Search Results (207,602)

The rational design of supported ruthenium catalysts for sustainable energy applications requires precise control over metal nanoparticle size, dispersion, and metal–support interactions. This study investigates the influence of mesoporous silica support topology—SBA-15 (2D hexagonal, cylindrical pores), SBA-12 (3D hexagonal structure), and SBA-3 (2D hexagonal)—on the structure and catalytic performance of 1 wt% ruthenium catalysts in CO₂ methanation and gas-phase toluene hydrogenation. Comprehensive characterization by nitrogen physisorption, low- and high-angle X-ray diffraction (XRD), H₂ temperature-programmed reduction (H₂-TPR), CO chemisorption, and transmission electron microscopy (TEM) revealed that support pore architecture dictates ruthenium particle size (1.2 nm for Ru/SBA-15, 2.8 nm for Ru/SBA-3, 4.3 nm for Ru/SBA-12) and dispersion (80%, 35%, 23%, respectively) through geometric confinement effects. Catalytic testing demonstrated contrasting structure–activity relationships: CO₂ methanation exhibited strong structure sensitivity with turnover frequency (TOF) increasing with particle size (Pearson’s r = 0.96), favoring Ru/SBA-3 and Ru/SBA-12 with near-optimal 3–4 nm particles, while toluene hydrogenation showed weaker structure sensitivity, with Ru/SBA-12 achieving the highest TOF owing to its larger particle size and higher crystallinity. These findings underscore the critical importance of tailoring mesoporous support topology to match reaction-specific structure sensitivity, providing fundamental insights for the design of bifunctional catalysts for hydrogenation reactions. Full article

Background: Artificial intelligence-based computer-aided detection (AI-CAD) systems are increasingly being used in endovascular practice to support time-sensitive detection, triage and prioritization tasks in imaging and procedural workflows. Despite rapid technological advancements and expanding regulatory clearances, the translation to lasting clinical benefit varies. Objective: This narrative review synthesizes AI-CAD applications in endovascular interventions and proposes an evaluation-oriented framework to support responsible clinical translation; this framework emphasizes detection-specific metrics, external validation, bias-aware assessment, and workflow integration. Methods: A structured narrative review was conducted using targeted searches in PubMed, Google Scholar, and IEEE Xplore (2020–2026); this review was supported by an examination of US FDA device databases and citation tracking. Evidence was assessed using a pragmatic hierarchical classification framework based on regulatory status and validation rigor. Results: AI-CAD applications were mapped across four main endovascular domains: neurovascular interventions (e.g., large vessel occlusion triage), coronary interventions (CCTA-based stenosis detection and intravascular imaging support), aortic interventions/EVAR (endoleak detection and sac monitoring), and peripheral interventions (lesion detection and angiographic decision support). Across the domains, performance reporting was heterogeneous and often relied on retrospective, single-center assessments. Key barriers to clinical readiness included acquisition variability and dataset shift due to artifacts, limited multicenter validation, annotation variability, and human–AI workflow factors. Evaluation priorities included whether to assess at the lesion level or case level, false positive burden and calibration, external validation under real-world heterogeneity, and clinical impact measures such as treatment timing and procedural decision-making. Conclusions: AI-CAD systems hold significant potential for improving endovascular care; however, clinical readiness depends on rigorous, endovascular feature-specific assessment and transparent reporting, beyond retrospective accuracy. The proposed evidence level framework and assessment checklist provide practical tools for distinguishing mature technologies from research prototypes and guiding future validation, implementation, and post-market monitoring. Full article

Background: Proton pump inhibitors (PPIs) are widely used to prevent acid-related complications, yet concerns persist about infectious harm. Observational studies have linked PPIs to Clostridioides difficile infection (CDI) and pneumonia whereas randomized controlled trials (RCTs) consistently show reductions in upper gastrointestinal bleeding. We therefore conducted a systematic review and meta-analysis restricted to randomized controlled trials to evaluate whether PPIs increase the risk of CDI, and to assess pneumonia and gastrointestinal bleeding to contextualize net clinical benefit. Methods: A comprehensive search of randomized controlled trials (RCTs) was conducted using several databases including PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and SCOPUS until July 2025. All published English-language RCTs that met the inclusion criteria were included. Random-effects models were utilized to calculate pooled odds ratios (ORs) with 95% confidence intervals. The risk of bias was assessed using the Cochrane Risk of Bias 2.0 Tool, and heterogeneity was quantified using I² statistics. Analysis was performed using STATA version 18 and RevMan 5.3. Results: Across eight RCTs (n = 30,019), PPIs did not increase C. difficile infection versus placebo (OR 1.29, 95% CI 0.82–2.02; p = 0.27; I² = 16%) with leave-one-out (LOO) analyses showing stable estimates. In six trials reporting pneumonia, there was no significant difference between groups (OR 1.00, 95% CI 0.92–1.09; p = 0.99; I² = 0%). For clinically important upper GI bleeding (seven trials), PPIs were associated with a statistically significant lower risk when compared to placebo (OR 0.51, 95% CI 0.27–0.94; p = 0.03; I² = 56%). Conclusions: Across randomized trials with follow-up ranging from 30 days to 3 years, PPI prophylaxis significantly reduced upper gastrointestinal bleeding without increasing the risk of CDI or pneumonia. These findings support the use of PPIs for prophylaxis when clinically indicated, while recognizing that larger trials are needed to better assess rare adverse events. Full article

