Search Results (4,694)

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Data exfiltration poses a major cybersecurity challenge because it involves the unauthorized transfer of sensitive information. Intrusion Detection Systems (IDSs) are vital security controls in identifying such attacks; however, their effectiveness in cloud computing environments remains limited, particularly against covert channels such as Internet Control Message Protocol (ICMP) and Domain Name System (DNS) tunneling. This study compares two widely used IDSs, Snort and Suricata, in a controlled cloud computing environment. The assessment focuses on their ability to detect data exfiltration techniques implemented via ICMP and DNS tunneling, using DNSCat2 and Iodine. We evaluate detection performance using standard classification metrics, including Recall, Precision, Accuracy, and F1-Score. Our experiments were conducted on Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instances, where IDS instances monitored simulated exfiltration traffic generated by DNSCat2, Iodine, and Metasploit. Network traffic was mirrored via AWS Virtual Private Cloud (VPC) Traffic Mirroring, with the ELK Stack integrated for centralized logging and visual analysis. The findings indicate that Suricata outperformed Snort in detecting DNS-based exfiltration, underscoring the advantages of multi-threaded architectures for managing high-volume cloud traffic. For DNS tunneling, Suricata achieved 100% detection (recall) for both DNSCat2 and Iodine, whereas Snort achieved 85.7% and 66.7%, respectively. Neither IDS detected ICMP tunneling using Metasploit, with both recording 0% recall. It is worth noting that both IDSs failed to detect ICMP tunneling under default configurations, highlighting the limitations of signature-based detection in isolation. These results emphasize the need to combine signature-based and behavior-based analytics, supported by centralized logging frameworks, to strengthen cloud-based intrusion detection and enhance forensic visibility. Full article

Cultural ecosystem services are intangible benefits people gain from ecosystems that enhance well-being. However, the Millennium Ecosystem Assessment indicates that about 70% of cultural ecosystem services are degraded or unsustainably used. To mitigate this decline, many regions and policies promote the assessment and mapping of cultural ecosystem services. Since 2005, related research and publications have increased, yet place-based cultural ecosystem services assessments remain limited. This study aims to clarify key aspects of cultural ecosystem services assessment, including categories, methods, and case study area types. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study systematically reviewed 163 articles on place-based cultural ecosystem services assessment from Web of Science and Scopus from 2010 to September 2024. The results show diverse ecosystem types, assessment categories, and methods, with urban ecosystems most frequently studied. Fourteen cultural ecosystem service categories were identified based on term definitions and relevance. Non-monetary methods, such as questionnaires and social media data, were most commonly applied. Future research trends will focus on spatial visualization and mapping of supply and demand of cultural ecosystem services, emphasizing public perception. These findings provide planners and decision-makers with more detailed and specific information to better manage, design, and develop regions in a sustainable and culturally sensitive way. Full article

When considering the adsorption effect, traditional experimental methods have faced significant challenges in obtaining the solute transport parameters for composite pollutants. Based on the adsorption test data of three types of composite pollutants collected from the Web of Science and China National Knowledge Infrastructure databases from 2014 to 2024, this study employed four commonly used machine learning models, that is, Random Forest (RF), Support Vector Machine (SVM), Back Propagation Neural Network (BPNN), and Decision Tree (DT) models, to establish adsorption isotherms of pollutants with liquid-phase equilibrium concentration as the horizontal coordinate and solid-phase adsorption capacity as the vertical coordinate, and systematically investigated the adsorption characteristics of combined pollutants in the porous aquifer. Subsequently, the Mean Square Errors (MSEs) and coefficients of determination, two commonly used evaluation metrics for regression models in machine learning, were chosen to estimate the prediction effect of datasets. Combined with the convection–diffusion equation, the adsorption kinetic parameters under the mutual interference of composite pollutants, namely, the retardation factor, were solved. The results show that for the adsorption isotherms of heavy metal composite pollutants, organic composite pollutants, and heavy metal and organic combined composite pollutants, SVM, BPNN, and RF models have the best prediction effect, respectively, and their MSEs are 0.032, 0.001, and 0.018. The adsorption isotherm fitting results indicate that the heavy metal composite pollutants and organic composite pollutants conform to the Freundlich model. The retardation factor of organic composite pollutants is significantly higher than that of heavy metal composite pollutants. Full article

