Search Results (4,379)

Biodiversity conservation schemes in collaboration with agriculture have been criticized in part for their limited ecological effectiveness. In this regard, biosphere reserves are internationally recognized as playing a model role in developing sustainable land use systems. To learn about farmers’ perspectives on insects, the role of biosphere reserves in their farm management, and their experiences with biodiversity measures, we interviewed farmers in the German biosphere reserves (BRs) Schaalsee, Schorfheide-Chorin, Middle Elbe, Bavarian Rhön and Black Forest, thereby drawing on the model function of these reserves for insect conservation. Their perceptions were then assessed by BR staff and insect conservation managers. Juxtaposing the views of farmers and insect conservation stakeholders provides comparative insights from multiple perspectives. Interviewed farmers perceived BR staff as holding substantial regional knowledge, which gives them a reputation as competent intermediaries between biodiversity conservation and agriculture. Although the economic incentives to change farm management are perceived as low, most farmers can be engaged in biodiversity conservation through their intrinsic motivations. Farmers were clustered into four motivational patterns that require targeted communication. Across motivational patterns, farmers identified administrative burdens, farm-level costs, and the limited reliability and flexibility of existing measures as major barriers. To better conserve insects and promote insect conservation measures among farmers, extension services are needed. BR staff and insect conservation managers agreed on most of the farmers’ needs identified through the interviews. Therefore, BR administrations may play a crucial role in insect conservation as they recognize the contribution of agriculture and can engage additional stakeholders beyond the sector. To establish insect conservation in BRs in the long term, regional extension staff for nature conservation and regional support schemes are most needed to provide site-specific, regionally adapted conservation measures. Full article

Libraries are increasingly recognized as strategic institutions that support innovation, creativity, and knowledge-based economic development. This study presents a narrative and conceptual review of scholarship on the role of libraries in supporting innovation and creative industries. Using a purposive rather than exhaustive search, the review examines a corpus of 26 peer-reviewed studies published between 2010 and 2025. Across the reviewed studies, libraries are represented as increasingly active knowledge institutions supporting innovation through technological infrastructure, digital services, makerspaces, collaborative environments, entrepreneurship support, and community engagement. Within this purposively selected corpus, public-library contexts and qualitative or case-based approaches are substantially represented, while fewer included studies address academic-library contributions to entrepreneurship, research commercialization, and knowledge transfer. These patterns describe the reviewed set and are not intended as prevalence estimates for the wider literature. The principal contribution is a structured synthesis and organization of existing knowledge: the review identifies recurring themes, clarifies relationships among previously separated strands of scholarship, highlights unresolved questions, and develops a future research agenda. An integrative library-specific framework is offered as a conceptual organizing device that brings these strands into a common input–process–outcome structure; it is not presented as a novel or empirically validated theory. Full article

International academic exchange is widely regarded as an important mechanism of knowledge transfer and international collaboration, yet relatively little is known about how countries’ structural positions within academic mobility networks relate to national innovation performance. This study examines the relationship between Erasmus academic mobility and innovation using a multidimensional quantitative framework integrating Social Network Analysis (SNA), econometric modelling, and inferential statistical analysis. Innovation performance is operationalised through the European Innovation Scoreboard (EIS), while Erasmus mobility data from 2008–2013 are analysed at both country and institutional levels. The empirical design combines regression analyses of mobility characteristics with two complementary network models: a classical model based on conventional centrality measures and an extended model incorporating weighted, influence-based, and temporal network metrics. The results indicate that overall mobility volume is only weakly associated with innovation performance, whereas mobility directed towards highly innovative countries exhibits substantially stronger associations. Network analyses further demonstrate that countries occupying more influential structural positions within Erasmus mobility networks consistently achieve higher innovation performance across multiple EIS dimensions. The comparison of the two analytical models shows that the extended multidimensional network representation captures complementary aspects of knowledge diffusion that remain only partially visible through conventional centrality measures. The study contributes conceptually by framing academic mobility as a knowledge network and methodologically by integrating econometric and network-based approaches within a unified analytical framework for examining international academic exchange and innovation. Full article

