Search Results (35,172)

Background/Objectives: Coleus aromaticus (Lamiaceae), also known as Cuban oregano or Indian borage, is a semi-succulent perennial species widely used in traditional medicine for its therapeutic and nutritional properties. While its essential oils and aromatic fraction have been extensively investigated, the characterization of its non-volatile metabolites remains limited. The aim of this study was to explore the chemical composition of fresh leaves with a focus on the non-volatile fraction. Methods: Fresh leaves of C. aromaticus were cryogenically treated with liquid nitrogen, ground, and subjected to three different extraction procedures: hydroalcoholic maceration, ethyl acetate maceration, and liquid–liquid partitioning to obtain a dichloromethane organic phase and a hydroalcoholic phase. Extracts and fractions were analyzed by HPTLC and HPLC for metabolic profiling. In addition, the Bligh–Dyer method was applied to separate polar and non-polar metabolites, which were subsequently characterized using NMR spectroscopy. Results: Chromatographic analyses highlighted the occurrence and distribution of organic acids, polyphenols (notably flavonoids), and proteinogenic amino acids. Spectroscopic data confirmed the presence of diverse polar and non-polar metabolites, providing a more detailed chemical fingerprint of C. aromaticus. This integrated approach broadened the phytochemical profile of the species beyond the well-documented essential oils. Conclusions: The results contribute to a better understanding of the non-volatile metabolites of C. aromaticus, offering novel insights into its chemical diversity. These findings highlight the potential of this plant as a valuable source of bioactive compounds, supporting its future application in nutraceutical and pharmaceutical research. Full article

This study examines how major global hotel groups construct sustainability through corporate communication, assessing both the thematic content and the internal coherence of their Environmental-Social-Governance (ESG) narratives. The research question is How do international hotel corporations construct sustainability through their corporate communication and ESG reporting? The research applies qualitative content analysis of sustainability reports from ten international hotel corporations and a four-layer discursive coherence model (performance, operational, narrative, strategic), the study analyses 888 coded quotations and 205 sustainability-theme occurrences in ATLAS.ti version 25, a qualitative data-analysis software. Results show that while measurable, performance-based disclosures dominate—such as digital food-waste monitoring, emissions-intensity reductions, and responsible sourcing—symbolic language remains strategically deployed to reinforce identity, purpose, and legitimacy. Across the sector, sustainability discourse converges around four recurring pillars: environmental performance leadership, community resilience, responsible business governance, and inclusive economic empowerment. The study advances theoretical work on sustainability communication by conceptualizing discursive coherence as an indicator of organizational authenticity and offers actionable insights for enhancing credibility and stakeholder trust in corporate ESG reporting. Full article

This study conducts a systematic bibliometric review of artificial intelligence (AI)-based approaches to tacit knowledge extraction and management. Drawing on data retrieved from Scopus and Web of Science, this study analyzes 126 publications published between 1985 and 2025 using VOSviewer and Biblioshiny to map citation networks, keyword co-occurrence patterns, and thematic evolution. The results identify nine major clusters spanning machine learning, natural language processing, semantic modeling, expert systems, knowledge-based decision support, and emerging hybrid techniques. Collectively, these findings indicate a field-wide shift from manual codification toward scalable, context-aware, and semantically enriched approaches that better support tacit knowing in organizational practice. Building on these insights, the paper introduces the AI–Tacit Knowledge Co-Evolution Model, which situates AI as an epistemic partner—augmenting human interpretive processes rather than merely codifying experience. The framework integrates Polanyi’s concept of tacit knowing, Nonaka’s SECI model, and sociotechnical learning theories to elucidate how human–AI interaction transforms the dynamics of knowledge creation. The review consolidates fragmented research streams and provides a conceptual foundation for guiding future methodological development in AI-enabled tacit knowledge management. Full article

