• Green technologies offer a window of opportunity for Latin American countries. • Entry into new technologies is positively associated with both local and global interactions, but only within green domains. • Policies should support global and local knowledge collaboration for developing technologies in green domains. This paper investigates whether the green transition represents a “green window of opportunity” for Latin American countries by examining how local and global knowledge networks shape technological specialisation in the region. We analyse the role of knowledge linkages—both local and global—and their technological nature (green versus non-green) in driving the emergence of new patent specialisations. Using United States Patent and Trademark Office (USPTO) data for 16 Latin American countries between 2000 and 2019, we estimate an entry model that relates the probability of developing new technological specialisations to network cohesion (local collaboration) and connectivity (global collaboration). Our results show that the green transition constitutes a window of opportunity: both local and global network interactions positively and significantly influence entry into new technologies, but only for green technological domains. Furthermore, our results highlight how global collaborations are more influential than local ones.

FedCAD: Cross-modal semantic alignment and distillation for cross-domain heterogeneous federated learning.

Conventional passenger next station prediction models are often restricted by rigid graph structures, failing to account for the dynamic interactions between passengers and stations. This limitation results in an insufficient representation of travel patterns and associated knowledge. To address these shortcomings, this paper proposes a novel approach that integrates reinforcement learning with knowledge graphs to enable a holistic fusion of heterogeneous data. The proposed method enriches the environmental variables within reinforcement learning frameworks and introduces collaborative updating mechanisms for representing passenger-station interactions based on human travel knowledge graphs. These enhanced representations are then employed to improve the accuracy of next-station prediction. To evaluate the effectiveness of the approach, ablation experiments and comparative analyses with classical algorithms were conducted. The results demonstrate that the proposed method significantly outperforms existing models in predicting next-stations, routes, and travel distances, establishing its efficacy in capturing complex passenger travel behaviors.

ABSTRACT Background The increasing emphasis on student-centered and technology-enhanced learning has positioned Web 2.0 applications as powerful tools for promoting collaborative inquiry and active knowledge construction in science education. Web 2.0 tools used for educational purposes in schools provide students with opportunities to engage in enjoyable and meaningful learning experiences simultaneously. This study focuses on examining the effects of Web 2.0 tools on both the academic achievement and attitudes toward the science course of Turkish primary school students participating in science activities. Purpose This study aims to determine the effect of the science activities supported by the Web 2.0 applications including Learningapps, Quizlet, Plickers, Wordwall ve Hihaho on fourth grade students’ achievement and attitudes towards Science Course. Sample The sample consisted of 47 students who participated in this study from a primary school in Turkey. In the experimental group consisting of 24 students, the course developed by the researcher was taught using materials utilizing Web 2.0 technologies. The functioning of the Science curriculum established over the 7 week period was improved by integrating Web 2.0 tools at various stages, including capturing interest, clarifying the subject matter, and assessing the topic. Design and Method The study was based on a model in which quantitative data analysis included independent and dependent samples t-tests, Mann Whitney U tests, Wilcoxon signed-rank tests. Result The results indicate that the use of Web 2.0 tools contributed to an increase in student success. Results from the science course attitude scale, which was administered using a Web 2.0 tool, revealed that 50% of the experimental group identified Science as their favorite subject, 63% expressed enjoyment of the activities, 67% showed a willingness to engage in the course, and 75% felt comfortable during the course.

Immune-related adverse events (irAEs) significantly impact the therapeutic efficacy of immune checkpoint inhibitors (ICIs) in lung cancer. However, a comprehensive understanding of the global research landscape and its evolution regarding irAEs is still lacking. We conducted a bibliometric analysis of 6787 publications from the Web of Science Core Collection (2015-2024) via CiteSpace and the R-bibliometrix package. The analyses included cocitation, coauthorship, and keyword burst detection to map collaborative networks, knowledge structure, and research trends. Research output demonstrated a remarkable compound annual growth rate of 30.4%. INSERM (France) and US institutions appeared as central hubs in collaborative networks, whereas China-despite its high publication volume-showed more limited international integration, suggesting a productivity‑influence mismatch. The knowledge base comprises three clusters: clinical evidence, translational research, and fundamental immunology. Research themes evolved from initial tumor-agnostic safety reports to a focus on organ-specific irAEs and precision toxicology. This study delineates the rapidly evolving landscape of irAE research in lung cancer immunotherapy, highlighting key collaborative patterns, knowledge structures, and thematic shifts. The findings provide a foundational perspective for guiding future research directions.