Background/Objectives: As precision medicine advances, attention to sex and gender determinants across epidemiological and clinical domains has intensified. However, in the audio-vestibular field, knowledge on sex- and gender-related aspects remains relatively limited. The main aim of this review has been to analyze the available gender medicine-based evidence in vestibular disorders. In particular, our investigation considered the following: (i) pathophysiology and clinical presentation, including differences in predominant signs and symptoms, diagnostic modalities and findings, underlying biological mechanisms associated with vestibular disorders across sex-specific groups; (ii) prognostic variables, including response to treatment, recovery rates, and long-term functional outcomes; (iii) the potential role of sex- and gender-specific diagnostic and therapeutic approaches in the management of vestibular disorders. Methods: Our protocol was registered on PROSPERO (CRD42025641292). A literature search was conducted screening PubMed, Scopus and Web of Science databases. After removal of duplicates and implementation of our inclusion/exclusion criteria, 67 included studies were identified and analyzed. Results: Several studies reported a higher incidence of vestibular dysfunctions among females, with proposed associations involving hormonal fluctuations, calcium metabolism and vitamin D. Estrogen receptors within the inner ear and their regulatory effects on calcium homeostasis have been proposed as potential mechanisms underlying these sex-specific differences. Furthermore, lifestyle factors, comorbidities and differential health-seeking behaviors between males and females may also modulate disease expression and clinical course. Conclusions: Gender-specific variables could not be independently analyzed because none of the included studies systematically reported gender-related data, representing a limitation of the available evidence. Current evidence suggests the presence of sex-related differences in the epidemiology and clinical expression of vestibular disorders, but substantial gaps remain regarding mechanisms, outcomes, and clinical implications. Future research should prioritize prospective, adequately powered studies specifically designed to assess sex and gender influences, integrating biological, psychosocial, and patient-reported outcomes, and adopting standardized sex- and gender-sensitive reporting frameworks. Full article

Background/Objectives: Family members are the principal providers of home-based care for migrant older adults. Linguistic, cultural, and structural barriers within health systems exacerbate the caregiver burden across emotional, physical and financial domains. Although home healthcare services may alleviate this burden, variability in access, cultural safety, and care coordination can also intensify it. This scoping review maps the evidence on the burden experienced by family caregivers who deliver home-based healthcare to migrant older adults and examines how these arrangements affect caregivers’ health and well-being. It synthesizes the literature on facilitators and barriers—including access, cultural-linguistic fit, coordination with formal services, and legal/immigration constraints—and distills implications for policy and practice to strengthen equitable, culturally responsive home care. Method: The Joanna Briggs Institute (JBI) scoping review framework was used to conduct the review. A comprehensive search was performed across six databases (CINAHL, Scopus, Web of Science, PsycINFO, MEDLINE and Sociological Abstracts) for articles published between 2000 and 2025. Studies were selected based on predefined inclusion criteria focusing on the family caregiver burden in providing home healthcare for migrant older adults. Data extraction and thematic analysis were conducted to identify key themes. Results: The review identified 20 studies across various geographical regions, highlighting four key themes: (1) Multidimensional Caregiver Burden, (2) The Influence of Gender, Family Hierarchy, and Migratory Trajectories on Caregiving, (3) Limited Access to Formal and Culturally Appropriate Support, and (4) Health Outcomes, Coping, and the Need for Community-Based Solutions. Conclusions: System-level reforms are required to advance equity in home healthcare for aging migrants. Priorities include establishing accountable cultural-safety training for providers; expanding multilingual access across intake, assessment, and follow-up; and formally recognizing and resourcing family caregivers (e.g., navigation support, respite, training, and financial relief). Investment in community-driven programs, frameworks and targeted outreach—co-designed with migrant communities—can mitigate isolation and improve uptake. While home healthcare is pivotal, structural inequities and cultural barriers continue to constrain equitable access. Addressing these gaps demands coordinated policy action, enhanced provider preparation, and culturally responsive care models. Future research should evaluate innovative frameworks that integrate community partnerships and culturally responsive practices to reduce the caregiver burden and improve outcomes for migrant families. Full article