Bentonite is the most widely used raw material for producing organoclays, which have numerous industrial and environmental applications. Due to their hydrophobicity, high swelling, and strong affinity for organic compounds, organoclays are effective in removing organic solvents from contaminated water originating from pipeline leaks, oil spills, traffic accidents, and industrial discharges. Such contamination not only degrades water quality but also forms surface films that hinder oxygen transfer, threatening aquatic ecosystems. In this study, two sodium bentonites with different specific surface areas (30 and 50 m²/g) were modified with three quaternary ammonium salts of varying molar masses and alkyl chain lengths (Sun, Arq, and Arm) to evaluate their performance in organic solvent sorption (gasoline, diesel, and kerosene). The materials were characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), differential thermal analysis (DTA), scanning electron microscopy (SEM), and swelling capacity and sorption efficiency. The swelling capacity was determined according to ASTM D5890-19 (Foster method) using gasoline, diesel, kerosene, toluene, and xylene, while the sorption efficiency was assessed following ASTM F726-17 in gasoline, diesel, and kerosene, chosen due to their high potential for water contamination and frequent occurrence in oil spill and leakage scenarios. These solvents also differ in polarity and aromatic content, providing a relevant model for hydrocarbon mixtures commonly found in the environment. Results showed that the interaction between the clay and the surfactant depended strongly on the modifier’s chemical structure. The sorption capacity increased with greater interlayer expansion, surfactant molar mass, and specific surface area of the clay. Among all samples, the Arm-modified natural bentonite (VLArm) exhibited the best performance, with adsorption capacities of up to 6 g/g for diesel, 5 g/g for gasoline, and 5 g/g for kerosene. These values exceeded most previously reported organoclays. These findings demonstrate that optimizing the combination of clay properties and surfactant chemistry can yield highly efficient, low-cost organoclays for environmental remediation of organic contaminants. Full article

Eight species of the medically important scorpion genus Hemiscorpius Peters, 1861 have been reported in Iran, three of which are responsible for most of the severe clinical cases of envenomation. However, morphological similarity complicates species delimitation in this genus, hindering the identification of species implicated in envenomations. The present study integrates morphology, DNA sequences, and ecological niche modeling to clarify the taxonomy and distribution of Hemiscorpius in southern Iran, providing taxonomic insights relevant to public health and biodiversity conservation. Morphometric analyses were performed to evaluate size and shape differences; molecular phylogenetic analyses were conducted on DNA sequences of the mitochondrial Cytochrome c Oxidase Subunit I gene; and species distribution models, based on occurrence records and bioclimatic variables, were developed. Morphometric analyses revealed significant interspecific differences and sexual dimorphism. A new species was identified and described as Hemiscorpius aratta sp. n. Molecular phylogenetic analysis confirmed the distinctiveness of the new species and revealed intraspecific variation in the type species, Hemiscorpius lepturus Peters, 1861, suggesting possible cryptic diversity. Southern Iran, particularly the coastline of the Persian Gulf and the Gulf of Oman, represents a diversity hotspot for Hemiscorpius. Topographical barriers such as the Jebal Barez, Makkoran, and Zagros Mountain ranges promoted isolation and speciation, leading to high levels of endemism in the genus. Ecological niche models revealed that the distributions of Hemiscorpius species are strongly influenced by temperature and precipitation. Coastal species are restricted to thermally stable maritime habitats, whereas semi-arid species occupy regions with higher temperature seasonality. Range-restricted species are habitat specialists, vulnerable to environmental change. This study reinforces the importance of integrating morphological, molecular, and ecological data for resolving taxonomic ambiguity. Full article

Wildfire occurrence in arid and semiarid landscapes is increasingly driven by shifts in climatic and biophysical conditions, yet its dynamics remain poorly understood in the mountainous environments of western Saudi Arabia. This study modeled wildfire probabilities across the Aseer, Al Baha, Makkah Al-Mukarramah, and Jazan regions via multisource Earth observation datasets from 2012–2025. Active fire detections from VIIRS were integrated with ERA5-Land reanalysis variables, vegetation indices, and Copernicus DEM GLO30 topography. A random forest classifier was trained and validated via stratified sampling and cross-validation to predict monthly burn probabilities. Calibration, reliability assessment, and independent temporal validation confirmed strong model performance (AUC-ROC = 0.96; Brier = 0.03). Climatic dryness (dew-point deficit), vegetation structure (LAI_lv), and surface soil moisture emerged as dominant predictors, underscoring the coupling between energy balance and fuel desiccation. Temporal trend analyses (Kendall’s τ and Sen’s slope) revealed the gradual intensification of fire probability during the dry-to-transition seasons (February–April and September–November), with Aseer showing the most persistent risk. These findings establish a scalable framework for wildfire early warning and landscape management in arid ecosystems under accelerating climatic stress. Full article

