Search Results (3,252)

This study analyzes the relationship between ergonomic and psychosocial risk factors and labor productivity using a bibliometric approach through a general analysis and one that includes inclusion criteria such as English language, open access, and primary research publications to identify only those articles that explicitly address the relationship between ergonomic and psychosocial risk factors and labor productivity. It is recognized that both physical and psychosocial conditions of the work environment directly influence workers’ health and organizational performance. For this purpose, a bibliometric review was conducted in academic databases, including Scopus, Web of Science, ScienceDirect, and Taylor & Francis, resulting in the selection of 4794 relevant articles for general analysis. Additionally, 116 relevant articles were selected based on the inclusion criteria. Tools and methodologies, such as Rayyan, Excel, VOSviewer 1.6.20, and PRISMA, were used to classify the studies and identify trends, collaboration networks, and geographical distribution. The results reveal a sustained growth in scientific production, with clusters on occupational safety and health, work environment factors, and the characteristics of the population, approach, and methodologies used in the studies. Likewise, Procedia Manufacturing, International Journal of Occupational Safety and Ergonomics, and Ergonomics stand out as the main sources of publication, while countries such as Sweden, Poland, and the United States lead the scientific production in this field. In addition, the network of co-occurrence of keywords evidences a comprehensive approach that articulates physical or ergonomic and psychosocial risk factors with organizational performance, while the network of authors shows consolidated collaborations and studies focused on analyzing the relationship between physical demands and musculoskeletal disorders from advanced ergonomic approaches. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies. Full article

Introduction: Breakfast has emerged as a critical factor in preventing cardiovascular diseases, driven not only by its nutritional content but also by its alignment with circadian rhythms. However, gaps remain in the literature regarding its clinical impact and thematic evolution. Objective: To characterize the global scientific output on the relationship between breakfast quality and cardiovascular health through a systematic bibliometric analysis. Methodology: The PRISMA 2020 protocol was applied to select 1436 original articles indexed in Scopus and Web of Science (1957–2025). Bibliometric tools, including R (v4.4.2) and VOSviewer (v1.6.19) were used to map productivity, impact, collaboration networks, and emerging thematic areas. Results: Scientific output has grown exponentially since 2000. The most influential journals are the American Journal of Clinical Nutrition, Nutrients, and Diabetes Care. The United States, United Kingdom, and Japan lead in publication volume and citations, with increasing participation from Latin American countries. Thematic trends have shifted from traditional clinical markers to innovative approaches such as chrononutrition, digital health, and personalized nutrition. However, methodological gaps persist, including a predominance of observational studies and an underrepresentation of vulnerable populations. Conclusions: Breakfast is a dietary practice with profound implications for cardiometabolic health. This study provides a comprehensive overview of scientific literature, highlighting both advancements and challenges. Strengthening international collaboration networks, standardizing definitions of a healthy breakfast, and promoting evidence-based interventions in school, clinical, and community settings are recommended. Full article

Community detection is widely recognized as a crucial area of research in network science. In recent years, multi-objective evolutionary algorithms (MOEAs) have been extensively employed in community detection tasks. Continuous coding is able to transform the discrete problem into a continuous one. However, conventional continuous coding methodologies frequently disregard the relationships between node structures, resulting in low-quality encoded populations that subsequently diminish community detection performance. Furthermore, continuous coding needs to be decoded into to label-based coding during the optimization process to compute objective functions. To alleviate this, we design the surrogate model adaptive switching strategy that selects the optimal surrogate model for the task. Subsequently, the surrogate-assisted evolutionary multi-objective community detection algorithm with core node learning is proposed. The core node learning method is employed to enhance the connection between nodes in augmented sequential coding, which helps initialize the population using the node similarity matrix. The core nodes of the network are subsequently identified based on node weights, which can be utilized to construct a surrogate model between the continuous coding and the objective function. The surrogate model is updated during the optimization process, which effectively improves both the accuracy and efficiency of community detection tasks. Experimental results obtained from synthetic and real-world networks demonstrate that the proposed algorithm exhibits superior performance compared to seven community detection algorithms. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The growing complexity of modern manufacturing has intensified the need for precise control of residual stresses to ensure structural reliability, dimensional stability, and material performance. This study conducts a bibliometric review using data from Scopus and Web of Science, covering publications from 2019 to 2024. Residual stress research in metal manufacturing has gained prominence, particularly in relation to welding, additive manufacturing, and machining—processes that induce significant stress gradients affecting mechanical behavior and service life. Emerging trends focus on simulation-based prediction methods, such as the finite element method, heat treatment optimization, and stress-induced defect prevention. Key thematic clusters include process-induced microstructural changes, mechanical property enhancement, and the integration of modeling with experimental validation. By analyzing the evolution of research output, global collaboration networks, and process-specific contributions, this review provides a comprehensive overview of current challenges and identifies strategic directions for future research in residual stress management in advanced metal manufacturing. Full article