The paper examines the phenomenon of urban envelopes, a conceptual parallel to building envelopes, which is considered an emerging theme in studies of the built environment. The term ‘envelope’ refers to various physical and non-physical occurrences in the built environment that delimit, enclose, or demarcate spatial configurations. In the first part of the paper, six distinct types of urban envelopes are identified: physical, programmatic, technological, ecological, environmental, and representational. These are defined based on a systematic literature review to clarify their form, role, and meaning in the context of contemporary cities. All six urban envelope types are formalised using ontology-building methods in Protégé and visualised through WebVOWL, producing domain-agnostic RDF/OWL models that support semantic interoperability. The results provide a concise definition of urban envelopes, which are becoming increasingly relevant in their non-physical representations, such as spaces of control (surveillance of public urban spaces), dynamic environmental and ecological phenomena (pollution, heat islands, and more), temporal or dynamic definitions of space use, and many others in the context of contemporary smart city development. The analysis of possible alignment with existing smart city-related ontologies is presented. By providing the methodology for linking urbanistic principles with data-driven smart city frameworks, the paper provides a unified methodological foundation for incorporating such emerging spatial phenomena into formal urban models. Full article

Fermented meat products rely on complex microbial ecosystems in which lactic acid bacteria (LAB) play a central role in safety, quality, and sensory development. In recent years, increasing demand for reduced-nitrite formulations, clean-label products, and improved risk management have driven renewed interest in microbial control strategies beyond traditional fermentation practices. This systematic review aims to synthesize current knowledge on the microbiological aspects of meat fermentation, spanning traditional spontaneous processes and modern approaches to microflora control, including starter cultures, biocontrol strategies, and omics-based tools. A systematic literature search was conducted in PubMed, Web of Science, Scopus, and Google Scholar, with the final search performed on 15 May 2025. After screening and eligibility assessment following PRISMA 2020 guidelines, 141 studies were included in the qualitative synthesis. The review integrates evidence on microbial succession, metabolic functions, pathogen inhibition, biogenic amine control, and flavour formation, with particular emphasis on advances in metagenomics, metabolomics, and predictive microbiology. Across studies, LAB-dominated ecosystems—particularly those involving Latilactobacillus sakei, Latilactobacillus curvatus, and Lactiplantibacillus plantarum—consistently emerge as the primary drivers of fermentation stability and safety. The strongest evidence supports the use of selected starter and protective cultures, bacteriocinogenic LAB, and omics-guided predictive control to enhance process reliability, support reduced-nitrite strategies, and mitigate microbiological risks without compromising product quality. Overall, the integration of traditional fermentation knowledge with data-driven microbial management provides a robust framework for developing safe, authentic, and sustainable fermented meat products. Full article

