Search Results (1,341)

Nature-Based Solutions (NBS) have emerged as a prominent and rapidly expanding strategic approach for addressing climate change, biodiversity loss, and socio-ecological resilience in cities, especially within European sustainability agendas where NBS are embedded in EU policy frameworks. Nevertheless, their practical implementation remains uneven across the globe, with relatively strong mainstreaming in Europe but more fragmented uptake in other regions. These disparities stem from a gap between policy-oriented NBS discourse and its translation into design and spatial practice. This study investigates how governance-related NBS research can be more effectively translated into landscape architecture and design practice. It integrates insights from narrative literature reviews, project-based experiences, and international conference sessions. We argue that effective NBS implementation requires alignment with landscape architecture (systems-multi-scale planning, design, site-specific ecological materialization and aesthetic mediation) and governance integration. Through comparative analysis of Global North and South contexts, we identify differences in institutional capacity, socio-cultural perception of nature, and knowledge systems that shape NBS implementation. We propose a multi-scale, co-creative framework connecting environmental knowledge, governance interface, and design practice. The findings demonstrate that NBS cannot succeed solely as a science and policy approach; instead, they must be spatially translated through culturally responsive and ecologically informed landscape design processes and maintenance. Full article

Background: Stroke remains a significant cause of persistent long-term disability globally. Post-stroke neuroplasticity is critical for neurological functional recovery and reducing disability. Nevertheless, the existing scoping reviews rarely systematically integrate its intrinsic mechanisms, predictive biomarkers, and actionable intervention strategies. This scoping review aims to map the current research landscape, synthesize the core research findings, and identify existing research gaps in this field. Methods: This scoping review was conducted following the PRISMA-ScR guidelines. Eligible studies published between 31 January 2021, and 31 January 2026, were retrieved from three major mainstream electronic databases: PubMed, Scopus, and Web of Science. All retrieved evidence was synthesized via narrative approach, focusing on core research findings concerning post-stroke neuroplasticity mechanisms, biomarkers, and therapeutic interventions. Results: The existing research on neuroplastic mechanisms following ischemic stroke is predominantly derived from in vitro and animal studies, which have collectively demonstrated multiple core adaptive alterations, including enhanced synaptic plasticity, dendritic and axonal structural remodeling, restored interhemispheric connectivity, endogenous neurogenesis, and functional reorganization of neural networks. In contrast, mechanistic investigations in human stroke patients primarily highlight compensatory activation within peri-infarct tissues and functionally remote brain regions. The relevant clinical biomarkers are mainly imaging and electrophysiological indicators. Research on therapeutic interventions has mainly focused on rehabilitation treatments such as non-pharmacological magnetic stimulation. Conclusions: This scoping review synthesized the current body of evidence on post-ischemic stroke neuroplasticity and identified critical research gaps, particularly regarding multimodal biomarkers and their translational relevance. The present findings confirm that neuroplastic alterations after ischemic stroke are regulated by multiple pathways, among which those involved in synaptic structure, dendrites, and neural network connections exert pivotal effects. Nevertheless, most existing investigations remain confined to in vitro experiments and animal models. The identification of neuroplasticity biomarkers has opened up new avenues for the development of targeted stroke therapies and offers promising prospects for rehabilitative treatment of stroke patients. Full article

Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real-life deployment. AutoML, a branch of ML automating model selection, automated architecture search, and the creation of model pipelines, may help overcome these shortcomings. While numerous NIDS applications employing automated ML techniques have been proposed, and recent surveys have mapped the AutoML framework landscape for network intrusion detection, no existing review critically audits the evaluation practice of this literature: the quality of its benchmark datasets, the reproducibility of its reported results, and the realism of its deployment assumptions. This paper critically reviews 26 research works published between January 2023 and June 2026, collected via a two-phase structured search: a documented keyword search across five databases (Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar), followed by full-text eligibility screening, citation chaining, and expert evaluation. Findings drawn from this collection capture trends observed among the selected studies, rather than reflecting the broader state of the field. Analysis of the corpus reveals that 88% of dataset-verified studies evaluate exclusively or partly on the legacy benchmark family (KDD-derived, CICIDS, UNSW-NB15, CIDDS), 21% evaluate on a single dataset only, and among attribute-verified studies only 32% release source code, 40% report statistical significance testing, and 36% include variance analysis, findings that collectively motivate the four contributions of this study. First, a recommended evaluation framework is proposed, addressing baseline parity, transparent search-space and budget reporting, nested cross-validation for selection-bias control, and stability reporting across multiple random seeds. Second, a dataset quality scoring framework is introduced, assessing five dimensions: overlap rate, duplication rate, label correctness, attack-type representativeness, and coverage of benign, IoT, and IIoT traffic. Third, a cross-domain justification is provided for neural architecture search (NAS) and meta-learning in NIDS, grounded in advances in federated NAS, out-of-distribution robustness, edge-constrained search cost reduction, and few-shot adaptation. Fourth, a structured research roadmap is outlined, targeting real-world validation, standardized benchmarks, curated datasets, resource-aware AutoML, and privacy-preserving federated NAS. In contrast to prior surveys of AutoML for network intrusion detection, which map frameworks and computational paradigms, this review contributes a formalized evaluation checklist, an explicit and partially empirically validated dataset quality scoring scheme, and evidence-based methodological guidance grounded in a transparent, fully enumerated study corpus. Full article