Bone metastasis remains a significant cause of morbidity and diminished quality of life in patients with advanced breast, prostate, and lung cancers. Emerging research highlights the pivotal role of reversible epigenetic alterations, including DNA methylation, histone modifications, chromatin remodeling complex dysregulation, and non-coding RNA networks, in orchestrating each phase of skeletal colonization. Site-specific promoter hypermethylation of tumor suppressor genes such as HIN-1 and RASSF1A, alongside global DNA hypomethylation that activates metastasis-associated genes, contributes to cancer cell plasticity and facilitates epithelial-to-mesenchymal transition (EMT). Key histone modifiers, including KLF5, EZH2, and the demethylases KDM4/6, regulate osteoclastogenic signaling pathways and the transition between metastatic dormancy and reactivation. Simultaneously, SWI/SNF chromatin remodelers such as BRG1 and BRM reconfigure enhancer–promoter interactions that promote bone tropism. Non-coding RNAs, including miRNAs, lncRNAs, and circRNAs (e.g., miR-34a, NORAD, circIKBKB), circulate via exosomes to modulate the RANKL/OPG axis, thereby conditioning the bone microenvironment and fostering the formation of a pre-metastatic niche. These mechanistic insights have accelerated the development of epigenetic therapies. DNA methyltransferase inhibitors (e.g., decitabine, guadecitabine) have shown promise in attenuating osteoclast differentiation, while histone deacetylase inhibitors display context-dependent effects on tumor progression and bone remodeling. Inhibitors targeting EZH2, BET proteins, and KDM1A are now advancing through early-phase clinical trials, often in combination with bisphosphonates or immune checkpoint inhibitors. Moreover, novel approaches such as CRISPR/dCas9-based epigenome editing and RNA-targeted therapies offer locus-specific reprogramming potential. Together, these advances position epigenetic modulation as a promising axis in precision oncology aimed at interrupting the pathological crosstalk between tumor cells and the bone microenvironment. This review synthesizes current mechanistic understanding, evaluates the therapeutic landscape, and outlines the translational challenges ahead in leveraging epigenetic science to prevent and treat bone metastases. Full article

Water science has always been a central part of modern scientific research. In this study, the viscosity and hydrogen bond structures of methanol aqueous solutions with different molar ratios were investigated via confocal microscopic Raman spectroscopy. The Raman spectra of methanol in the CH and CO stretching regions were measured in order to investigate the structure of water/methanol molecules. The points of transition were identified by observing changes in viscosity following changes in concentration, and the bands were assigned to the C-H bond vibration shifts where the molar ratios of methanol and water were 1:3 and 3:1. Furthermore, the large band shift of 19 cm⁻¹ between the methanol solutions with the lowest and highest concentrations contained three hydrogen bond network modes, affecting the viscosity of the solution. This study provides an explanation for the relationship between the microstructures and macroscopic properties of aqueous solutions. Full article

Higher education and science (HES) is one of the key drivers of a country’s economic growth. In this study, we examine national projects and research capacity in HES in Kazakhstan from 2014 to 2024. We conducted a content review and scientometric analysis with network and temporal visualizations. Our data sources included policy documents, statistical reports, and the Scopus database. Our findings suggest that, while Kazakhstan aligns with global trends in the field (e.g., digitalization, scientometrics monitoring, and internationalization), these are achieved through a state-led, policy-driven approach shaped by its post-Soviet context. Additionally, we note a dual structure in Kazakhstan’s HES sector, characterized by a strong top-down direction and increasing institutional engagement. In terms of the thematic trends from the temporal analysis, the country experienced a three-staged evolution: foundational reforms and system modernization (2014–2017), capacity building and evaluation (2018–2021), and, most recently, strategic expansion, inclusivity, and globalization (2022–2024). Throughout the analyzed period, low R&D intensity, disciplinary imbalances, and structural barriers still undermine desired development efforts in HES. The analyzed case of Kazakhstan can serve as “lessons learned” for policymakers and researchers working in the science evaluation and scholarly communication area in similar emerging or transition countries. Full article

