Search Results (1,439)

Introduction: The anonymisation of Personal Data (PD) and its release as Open Data (OD) hold considerable potential for innovation across health, research, public administration, and the economy. However, practical experiences regarding data anonymisation and OD publication remain underexplored in Germany. This study empirically investigates the current state of anonymised data practices, the barriers to implementation, and the desired support mechanisms for publishing formerly PD as OD. Methods: Embedded in a mixed-methods approach, this cross-sectional study examines research interest in the collection, processing, and use of anonymised data, as well as potential barriers and support services for the anonymisation and publication of former PD. A nationwide online survey was conducted in October–November 2024 via LimeSurvey. A total of 215 responses were included in the descriptive analysis. Results: The findings indicate limited experience with PD anonymisation and OD publication across industries. The potential added value of these processes was often not fully recognised, and data-handling responsibilities were rarely standardised. Data collectors, data protection officers, and IT departments were identified as the most frequently involved parties in these processes. Technical and educational support were the most desired forms of assistance. Discussion: To foster broader OD utilisation, stakeholders require comprehensive support. According to the sample, specific training and further education on the anonymisation and publishing process, as well as the desired software, are most important. Developing standardised process descriptions that integrate ethical and legal considerations, supported by national networks or governmental institutions, could significantly enhance the responsible and effective use of anonymised OD in Germany. Full article

The rising presence of artificial intelligence (AI) across European industries is gradually reshaping how societies manage resources, reduce waste, and pursue long-term sustainability. While researchers widely acknowledge the economic and social implications of AI, they have not yet sufficiently explored its contribution to advancing a circular economy. This study examines how varying levels of AI adoption across EU Member States relate to material footprint, resource productivity, waste generation, and recycling performance. The analysis draws on harmonized Eurostat data from 2023, the most recent year for which complete and comparable indicators are available, enabling a coherent cross-sectional perspective that reflects the period when AI began to exert a more visible influence on economic and environmental practices. By combining measures of AI uptake with key circular economy indicators and applying factor analysis, neural network modelling, and cluster analysis, the study identifies underlying patterns and country-specific profiles. The results suggest that higher AI adoption is often associated with greater resource productivity and more efficient material use. However, its effects on waste generation and recycling remain uneven across Member States. These findings indicate that AI can support circular economy objectives when embedded in coordinated national strategies and supported by robust institutional frameworks. Strengthening the alignment between digital innovation and sustainability goals may help build more resilient, resource-efficient economies across Europe. Full article

Background: Recent research suggests that damage to right hemisphere regions homotopic to the left hemisphere language network affects language abilities to a greater extent than previously thought. However, few studies have investigated acute disruption of language after lesion to the right hemisphere. Here, we examined lesion correlates of acute speech deficits following left and right hemisphere ischemic stroke to clarify the neural architecture underlying early language dysfunction. Methods: We retrospectively analyzed 410 patients (225 left, 185 right hemisphere lesions) from the Stroke Outcome Optimization Project dataset. Presence and severity of speech deficits was measured using the National Institute of Health Stroke Scale Best Language subscore within 48 h of onset. Manual lesion masks were derived from clinical MRI scans and normalized to MNI space. Lesion-symptom mapping was conducted using voxelwise and region-of-interest analyses with permutation correction (5000 iterations; p < 0.05), controlling for total lesion volume. Results: Speech deficits were observed in 53.7% of the cohort (58.2% left, 48.1% right hemisphere lesions). In the full sample, the presence of speech deficits was associated with bilateral subcortical and perisylvian damage, including the external and internal capsules, insula, putamen, and superior fronto-occipital fasciculus. Severity of speech deficits localized predominantly to left hemisphere structures, with peak associations in the external capsule (Z = 6.39), posterior insula (Z = 5.64), and inferior fronto-occipital fasciculus (Z = 5.43). In the right hemisphere cohort, the presence and severity of speech deficits were linked to homologous regions, including the posterior insula (Z = 3.70) and external capsule (Z = 3.63), although with smaller effect sizes relative to the left hemisphere cohort. Right hemisphere lesions resulted in milder deficits despite larger lesion volumes compared with left hemisphere lesions. Conclusions: Acute speech impairment following right hemisphere stroke is associated with damage to a homotopic network encompassing perisylvian cortical and subcortical regions analogous to the dominant left hemisphere language network. These findings demonstrate that damage to the right hemisphere consistently results in acute speech deficits, challenging the traditional left-centric view of post-stroke speech impairment. These results have important implications for models of bilateral language representation and the neuroplastic mechanisms supporting language recovery. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