Background/Objectives: Early residency is not only about building clinical knowledge. It also involves learning how to communicate, collaborate, and regulate one’s behavior throughout real-world pressure—skills that shape teamwork and everyday patient encounters. Describing the behavioral profiles of early-career physicians can therefore be useful for mapping strengths and developmental needs in training. Methods: A cross-sectional exploratory study was conducted between May and June 2023 among 83 medical residents from a major Romanian training centre. Participants completed the Harrison Assessment, which yields trait-level scores relevant to workplace functioning. Individual traits were summarized, and complementary characteristics were examined as trait pairs, using a balance/imbalance classification to describe configuration-level profiles. Results: Across five of the six trait pairs, non-balanced configurations represented 60.24–65.06% of the cohort; Delegation showed a larger balanced group (55.42%). These distributions describe heterogeneity within the participating cohort at one time point and do not establish developmental sequence, competence, or clinical consequence. Conclusions: Configuration-level profiling may offer a descriptive way to examine how complementary behavioral tendencies co-occur. Replication, population-specific psychometric evaluation, longitudinal follow-up, and validation against external outcomes are required before educational or clinical utility can be inferred. Full article

Multi-source cross-domain hyperspectral image (HSI) classification is challenged by heterogeneous sensor configurations, scene-dependent distribution shifts, and limited labeled target data, which hinder effective knowledge transfer across multiple scenes. Motivated by progressive feature correction, we propose a three-stage Residual Feature Discrepancy Refinement (RFD) framework for collaborative representation learning across heterogeneous HSI domains. RFD formulates this correction as a deterministic, discrepancy-conditioned residual refinement process. First, domain-specific encoders project four source domains and the target domain, which may have unequal spectral dimensions and label spaces, into a common-dimensional feature space. Adaptive severity and domain weighting uses first- and second-order feature discrepancies to estimate source-specific conditioning coordinates and collaborative contribution weights. A shared discrepancy-conditioned residual refiner then performs multi-step feature refinement to reduce domain-dependent statistical deviations. Finally, an exponential-moving-average historical prototype memory stabilizes target adaptation, followed by cosine 1-nearest-neighbor classification. Across ten randomized runs, RFD achieves mean overall accuracies of 94.65%, 94.87%, and 96.95% on NC12, Salinas, and WHU-Hi-LongKou, respectively, and obtains the highest mean overall accuracy, average accuracy, and $\kappa$ among the evaluated unified-protocol methods. Full article

Most students in the United States experience engineering education as primarily technical knowledge and communication in English-dominant settings. Such engineering education is at odds with the sociotechnical realities of engineers who collaborate with and communicate their work to audiences of various backgrounds, from peers to communities and other stakeholders. Yet assimilated into the dominant societal language and the culture of engineering, students are under the impression they must compartmentalize their own ways of speaking and knowing. This design-based research study examines how language functions as technology in engineering communication systems, particularly among sixth-grade students’ engineering media production. We conceptualize language as the vast range of resources that individuals and communities use for communication, sensemaking, and problem-solving. Languages, like technological artifacts, are human-driven systems designed to meet the needs of people. Through a multiphase analysis of student interviews and media artifacts, the findings suggest that most learners privileged the language and cultural norms of the discipline while a few centered their communities’ cultural and language practices. In the first case, students experienced how language norms in engineering within K-12 science classrooms are technologies for assimilation into disciplinary communities, while in the second case, students used their language as a technology for resisting monolingual and monocultural norms. This work points to a need to focus on the ways language norms influence students’ experiences when producing engineering communication for broader communities. By conceptualizing language as a technology, this study provides a sociotechnical approach for understanding communicative practices in engineering and science classrooms while offering guidance for educators to design linguistically inclusive learning spaces. Full article