Biflavonoids, a unique class of naturally occurring polyphenolic compounds formed by the dimerization of flavonoid units, have attracted increasing scientific interest due to their diverse biological activities and structural complexity. This review provides a comprehensive overview of their natural occurrence, synthetic strategies, and antimicrobial properties, with a particular focus on their antibacterial, antifungal, and antiviral activities. Comparative analyses indicate that biflavonoids often display enhanced or distinct biological effects compared to their monomeric flavonoid counterparts, suggesting that dimerization plays a crucial role in modulating bioactivity. Overall, biflavonoids represent an underexplored yet highly promising group of natural compounds with significant potential for the development of novel antimicrobial and antiviral agents. Continued interdisciplinary research integrating natural product chemistry, pharmacology, and computational modeling will be essential to fully realize their therapeutic potential. Full article

Cities increasingly utilise digital technologies to tackle climate risks and urban shocks, yet their real impact on resilience remains uncertain. This paper systematically reviews 115 peer-reviewed studies (2012–2024) to explore how smart city technologies engage with planning instruments, governance arrangements, and social processes, following PRISMA 2020 and combining bibliometric co-occurrence mapping with a qualitative synthesis of full texts. Three themes organise the findings: (i) urban planning and design, (ii) smart technologies in resilience, and (iii) strategic planning and policy integration. Across these themes, Internet of Things (IoT) and geographic information system (GIS) applications have the strongest empirical support for enhancing absorptive and adaptive capacities through risk mapping, early warning systems, and infrastructure operations, while artificial intelligence, digital twins, and blockchain remain largely at pilot or conceptual stages. The review also highlights significant geographical and hazard biases: most cases come from high-income cities and concentrate on floods and earthquakes, while slow stresses (such as heat, housing insecurity, and inequality) and cities in the Global South are under-represented. Overall, the study promotes a “smart–resilience co-production” perspective, demonstrating that resilience improvements rely less on technology alone and more on how digital systems are integrated into governance and participatory practices. Full article

The critical need to preserve cultural relics has sustained longstanding interest in their corrosion and conservation, research vital to extending artifact lifespan and maintaining historical authenticity. Although scholarly output in this field grows annually, the expanding volume of literature makes it difficult to systematically identify research hotspots and forecast trends. This lack of clarity can lead to redundant efforts and hinder the practical application of preservation technologies. Existing reviews often focus on specialized subtopics, leaving a comprehensive overview lacking. To address this gap, this study conducts a systematic bibliometric analysis of 4983 relevant publications from the WOS Core Collection (1961–2025). Through a multi-dimensional examination of annual publication trends, keyword co-occurrence, contributions from countries and authors, and institutional collaborations, we elucidate the field’s development and intellectual structure. Our findings reveal key research hotspots, including corrosion mechanisms, novel protective materials, micro-environmental control, and multidisciplinary detection methods, whose evolution shows distinct temporal patterns. Furthermore, an analysis of collaborative networks indicates that progress is increasingly driven by institutional and international cooperation, steering the field toward greater systematization and refinement. Full article