Riparian ecosystems provide key ecosystem services, yet their degradation is accelerating under growing human pressures. This study performs a systematic and bibliometric assessment to identify global trends in riparian restoration, specifying three objectives: (i) analyze the temporal evolution of scientific production, (ii) evaluate geographical patterns and North–South asymmetries, and (iii) identify dominant restoration approaches and research gaps. A total of 322 documents (1984–2025) were analyzed using productivity indicators, Lotka-based authorship patterns, co-authorship networks, keyword co-occurrence, and a logistic growth model fitted to annual publication counts, combined with descriptive statistics. Annual scientific output showed a steady 4% growth, while 78.2% of studies were led by institutions in the Global North, mainly in North America (39.1%), Europe (17.8%), and Asia (18.5%), highlighting geographical biases and limited representation of tropical regions. Restoration efforts were centered on natural regeneration and tree planting, with less emphasis on cultural ecosystem services and community participation. Despite scientific advances, challenges persist in adopting adaptive and socio-ecologically grounded approaches, especially in underrepresented regions. Strengthening science–policy links, promoting interdisciplinary collaborations, and expanding community involvement are essential to enhance riparian resilience and sustainability. We call for co-creation processes that integrate traditional knowledge and position local communities as partners in restoration efforts. Full article

The global prevalence of non-alcoholic fatty liver disease (NAFLD) is 25%, and its onset is closely related to fatty acid metabolism disorders. With the rise of the concept of non-drug treatment, the intervention potential of unsaturated fatty acids (especially ω-3/ω-6 fatty acids) has become a research hotspot, but the field’s development trend has not been systematically evaluated. Based on bibliometric analysis, 4509 NAFLD fatty acid-related articles in the Web of Science core collection were retrieved, and CiteSpace and VOSviewer were used to analyze the country, institution, and author cooperation networks, and keyword evolution. The annual publication volume peaked in 2022 (316 articles). China led the research output, but the United States had a significant lead in influence. The key author cluster was centered on Sanyal Arun (USA) and Li Y (China); the University of California system and the French National Institute of Health were high-impact institutions. The research topic has shifted from pathological mechanisms (“insulin resistance” and “oxidative stress”) to clinical intervention (“ω-3 fatty acids” and “double-blind trials”). The research on fatty acids in NAFLD has shifted from a stable period to a transitional period. The key words “ω-3 fatty acids”, “double-blind trials”, and “short-chain fatty acids” indicate that nutritional intervention has entered the evidence-based verification stage. Future research should explore the therapeutic potential of unsaturated fatty acids (e.g., ω-6/ω-9 fatty acids) and specialty oils, such as Torreya grandis oil, as novel dietary interventions. Full article