Fire drills are considered very useful in different professional environments and public buildings, so they could also help to train citizens in domestic fire safety. According to the last available data, 172 deaths were reported during one year in the 19,411 domestic fires that occurred in Spain. Drills could be convenient in domestic fire safety training to reduce these fatal data. This pilot study, with 20 participants, explores the integration of smoke-diving drills in a safety training programme, developed at the Citizen School for Risk Prevention, in Zaragoza (Spain), to improve citizens’ knowledge and safe behaviour in domestic settings. A fire in a small apartment was simulated and, during this experimental smoke-diving drill, participants had to successfully escape in a limited time. This drill started in a room with zero visibility (very common in domestic fires) and (breathable, non-toxic) smoke, combined with high temperature. Due to the absence of light, two thermal cameras were used to record the participants’ behaviour. Two surveys, before and after the drill, were used to assess the acquired knowledge, in addition to the analysis of the thermal videos. Results revealed both positive and negative aspects of this experimental drill, thanks to the collaboration with professional firefighters, who participated as citizen scientists. Full article

Firms operating in import-dependent and information-intensive supply environments require organizational capabilities that support continuity and adaptation under disruption. This study examines the relationships of knowledge management and innovation ambidexterity with business resilience among cosmetic raw-material supplier firms in Indonesia. A cross-sectional survey of 189 senior representatives, each representing one firm, was analyzed using partial least squares structural equation modeling (PLS-SEM). The focal structural model showed that knowledge management was positively associated with business resilience (beta = 0.472, t = 5.850, p < 0.001), while innovation ambidexterity was also positively associated with business resilience (beta = 0.443, t = 5.876, p < 0.001). Together, the two organizational capabilities accounted for 67.50% of the variance in business resilience (R2 = 0.675). The measurement results supported the hierarchical representation of knowledge management through knowledge acquisition and knowledge transfer capability, innovation ambidexterity through exploratory and exploitative innovation, and business resilience through reengineering and collaboration, although construct separation should be interpreted cautiously. Drawing on the knowledge-based view and dynamic capabilities perspective, the findings provide context-specific evidence that knowledge-related and innovation-related capabilities are associated with organizational resilience in an upstream, import-dependent industry in an emerging economy. The study contributes primarily through its empirical and contextual positioning rather than through the development of a new theoretical mechanism. Environmental and social sustainability outcomes were not directly measured. Full article

Knowledge-graph-enhanced recommendation leverages external knowledge to characterize item semantics and alleviate the limitations of representation learning under sparse user–item interactions. However, existing methods inadequately model the correspondence between item-level collaborative signals and knowledge semantics, and most graph augmentation strategies rely on random perturbations that fail to distinguish edge-specific retention values. To address these limitations, we propose Knowledge-Stability-Guided Dual-Graph Contrastive Learning for Recommendation (KSDGCL). KSDGCL learns collaborative representations from the user–item interaction graph and knowledge-semantic representations from the knowledge graph, and introduces a cross-graph semantic alignment objective to strengthen the semantic correspondence between the collaborative and knowledge-semantic representations of the same item. To construct informative augmented views, KSDGCL defines edge-level knowledge stability by measuring the consistency of preference matching for the same user–item interaction edge across knowledge-perturbed views. The resulting stability scores are converted into edge retention probabilities to guide augmented interaction graph construction, thereby preserving valuable collaborative relations. Finally, multi-view representations from the original interaction graph, the knowledge graph, and the augmented interaction graphs are fused into comprehensive representations for recommendation. Experiments on Amazon-Book and LastFM show Recall@20 gains of 3.7% and 5.6% over the strongest baseline, respectively, demonstrating the effectiveness of KSDGCL in improving recommendation performance. Full article

