Search Results (818)

Background/Objectives: As safe, eco-friendly, and legally compliant solutions for the disposal of unwanted medications, drug take-back systems have attracted extensive research attention. However, there is a lack of systematic mapping of global trends, collaborative networks, research themes, and hotspots in this field. This study aimed to conduct a comprehensive bibliometric analysis and review of global academic research on drug take-back programs. Methods: Peer-reviewed research articles on drug take-back programs, published between 2005 and 2025, were retrieved from the Web of Science Core Database. Microsoft Office Excel 2019, VOSviewer (v.1.6.17), and CiteSpace (v.6.1.R3 Advanced) were used to assess publication/citation trends, countries, institutions, authors, journals, disciplines, references, and keywords. Narrative analysis was employed to synthesize data from the included articles and identify core research themes. Results: A total of 149 eligible articles with 4520 citations were included, involving 619 authors, 52 countries/regions, 310 institutions, and 95 journals. Publication/citation counts increased significantly between 2005 and 2025. The United States led in both publication output and collaborative research; Mercer University was the most influential institution, but international and cross-institutional collaboration remained limited. Environmental Sciences ranked first among disciplinary categories in drug take-back research, followed by Pharmacology/Pharmacy. Core research themes underpinning this field included stakeholders’ knowledge–attitude–practice assessment (76 articles), returned medication treatment (37 articles), intervention evaluation (25 articles), policy analysis (7 articles), and the role of drug take-back programs in mitigating environmental and public health hazards caused by medicine wastes (4 articles). Conclusions: Scholarly attention to drug take-back programs has grown steadily. Future research should prioritize cross-sectoral and international cooperation, develop and adopt evidence-based interventions to optimize the safety, sustainability, and accessibility of drug take-back systems on a global scale. Full article

Aim: With the intensification of population aging, the public health challenges posed by multimorbidity have become increasingly severe. This study employs bibliometric analysis to elucidate research hotspots and trends in the field of multimorbidity against the backdrop of global aging. The immediate aim is to systematically map the intellectual landscape and evolving patterns in multimorbidity research. The ultimate long-term aim is to provide a scientific basis for optimizing chronic disease prevention systems and guiding future research directions. Methods: The study adopted the descriptive research method and employed a bibliometric approach, analyzing 8129 publications related to multimorbidity from the Web of Science Core Collection. Using CiteSpace, we constructed and visualized several knowledge structures, including collaboration networks, keyword co-occurrence networks, burst detection maps, and co-citation networks within the multimorbidity research domain. Results: The analysis included 8129 articles from 2004 to 2024, published across 1042 journals, with contributions from 740 countries/regions, 33,931 institutions, and 40,788 authors. The five most frequently occurring keywords were prevalence, health, older adult, mortality, and risk. The top five contributing countries globally were the United States, the United Kingdom, Germany, China, and Spain. Five pivotal research trajectories delineate the intellectual architecture of this field: ① Evolution of Disease Cluster Management: Initial investigations (2013–2014) prioritized disease cluster coordination within general practice settings, particularly cardiovascular comorbidity management through primary care protocols and self-management strategies. ② Paradigm Shifts in Health Impact Assessment: Multimorbidity outcome research demonstrated sequential transitions—from physical disability evaluation (2013) to mental health consequences like depression (2016), culminating in current emphasis on holistic health indicators including frailty syndromes (2015–2019). ③ Expansion of Risk Factor Exploration: Analytical frameworks evolved from singular physical activity metrics (2014) toward comprehensive lifestyle-related determinants encompassing behavioral and environmental dimensions (2021). ④ Emergence of Polypharmacy Scholarship: Medication optimization studies emerged as a distinct research stream since 2016, addressing therapeutic complexities in multimorbidity management. ⑤ Frontier Investigations: Cutting-edge directions (2019–2021) feature cardiometabolic multimorbidity patterns and their dementia correlations, signaling novel interdisciplinary interfaces. Conclusions: The prevalence of multimorbidity is on the rise globally, particularly in older populations. Therefore, it is essential to prioritize the prevention of cardiometabolic conditions in older adults and to provide them with appropriate and effective health services, including disease risk monitoring and community-based chronic disease care. Full article

The forecasting of Drug-Drug Interactions (DDIs) is essential in pharmacology and clinical practice to prevent adverse drug reactions. Existing approaches, often based on neural networks and knowledge graph embedding, face limitations in modeling correlations among drug features and in handling complex BioKG relations, such as one-to-many, hierarchical, and composite interactions. To address these issues, we propose Rot4Cap, a novel framework that embeds drug entity pairs and BioKG relationships into a four-dimensional vector space, enabling effective modeling of diverse mapping properties and hierarchical structures. In addition, our method integrates molecular structures and drug descriptions with BioKG entities, and it employs capsule network–based attention routing to capture feature correlations. Experiments on three benchmark BioKG datasets demonstrate that Rot4Cap outperforms state-of-the-art baselines, highlighting its effectiveness and robustness. Full article