ABSTRACT Integrating sustainability is vital for the commercial furniture industry, yet business-to-business enterprises often struggle with fragmented value prioritization during green innovation. This study develops a synergy-based framework to evaluate value drivers using a hybrid approach integrating the Grey-Decision Making Trial and Evaluation Laboratory and the Analytic Network Process. Key insights include: (i) knowledge sharing, green brand image, and supply chain collaboration are top strategic priorities; and (ii) sustainable transformation is driven by the formal institutionalization of user demands through mandatory standards. Discussions highlight that collaborative knowledge sharing resolves inherent conflicts between production costs and aesthetic requirements. Consequently, manufacturers should shift toward proactive regulatory anticipation, while designers utilize quantitative assessment tools for empirical justification. The scientific value of this paper lies in providing a quantifiable decision-making model that bridges institutional theory and industrial design. Findings offer a tool for formulating data-driven sustainable strategies.

This phenomenographic study identified six qualitatively distinct conceptions of GenAI-mediated learning among 15 Taiwanese undergraduates through interviews: (1) answer acquisition, (2) cognitive offloading, (3) future competency development, (4) question-driven exploration, (5) critical judgment engagement, and (6) collaborative creativity. Notably, “answer acquisition” emerged as the most common conception. These conceptions form a hierarchical spectrum, varying along dimensions of interaction mode with GenAI, perceived GenAI roles, and learner agency, and illustrate a progression from instrumental use toward collaborative knowledge construction. Understanding these variations could inform pedagogical strategies that encourage learners to move beyond surface-level engagement. Consequently, educators could promote deeper learning through critical reflection and collaborative co-construction, which are essential for supporting the responsible and transformative use of GenAI in educational contexts.

ABSTRACT While university-industry knowledge collaborations’ (UICs’) tangible outcomes have been extensively studied, UICs’ intangible outcomes and consequential perceived employability for students have largely been missed in the literature. This study aims to examine the relationships between UICs’ intangible outcomes for students, student engagement and students’ perceived employability with the incorporation of student engagement as a mediator. A survey was conducted with 535 students studying across different universities in Vietnam. The study found that UICs’ intangible outcomes, which were personal management skills, interpersonal skills and career planning, positively influenced student engagement, while student engagement positively influenced perceived employability. The study also demonstrated that student engagement mediated the relationships between personal management skills, interpersonal skills and career planning with perceived employability. Therefore, student engagement is a critical factor to convey the impact of UICs on perceived employability. The research findings can help universities, industries and governments realize an area of focus and priority if they want to improve the quality of education, future workforce and innovations.

This study aimed to develop contextualized emergency nursing educational materials tailored to the Lao People's Democratic Republic (Lao PDR). Emergency nurses in Lao PDR face challenges arising from the lack of standardized, culturally relevant learning resources. Existing materials are often adopted from other countries and do not adequately reflect local needs, contributing to inconsistencies in clinical practice. Developing locally tailored resources, supported by official development assistance, is therefore essential for improving emergency nursing education and practice. This study used a methodological design based on the Four-Door Model-Define, Design, Develop, and Disseminate-from 2021 to 2024. A situational analysis was conducted using a mixed-methods approach that included a quantitative survey of 70 nursing professionals and qualitative interviews with 52 stakeholders. Content validity and suitability were evaluated by a panel of six local experts using standardized validation forms and the Suitability Assessment of Materials (SAM). Ten emergency nursing domains were developed to address both universal principles, including triage protocols and life support interventions, and Lao PDR-specific challenges. Validity and suitability were supported by a SAM score of 77.3%, indicating clinical accuracy and cultural appropriateness. The 269-page coursebook was distributed to partner nursing colleges, three central hospitals, and international organizations. This study developed the first comprehensive emergency nursing educational materials tailored to the Lao PDR context and written in the local language. These materials support continuing professional development and license renewal every 5 years. They also exemplify an international development paradigm that prioritizes recipient-country ownership and sustainable capacity building through collaborative knowledge creation.

LLM-augmented causal-knowledge heterogeneous graph framework for interpretable reasoning and collaborative knowledge fusion in automotive chip production

The global shortage of healthcare professionals is a critical challenge driven by demographic changes, workforce attrition, and increasing healthcare demands. Older professionals contribute valuable experience and interpersonal skills but are often underutilized due to stereotypes, health concerns, and challenges in adapting to new technologies. This systematic review, conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 reporting guideline, examined strategies to recruit and retain older healthcare professionals and to promote intergenerational collaboration. A comprehensive literature search was performed in PubMed, Medline, and Google Scholar for studies published before March 2025 that addressed recruitment and retention strategies, workplace diversity, and collaboration across generations. Of 224 records initially identified, 84 met the inclusion criteria and were synthesized narratively. Effective recruitment strategies included community-based outreach and flexible work arrangements, while retention measures such as phased retirement, mentoring programs, and workplace health initiatives supported knowledge transfer, job satisfaction, and workforce stability. Digital knowledge management and structured mentoring programs further facilitated intergenerational collaboration. A structured approach to workforce planning that integrates diversity and inclusivity can help mitigate healthcare workforce shortages by leveraging the strengths of different generations. Ensuring adaptable working conditions, intergenerational collaboration, and continuous knowledge exchange can strengthen workforce resilience and support the sustainability of healthcare systems. Future research should evaluate the long-term effects of these strategies on workforce stability and patient outcomes.