Artificial intelligence (AI) is increasingly reshaping production management by enabling data-driven optimization, predictive decision-making, and more adaptive operational systems across manufacturing. With research activity in this area growing rapidly, the literature is fragmented; thus, understanding the current reconfigurations of production management driven by AI has become difficult, though necessary. This study offers a comprehensive analysis of scientific publications (n = 439) on AI in production management published between 2016 and 2026. Research was conducted through an adapted selection process guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to ensure transparency, while data were analyzed using VOSviewer and complementary statistics, combining keyword co-occurrence mapping, subject area classification, and country and author collaboration networks. Findings indicate an increase in research activity, with computer science, engineering and telecommunications emerging as the primary foundations of the field. The analysis identifies three interconnected thematic directions: AI-enabled smart manufacturing and operational optimization, including scheduling, production planning, predictive maintenance, and quality control; sustainability- and resilience-oriented production and supply chain transformation; and digital twin-based, human-centered, and knowledge-driven production systems. Moreover, international collaboration patterns show a highly globalized research landscape led by Germany, France, Italy, Sweden, Norway and South Korea. Overall, bibliometric patterns suggest an emerging framing of AI in production management research which needs more attention and further development. Full article

Glucagon-like peptide-1 (GLP-1) analogues are a major class of peptide therapeutics used to treat metabolic diseases. GLP-1-derived peptides are characterized by dynamic conformational ensembles in which folding, intermolecular assembly, and aggregation are strictly coupled processes. This study focused on the effects of sequence modifications, such as helix-promoting residues and backbone constraints on the helix-coil equilibrium, as well as lipidation, which creates competing equilibria among monomeric, oligomeric, and albumin-bound forms. These coupled equilibria simultaneously enhance pharmacokinetic properties and modulate conformational stability. We also explored how environmental conditions such as ionic concentration and temperature affect conformation, and emphasize how manufacturing processes act as external perturbations that could impact structural integrity. Moreover, we focus on the increasingly emerging new multi-agonist peptides, noting that their increased sequence complexity broadens conformational diversity and poses challenges to existing design methods. Despite significant experimental progress, predictive models capable of mapping the intricate interconnections among peptide sequences, lipidation patterns, and aggregation pathways remain critically limited. This highlights the importance of integrating biophysics, computation, and process science. The review points out that designing effective GLP-1 therapeutics rationally depends on managing conformational distributions across complex energy landscapes, not just stabilizing individual structures, in order to offer a new framework for developing the next generation of peptide drugs. Full article

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic constraints. To overcome this limitation, we propose Geo-InkGAN, a geo-heuristic framework that integrates geographic principles with generative processes to achieve high-fidelity ink-wash style synthesis. A key component of our approach is an adaptive optimization strategy grounded in the Slope Standard Deviation (SSD). By establishing a quantitative relationship between geomorphological entropy and the cycle-consistency loss weight (${\lambda }_{cyc}$), we effectively address the Pareto trade-off between geomorphic accuracy and esthetic representation. Our results indicate that alluvial plains benefit from low-intensity constraints to facilitate fluid ink diffusion, whereas rugged terrains require high-intensity constraints to maintain the integrity of the topological framework. Additionally, the HCEG-SE mechanism (Hillshade-Contour Edge-Guided Stroke Enhancement) narrows the semantic divide between terrain skeletons and artistic textures by combining multi-directional non-photorealistic rendering with precise edge extraction techniques. Evaluated across five geomorphologically diverse regions—from karst towers to loess plateaus—Geo-InkGAN demonstrably surpasses existing benchmarks in Geomorphological Structure Correlation (GSC). This geomorphology-aware approach advances the scientific rigor of AI-driven cartography and offers a refined methodology for the cultural representation of digital twin landscapes. Full article