Person re-identification (re-ID) aims to retrieve images of a given individual from different camera views. Obstacles obstructing parts of a pedestrian’s body often result in incomplete identity information, impairing recognition performance. To address the occlusion problem, a method called Multi-Branch Interaction Network (MBIN) is proposed, which exploits the information interaction between different branches to effectively characterize occluded pedestrians for person re-ID. The method consists primarily of a hard branch, a soft branch, and a view branch. The hard branch enhances feature robustness via a unified horizontal partitioning strategy. The soft branch improves the high-level feature representation via multi-head attention. The view branch fuses multi-view feature maps to form a comprehensive representation via a dual-classifier fusion mechanism. Moreover, a mutual knowledge distillation strategy is employed to promote knowledge exchange among the three branches. Extensive experiments are conducted on widely used person re-ID datasets to validate the effectiveness of our method. Full article

Background: Knowledge distillation (KD) compresses deep neural networks by transferring knowledge from a high-capacity teacher model to a lightweight student model. However, conventional evaluation metrics such as accuracy, mAP, IoU, or RMSE focus mainly on task performance and overlook how effectively the student internalizes the teacher’s knowledge. Methods: This study introduces the Knowledge Retention Score (KRS), a composite metric that integrates intermediate feature similarity and output agreement into a single interpretable score to quantify knowledge retention. KRS was primarily validated in computer vision (CV) through 36 experiments covering image classification, object detection, and semantic segmentation using diverse datasets and eight representative KD methods. Supplementary experiments were conducted in natural language processing (NLP) using transformer-based models on SST-2, and in time series regression with convolutional teacher–student pairs. Results: Across all domains, KRS correlated strongly with standard performance metrics while revealing internal retention dynamics that conventional evaluations often overlook. By reporting feature similarity and output agreement separately alongside the composite score, KRS provides transparent and interpretable insights into knowledge transfer. Conclusions: KRS offers a stable diagnostic tool and a complementary evaluation metric for KD research. Its generality across domains demonstrates its potential as a standardized framework for assessing knowledge retention beyond task-specific performance measures. Full article

Fire and invasive plant species interactions are critical drivers of biodiversity loss and ecological change in grassland ecosystems worldwide. However, research efforts on this topic are often fragmented, regionally based, and lack synthesis across disciplines. This study aims to map the intellectual structure, collaboration networks, thematic focus, and knowledge gaps in research on fire-invasive species interactions in grassland restoration. A systematic bibliometric analysis was conducted using the Web of Science Core Collection, focusing on peer-reviewed English-language articles published between 1990 and 2024. The search strategy targeted studies addressing fire regimes and invasive plant species in grassland ecosystems, using co-authorship, co-occurrence and thematic clustering analyses to reveal collaboration patterns and research trends. The results highlight a concentration of publications in key ecological journals, with a dominant contribution from institutions in the Global North, through growing representation from the Global South, particularly South Africa, is evident. Thematic clusters are centred on biological invasions, fire regimes, species traits and ecosystem resilience, while long-term post-fire recovery and studies from underrepresented regions remain critical knowledge gaps. This synthesis emphasises the need for interdisciplinary, regionally inclusive and policy-aligned research to inform effective grassland restoration strategies in the context of fire and invasive species challenges. Full article

Additive manufacturing (AM) demands materials that combine precise printability with reliable thermal and mechanical performance. Polyglycerol (PG)-based macromolecular systems offer exceptional tunability through controlled architecture and chemical modification, enabling their use across both light-based and extrusion AM platforms. Strategic enhancements such as chemical functionalization, network formation, and hybrid reinforcement have expanded their capabilities from biomedical to structural applications, delivering improved stability, strength, and functionality. Despite these advances, performance-processing trade-offs and dispersion challenges remain barriers to widespread adoption. This review synthesizes current knowledge on PG-based materials in AM, mapping key structure-property-processing relationships and identifying strategies to advance their development as versatile and sustainable options for next-generation manufacturing. Full article