Background/Objectives: Palliative care education is a core component of undergraduate nursing preparation; however, many nursing students report limited exposure and confidence in providing end-of-life care. Digital and web-based educational approaches have increasingly been adopted to address gaps in palliative care training and to provide flexible, scalable learning opportunities. This mixed-methods systematic review examined the use of digital and web-based approaches in palliative care education for pre-registration nursing students. The aim was to synthesize existing evidence on educational outcomes, confidence development, practice preparation, and acceptability to guide future design and implementation of technology-enhanced learning in this field. Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The search was conducted across Medline (Ovid), Embase, CINAHL, Scopus and PsycINFO in October 2025. Studies employing qualitative, quantitative, or mixed-methods designs were eligible if they evaluated fully digital or web-based palliative care educational interventions for nursing students. Screening, quality appraisal, and data extraction were undertaken independently by multiple reviewers. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). Extracted data were synthesized narratively to integrate qualitative and quantitative findings. Results: The search yielded 1826 records; after removing duplicates and applying eligibility criteria, 12 studies were included in the final synthesis. Considerable heterogeneity in design and outcomes was observed. Most included studies reported improvements in students’ knowledge, self-efficacy, and reflective capacity, alongside high levels of acceptability. Conclusions: Digital and technology-enhanced learning appears feasible and acceptable for palliative care education; however, the current evidence base is limited by methodological heterogeneity, reliance on self-reported outcomes, and predominantly short-term evaluations. Further rigorous, large-scale studies with objective outcome measures are required to determine sustained educational and practice impact. Full article

This study provides a systematic overview of Natural Language Processing (NLP)-based frameworks for Cyber Threat Intelligence (CTI) and the early prediction of cyberattacks in Industry 4.0. As digital transformation accelerates through the integration of IoT, SCADA, and cyber-physical systems, manufacturing environments face an expanding and complex cyber threat landscape. Following the PRISMA 2020 systematic review protocol, 80 peer-reviewed studies published between 2015 and 2025 were analyzed across IEEE Xplore, Scopus, and Web of Science to identify methods that employ NLP for CTI extraction, reasoning, and predictive modelling. The review finds that transformer-based architectures, knowledge graph reasoning, and social media mining are increasingly used to convert unstructured data into actionable intelligence, thereby enabling earlier detection and forecasting of cyber threats. Large Language Models (LLMs) demonstrate strong potential for anticipating attack sequences, while domain-specific models enhance industrial relevance. Persistent challenges include data scarcity, domain adaptation, explainability, and real-time scalability in operational-technology environments. The review concludes that NLP is reshaping Industry 4.0 cybersecurity from reactive defense toward predictive, adaptive, and intelligence-driven protection, and it highlights the need for interpretable, domain-specific, and resource-efficient frameworks to secure Industry 4.0 ecosystems. Full article

Lichens, symbiotic associations between fungi and photobionts, are essential and sensitive bioindicators of environmental change. Despite their resilience, lichens face increasing threats from air pollution, land-use change, unsustainable harvesting, and climate change. This study presents a bibliometric analysis of global research on lichen threats between 1981 and 2024, using data from Scopus and Web of Science, combined with an additional analysis based on the database Recent Literature on Lichens (RLL). A total of 319 research publications were analyzed through VOSviewer (version 1.6.20)and Biblioshiny (R core team version 4.5.2) to assess temporal trends, thematic evolution, authorship, and geographical distribution of affiliations, and 1354 publications from RLL were studied for frequent authors and geographical distribution of study sites. Results show that research output was initially dominated by air pollution studies (1981–2004) but shifted after 2005 toward conservation and climate change impacts, with a sharp increase after 2017. North America and a few European countries led in scientific production, while biodiversity-rich regions in Africa, South America, and Southeast Asia remained underrepresented. Despite increasing publication trends, collaboration remains moderate (23% international co-authorship), and many threatened species remain unassessed. Recovery measures emphasize habitat protection, improved forest management, pollution control, integration of lichens into global biodiversity frameworks, and enhanced international collaboration. This study provides a systematic overview of how lichen conservation research has evolved, suggesting strategies for decelerating lichen diversity loss under accelerating global change. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article