Generative artificial intelligence (GenAI) is rapidly permeating research practices, yet knowledge about its use and topical profile remains fragmented across tools and disciplines. In this study, we present a cross-disciplinary map of GenAI research based on the Web of Science Core Collection (as of 4 November 2025) for the ten tool lines with the largest number of publications. We employed a transparent query protocol in the Title (TI) and Topic (TS) fields, using Boolean and proximity operators together with brand-specific exclusion lists. Thematic similarity was estimated with the Jaccard index for the Top–50, Top–100, and Top–200 sets. In parallel, we computed volume and citation metrics using Python and reconstructed a country-level co-authorship network. The corpus comprises 14,418 deduplicated publications. A strong concentration is evident around ChatGPT, which accounts for approximately 80.6% of the total. The year 2025 shows a marked increase in output across all lines. The Jaccard matrices reveal two stable clusters: general-purpose tools (ChatGPT, Gemini, Claude, Copilot) and open-source/developer-led lines (LLaMA, Mistral, Qwen, DeepSeek). Perplexity serves as a bridge between the clusters, while Grok remains the most distinct. The co-authorship network exhibits a dual-core structure anchored in the United States and China. The study contributes to bibliometric research on GenAI by presenting a perspective that combines publication dynamics, citation structures, thematic profiles, and similarity matrices based on the Jaccard algorithm for different tool lines. In practice, it proposes a comparative framework that can help researchers and institutions match GenAI tools to disciplinary contexts and develop transparent, repeatable assessments of their use in scientific activities. Full article

Water pollution is a global issue that threatens human health and ecosystems, driving the need for advanced purification technologies. Traditional methods face limitations in cost and efficiency, prompting the emergence of green nanomicrobiology as a sustainable alternative. This interdisciplinary approach integrates nanotechnology and microbiology to develop advanced materials capable of eliminating contaminants. To assess scientific advancements in this field, a bibliometric analysis was conducted based on publications indexed in Scopus, utilizing tools such as VOSviewer 1.6.20 and RStudio 2025.09 to identify trends, institutional collaborations, and development patterns. The findings reveal a significant increase in scientific output between 2010 and 2025, with growing research on nanocomposites, adsorption processes, and hybrid microbiological systems. Notably, metallic nanoparticles and functionalized biopolymers, such as modified bacterial cellulose, demonstrate high efficiency in removing heavy metals and toxic residues. The study also highlights China’s pivotal role in scientific collaboration, with an expanding network of partnerships. Despite these advancements, challenges remain regarding industrial scalability, long-term toxicity, and regulatory frameworks. Integrating artificial intelligence and metagenomics could enhance these systems, strengthening their impact on water sustainability. Full article

The increasing complexity of water distribution systems (WDSs) and the growing demand for sustainable infrastructure management have spurred interest in Building Information Modelling (BIM) and Digital Twin (DT) technologies. This study presents a comprehensive bibliometric and thematic literature review aiming to identify key trends, research clusters, and knowledge gaps at the intersection of BIM, DT, and WDSs. Using the Scopus database, 95 relevant publications from 2004 to 2024 were systematically analyzed. VOSviewer was applied to create, visualize, and analyze maps of countries, journals, documents, and keywords based on citation, co-citation, collaboration, and co-occurrence data. The results indicate a sharp rise in scholarly attention after 2020, with dominant contributions from European institutions. Co-authorship networks show limited global interconnectedness, suggesting that developing countries should especially prioritize integrated DT and BIM for more inclusive and diverse research partnerships. This study characterizes the state of the art and future requirements for research on the use of DT and BIM technologies in WDSs and makes a noteworthy contribution to the body of knowledge. Future research should focus on integrating DT and BIM technologies with ML, which represents scalability challenges of real-time anomaly detection integration models, advancing decision-making and operational resilience in WDNs. Full article

This study is based on the data from co-authored papers, collaborative patents, and jointly authored varieties involving Chinese agricultural enterprises, universities, and research institutions from 2011 to 2023. We construct a three-dimensional dynamic equation system to model the agricultural industry–university–research (I-U-R) collaborative innovation network operation mechanism. Inspired by the Belousov–Zhabotinsky (B-Z) reaction, we model a three-variable oscillator with the state variables (network structure embeddedness, partner heterogeneity, and collaborative innovation output) to represent three primary substances in the chemical oscillators. This study investigates the network’s operational patterns and its determinants. Findings reveal that the patent network operates more efficiently than the paper and variety networks. Dependence on external government support increases with innovation complexity, coordination difficulty, and social value. Although a “structural optimization–resource agglomeration–output explosion” state is theoretically attainable under threshold conditions, the observed reality reflects “marginal structural optimization–continuous resource depletion–zero output growth”. Among the entities, eighteen are active leaders, forty-two constitute a stable but low-dynamism backbone, and ninety are general participants with limited innovation capacity. Significant structural contradictions highlight the need for targeted policy interventions to guide the network toward a more advanced and orderly state. Full article