Semantic technologies based on ontologies and knowledge graphs are increasingly recognized for their potential to enhance safety, risk, and emergency management in railway systems. This paper presents a systematic literature review aimed at identifying how ontologies, knowledge graphs, and the technologies based on them are applied within the domain of railway safety and assessing their contributions. A total of 53 relevant papers were analyzed using a structured review process, covering four main areas: risk management, safety management, emergency management, and accident analysis. The results reveal that ontologies and knowledge graphs support proactive hazard identification, formalization of safety knowledge, intelligent emergency response, and detailed accident causation modeling. Moreover, they enable semantic interoperability, reasoning, and automation across complex socio-technical railway systems. Despite their benefits, challenges remain regarding data heterogeneity, scalability, and the lack of semantic standardization. This study identifies the most relevant models and technologies, such as SRAC, SRI-Onto, and transformer-based graph neural networks, highlighting their role in advancing intelligent railway safety solutions. This work contributes a detailed map of the current state of semantic applications in railway safety and offers insight into emerging opportunities for future development. Full article

The built environment plays a crucial role in shaping residents’ quality of life and emotional well-being. In the context of growing efforts to promote livable and walkable cities, a key question emerges: how can emerging technologies—particularly virtual reality (VR)—be leveraged to evaluate and enhance urban environments through the lens of pedestrian emotional perception? This study systematically reviewed the literature published between 2015 and 2024 in the China National Knowledge Infrastructure (CNKI) and Web of Science (WOS) databases, ultimately identifying 37 Chinese-language and 113 English-language journal articles. Using bibliometric analysis and CiteSpace, the research mapped publication trends, research hotspots, and disciplinary networks across linguistic contexts. Results reveal that Chinese-language studies often emphasize embodied cognition and electroencephalogram (EEG) monitoring, while English-language studies focus more on VR application in stress recovery and health assessment. Based on this synthesis, this study proposes a “sensory–cognitive–affective” framework and a set of spatial intervention strategies, offering a novel perspective for emotion-driven urban design. The findings highlight a paradigm shift from engineering-oriented planning to human-centered approaches, with VR technologies serving as a critical enabling tool. This review contributes both conceptual and methodological foundations for future research at the intersection of immersive technologies, built environment studies, and urban emotional well-being. Full article

The green economy and the broader green transition have become central themes in global sustainability efforts, reflecting a strategic shift in addressing environmental challenges through economic transformation. This study provides a systematic bibliometric analysis of 1014 peer-reviewed publications indexed in Scopus on the green transition within the framework of sustainable development, covering the period 1990–2024. The findings show a rapid growth in research output after 2015, culminating in 360 publications in 2024. China, Italy, and the Russian Federation emerge as the most active contributors, while collaboration networks reveal both established partnerships and emerging participation from Central and Eastern Europe. Influential authors include Mahmood Haider and Fabio Iraldo, and major publication outlets are the Journal of Cleaner Production, Sustainability (Switzerland), and Ecological Economics. Four thematic clusters—renewable energy, climate policy, circular economy, and green innovation—highlight dominant research trajectories and persistent knowledge gaps. By mapping authors, sources, keyword co-occurrences, and citation structures, this study offers a structured foundation for future research and a clearer understanding of how the green transition is conceptualized within sustainability scholarship. Full article

This study conducts a comprehensive bibliometric analysis of research applying data envelopment analysis (DEA) to the evaluation of sustainability and performance in complex socioeconomic systems between 2010 and mid-2025. DEA has become an increasingly valuable tool for measuring efficiency, benchmarking practices, and supporting decision-making in contexts where sustainability challenges intersect with economic, environmental, and governance dimensions. To capture global research dynamics, we extracted and merged bibliographic data from Web of Science and Scopus, analyzing publication trends, thematic clusters, co-authorship networks, citation structures, and keyword co-occurrences using bibliometric tools such as VOSviewer and Bibliometrix. Our findings reveal a consistent growth trajectory of the field, with research outputs peaking in 2020 and subsequently diversifying across multiple thematic areas. Conceptual mapping highlights two dominant domains: (i) policy, governance, and planning and (ii) environmental, ecological, and management applications, both linked through the overarching theme of sustainable development. The analysis further underscores the geographic diversity of contributions, the concentration of knowledge in key publication outlets, and the increasing connectivity of international collaboration networks. By identifying thematic gaps and underexplored intersections, this study emphasizes the need for more interdisciplinary approaches that integrate bibliometric insights with practical sustainability outcomes. The results provide a structured overview of the field’s evolution, offering researchers and policymakers a valuable reference point for advancing DEA applications in sustainability research. Full article

