mispronunciation detection (MD) model that computationally mimics listener adaptation: it is first pre-trained on multi-speaker Librispeech, then fine-tuned on the non-native L2-ARCTIC corpus that represents a specific learner population. Fine-tuning, using either synthetic or human MD labels, constrains updates to the phonetic space (i.e., the representation space used to encode phone-level distinctions, the learned phone/phonetic embedding space, and its alignment with acoustic representations), which means that only the phonetic module is updated while the rest of the model stays fixed. Relative to the pre-trained model, L2 adaptation substantially improves MD recall and F1, increasing ROC–AUC from 0.72 to 0.85. The results support a target-population norm and inform the design of perception-aligned, fairer automatic pronunciation assessment systems. Full article

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

Russia’s 2022 invasion precipitated one of Europe’s largest episodes of forced academic mobility, compelling universities to shift from emergency access to durable inclusion. This article investigates how Ukrainian students are integrated into Romanian higher education through a qualitative case study at Ovidius University of Constanta, undertaken within the Erasmus+ EIUS project. We analysed a participatory focus-group workshop (“Building Bridges,” May 2024) involving 72 participants (15 Ukrainian students, 31 Romanian students, 26 academic staff). Transcripts were coded via reflexive thematic analysis and interpreted through a SWOT lens to connect lived experience with institutional strategy. Findings indicate that integration generates tangible pedagogical and social value—diversity enriches coursework, empathy strengthens peer collaboration, and exposure to multilingual classrooms catalyses instructional innovation. Yet systemic fragilities persist: language anxiety (“translation silence”), fragmented support pathways, and limited access to counselling shift emotional labour onto faculty and peers. Opportunities cluster around Erasmus+ infrastructures, bilingual materials, and co-created projects that transform access into participation; threats include latent prejudice, social isolation, compassion fatigue, and policy discontinuity as crisis attention wanes. We advance the concept of institutionalised solidarity—a multi-level inclusion model that couples emotional infrastructures (mentoring, trauma-informed pedagogy, counselling) with organizational infrastructures (integration offices, linguistic scaffolding, adaptive assessment). The study contributes an empirically grounded framework for moving from humanitarian reaction to sustainable academic inclusion and offers actionable guidance for European universities seeking resilience under protracted disruption. Full article

Research laboratories in universities face a complex challenge: they must manage multiple projects, diverse teams, and tight deadlines, often with limited resources. While the business world has long used agile and quality management tools to navigate such complexity, these methods are surprisingly rare in academic research. In this study, we set out to bridge this gap. We implemented a combined management model, blending agile Scrum practices with proven quality tools like the Ishikawa diagram and PDCA cycle, within a pharmaceutical sciences research lab. Over a six-month period, we diagnosed key issues, created a structured action plan, and introduced an online platform to monitor progress continuously. Our approach led to a significant increase in productivity, with 65% of targeted articles being published or submitted and 75% of general lab activities completed. Perhaps just as importantly, communication improved dramatically, and the lab successfully met all its institutional deadlines. We conclude that this hybrid framework is not just a theoretical idea but a practical and powerful innovation. It provides a tangible blueprint for other research groups looking to enhance their productivity, streamline communication, and build a more adaptive and effective research culture in the face of academic complexity. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

As urbanization and extreme weather conditions intensify, the comprehensive performance requirements for building waterproofing systems are becoming more demanding. Single-layer waterproof membranes often struggle to meet usage requirements in complex environments, leading to the gradual rise of composite waterproof systems. This paper selects three different types of waterproof membranes, ultra-thin reinforced self-adhesive polymer-modified bitumen waterproof membrane, polymer self-adhesive waterproof membrane, and polymer-modified bitumen root penetration-resistant waterproof membrane, and conducts a systematic study on their compatibility and durability. Through tensile performance, low-temperature flexibility, and peel compatibility tests, combined with thermal oxidative aging experiments at different aging times, the mechanical behavior, low-temperature adaptability, and interfacial bonding characteristics of the membranes were analyzed. The results show that the three membranes differ significantly in tensile performance. The root penetration-resistant membrane has the highest strength but is more brittle, the polymer self-adhesive membrane has lower strength but better stability, and the ultra-thin reinforced membrane performs better initially but lacks durability. In terms of low-temperature flexibility, the root penetration-resistant membrane demonstrates superior crack resistance and aging resistance. These divergent aging responses are closely related to differences in reinforcement structure, polymer modification, and the thermal–oxidative sensitivity of the bituminous adhesive layers. Peel compatibility tests show that the peel strength of the composite membranes of the ultra-thin reinforced and polymer self-adhesive membranes is significantly improved, indicating a good synergistic effect and compatibility. Overall, different waterproof membranes exhibit distinct compatibility mechanisms and aging patterns in composite applications, providing a scientific basis for the design and optimization of composite waterproof systems. Full article