Under the combined pressures of dual carbon policy constraints, the integration of intelligent construction technologies, and intensifying market competition, the development of a scientific and robust performance evaluation system has become essential for building construction enterprises seeking to enhance their core competitiveness. Traditional evaluation methods, however, often suffer from incomplete indicator systems and limited capability in addressing high-dimensional and nonlinear problems, rendering them inadequate for the evolving demands of the industry. To address these challenges, this study proposes a performance evaluation model for building construction enterprises based on the least squares support vector machine (LSSVM), optimized by an improved Pied Kingfisher Optimizer (IPKO). Drawing on environment–behavior theory, the model incorporates three environmental and ten behavioral factors. To overcome the limitations of the original PKO algorithm—namely, insufficient exploration capability and weak local search—the exploration phase of PKO is integrated with that of the Marine Predators Algorithm. Empirical results demonstrate that: (1) the proposed IPKO outperforms Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sparrow Search Algorithm (SSA), Dung Beetle Optimizer (DBO), Ospery Optimization Algorithm (OOA), and the original PKO in most benchmark functions; (2) the ReliefF feature selection algorithm improves the model’s test set accuracy by approximately 2.18%; and (3) the IPKO-LSSVM model achieves 6.53%, 4.16%, and 6.74% higher prediction accuracy than Backpropagation Neural Networks (BPNN), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost), respectively. These findings highlight the model’s effectiveness in addressing small-sample, high-dimensional, and nonlinear problems, offering a scientifically sound and practical tool for performance evaluation in building construction enterprises. Full article

Background: Gastrointestinal (GI) cancers remain a major global health concern, where timely and accurate interpretation of endoscopic findings plays a decisive role in patient outcomes. In recent years, deep learning–based decision support systems have shown considerable potential in assisting GI diagnosis; however, their broader adoption is often limited by patient privacy regulations, uneven data availability, and the fragmented nature of clinical data across institutions. Federated learning (FL) offers a practical solution by enabling collaborative model training while keeping patient data local to each hospital. Methods: Vision Transformers (ViTs) are particularly well suited for endoscopic image analysis due to their ability to capture long-range contextual information. Nevertheless, their high computational and communication costs pose a significant challenge in federated settings, especially when data distributions vary across clients. To address this issue, we propose a privacy-preserving federated framework that combines ViTs with a Clinical-Entropy Guided Quantum Evolutionary Algorithm (CEQEA) for adaptive token pruning. The CEQEA leverages the diagnostic diversity of each client’s local dataset to guide population initialization, evolutionary updates, and mutation strength, allowing the pruning strategy to adapt naturally to different clinical profiles. Results: The proposed framework was evaluated on curated upper- and lower-GI tract subsets of the HyperKVASIR dataset under realistic non-IID federated conditions. On the final test sets, the model achieved a mean micro-averaged accuracy of 92.33% for lower-GI classification and 90.19% for upper-GI classification, while maintaining high specificity across all diagnostic classes. At the same time, the adaptive pruning strategy reduced the number of tokens processed by approximately 40% and decreased the number of required federated communication rounds by 33% compared to ViT-based federated baselines. Conclusions: Overall, these results indicate that entropy-aware, quantum-inspired evolutionary optimization can effectively balance diagnostic performance and efficiency, making transformer-based models more practical for privacy-preserving, multi-institutional gastrointestinal endoscopy. Full article