This study examines the use of artificial neural networks (ANNs) to classify financial risks in micro-, small-, and medium-sized enterprises (MSMEs) in Montenegro and the wider Western Balkan region. The economies in this region share structural similarities, such as a high concentration of MSMEs, limited access to finance, and vulnerability to macroeconomic volatility, which make financial risk assessment particularly challenging. Traditional statistical and econometric methods often fail to capture the complex, nonlinear interdependencies among financial and operational indicators, resulting in the inaccurate classification of high-risk MSMEs. By applying advanced machine learning (ML) techniques, neural networks (NNs) can identify intricate patterns in multidimensional financial data, significantly improving the accuracy and reliability of risk classification. In this research, a predictive model was developed using key financial and operational variables of MSMEs, enabling the accurate classification of MSMEs in terms of financial instability and insolvency. Empirical validation shows that NNs outperform conventional methods in accuracy, sensitivity, and generalisation. This approach offers tangible benefits for investors, credit institutions, and MSME managers, supporting improvements in early warning systems, optimisation of credit decision-making, and strengthening MSMEs’ financial resilience and sustainability. The methodology also advances risk quantification tools, providing robust indicators for strategic planning and resource management. By focusing the analysis on Montenegro and the Western Balkans, this study demonstrates that regional economic and structural similarities support the adaptation of NN models for precise financial risk classification, offering actionable insights to enhance MSME performance and regional economic stability. Full article

In this study, we propose the Integrated Developmental Entrepreneurship Resiliency Framework (IDERF), a conceptual model positioning universities as orchestrators of stakeholder networks for entrepreneurial resilience and sustainability. Review and analysis of historical and contemporary research revealed gaps in existing approaches to sustainable entrepreneurship. Entrepreneurship education has evolved from isolated curricula to formal programs that incorporate experiential learning and multilateral institutional access, which appreciably enhance entrepreneurial resilience and venture longevity. The integration of resilience theory with entrepreneurship research has identified multi-level sustainment factors across the disciplines of psychology, organizational theory, and structural economic development. The IDERF addresses this limitation by adapting the triple helix model to a quadruple helix framework that encompasses academia, government, industry, and community stakeholders. Our proposed conceptual framework was developed through conceptual synthesis based on a structured literature review of 212 publications on university-led entrepreneurship programs and entrepreneur sustainability and resilience since 1940. Our findings revealed the need for more resiliency-focused entrepreneurship program designs, synthesis between resilience and sustainability education, analysis of educational program impacts on business development sustainability, and practical entrepreneur training in real-world economic contexts. The resulting IDERF encompasses five dimensions of adaptive entrepreneurial capacity, stakeholder governance, economic transformation, social–environmental integration, and institutional reform as novel components of entrepreneurial resilience and sustainability. We propose an integrated mixed-methods research agenda that includes proposed research questions to instigate the development of measurement frameworks and cross-cultural validation to empirically test the IDERF’s effectiveness in fostering entrepreneurial resilience across diverse contexts and economic regions. Full article