Wastewater systems are critical reservoirs where antibiotic resistance genes, antibiotic-resistant bacteria, and pathogens converge and disseminate into receiving waters, posing risks to ecosystems and public health. Metagenomics enables culture-independent surveillance of resistome and pathogens in wastewater. After the COVID-19 pandemic, the rapid expansion of wastewater-based epidemiological surveillance, together with growing emphasis on the One Health framework, has further promoted the integration of wastewater metagenomic monitoring with public-health surveillance strategies. However, no bibliometric study has systematically mapped the global research landscape at the intersection of metagenomics, wastewater systems, antimicrobial resistance, and pathogen surveillance. This study retrieved 1161 publications from the Web of Science Core Collection and used CiteSpace to conduct bibliometric analyses. From 2010 to 2025, annual publications increased from 1 to 219, with 72.7% of the total output concentrated between 2021 and 2025. China led in publication output but showed low betweenness centrality, whereas Australia and Sweden served as key intermediaries. Keyword analysis revealed a gradual thematic evolution from the basic detection of antibiotic resistance genes in activated sludge, through studies of dissemination mechanisms, to recent work on One Health and wastewater surveillance. Literature co-citation analysis showed that integration between environmental monitoring and public health literature remains limited, suggesting that the translation of metagenomic surveillance data into health risk assessment frameworks is still at an early stage. By mapping the field’s knowledge structure and gaps, this review highlights priorities for advancing wastewater-based Antimicrobial Resistance surveillance, including standardizing analytical methods, developing artificial intelligence-assisted resistome analysis, promoting equitable participation from underrepresented regions, and operationalizing One Health surveillance, thereby supporting the translation of wastewater monitoring into actionable public-health solutions. Full article

Targeted Protein Degradation (TPD) has emerged as a transformative paradigm in drug discovery, offering a robust strategy to address “undruggable” targets. This study presents the first 25-year longitudinal scientometric analysis (2001–2025) of the TPD field, integrating data from 2750 publications across the Web of Science Core Collection, Scopus, and PubMed to map the global research landscape and therapeutic innovations. The results indicate that TPD research entered an explosive growth phase post-2016. China leads in publication volume (1247 papers), while the USA maintains dominance in citation impact (H-index = 80) and foundational leadership. The Chinese Academy of Sciences and Harvard University were identified as core institutions, with Craig M. Crews confirmed as a pivotal scholar. Thematic analysis reveals a systematic evolution from foundational ubiquitin-proteasome mechanisms to the clinical translation of advanced modalities, including Proteolysis-Targeting Chimeras (PROTACs), molecular glues, and non-ubiquitin-dependent platforms like LYTACs and AUTACs. Clinical viability is evidenced by the FDA approval of the agent ARV-471 for oncology. Despite this progress, critical challenges remain regarding E3 ligase expansion, molecular design optimization, and off-target toxicity. This review provides a data-driven roadmap for future TPD development, bridging the gap between academic output and real-world translational science to guide researchers, clinicians, and industry partners in navigating this dynamic therapeutic frontier. Full article

Deep eutectic solvents (DESs) have undergone a remarkable transformation over the past two decades, evolving from a laboratory curiosity into one of the most actively investigated solvent platforms in green chemistry. Yet, despite this rapid expansion, and although the field is well served by numerous topical reviews, it still lacks a corpus-wide, cross-disciplinary synthesis capable of guiding strategic research priorities, identifying critical knowledge gaps, and informing policy and industrial investment decisions. The present work addresses this need through a thorough analysis of global DES research from 2003 to 2025, based on a deduplicated corpus of 17,757 publications retrieved from the Web of Science Core Collection and Scopus following PRISMA-adapted screening guidelines. The analysis maps temporal publication dynamics, geographic and institutional contributions, thematic evolution, journal landscape, component usage patterns, international collaboration networks, market projections, and alignment with the United Nations Sustainable Development Goals. The results document an exponential growth trajectory—from a single publication in 2004 to 3954 in 2025 (CAGR > 30%)—and reveal a clear thematic transition from early electrochemistry-dominated research toward extraction, pharmaceutical, and environmental applications, with machine-learning-assisted design and hydrophobic DES formulations emerging as the most dynamic current frontiers. China leads global output with 6819 publications (38.4%), while the United States and Malaysia achieve the highest citation-per-publication ratios among the leading nations (≈46.7 and ≈38.9, respectively, versus ≈27.6 for China), and Spain pairs a comparatively modest output with a high h-index, indicating that impact is large relative to volume. Type III DESs and NADESs collectively account for approximately 69% of the literature, with choline chloride present in 72% of reported formulations. The global DES market, valued at approximately USD 166 million in 2024, is projected to reach USD 370 million by 2030. Despite this progress, critical translational barriers persist: fewer than 0.3% of publications include techno-economic or life cycle assessment analysis, standardized characterization protocols remain absent, and toxicological datasets are systematically incomplete. This panoramic analysis is intended to serve as an evidence-based reference for researchers prioritizing future directions, for funding agencies assessing the maturity and needs of the field, and for industrial stakeholders evaluating the readiness of DES technologies for scale-up. Full article