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

Background: International migration is a growing global phenomenon involving diverse groups, such as labor migrants, international marriage migrants, refugees, and international students. International migrants face unique mental health challenges influenced by adversities such as social isolation and limited access to mental health services. This study employs bibliometric methods to systematically analyze the global body of literature on international migrants’ mental health. Methods: The literature on the mental health of international migrants published until October 2024 was searched using the Web of Science database. The search terms included (‘International migrants’ OR ‘migrant workers’ OR ‘international students’ OR ‘refugees’ OR ‘asylum seekers’ OR ‘smuggled migrants’) AND ‘mental health’. VOSviewer was used to conduct bibliometric analysis, focusing on co-authorship patterns, keyword co-occurrence, and citation networks. Results: Over the past four decades, research on the mental health of international migrants has grown substantially, with major migration destinations such as the United States, Europe, and Australia playing prominent roles in this field. ‘Post-traumatic stress disorder (PTSD)’ was the most frequent keyword in publications, with strong links to ‘trauma’ and ‘depression’. In recent years, with the impact of global socioenvironmental changes and emergencies such as the COVID-19 pandemic, the research focus has gradually shifted towards social support, service accessibility, and cultural adaptation. Conclusions: International migration is a far-reaching global phenomenon, and addressing the mental health of migrant populations is essential for advancing public health, social cohesion, and sustainable development. This study provides the first bibliometric overview of research in this domain, mapping its thematic evolution and collaborative structure. The findings offer valuable insights into the field’s development and may support future interdisciplinary collaboration and the formulation of culturally informed, evidence-based approaches in migrant mental health. Full article

Improving curriculum structure is critical for enhancing student success and on-time graduation, yet few methods exist to evaluate how prerequisite paths shape student progression and graduation outcomes. This study proposes a data-driven, graph-based framework that integrates course prerequisite networks with student performance data to systematically analyze curricular structure and student outcomes. We identify high-risk courses by jointly modeling their structural importance and pass rates, and quantify the time and survivability of different prerequisite paths using probabilistic models. Additionally, we introduced grade transition patterns to capture more nuanced transitions in student performance and pinpoint bottlenecks along prerequisite paths. Applying the model on four science and engineering majors from a public institution, the results not only identify high-risk courses often missed in conventional analyses, but also reveal path-level disparities and structural bottlenecks that affect student progression and time to graduation. For example, in the Computer Science major, we identified that the architecture and operating systems pathway is more challenging than the software engineering pathway. A closer examination of the course pairs along this trajectory revealed that the difficulty stems from a significant drop in student performance between a prerequisite–successor course pairs.This type of analysis fills a gap in conventional curriculum studies, which often overlook path-level dynamics, and offers actionable insights for educators a to identify high risk curricular components. Full article

People living with HIV (PLWH) faced diverse challenges during the COVID-19 pandemic, including disruptions to care, housing instability, emotional distress, and economic hardship. This study used graph-based clustering methods to analyze pandemic-era experiences of PLWH in a national sample from the NIH’s All of Us dataset (n = 242). Across three graph configurations we identified consistent subgroups shaped by social connectedness, housing stability, emotional well-being, and engagement with preventive behaviors. Comparison with an earlier local study of PLWH in Illinois confirmed recurring patterns of vulnerability and resilience while also revealing additional national-level subgroups not observed in the smaller sample. Subgroups with strong social or institutional ties were associated with greater emotional stability and proactive engagement with COVID-19 preventive behaviors, while those facing isolation and structural hardship exhibited elevated distress and limited engagement with COVID-19 preventive measures. These findings underscore the importance of precision public health strategies that reflect the heterogeneity of PLWH and suggest that strengthening social support networks, promoting housing stability, and leveraging institutional connections may enhance pandemic preparedness and HIV care in future public health crises. Full article

This study presents a computational analysis of industry-specific advertising message strategies through the theoretical lens of the FCB (Foote, Cone & Belding) grid framework. Leveraging the AiSAC (AI Analysis System for Ad Creation) system developed by the Korea Broadcast Advertising Corporation (KOBACO), we analyzed 27,000 Korean advertisements across five major industries using advanced machine learning techniques. Through Latent Dirichlet Allocation topic modeling with a coherence score of 0.78, we identified five distinct message strategies: emotional appeal, product features, visual techniques, setting and objects, and entertainment and promotion. Our computational analysis revealed that each industry exhibits a unique “message strategy fingerprint” that significantly discriminates between categories, with discriminant analysis achieving 62.7% classification accuracy. Time-series analysis using recurrent neural networks demonstrated a significant evolution in strategy preferences, with emotional appeal increasing by 44.3% over the study period (2015–2024). By mapping these empirical findings onto the FCB grid, the present study validated that industry positioning within the grid’s quadrants aligns with theoretical expectations: high-involvement/think (IT and Telecom), high-involvement/feel (Public Institutions), low-involvement/think (Food and Household Goods), and low-involvement/feel (Services). This study contributes to media science by demonstrating how computational methods can empirically validate the established theoretical frameworks in advertising, providing a data-driven approach to understanding message strategy patterns across industries. Full article