Background: For very-rare cancers such as pheochromocytoma and paraganglioma (PPGL), center-level case volume is uniformly low, rendering the traditional volume–outcome paradigm uninformative. This study examines whether cancer programs’ institutional resources, after adjusting for tumor-specific case volume, impact overall survival (OS) after surgery. Methods: The 2004–2021 National Cancer Database was queried for patients with a diagnosis of PPGL with malignant potential. Demographics, clinicopathologic characteristics, socioeconomic status, and treatment and survival variables—together with program resource tier (high resource = Academic/Research + Comprehensive Community Cancer Programs; low resource = Community Cancer + Integrated Network Programs), were extracted. IPW-Cox proportional hazard model and survival analysis were performed. Results: 1306 patients were identified, of whom 1066 (81.6%) were treated at high-resource programs. Mean age was 59.0 years and 55.1% were female (n = 719). Median follow-up was 61.7 months (maximum 207 months). Mortality was 28.3% (n = 278). Age, race, median income, tumor size, and surgical approach did not differ by resource tier. Patients treated at high- vs. low-resource programs differed by Charlson– Deyo score (p = 0.008), gender (p = 0.033), insurance status (p = 0.004), and distance traveled to facility (p < 0.001). On adjusted survival analysis, treatment at a high-resource program was associated with improved OS (HR = 0.64, p = 0.043) and a mean survival advantage of 23 months (p = 0.009) vs. a low-resource program. Age (HR = 1.03), tumor size >10 cm (HR = 4.18), and metastasis (HR = 4.17) independently predicted worse OS. Conclusions: Despite uniformly low PPGL case volumes nationally, treatment at high-resource cancer programs was associated with a 23-month longer mean survival and a 36% lower risk of death compared with low-resource cancer programs. Further studies are needed to identify the specific institutional factors that drive this survival advantage in rare cancers. Full article

This study conducts a bibliometric review of Bitcoin research in the Business and Economics domains, using VOSviewer to visualize network structures and Bidirectional Encoder Representations from Transformers Topic (BERTopic) to derive semantically coherent topic clusters. The analysis identifies five major research themes: (1) Diversification, hedging, and safe-haven properties; (2) Market dynamics, efficiency, and investor behavior; (3) Bitcoin price and volatility prediction attempts; (4) Environmental impact of Bitcoin; and (5) Financial impact of Central Bank Digital Currency (CBDC). Based on these themes, the study recommends further investigation into the influence of Exchange-Traded Fund (ETF) approvals, regulatory frameworks, and institutional investor participation on Bitcoin’s safe-haven potential; the role of market dynamics and regulatory interventions; early detection of herding behavior and price bubbles; the integration of machine learning and deep-learning models for price prediction; the environmental costs associated with mining; and the evolving regulatory and implementation challenges of CBDCs. Overall, this review synthesizes existing scholarship and outlines future research directions for the rapidly evolving cryptocurrency ecosystem. Full article

Common bean (Phaseolus vulgaris L.) is a cornerstone of global food security, yet its production is persistently challenged by biotic and abiotic stresses. This study conducted a bibliometric analysis following PRISMA guidelines on 549 documents published between 1971 and mid-2025, using Biblioshiny, VOSviewer, and CiteSpace. Results reveal a scientific output concentrated in leading institutions such as Michigan State University (MSU, USA) and the International Center for Tropical Agriculture (CIAT, Colombia). Collaboration networks are dominated by influential authors including Beebe, S. and Kelly, J.D., with Euphytica and Crop Science emerging as primary publication outlets. Research trends highlight salinity tolerance, oxidative stress, and chromosomal mapping, where advanced technologies such as SNP chips have supplanted RAPD markers. Critical challenges remain, including limited phenotyping capacity and the complexity of polygenic resistance, with urgent implications for developing countries where beans are vital for food security but face barriers to technology adoption and restricted participation in global research networks. Concurrently, mitigation strategies have shifted toward sustainable approaches, incorporating beneficial microorganisms for biotic stress and bio-stimulants or plant extracts for abiotic stress. Since 2020, the field has increasingly embraced multifunctional strategies leveraging natural mechanisms to enhance crop resilience. This analysis offers a comprehensive knowledge base to guide future research agendas. Full article