The rapid integration of artificial intelligence (AI) into university English education has prompted a profound rethinking of language teachers’ professional identities and practices. Based on a systematic review of recent empirical studies, this paper addresses two core questions: how teacher roles are reconfigured under AI-enabled instruction, and what pedagogical innovations are needed to sustain the humanistic essence of education. Grounded in AI-TPACK, double stimulation theory, and collaborative knowledge creation, this study identifies six emerging teacher roles: AI Collaborator, Learning Architect, Humanistic Guide, Data Interpreter, Ethical Mediator, and Epistemic Agent. It also proposes a three-dimensional pedagogical transformation model. The paper argues that although AI improves instructional efficiency and personalized support, human elements—including empathy, critical dialogue, moral guidance, and situated interaction—remain irreplaceable. Practical pathways for teacher development are provided, including tiered training, virtual knowledge-building communities, and institutional support. This framework offers actionable guidance for university English teachers to achieve human-AI collaborative teaching while upholding educational values.

This paper examines the terminology employed across PubMed publications to refer to medical animations in patient education, also looking at how content is assessed, and the ways in which narrative clarity, aesthetic engagement, and scientific rigor are balanced. The findings reveal both terminological ambiguity when labeling “medical animation” for lay audiences, and heterogeneous content evaluation frameworks. The paper also highlights the absence of medical animations as an area of ​​research in translation research despite their growing role in multilingual health communication. And yet, their nature invites inquiry into multimodal translation strategies. Furthermore, medical animations can serve as a pedagogical tool for cultivating competencies that include multimodal literacy, visual literacy, collaborative knowledge negotiation, boundary-crossing inquiry, interdisciplinary conversation of narrative modes, scientific analysis, and creative synthesis of medical science, technology, visual communication, and language practices.

Depression is a leading global health burden increasingly linked to dysregulation in glutamatergic signaling. Ionotropic glutamate receptors (iGluRs), including NMDA, AMPA, and kainate subtypes, have emerged as pivotal therapeutic targets. However, the rapid expansion of literature has led to a fragmented research landscape, creating an urgent need for a structured overview. This study aims to provide a comprehensive bibliometric analysis to map the evolution, knowledge hubs, and emerging frontiers of iGluR research in depression.We retrieved 6,843 publications (1975-2025) from Web of Science, PubMed, and Scopus, utilizing CiteSpace, VOSviewer, and Bibliometrix for visualization and trend analysis. Analysis revealed a 7.31% annual growth rate, with the United States and China leading global productivity. Research has transitioned from basic synaptic transmission mechanisms to clinical innovations, with "NMDA receptor" and "treatment-resistant depression" identified as primary hotspots. These findings provide a quantitative framework that clarifies the shift from preclinical models to rapid-acting antidepressant discovery. Here we show that bibliometric mapping provides a coherent picture of how iGluRs research in depression has evolved. The prominence of ketamine therapy highlights a successful translational trajectory. By identifying key collaborative networks and knowledge gaps, this study provides a strategic roadmap to guide future mechanistic work and the development of next-generation rapidly acting antidepressants.

Abstract To address the difficulty in dynamically coupling student competencies with medical job requirements in clinical medical education, this paper applies a career planning model based on a dynamic coupling mechanism. First, the analytic hierarchy process (AHP)-entropy weight method is used to quantify student clinical competencies. This method, combined with the LSTM (long short-term memory) algorithm, predicts future departmental talent needs. A weighted Euclidean distance and cosine similarity fusion algorithm is designed to dynamically match competencies with positions. Collaborative filtering and knowledge graph techniques are further incorporated to generate personalized career path recommendations. Competency assessment and recommended paths are dynamically updated through an online learning mechanism. Finally, SHAP (Shapley additive explanations) interpretability analysis is integrated to visualize the contribution of each competency dimension to the recommended results. Experimental results demonstrate that the proposed model achieves high competency assessment accuracy (average 0.855) and job prediction accuracy (average 7.47%). The overall adoption intention for the top-1 recommended path is as high as 74.2%, effectively improving the scientificity, precision, and practicality of medical students' career planning.