The flower is the primary infection site of Sclerotinia sclerotiorum in the disease cycle of Sclerotinia white mold in rapeseed. Therefore, designing and breeding floral resistance cultivars would be an efficient strategy to control this disease. Nevertheless, a standardized system for evaluating floral resistance to this pathogen is currently lacking. To resolve this gap, we developed a mycelial suspension inoculation method for rapeseed flowers under both greenhouse and field conditions. Furthermore, we established a disease severity rating system for individual flowers and a floral resistance rating system for germplasms through field assays for 35 core rapeseed germplasms. The practicality and effectiveness of the floral resistance evaluation system were further validated in both greenhouse and field. With this system, we identified R4879 (Hungry Gap) as a flower-resistant germplasm in two-year field trials. Taken together, this study provides a methodological foundation for evaluation of rapeseed floral resistance to S. sclerotiorum, thereby supporting breeding for floral resistant rapeseed varieties. Full article

This study develops and tests an Engagement–Experience–Co-creation–Loyalty (EECL) framework explaining how hospitality brand engagement (HBE) is translated into multidimensional hotel loyalty through two parallel mechanisms: Hospitality brand experience (HBX) and hospitality value co-creation (HVCC). A variance-based PLS-SEM model with seven reflective latent constructs and 57 indicators was estimated using data from 1407 members of four global hotel loyalty programs; generational cohort was used only as a grouping variable in multi-group analysis, not as an additional construct. MICOM established measurement invariance across Generation Z, Millennials, Generation X, and Baby Boomers. HBE is positively associated with both HBX and HVCC, and both mechanisms transmit its relationship to cognitive, affective, and conative loyalty. These three attitudinal facets jointly predict action loyalty, supporting a parallel rather than strictly staged loyalty-formation logic in hotel loyalty-program contexts. Younger cohorts translate engagement more strongly into experience and co-creation, whereas older cohorts rely more on experience when forming cognitive loyalty. The study contributes a hospitality-specific, predictive, and cohort-sensitive explanation of how engagement is converted into hotel loyalty. Full article

Soil-dwelling predatory mites (Acari: Mesostigmata) are key components of decomposer-based soil food webs and contribute to the regulation of soil microarthropods, including agricultural pests. Despite their ecological and applied importance, the predatory mite fauna of Slovenia has remained poorly documented. This study provides the first systematic inventory of soil-dwelling mesostigmatid mites in Slovenia, based on standardized sampling conducted between July and October 2024 and between June and September 2025. Samples were collected from a range of organic substrates, including stable manure, compost, vermicompost, decomposing plant material and forest litter, and mites were extracted using a modified Berlese–Tullgren method. In total, 31 predatory mite taxa belonging to nine families were recorded, with all species except Macrocheles glaber being reported for the first time in Slovenia. Diversity analyses, based on species richness, Shannon index and minimum confirmed abundance, revealed clear differences in community structure among substrate types. Manure- and compost-based substrates showed the highest species richness and abundance, whereas forest litter supported lower diversity but more even communities. Several recorded genera include species with documented or potential relevance for the suppression of soil-dwelling pests such as Rhizoglyphus spp. These findings provide baseline data for future faunistic, ecological and applied research and improve our understanding of predatory mite communities in organically enriched agroecosystems. Full article

As oil and gas development increasingly targets low and ultra-low permeability reservoirs, conventional recovery techniques often prove insufficient for mobilizing residual oil. Surfactant flooding, a key chemical enhanced oil recovery (EOR) technology, thus requires careful system optimization and mechanistic investigation. This study focuses on low-permeability reservoirs in the Changqing Oilfield, evaluating three surfactant systems—YHS-Z1 (a 7:3 mass ratio blend of hydroxypropyl sulfobetaine and cocamide),YHS-Z2 (a polyether carboxylate, a nonionic-anionic composite) and a middle-phase microemulsion system (Heavy alkylbenzene sulfonate and hydroxysulfobetaine were combined with a mass ratio of 7:3)—through a series of experiments including interfacial tension measurement, contact angle analysis, static and dynamic oil displacement tests, as well as emulsion transport/retention index assessments, to comprehensively characterize their oil displacement properties. Based on the experimental data, this study constructed four classical regression models: Ridge Regression, Random Forest (RF), Gradient Boosting Regression (GBR), and Support Vector Regression (SVR), and conducted a comparative analysis of their predictive performance. The results demonstrate that the Random Forest (RF) model achieved the optimal prediction performance, with a Mean Absolute Error (MAE) of 1.8245, a Mean Absolute Percentage Error (MAPE) of 4.78%, and a coefficient of determination (R²) of 0.9428 on the training set. Further analysis using the SHapley Additive exPlanations (SHAP) algorithm revealed that the retention index is the primary global factor (accounting for 49.79% of the variance), while significant intergroup differences exist in the primary factors across different surfactant systems. Concurrently, single-factor and multi-factor sensitivity analyses were conducted to elucidate synergistic effects and threshold behaviors among parameters. The optimal parameter combination, identified via a random search method, achieved a predicted recovery factor of 45.61%, representing a 6.57% improvement over the highest experimental value. This study demonstrates that machine learning methods can effectively identify the dominant factors in oil displacement and enable synergistic parameter optimization, thereby providing a theoretical foundation for the efficient development of surfactant flooding in low-permeability reservoirs. Full article