Background: Osteoarthritis (OA) is a complex whole-joint disease imparting a substantial global socioeconomic burden. Photobiomodulation (PBM) has attracted increasing research interest as a non-pharmacological intervention for OA, but the intellectual structure and developmental trajectory of this research field remain incompletely understood. Objective: This study systematically mapped the intellectual landscape, research hotspots, thematic structure, and translational characteristics of PBM research in OA using bibliometric methods. Methods: Bibliographic records were retrieved from the Web of Science Core Collection database. Following data cleaning, 422 publications (1988–2026) were analyzed using the bibliometrix package in R and VOSviewer software. Results: Publication output increased substantially after 2015, with a marked rise after 2020. Keyword co-occurrence analysis classified 71 core keywords into seven clusters, revealing a dual-axis knowledge structure comprising a mechanistic biology axis (inflammation, chondrocytes, oxidative stress, and cartilage) and a clinical rehabilitation axis (pain, WOMAC, exercise, and physical therapy). Overlay visualization and thematic map analyses indicated a gradual shift in research focus from symptom-oriented rehabilitation research toward mechanistic investigations and regenerative medicine-related approaches involving platelet-rich plasma and mesenchymal stem cells. Reference co-citation analysis identified two major citation clusters connected through studies related to inflammation, pain management, and rehabilitation. Conclusions: The PBM-OA literature is characterized by a translational knowledge structure integrating mechanistic biology and rehabilitation-oriented research. Notably, recent publication trends indicate increasing scholarly attention to regenerative medicine-related approaches while continuing to position PBM within exercise-centered conservative management. To strengthen the evidence base and guide future investigations, future research should prioritize protocol standardization, dose–response validation, and long-term structural outcomes. Full article

Metal-contaminated mining soils require management options that reduce environmental risk while enabling the controlled reuse of degraded land. Following the PRISMA extension for Scoping Reviews (PRISMA-ScR), this review synthesizes evidence on the use of non-food crops for the phytomanagement of metal-contaminated mining soils, with particular emphasis on crop establishment, agronomic performance, metal uptake and partitioning, biomass safety, valorisation pathways, and safe land reuse. Searches conducted in Web of Science, Scopus, and ScienceDirect, complemented by Google Scholar and manual screening, identified 7223 records; after duplicate removal and eligibility assessment, 85 publications were included in the final synthesis. The evidence indicates that non-food crops can support phytostabilization, exclusion-based phytomanagement, biomass production, and, in selected cases, phytoextraction. However, their suitability is strongly site-specific and depends on substrate constraints, contaminant behaviour, biomass quality, and residue-management requirements. Field and pilot-scale evidence remains less frequent than pot and greenhouse studies, which limits the direct transfer of findings to heterogeneous post-mining landscapes. Biomass safety emerged as a critical decision point because harvested biomass and conversion residues may become secondary contamination pathways. Based on the evidence map, this review proposes a seven-step conceptual decision-support framework linking site diagnosis, management objective definition, crop pre-selection and field-performance screening, metal-risk behaviour assessment, biomass safety assessment, land-reuse matching, and adaptive monitoring. The proposed framework is intended as a screening and planning tool and requires site-specific validation before operational implementation. Full article