Background and Objectives: Preterm birth is a common obstetric problem. Attention-deficit hyperactivity disorder (ADHD) affects an increasing number of children. There is evidence that all subcategories of preterm birth are related to the occurrence of ADHD. The present article reviews the findings of the last two years regarding this association. Materials and Methods: PubMed was screened for relevant articles published in English between January 2024 and November 2025. Keyword combinations of the words “preterm birth”, “prematurity”, “attention deficit disorder”, “attention disorders”, “ADHD”, “preterm” and “attention deficit hyperactivity disorder” were used. A total of 28 articles were retrieved, reviewed and selected. Results: Preterm birth results in higher risk of ADHD, while early preterm births are characterized by an even higher risk of ADHD. Additionally, postnatal complications commonly experienced by preterm infants are associated with the presence of ADHD. It seems that the maternal use of ADHD medications during pregnancy is associated with a high risk of preterm birth, although there is a concern about the role of other psychotropic medications during pregnancy. Various neurodevelopmental disorders are also associated with preterm birth. Maternal use of glucocorticoids combined with preterm birth leads to higher risk of ADHD. However, the outcome of ADHD is shaped by a wide range of social, familiar and biological factors. Conclusions: Prematurity is a significant risk factor for the development of ADHD symptoms in children. However, many biological, environmental, and psychosocial factors, such as neurodevelopmental vulnerability, perinatal complications, maternal health and adverse psychosocial factors, act as regulators in this relationship. Researching and understanding these associations will help in implementing preventive measures in children who are at increased risk of developing ADHD. Full article

This review examines data-driven road traffic safety modeling, aiming to provide a comprehensive overview of the state-of-the-art and persistent research gaps. The study is structured around data sources, influencing factors, reactive and proactive modeling approaches, and key challenges. Data sources, including crashes, trajectories, traffic, roadway geometry, and environmental data, are first reviewed in the context of reactive and proactive safety analysis. To address the substantial heterogeneity across studies, a vote-counting strategy is adopted to aggregate directional evidence reported in the literature. The synthesis indicates that traffic demand variables exhibit consistently positive associations with crash occurrence, while speed-related effects are strongly context-dependent. Road geometry and surface conditions have largely consistent directional impacts on safety outcomes. From a methodological perspective, reactive approaches remain dominant, while proactive approaches exhibit potential for early risk identification but remain insufficiently validated due to data quality constraints. In addition, empirical evidence on conflict–crash relationships is still limited. Notably, model performance varies substantially across safety tasks, with algorithm effectiveness primarily driven by data structure, outcome definition, and aggregation level, rather than by the intrinsic superiority of any single approach. Overall, this review highlights challenges related to data integration, spatio-temporal modeling, interpretability, and transferability, and provides practical guidance for model selection in operational road safety analysis. Full article

Saproxylic insects are key forest components but highly vulnerable to practices that reduce deadwood quality and diversity. We investigated the response of Platycerus caraboides (Coleoptera: Lucanidae)—an elusive, cool-adapted stag beetle associated with moist, small-diameter decayed wood—to forest coppicing in the Euganean Hills (northeastern Italy). Surveys were conducted both before (2017–2020) and after coppicing (2021–2025) to compare plots that had undergone coppicing with those that remained uncoppiced. Field investigation focused on the volume of downed woody debris and on P. caraboides occurrence, quantified as encounter rates of deadwood bearing stag beetle oviposition scars or the evidence of stag beetle presence. Coppicing and wood harvesting reduced the overall volume of deadwood but did not significantly affect the amount of small-diameter downed woody debris. Nevertheless, P. caraboides showed consistently lower encounter rates in coppiced areas, particularly during the initial survey period, suggesting that altered microclimatic conditions and reduced debris quality may hinder colonization or larval development. These findings underscore the need to retain small- and medium-diameter woody debris, maintain partial canopy cover, and enhance structural heterogeneity to conserve overlooked, cool-adapted saproxylic species—especially under climate change and in line with EU biodiversity restoration goals. Full article