Multi-stakeholder supply chains require decision mechanisms capable of simultaneously interpreting dynamic system states, coordinating conflicting stakeholder interests, and managing uncertainty across interconnected operational decisions. This study proposes an integrated Human–AI Collaborative Neuromorphic Digital Twin framework in which synchronized supply-chain data are transformed into cognitive representations, enriched through human–AI collaborative intelligence, strategically coordinated through adaptive game-theoretic interactions, and subsequently mapped into a unified decision-knowledge representation for fuzzy multi-objective optimization. The principal innovation lies in this closed and interconnected decision architecture, where the outputs of cognitive, collaborative, and strategic intelligence layers are explicitly fused and transferred to the optimization space rather than being applied as independent analytical modules. The framework jointly optimizes economic, environmental, service, resilience, energy, and operational-risk objectives under fuzzy uncertainty. Evaluation was conducted using combined real and statistically consistent simulated data across six operational scenarios ranging from baseline conditions to a critical scenario involving simultaneous demand growth, capacity restrictions, cost escalation, uncertainty, and stakeholder conflicts. Results demonstrate progressive improvements in decision quality under increasingly complex conditions; in the critical scenario, Human–AI collaboration achieved a 19.6% cost improvement, while the service level reached 99.4%. The findings demonstrate that integrating cognitive representation, collaborative intelligence, strategic adaptation, and fuzzy optimization provides a unified mechanism for adaptive multi-stakeholder supply-chain decision-making. Full article

The increasing availability of construction data and advances in artificial intelligence (AI) have accelerated the adoption of time series forecasting across construction management. However, a comprehensive understanding of the field’s knowledge structure, methodological evolution, and future directions remains limited. To address this gap, a scientometric and qualitative review was conducted on 192 journal articles published between 2010 and December 2025 and retrieved from the Web of Science Core Collection and Scopus databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. VOSviewer was employed to visualize the knowledge structure, collaboration networks, and research themes. The results indicate sustained growth in research activity since 2010, accompanied by increasing international collaboration. Six major research streams were identified: cost estimation and forecasting, safety and risk management, schedule and performance monitoring, productivity and resource management, sustainability and waste management, and emerging methods and future technological directions. The findings reveal a clear transition from traditional statistical approaches, including AutoRegressive Integrated Moving Average (ARIMA) and vector error correction (VEC) models, toward machine learning, deep learning, and hybrid forecasting frameworks. At the same time, traditional methods remain important because of their interpretability and practical applicability. Three persistent challenges were identified: data quality and availability, model interpretability, and practical implementation. Future research is expected to focus on lightweight real-time forecasting, multimodal data fusion, explainable AI, and physics-informed forecasting models. This review provides an integrated understanding of the field and a research agenda for future methodological and practical development. For practitioners, it further highlights that the value of forecasting models depends not only on predictive accuracy but also on interpretability, computational efficiency, data requirements, and practical deployability in construction decision-making. Full article

This study aims to systematically assess urban Chinese college students’ awareness and depth of understanding of the Sustainable Development Goals (SDGs), examine how digital media exposure and academic discipline shape SDG-related cognitive development, and provide empirical evidence to inform the design of sustainable development education in higher education institutions. A questionnaire survey was administered in China. Descriptive statistics, multinomial logistic regression, path analysis, and correlation analysis were employed to examine the relationships among digital media use, urban context, disciplinary background, and SDG cognition. The results are as follows. Digital media exposure and city tier were both associated with variations in SDG cognition, and a significant nonlinear threshold was observed at approximately two hours of daily media use. A clear threshold effect emerges, with students using digital media for more than two hours per day demonstrating markedly higher cognitive levels. Although overall awareness is relatively high, knowledge remains broad but superficial, with weaker understanding of ecological and collaborative goals. Disciplinary differences are evident—medical and agricultural students show stronger comprehension, while arts and humanities students lag behind—yet support for interdisciplinary collaboration is substantial. Participation in SDG-related learning activities significantly enhances attitudinal and behavioral change. This study advances the literature by integrating digital media exposure, urban hierarchy, and academic discipline into a unified analytical framework to explain variations in SDG cognition among Chinese urban college students. It identifies a significant media use threshold effect (over two hours per day) that enhances cognitive accumulation, revealing a nonlinear mechanism rarely examined in prior research. By uncovering disciplinary heterogeneity and the relative weakness in ecological and collaborative goal awareness, the study moves beyond descriptive assessments to provide targeted, evidence-based implications for curriculum design and interdisciplinary sustainable development education in higher education institutions. Full article