Background: Artificial intelligence is increasingly influencing health care and policy, yet the global research landscape linking artificial intelligence and health care policy remains underexplored. This study aimed to map publication trends, major contributors, collaboration networks, citation structures, and emerging themes in this field. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection. The search was performed on 3 May 2026, and covered publications from 2000 to 3 May 2026. The final dataset included 347 peer-reviewed English-language original research and review articles. Biblioshiny, VOSviewer, and CiteSpace were used to analyze publication trends, country and institutional contributions, author and journal productivity, collaboration networks, citation and co-citation structures, keyword patterns, and thematic evolution. Results: Publications increased markedly after 2020 and reached their highest annual output in 2025. The 2026 publication count was lower because data for that year were partial at the time of database retrieval. Researchers from 82 countries and 900 institutions contributed to the field, with the United States leading in output, followed by China, England, Canada, and India. Harvard Medical School was the most productive institution, whereas Harvard University had the highest institutional centrality. Frontiers in Public Health published the most articles, and PLOS ONE was the most frequently co-cited journal. The most cited article was “Artificial intelligence and the future of global health.” Key research themes included machine learning, COVID-19, health policy, risk, large language models, interpretable machine learning, neural network-assisted screening, socioeconomic perspectives, and public health applications. Conclusions: Research on artificial intelligence and health care policy has expanded rapidly, particularly in recent years, and is increasingly centered on predictive modeling, public health decision-making, and emerging artificial intelligence technologies. These findings highlight influential contributors, evolving themes, and future directions for researchers, policymakers, and health care leaders. Full article

Background: Returning to sport (RTS) after anterior cruciate ligament (ACL) injury remains a complex challenge in sports medicine, requiring integration of physical recovery, functional performance, and psychological readiness. Although ACL rehabilitation and RTS outcomes have been extensively investigated, the intellectual structure and evolution of this research field have not been comprehensively synthesized. Objective: This study aimed to systematically map the global research landscape of ACL injury and RTS using bibliometric analysis. Methods: A total of 1368 Scopus-indexed publications published between 1997 and April 2026 were analyzed using performance analysis and science mapping. Results: The study identified leading authors, institutions, countries, funding agencies, and journals, and examined collaboration networks, thematic structures, and emerging trends. Scientific output grew substantially after 2012, with accelerated expansion between 2020 and 2025. The United States was the most productive country, and La Trobe University (Australia) the leading institution; the National Institutes of Health (NIH) was the principal funding source. Among 4652 authors, K.E. Webster was the most prolific contributor, and the Orthopaedic Journal of Sports Medicine was the most productive journal. Science mapping showed a shift from surgical and structural perspectives toward multidimensional frameworks emphasizing functional recovery, psychological readiness, neuromuscular performance, injury prevention, and athlete-centered outcomes. Emerging priorities include psychological and neurocognitive readiness, rehabilitation and performance optimization, biomechanical and functional assessment, surgical innovation, outcome validation, and machine learning applications. Conclusions: This bibliometric overview offers clinicians, rehabilitation specialists, and researchers an evidence base for guiding future investigation into functional recovery, re-injury risk reduction, and long-term athlete outcomes. Full article

Vertical greenery is increasingly applied in modern cities for environmental improvement and landscape enhancement. Given the insufficient coverage of recent developments in research and practice by prior reviews, this paper conducts a systematic review based on literature from Web of Science and global patent databases following PRISMA guidelines, with CiteSpace used for bibliometric analysis. This study summarizes the theoretical achievements of vertical greenery in ecological environment, building energy efficiency and technical materials. It also analyzes practical innovations via patent mining—a new supplement compared with traditional reviews. The environmental impacts of both outdoor and indoor vertical greenery are elaborated on: outdoor systems improve urban microclimate, noise control and air quality; indoor systems enhance indoor comfort, air purification and people’s mental status. Current innovations are categorized into structure and equipment, intelligent management, and social–cultural values. The outcomes of this work offer practical guidance for the design, construction and maintenance of vertical greenery in real projects. This paper also identifies future research priorities for the long-term development of vertical greenery. Full article

Fermentation is a foundational process that has historically underpinned the development of global civilizations. By extending the shelf life of perishable ingredients while enhancing flavor, nutrition, and bioactive properties, fermentation has provided the food security necessary for societies to flourish. Traditionally, these processes utilized locally available raw materials—such as milk, cereals, fruits, and vegetables—to produce a diverse array of non-alcoholic, alcoholic, and functional foods. This review explores the evolution of prominent ancient fermentation products and the contemporary movement to revive their authentic sensory profiles, including unique aromas and textures. Furthermore, it examines the transition from traditional artisanal methods to modern industrial production, where the use of standardized starter cultures and precise process parameters ensures product uniformity for the global market while employing precision fermentation to improve traditional fermentation products. By bridging ancestral wisdom with modern food science, this review highlights the enduring relevance of fermentation in the current food landscape. Full article