Search Results (22,600)

Inland depression irrigation districts in the arid regions of Xinjiang, owing to the absence of natural drainage conditions, exhibit unique groundwater-salt dynamics and face prominent risks of soil salinization, thus necessitating clarification of their water-salt transport mechanisms to ensure sustainable agricultural development. This study takes the Karamay Agricultural Comprehensive Development Zone as the research subject. The study examines the distribution characteristics of soil salinity, groundwater depth, and Total Dissolved Solids (TDS) of groundwater across diverse soil textures, elucidates the correlative relationships between groundwater dynamics and soil salinity, and forecasts the evolutionary trajectory of groundwater levels within the irrigation district. The findings reveal that groundwater depth in silty soil regions (3.24–3.11 m) substantially exceeds that in silty clay regions (2.43–2.61 m), whereas TDS of groundwater demonstrates marginally elevated concentrations in silty clay areas (19.05–16.78 g L⁻¹) compared to silty soil zones (18.18–16.29 g L⁻¹). Soil salinity exhibits pronounced surface accumulation phenomena and considerable inter-annual seasonal variations: manifesting a “spring-peak, summer-trough” pattern in 2023, which inversely transitioned to a “summer-peak, spring-trough” configuration in 2024, with salinity hotspots predominantly concentrated in silty clay distribution zones. A significant sigmoid functional relationship emerges between soil salinity and groundwater depth (R² = 0.73–0.77), establishing critical depth thresholds of 2.44 m for silty soil and 2.72 m for silty clay, beneath which the risk of secondary salinization escalates dramatically. The XGBoost model demonstrates robust predictive capability for groundwater levels (R² = 0.8545, MAE = 0.4428, RMSE = 0.5174), with feature importance analysis identifying agricultural irrigation as the predominant influencing factor. Model projections indicate that mean groundwater depths across the irrigation district will decline to 2.91 m, 2.76 m, 2.62 m, and 2.36 m over the ensuing 1, 3, 5, and 10 years, respectively. Within a decade, 73.33% of silty soil regions and 92.31% of silty clay regions will experience groundwater levels below critical thresholds, subjecting the irrigation district to severe secondary salinization threats. Consequently, comprehensive mitigation strategies encompassing precision irrigation management and enhanced drainage infrastructure are imperative. Full article

Background/Objectives: Rheumatoid neutrophilic dermatosis (RND) is a rare extra-articular manifestation of rheumatoid arthritis (RA) with variable clinical presentations. Although typically non-blistering, a rare bullous or vesiculobullous subtype has been described, mainly in patients with seropositive and active RA, and may mimic autoimmune blistering diseases. The objective of this review was to systematically summarize the clinical, histopathological, immunopathological, and therapeutic features of vesiculobullous rheumatoid neutrophilic dermatosis. Methods: A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines utilizing the PubMed, MEDLINE, and Google Scholar databases, which were searched through December 2025. Case reports and case series describing vesiculobullous or bullous RND with extractable patient-level data were included. Non-English articles were translated. Demographic, clinical, histopathological, immunopathological, microbiological, and therapeutic data were extracted and analyzed using Statistica 12.0 software. Results: Results were synthesized descriptively due to clinical heterogeneity and limited sample size. Thirty reported cases were identified, of which 28 non-duplicate cases were included. The mean patient age was 60.8 ± 14.9 years, with a female predominance (male-to-female ratio, 1:2.5). Most patients were of Asian descent (67.9%). Bullous or vesicular lesions most frequently involved the lower legs (64.3%), palms and soles (41.7%), and thighs (35.7%). Rheumatoid factor data were available in 67.9% of patients, all indicating high RA activity. Histopathological examination was reported in 71.4% of cases and most commonly demonstrated a predominantly neutrophilic infiltrate, often dense and extending throughout the dermis, with subepidermal blister formation being the most frequent pattern. Direct immunofluorescence, serological testing for autoimmune bullous diseases, and microbiological investigations were predominantly negative. Dapsone and systemic corticosteroids, alone or combined with RA-specific therapies, were the most commonly used treatments. Conclusions: This review represents the most comprehensive synthesis to date focused exclusively on the bullous/vesiculobullous subtype of RND, highlighting key diagnostic features such as neutrophil-predominant histopathology, negative direct immunofluorescence, and favorable response to dapsone. Full article

Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies. Full article

This study documents the ethnomedicinal knowledge of Konak (Malatya, Eastern Anatolia, Türkiye), a region with rich plant diversity but no prior comprehensive research. The aim of the study is to systematically document and analyze the ethnomedicinal practices of Konak village, focusing on plant taxa (species, subspecies and varieties) used, preparation methods, and therapeutic applications. Data were collected through semi-structured interviews with 68 local informants. Quantitative analysis was performed using Informant Consensus Factor (F_IC) and Use Value (UV) indices. Plant specimens were collected, identified, and deposited in the herbarium. The study documented 86 plant taxa from 35 families used in 230 therapeutic applications. Lamiaceae, Asteraceae, and Rosaceae were the most represented families. High F_IC values were recorded for colds (F_IC = 0.95), stomach pain (F_IC = 0.92), and inflammation (F_IC = 0.90), indicating strong community consensus. The most frequently cited species were Origanum vulgare subsp. gracile, Mentha spp., and Rosa canina. There are novel or locally specific uses, with 13 taxa having no previously recorded ethnomedicinal applications in the reviewed literature. The findings reveal Konak as a significant repository of ethnomedicinal knowledge. High-F_IC taxa represent prime candidates for phytochemical and pharmacological research to validate traditional uses and support evidence-based phytotherapy. This study enriches regional ethnopharmacological data and highlights candidate taxa for pharmacological validation. Full article

The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrained hardware remains a significant challenge, especially on the Artificial Internet of Things (AIoT). This study presents an AIoT-based framework for automated English-speaking assessment that integrates architecture and system design, ASR benchmarking, and reliability analysis on edge devices. The proposed AIoT-oriented architecture incorporates a lightweight scoring framework capable of analyzing pronunciation, fluency, prosody, and CEFR-aligned speaking proficiency within an automated assessment system. Seven open-source ASR models—four Whisper variants (tiny, base, small, and medium) and three Vosk models—were systematically benchmarked in terms of recognition accuracy, inference latency, and computational efficiency. Experimental results indicate that Whisper-medium deployed on the Raspberry Pi 5 achieved the strongest overall performance, reducing inference latency by 42–48% compared with the Raspberry Pi 4 and attaining the lowest Word Error Rate (WER) of 6.8%. In contrast, smaller models such as Whisper-tiny, with a WER of 26.7%, exhibited two- to threefold higher scoring variability, demonstrating how recognition errors propagate into automated assessment reliability. System-level testing revealed that the Raspberry Pi 5 can sustain near real-time processing with approximately 58% CPU utilization and around 1.2 GB of memory, whereas the Raspberry Pi 4 frequently approaches practical operational limits under comparable workloads. Validation using real learner speech data (approximately 100 sessions) confirmed that the proposed system delivers accurate, portable, and privacy-preserving speaking assessment using low-power edge hardware. Overall, this work introduces a practical AIoT-based assessment framework, provides a comprehensive benchmark of open-source ASR models on edge platforms, and offers empirical insights into the trade-offs among recognition accuracy, inference latency, and scoring stability in edge-based ASR deployments. Full article

The shift toward Industry 5.0 places human-centred and digitally integrated metrology at the core of modern manufacturing, particularly in the automotive sector, where portable Laser Line Triangulation (LLT) systems must combine accuracy with operator usability. This study addresses the challenge of operator-induced variability by evaluating how algorithmic strategies and mechanical support features jointly influence the performance of a portable LLT device derived from the G3F sensor. A comprehensive Measurement System Analysis was performed to compare three feature extraction algorithms—GC, FIR, and Steger—and to assess the effect of a masking device designed to improve mechanical alignment during manual measurements. The results highlight distinct algorithm-dependent behaviours in terms of repeatability, reproducibility, and computational efficiency. More sophisticated algorithms demonstrate improved sensitivity and feature localisation under controlled conditions, whereas simpler gradient-based strategies provide more stable performance and shorter processing times when measurement conditions deviate from the ideal. These differences indicate a trade-off between algorithmic complexity and operational robustness that is particularly relevant for portable, operator-assisted metrology. The presence of mechanical alignment aids was found to contribute to improved measurement consistency across all algorithms. Overall, the findings highlight the need for an integrated co-design of algorithms, calibration procedures, and ergonomic aids to enhance repeatability and support operator-friendly LLT systems aligned with Industry 5.0 principles. Full article