In recent years, as a critical measure of marine fishery resource conservation and ecological restoration in China, stock enhancement has become a hotspot in fishery science research both domestically and internationally. Stock enhancement refers to the replenishment and restoration of biological resources in natural waters through artificial propagation, seed rearing, and releasing, thereby alleviating fishing pressure, improving the ecological environment, and promoting the sustainable utilization of fishery resources. As a major marine fishery nation, China attaches great importance to stock enhancement, utilizing it as an important means to advance the construction of marine ecological civilization, promote green fishery development, and achieve fishery resource recovery. Bibliometric methods allow for the systematic review and visual analysis of massive academic literature, helping to identify research hotspots, reveal knowledge structures, and track disciplinary evolutionary trends. This study systematically investigates literature related to stock enhancement in the China National Knowledge Infrastructure (CNKI) and the Web of Science (WoS) core databases by combining bibliometric analysis, CiteSpace visualization analysis, and Excel statistical analysis. A total of 495 relevant publications were retrieved from CNKI, and 489 from WoS. Concurrently, policy documents from China over the past two decades concerning stock enhancement, aquatic biological resource conservation, and ecological restoration were compiled and analyzed. The results indicate that current research hotspots are primarily concentrated on resource recovery, release effect evaluation, artificial reefs, genetic diversity, ecological restoration, and the collaborative construction of marine ranching. Furthermore, research methodologies have progressively evolved from traditional resource replenishment evaluation toward more refined techniques, such as molecular markers, otolith marking, acoustic telemetry, and ecosystem level assessments. Overall, China’s stock enhancement research is steadily transitioning toward ecological, scientific, precise, and intelligent paradigms, with the long-term monitoring of release effects, collaborative ecosystem restoration, and smart resource management emerging as pivotal future research trends. This study provides a valuable reference for theoretical research, policy formulation, and resource conservation practices in China’s stock enhancement domain. Full article

Knowledge translation (KT) involves the co-creation, exchange and dissemination of knowledge to increase evidence-informed public health. Increasingly, there is a demand to strengthen evidence-informed public health, and with this, a need to understand how to support KT efforts within public health systems. Whilst a variety of KT frameworks have been developed, frameworks and studies that provide insight into KT initiatives that operate across the individual, organisational and system levels are lacking. This paper presents the development of a KT framework used by a government-funded sexual health and blood-borne virus capacity-building partnership for research and evaluation in an Australian jurisdiction. The approaches taken and practical insights into operationalising a collaborative approach to KT are described. The process of developing the framework included inputs from the literature, a needs assessment survey, and stakeholder consultation. The final framework is composed of a conceptual model and a suite of KT strategies that reflect the dynamic and complex nature of KT in public health. The framework illustrates the broader influences of KT, key strategies, and guiding principles. Findings have relevance for service providers, researchers and policymakers (including funders) seeking ways to engage in KT at the individual, organisational and system levels. Full article

There is an extensive body of knowledge related to open innovation and university–industry collaboration (UIC) emphasizing the benefits and challenges of such partnerships. This study examines the relevance of the CESM (Composition, Environment, Structure, Mechanisms) systemism model as a management tool in a case study of a center for research-based innovation. The research investigates how governance structures, trust-building mechanisms, resource allocation, and evolving partner roles interact over time to shape collaboration quality and innovation capability. The results show that the CESM system model provides a coherent framework for diagnosing performance-shaping factors, identifying systemic interdependencies, and guiding targeted managerial interventions. The findings highlight the central role of trust and innovation capability as emergent properties of the interaction between structural, relational, and organizational elements. The study concludes that the CESM model has the potential to offer both analytical and practical value by providing insight and assistance in managing innovation ecosystems. Full article