Artificial Intelligence (AI) has gained prominence on sustainability agendas while raising ethical, social, and environmental challenges. This study synthesizes evidence and maps the scientific production on Human-Centered AI (HCAI) at the interface with the Sustainable Development Goals (SDGs) for 2020–2024. Searches in Scopus and Web of Science (Boolean operators; thematic and temporal filters), followed by deduplication, yielded 265 articles, which were analyzed with Bibliometrix/Biblioshiny version 5.1.1 and VOSviewer version 1.6.20 (0) to generate term co-occurrence maps, collaboration networks, and bibliographic coupling. The results indicate accelerated growth and diffusion of the topic, with journals such as Sustainability, IEEE Access, and Applied Sciences standing out. Three interdependent axes were identified: (i) technical performance, with emphasis on machine learning and deep learning; (ii) explainability and human-centeredness (XAI, ethics, and algorithmic governance); and (iii) socio-environmental applications oriented toward the SDGs. Underrepresentation of the Global South, particularly Brazil, was observed. It is concluded that HCAI is being consolidated as an emerging interdisciplinary field with potential to accelerate the SDGs, although there remains a need to integrate ethical, regional, and impact-assessment dimensions more systematically to achieve global targets effectively. Full article

Smart contracts are deployed and represented as bytecodes in blockchain networks, and these bytecodes are machine-readable codes. Only a small number of deployed smart contracts have their verified human-readable code publicly accessible to blockchain users. To improve the understandability of deployed smart contracts, we explored rule-based classification of smart contracts using iterative integration of fingerprints of relevant function interfaces and keywords. Our classification system included categories for standard contracts such as ERC20, ERC721, and ERC1155, and non-standard contracts like FinDApps, cross-chain, governance, and proxy. To do this, we first identified the core function fingerprints for all ERC token contracts. We then used an adapted header extractor tool to verify that these fingerprints occurred in all of the implemented functions within the bytecode. For the non-standard contracts, we took an iterative approach, identifying contract interfaces and relevant fingerprints for each specific category. To classify these contracts, we created a rule that required at least two occurrences of a relevant fingerprint keyword or interface. This rule was stricter for standard contracts: the 100% occurrence requirement ensures that we only identify compliant token contracts. For non-standard contracts, we required a minimum of two relevant fingerprint occurrences to prevent hash collisions and the unintentional use of keywords. After developing the classifier, we evaluated its performance on sample datasets. The classifier performed very well, achieving an F1 score of over 99% for standard contracts and a solid 93% for non-standard contracts. We also conducted a risk analysis to identify potential vulnerabilities that could reduce the classifier’s performance, including hash collisions, an incomplete rule set, manual verification bottlenecks, outdated data, and semantic misdirection or obfuscation of smart contract functions. To address these risks, we proposed several solutions: continuous monitoring, continuous data crawling, and extended rule refinement. The classifier’s modular design allows for these manual updates to be easily integrated. While semantic-based risks cannot be completely eliminated, symbolic execution can be used to verify the expected behavior of ERC token contract functions with a given set of inputs to identify malicious contracts. Lastly, we applied the classifier on contracts deployed Ethereum main network. Full article

Objective: We investigated the relationship between venous thromboembolism (VTE) and pelvic and para-aortic lymphadenectomy (LND) within the first 90 days post gynaecological cancer surgery. Methods: A retrospective cohort analysis was conducted on 1021 patients who underwent gynaecological cancer surgery between 2006 and 2019. Univariate and multivariate analysis was performed to assess the effects of LND and lymph node (LN) metastasis on VTE occurrence within 90 days post-surgery. Results: A total of 41 patients developed VTE within 90 days post-surgery. Para-aortic LND was significantly associated with VTE (p < 0.001), with the highest rates (14.6%) observed in patients who had >10 para-aortic LN removed. In patients with pelvic LN metastasis, 10 (7.5%) developed VTE; patients with >5 pelvic nodes positive for metastasis had an HR = 4.83 (95% CI: 0.99–13.9) after adjustment for age, duration of hospital stay, and surgical approach. The highest VTE rates (27.3%) occurred in patients with >5 para-aortic LN positive for metastasis, HR = 3.79 (95% CI 1.44–14.23) after adjustment for age, duration of hospital stay, and surgical approach (p = 0.011). Conclusions: Para-aortic LND significantly increases VTE risk within the first 90 days post-surgery. The risk is further amplified in cases with metastatic LN and persists even after adjustment for other risk factors for VTE. While extended thromboprophylaxis is standard for all cancer patients, our findings suggest that para-aortic LND—especially with nodal metastases—may help identify those who would benefit most from a more tailored, risk-based prophylaxis approach. Full article