Machine learning (ML) algorithms have promoted the development of predictive modeling of mineral prospectivity, enabling data-driven decision-making processes by integrating multi-source geological information, leading to efficient and accurate prediction of mineral exploration targets. However, it is challenging to conduct ML-based mineral prospectivity mapping (MPM) in under-explored areas where scarce data are available. In this study, the Narigongma district of the Qiangtang block in the Himalayan–Tibetan orogen was chosen as a case study. Five typical alterations related to porphyry mineralization in the study area, namely pyritization, sericitization, silicification, chloritization and propylitization, were extracted by remote sensing interpretation to enrich the data source for MPM. The extracted alteration evidences, combined with geological, geophysical and geochemical multi-source information, were employed to train the ML models. Four machine learning models, including artificial neural network (ANN), random forest (RF), support vector machine and logistic regression, were employed to map the Cu-polymetallic prospectivity in the study area. The predictive performances of the models were evaluated through confusion matrix-based indices and success-rate curves. The results show that the classification accuracy of the four models all exceed 85%, among which the ANN model achieves the highest accuracy of 96.43% and a leading Kappa value of 92.86%. In terms of predictive efficiency, the RF model outperforms the other models, which captures 75% of the mineralization sites within only 3.5% of the predicted area. A total of eight exploration targets were delineated upon a comprehensive assessment of all ML models, and these targets were further ranked based on the verification of high-resolution geochemical anomalies and evaluation of the transportation condition. The interpretability analyses emphasize the key roles of spatial proxies of porphyry intrusions and geochemical exploration in model prediction as well as significant influences everted by pyritization and chloritization, which accords well with the established knowledge about porphyry mineral systems in the study area. The findings of this study provide a robust ML-based framework for the exploration targeting in greenfield areas with good outcrops but low exploration extent, where fusion of a remote sensing technique and multi-source geo-information serve as an effective exploration strategy. Full article

The recent growth of generative artificial intelligence (AI) has brought new possibilities and revolutionary applications in many fields. It has also, however, created important ethical and security issues, especially with the abusive use of deepfakes, which are artificial media that can propagate very realistic but false information. This paper provides an extensive bibliometric, statistical, and trend analysis of deepfake research in the age of generative AI. Utilizing the Web of Science (WoS) database for the years 2014–2024, the research identifies key authors, influential publications, collaboration networks, and leading institutions. Biblioshiny (Bibliometrix R package, University of Naples Federico II, Naples, Italy) and VOSviewer (version 1.6.20, Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) are utilized in the research for mapping the science production, theme development, and geographical distribution. The cutoff point of ten keyword frequencies by occurrence was applied to the data for relevance. This study aims to provide a comprehensive snapshot of the research status, identify gaps in the knowledge, and direct upcoming studies in the creation, detection, and mitigation of deepfakes. The study is intended to help researchers, developers, and policymakers understand the trajectory and impact of deepfake technology, supporting innovation and governance strategies. The findings highlight a strong average annual growth rate of 61.94% in publications between 2014 and 2024, with China, the United States, and India as leading contributors, IEEE Access among the most influential sources, and three dominant clusters emerging around disinformation, generative models, and detection methods. Full article

Artificial Intelligence (AI) has become deeply embedded in the advertising industry, presenting both opportunities and challenges. Understanding this transformation requires mapping the dyad’s structure and identifying the conditions that shape its evolution. This study applies a scientometric framework, integrating bibliometric network analysis with statistical modeling, to examine AI advertising as a knowledge ecosystem. By analyzing patterns of collaboration, thematic convergence, and structural centrality, we interpret how scholarly networks generate, connect, and diffuse ideas in ways that influence both academic and industry practices. The findings reveal that the field’s growth is underpinned by interconnected clusters of expertise with strategic opportunities emerging from interdisciplinary integration and global collaboration. Simultaneously, consolidating influence among a few dominant actors raises questions about diversity, access, and the balance between innovation and ethical responsibility. Statistical analyses conducted in SPSS Statistics version 29.0.2.0 further identify the bibliometric and structural factors that most predict citation impact, strengthening the study’s contribution to understanding how influence is built and sustained in AI-driven advertising research. Full article

Real estate plays a pivotal role in fostering national economic growth and ensuring social stability. In China, housing constitutes the largest fixed asset for the majority of households. Given the extensive network of upstream and downstream industries associated with real estate, the government places significant emphasis on its regulation and development, employing a variety of policy instruments to maintain market stability. This study adopts a quantitative approach to conduct a text analysis of China’s real estate policies through the lens of knowledge mapping and LDA topic modeling, while also comparing policy content across 21 different cities. The findings indicate that real estate policy in China transcends mere market regulation. It also encompasses governance within the construction industry as well as provisions for housing security. Furthermore, due to the diverse roles that real estate plays in economic development and urban construction, there is notable regional heterogeneity in policy priorities. By text analysis of real estate policies, this study provides a systematic overview of policy content, thereby laying a foundation for more nuanced and regionally differentiated research within the realm of real estate policy. Full article