Objective: To investigate the relationship between the number of previous implantation failures (IFs) and embryo ploidy status, as well as subsequent clinical outcomes, in women with recurrent implantation failure (RIF) undergoing preimplantation genetic testing for aneuploidy (PGT-A). Methods: This retrospective cohort study included 422 women with RIF who underwent their first PGT-A cycle between 2017 and 2022. Participants were stratified by maternal age (<38 years, n = 292; ≥38 years, n = 130) and by the number of previous IFs, categorized as 3, 4, or ≥5. The primary outcomes were embryo ploidy rates (euploidy, aneuploidy, and mosaicism). Secondary outcomes included reproductive outcomes after single euploid blastocyst transfer (biochemical pregnancy, clinical pregnancy, ongoing pregnancy, live birth, and pregnancy loss) and neonatal birth weight. Results: Women aged ≥38 years had a significantly lower euploidy rate than those <38 years (24.8% vs. 47.3%, p < 0.001). Ploidy distribution did not differ significantly across IF categories. Among women aged <38 years with ≥5 IFs, a greater number of previous embryo transfer attempts was independently associated with higher odds of live birth after euploid embryo transfer (adjusted OR = 1.258, 95% CI: 1.051–1.505; p = 0.012). Neonatal weight did not differ significantly across IF categories. Conclusions: The number of previous IFs was not independently associated with embryo ploidy or clinical outcomes after euploid transfer, whereas advanced maternal age was strongly associated with a lower likelihood of obtaining euploid embryos. In younger women with ≥5 IFs, a greater number of previous embryo transfer attempts was associated with live birth after euploid transfer; however, this exploratory subgroup finding should be interpreted cautiously and requires prospective validation. Because this study did not directly evaluate therapeutic strategies, any potential role for individualized endometrial evaluation or optimization should be considered as hypothesis-generating rather than supported by the present data. Full article

The COVID-19 pandemic has been associated with a substantial number of deaths, exposing many individuals to bereavement under particularly adverse circumstances, as public health restrictions often prevented individuals from engaging in customary farewell and mourning practices. In this context, the development of interventions capable of mitigating the psychological impact of grief is of critical importance. This mixed-methods study, with a predominantly qualitative design, aimed to develop and pilot-test a group intervention grounded in cognitive-narrative theory for individuals experiencing bereavement during the COVID-19 pandemic, in Portugal. Four patients aged between 18 and 65 years (M = 49.25; SD = 21.24) participated in the 6-week intervention, between July and August 2022. Quantitative data were collected using the Grief and Meaning Reconstruction Inventory, the Prolonged Grief Assessment Instrument, and the Hospital Anxiety and Depression Scale, with pre- and post-intervention comparisons. To assess the intervention process, participants completed an individual evaluation form, and a group interview was conducted at the end of the intervention. The results indicated a clinically significant reduction in feelings of emptiness and loss of meaning in most participants, with improved meaning-making related to the loss. The thematic analysis performed on the qualitative data highlighted the strengths of the intervention (e.g., adjustment to grief and sharing) and some areas for improvement (e.g., more regular feedback and group composition). Despite limitations, particularly the small sample size, the findings are promising and support further evaluation of this intervention in larger samples of individuals diagnosed with prolonged grief. Full article

Flooding is one of the most persistent and destructive natural hazards in West Virginia. However, community-scale assessments that connect social vulnerability with physical flood vulnerability are still limited. Existing floodplain management plans often focus on infrastructure and hydrology, overlooking how socioeconomic disparities shape resilience. This study assesses flood resilience in West Virginia communities by connecting socioeconomic vulnerability with physical flood vulnerability. Using data from the American Community Survey (ACS) and state floodplain maps, we developed a Socioeconomic Vulnerability Index (SEVI) and combined it with physical indicators, such as the percentage of residential buildings in the 100-year floodplain, the share of mobile homes in flood-prone areas, the presence of essential facilities and community assets within flood zones, and the proportion of roads submerged by at least one foot of water. Incorporated and unincorporated communities were analyzed separately to reflect differences in governance and service capacity. The results reveal that high flood vulnerability areas often coincide with high socioeconomic vulnerability, especially in the southern and southeastern counties, where long-term economic decline has increased risks. Communities like McDowell and Mingo face a combined challenge of social and physical vulnerability, adding pressure to populations already dealing with limited resources. These findings emphasize the importance of integrated resilience planning that combines physical protection with social support. Considering the increasing intensity of extreme precipitation events associated with climate change, these findings also highlight the importance of incorporating long-term climate considerations into flood resilience planning. Policy suggestions include expanding targeted flood insurance subsidies for low-income households, prioritizing the relocation or retrofitting of mobile homes and essential facilities, investing in green and open spaces, and encouraging community-based mitigation strategies. Together, these actions can lower long-term flood risks while addressing structural inequalities that make certain populations more vulnerable. Full article