Worldwide the adoption of electric vehicles (EVs) is recognized as a key strategy for reducing transport-related greenhouse gas (GHG) emissions, a major contributor to global warming and climate change. The objective of this pilot study is to examine the key variables that might have influenced electric vehicle (EV) purchase decisions among current EV owners and how they are aligned or different for the prospective EV owners in North Carolina (NC). By adopting a web-based survey for data collection, the study specifically aims to identify economic, demographic, environmental, and commuting behaviors, along with existing government policies and incentives that might motivate consumer choices regarding EV adoption. Most existing EV owners who participated in the survey identified themselves as college-educated White men with USD 100 K or higher income, have more than two cars, commute more than 30 min, and live in single-family homes with EV charging. In contrast, among non-EV owners who plan to adopt an EV within the next three years, a significant proportion are non-White, women, and earn USD 50,000 or less annually. While home charging is important to both current EV owners and non-EV owners, EV incentive policies and proximity to public changing stations are found to be more important to non-EV owners’ decision to adopt EVs. A reasonable conclusion from this research is that expanding EV-friendly policies, incentives, and infrastructure will encourage first-time EV ownership in NC while providing deeper insights into the dynamics of sociodemographic among both EV owners and non-EV owners. Full article

Objectives: This systematic review and meta-analysis aimed to evaluate microbial adhesion and biofilm formation on additively manufactured composite-based orthodontic clear aligners compared with thermoformed aligners and other conventional polymeric materials. The influence of material composition, surface roughness, post-processing parameters, and cleaning protocols on microbial colonization was also assessed. Methods: A comprehensive search of PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Library was conducted up to September 2025. Only in vitro studies investigating microbial adhesion, biofilm biomass, or microbiome changes on three-dimensional (3D)-printed aligner composites were included. Primary outcomes consisted of colony-forming units (CFU), optical density (OD) from crystal violet assays, viable microbial counts, and surface roughness. Risk of bias was assessed using the RoBDEMAT tool. Data were narratively synthesized, and a random-effects meta-analysis was performed for comparable datasets. Results: Five studies fulfilled the inclusion criteria, of which two in vitro studies were eligible for meta-analysis. Microbial adhesion and biofilm accumulation were influenced by the manufacturing technique, composite resin formulation, and surface characteristics. Certain additively manufactured aligners exhibited smoother surfaces and reduced bacterial adhesion compared with thermoformed controls, whereas others with increased surface roughness showed higher biofilm accumulation. Incorporating bioactive additives such as chitosan nanoparticles reduced Streptococcus mutans biofilm formation without compromising material properties. The meta-analysis, based on two in vitro studies, demonstrated higher OD values for bacterial biofilm on 3D-printed aligners compared with thermoformed aligners, indicating increased biofilm biomass (p < 0.05), but not necessarily viable bacterial load. Conclusions: Microbial adhesion and biofilm formation on 3D-printed composite clear aligners are governed by resin composition, additive manufacturing parameters, post-curing processes, and surface finishing. Although certain 3D-printed materials display antibacterial potential, the limited number of studies restricts the generalizability of these findings. Clinical Significance: Optimizing composite formulations for 3D printing, alongside careful post-curing and surface finishing, may help reduce microbial colonization. Further research is required before translating these findings into definitive clinical recommendations for clear aligner therapy. Full article

Amid increasing urbanization and escalating global mental health concerns, understanding the environmental determinants of mental illness has become a research priority. This study presents a bibliometric analysis of global research exploring the intersection of green spaces and mental illness. Drawing on 2136 peer-reviewed articles and review papers published between 1990 and 2024 from the Web of Science Core Collection and Scopus, this study examined publication trends, geographic and institutional contributions, research hotspots, and thematic evolution. Findings reveal a sharp increase in scholarly output since 2012, reflecting heightened interdisciplinary engagement and alignment with global frameworks, such as the United Nations Sustainable Development Goals. China, the United States, and the United Kingdom are leading contributors. Keyword co-occurrence analysis highlights major themes such as green spaces, mental health, physical activity, urban planning, and air pollution. Despite notable progress, the field faces methodological inconsistencies, limited integration of air quality data, and a lack of representation from low- and middle-income countries. This study offers a comprehensive overview of the research progress and gaps, supporting the development of nature-based strategies and sustainable urban planning to mitigate mental illness and promote psychological resilience. Full article