MSMEs in the food and beverage (F&B) sector play a strategic role in labor absorption and regional development in Bandar Lampung. However, many business owners still face challenges such as limited innovation and low adoption of digital technology, which hinder their growth and performance in a dynamic market. This study aims to examine the influence of Information and Communication Technology (ICT) adoption and open innovation on the business resilience of F&B MSMEs. Using a quantitative approach, the population of this study consisted of 1,183 F&B MSME owners in Bandar Lampung who utilize ICT. A sample of 112 respondents was determined using the Slovin method with a purposive sampling technique. Data were collected through structured questionnaires with Likert scales and analyzed using multiple linear regression techniques via SPSS 25 software. The results indicate that ICT adoption (β =0,685; p =0,000) and open innovation (β =0,251$; p =0,000$) have a positive and significant effect on business resilience both partially and simultaneously. The model achieved a coefficient of determination (R2) of 0.694, meaning the independent variables explain 69.4% of the variance in business resilience.The implications of this research emphasize that strengthening internal digital literacy and external knowledge collaboration, particularly customer involvement, is crucial for increasing competitiveness and ensuring the business sustainability of MSMEs amidst local economic pressures.

This paper proposes a “play triad” of curiosity, divergent thinking and a freedom to fail which fosters a healthy learning environment for successful medical education. Traditional medical education has focused on individual mastery and rote memorization, but a shift towards small group learning centering on adaptability, collaboration, and clinical application of knowledge is needed. The success of this communal learning is fostered through the elements of play, a curiosity-driven exploration of hidden potential. Curiosity is the importance of moving from knowing right answers to slowing down and grappling with the why behind the answers we hold true. Divergent thinking builds on the complexity of medical education and being willing to value creative exploration and multiple perspectives. Finally, freedom to fail is the awareness that “wrong answers” are often our greatest teachers. By framing learning around the elements of play, educators can cultivate a growth mindset, creativity, and critical thinking, enabling students to navigate the uncertainties and challenges of the medical profession.

Deep neural networks (DNNs) deployed on edge devices often suffer from severe performance degradation when exposed to dynamic and continually shifting environments. Test-time adaptation (TTA) has emerged as a promising solution by updating models online with incoming test data. However, edge deployment poses unique challenges: limited computational resources, latency caused by adaptation delays, and knowledge isolation across devices. The situation becomes even more complex in open-world scenarios, where the presence of unknown categories further disrupts adaptation. To overcome these limitations, we propose TeamTTA, a cloud-integrated framework designed for efficient multi-device collaboration open-set test-time adaptation. Specifically, TeamTTA aggregates reliable samples from multiple edge devices through crowdsourcing, uploads them to the cloud, and maintains a memory buffer for continual adaptation. A large vision model (LVM) in the cloud leverages its zero-shot generalization ability to filter out open-set samples and acts as a teacher model, distilling its knowledge into a replicated student edge model stored in the cloud. The adapted model parameters, or alternatively global statistics under poor network conditions, are then transmitted back to the edge devices for efficient inference. Extensive experiments on standard public TTA benchmarks, including corrupted and open-set datasets, show that TeamTTA achieves superior adaptation accuracy, robustness to distribution shifts, and communication efficiency, outperforming state-of-the-art TTA baselines. These results validate the effectiveness of integrating cloud-edge collaboration and LVM-driven knowledge distillation for real-world edge intelligence.

ABSTRACT Many international organizations, such as the United Nations, encourage public participation in formulating water policy; however, there is a lack of research evaluating the impact of this input. This study explored the mechanisms and analyzed how those mechanisms influence community involvement in making water policy in the East Coast Gulf River Basin of Thailand. The study used a qualitative research approach, involving interviews with 39 key informants and questionnaires with 403 respondents. Findings indicate that water user groups, water committees, and consultation processes serve as the primary mechanisms for engagement. Secondary mechanisms—those fostering deeper collaboration, reflective learning, and ongoing knowledge sharing—are less commonly utilized. If implemented, these mechanisms could contribute to transforming institutional, community, and cultural norms. To improve water policy and its desired outcomes, forms of engagement must move beyond legal compliance toward learning and behavior change. Such a shift must emerge from the bottom up, grounded in the local context. Further study is required to examine forms of consultation and their temporal impact on longer term policy outcomes within a continuous feedback process.

The rapid advancement of digital technologies has strengthened the strategic importance of digital literacy in fostering knowledge‐sharing behavior within organizations. This study examines the effect of digital literacy on employees’ knowledge‐sharing intention in Kota Yogyakarta and Kota Bandung, two major urban centers in Indonesia. A mixed‐methods approach was employed, involving 60 respondents through questionnaire surveys supported by narrative analysis. Quantitative data were analyzed using validity and reliability tests and simple linear regression. The results indicate that digital literacy has a significant positive influence on knowledge‐sharing intention. Furthermore, employees in Kota Bandung exhibit higher knowledge‐sharing intention compared to those in Kota Yogyakarta, suggesting contextual differences in digital readiness and knowledge culture. These findings highlight the need for organizations to strengthen continuous digital literacy initiatives as a strategy to enhance collaborative knowledge practices in the workplace.