Background: Vitamin B12 deficiency is a recognized but frequently under-integrated cause of global developmental delay (GDD) in infancy and early childhood. Early diagnosis is critical because neurological impairment may be partially or completely reversible with timely treatment. Objective: This narrative review aims to synthesize current evidence on the role of vitamin B12 deficiency in the diagnostic evaluation of GDD, with a focus on clinical phenotype, risk factors, biomarkers, treatment outcomes, and practical integration into contemporary diagnostic algorithms. Methods: A structured, non-systematic search of PubMed/MEDLINE, Embase, and Web of Science was performed to identify clinical studies, case series, reviews, and guideline documents addressing pediatric vitamin B12 deficiency and neurodevelopmental delay. Results: Vitamin B12 deficiency in early childhood is most commonly associated with maternal deficiency and exclusive breastfeeding without adequate supplementation. Evidence from recent clinical and observational studies indicates that vitamin B12 deficiency may present with nonspecific neurological symptoms, including developmental regression, hypotonia, and feeding difficulties. Incorporating vitamin B12 assessment—using serum vitamin B12, holotranscobalamin, methylmalonic acid, and homocysteine—into early diagnostic algorithms for GDD may facilitate timely identification of a treatable cause of neurodevelopmental impairment. The proposed diagnostic framework emphasizes early biochemical evaluation in infants with unexplained developmental delay, thereby supporting prompt treatment during a critical window of neurological reversibility. Conclusions: Targeted assessment of vitamin B12 status in children with GDD, together with evaluation of maternal status, represents a clinically relevant approach to identifying a potentially preventable and treatable cause of neurodevelopmental impairment. Integration of functional biomarkers into diagnostic pathways and the development of pediatric-specific reference standards are key priorities for future research and clinical practice. Full article

The rapid digitalization of cities is reshaping urban planning practices; however, significant gaps persist between technological investments and institutional governance capacity, particularly in transition economies. This study investigates how digital tools can be systematically embedded within planning processes to improve decision-making quality, coordination, and administrative efficiency. Drawing on urban governance theory and an empirical implementation study conducted in Yerevan, Armenia (population 1.1 million) between 2019 and 2023, the paper develops and operationalizes a multi-layer governance framework that aligns digital instruments—including geospatial information systems, performance dashboards, and decision-support platforms—with strategic, tactical, and operational levels of city management. The framework is evaluated through institutional analysis of municipal policy documents, planning databases, and semi-structured interviews with planning officials. The results reveal substantial governance barriers, including data fragmentation, organizational silos, and limited digital capacity. Framework-based implementation produced measurable improvements: planning decision cycles shortened by 43%, GIS utilization increased from 18% to 68% of eligible projects, inter-agency data sharing rose sixfold, and annual cost savings of approximately $1.2 million were achieved through reduced duplication and faster approvals. By combining conceptual design with empirical validation, the study advances digital urban governance research and offers a transferable, evidence-based model for implementing resilient and efficient data-driven planning systems in resource-constrained contexts. Full article

The rise in emerging viral outbreaks has intensified the need for novel antiviral therapies and highlighted the untapped potential of natural products. Influenza viruses, particularly avian strains, continue to evolve rapidly, yet the antiviral properties of Jordan’s native plants remain largely unexplored. This study focused on avian influenza and screened twelve endemic plant species, using ethanol to selectively extract polar phytochemicals likely to interact with the hydrophilic active site of neuraminidase (NA). Among these, Arbutus andrachne leaf and fruit extracts emerged as potent in vitro inhibitors of recombinant N9 neuraminidase, a key enzyme in influenza replication, with IC₅₀ values of 31.6 µg/mL and 32.9 µg/mL, respectively. LC-MS analysis identified hyperoside as the major shared flavonoid in both extracts, which may contribute to the observed inhibitory activity. These findings support the potential of A. andrachne as a natural source for herbal preparations with antiviral activity. Full article