The UK’s net-zero by 2050 commitment necessitates urgent housing sector decarbonisation, as residential buildings contribute approximately 17% of national emissions. Post-1950 construction prioritised speed over efficiency, creating energy-deficient housing stock that challenges climate objectives. Current retrofit policies focus primarily on technological solutions—insulation and heating upgrades—while neglecting broader sustainability considerations. This research advocates systematically integrating Circular Economy (CE) principles into residential retrofit practices. CE approaches emphasise material circularity, waste minimisation, adaptive design, and a lifecycle assessment, delivering superior environmental and economic outcomes compared to conventional methods. The investigation employs mixed-methods research combining a systematic literature analysis, policy review, stakeholder engagement, and a retrofit implementation evaluation across diverse UK contexts. Key barriers identified include regulatory constraints, workforce capability gaps, and supply chain fragmentation, alongside critical transition enablers. An evidence-based decision-making framework emerges from this analysis, aligning retrofit interventions with CE principles. This framework guides policymakers, industry professionals, and researchers in the development of strategies that simultaneously improve energy-efficiency, maximise material reuse, reduce embodied emissions, and enhance environmental and economic sustainability. The findings advance a holistic, systems-oriented approach, positioning housing as a pivotal catalyst in the UK’s transition toward a circular, low-carbon built environment, moving beyond isolated technological fixes toward a comprehensive sustainability transformation. Full article

Oriented object detection in remote sensing images remains a challenging task due to arbitrary target rotations, extreme scale variations, and complex backgrounds. However, current rotated detectors still face several limitations: insufficient orientation-sensitive feature representation, feature misalignment for rotated proposals, and unstable optimization of rotation parameters. To address these issues, this paper proposes an enhanced Rotation-Sensitive Feature Pyramid Network (RSFPN) framework. Building upon the effective Oriented R-CNN paradigm, we introduce three novel core components: (1) a Dynamic Adaptive Feature Pyramid Network (DAFPN) that enables bidirectional multi-scale feature fusion through semantic-guided upsampling and structure-enhanced downsampling paths; (2) an Angle-Aware Collaborative Attention (AACA) module that incorporates orientation priors to guide feature refinement; (3) a Geometrically Consistent Multi-Task Loss (GC-MTL) that unifies the regression of rotation parameters with periodic smoothing and adaptive weight mechanisms. Comprehensive experiments on the DOTA-v1.0 and HRSC2016 benchmarks show that our RSFPN achieves superior performance. It attains a state-of-the-art mAP of 77.42% on DOTA-v1.0 and 91.85% on HRSC2016, while maintaining efficient inference at 14.5 FPS, demonstrating a favorable accuracy-efficiency trade-off. Visual analysis confirms that our method produces concentrated, rotation-aware feature responses and effectively suppresses background interference. The proposed approach provides a robust solution for detecting multi-oriented objects in high-resolution remote sensing imagery, with significant practical value for urban planning, environmental monitoring, and security applications. Full article

The decision-making process for the adaptive reuse of cultural heritage requires the evaluation of multiple criteria because of its multifaceted structure. The criteria determined through a literature review were weighted by experts and ranked according to their degree of importance via the DEMATEL method, which is a multicriteria decision-making technique. This study, conducted by integrating the importance levels of the criteria determined by the DEMATEL method with Geographic Information Systems (GIS) techniques, was applied to Yediocak Primary School, one of the significant buildings in Osmaniye, affected by the 2023 Kahramanmaraş Pazarcık Earthquake and heavily damaged during the event. The DEMATEL analysis demonstrated that economic value, regional potential, and compatibility with the new function are the primary cause-group criteria, whereas architectural, cultural, and social values are predominantly situated within the effect group. The spatial assessment yielded a low suitability score for the current primary school function (0.3954). The hybrid DEMATEL + GIS index (0.2598) confirmed that a building’s reuse as a high-occupancy school is constrained by seismic risk, its position on a heavily trafficked corridor, and relatively limited access to healthcare and emergency assembly areas. This study aimed to establish a new framework for the adaptive reuse of historic buildings. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major global health threat that reduces antibiotic effectiveness and increases healthcare burdens. Countries in the Asia–Pacific region face a particularly high AMR burden, necessitating international collaboration, education, and practical training to combat this growing crisis. This study describes the design, implementation, and educational outcomes of the Infection Diagnosis Workshop, a short-term international program primarily targeting undergraduate medical sciences students that integrates AMR-focused hands-on clinical microbiology training and lectures, alongside cross-cultural collaboration and scientific English communication. Methods: The Infection Diagnosis Workshop was implemented as a four-day program combining lectures with hands-on laboratory activities. Training emphasizes the detection and analysis of antibiotic-resistant bacteria through environmental sampling, bacterial culturing, phenotypic and genotypic resistance detection, and species identification, core components that have remained consistent since the workshop’s establishment. Students also attended lectures on AMR science, global impact, and management strategies. Group discussions and collaborative tasks encouraged interdisciplinary learning. A thematic analysis of student feedback essays from previous workshop cohorts was conducted to identify key concepts, learning outcomes, and shared experiences. All participants provided informed consent for the use of their written feedback. Results: Thematic analysis revealed key learning outcomes categorized into three themes: (1) Knowledge, Awareness, and Technical Skills; (2) Cultural Understanding and Cross-Cultural Collaboration; and (3) English Language and Communication Skills. Students reported increased AMR knowledge, improved laboratory proficiency, enhanced cultural adaptability, and greater confidence in English communication. They also expressed a deeper appreciation for interdisciplinary and international approaches to AMR. Conclusions: The Infection Diagnosis Workshop effectively integrated practical laboratory training with international and cross-cultural engagement. The program strengthened student competencies and contributed to building global partnerships essential for combating AMR. Full article