Background/Objectives: Missed Nursing Care is a global concern that affects nurses’ well-being and patients’ safety. Despite global recognition of Missed Nursing Care, there is limited synthesized evidence that determines its characteristics in a Middle Eastern context. The purpose of the study is to synthesize the existing evidence about the prevalence of Missed Nursing Care among nurses in hospitals, the types of care missed, and reasons for Missed Nursing Care in the Middle East. Methods: A systematic literature review is conducted by using a comprehensive search in CINAHL, Scopus, and ScienceDirect databases for studies published between 2020 and 2025 and utilizing the MISSCARE Survey. Results: 25 studies met the inclusion criteria. The reported prevalence of Missed Nursing Care ranged between 1.06 and 2.9 out of five, indicating a low to moderate level. Frequent missed care activities included ambulation, hygiene, mouth care, and patient teaching. Contributing factors were staffing shortages, heavy workload, resource limitations, and communication issues. Missed Nursing Care critically affected patients’ outcomes, reduced job satisfaction, and caused moral distress and a higher intent to leave the profession. Conclusions: Missed Nursing Care remains a significant, complex challenge in the Middle East. Therefore, understanding this phenomenon in the region is needed. Collaborative efforts among policymakers, administrators, and nursing leaders are essential to implement targeted interventions, supportive policies, and ongoing research to minimize Missed Nursing Care across the Middle East. Full article

Kazakhstan, located in Central Asia, is experiencing faster warming than the global trend, making it an important region regarding the study of how climate change is affecting climatic zones. This research aims to identify projected shifts in Köppen–Geiger climate zones under high-emission Shared Socioeconomic Pathway (SSP) 5-8.5 climate scenarios. The Köppen–Geiger climate classification system is a practical tool that effectively captures climate types based on just two variables: temperature and precipitation. Monthly temperature and precipitation data from Climatic Research Unit (CRU,) ERA5-Land, and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles from 1951 to 2100 were used to generate climatic zone maps. CMIP6 models were evaluated against meteorological station data and ERA5-Land, with bias metrics used to identify the best-performing models for temperature and precipitation in Kazakhstan. Based on these results, two inter-model datasets were developed and used to generate Köppen–Geiger climate maps for high-emission scenarios for the 2061–2100 time period. This research resulted in two key outcomes. First, to facilitate this analysis, a Google Earth Engine (GEE) application was developed as an open accessible tool for dynamic visualization of Köppen–Geiger climate maps. Second, projected maps based on CMIP6 SSP5-8.5 scenario projections indicate that southern Kazakhstan may shift to BSh (Hot Semi-Arid) and Csa (Mediterranean) climates, and the southwest region of the country is projected to shift to a BWh (Hot Desert) climate. These projected Köppen–Geiger climate maps contributed to climate adaptation efforts by identifying regions at risk of desertification and aridification. This study provides a comprehensive analysis of climate zone transformations in Kazakhstan and offers a practical scalable geovisualization tool for monitoring climate change impacts. This allows users easy access to climate-related information and insights into data processing procedures. Full article

The wnt gene family encodes a group of highly conserved secreted glycoproteins that play essential roles in vertebrate development, including tissue patterning, cell differentiation, and gonadal regulation. However, the genomic organization, evolutionary dynamics, and functional roles of Wnt signaling components in flatfish remain poorly understood. In this study, we performed a comprehensive genome-wide identification, evolutionary characterization, expression profiling, and functional analysis of wnt genes in Cynoglossus semilaevis, a flatfish species exhibiting ZW/ZZ sex determination and temperature-induced sex reversal. A total of 20 wnt genes were identified and classified into 13 subfamilies, displaying conserved structural organization and phylogenetic relationships consistent with other teleosts. Chromosomal mapping revealed lineage-specific WNT clusters, including a unique wnt3–wnt7b–wnt5b–wnt16 block, as well as syntenic associations with reproduction-related genes (e.g., adipor2, sema3a, nape-pld, erc2, lamb2), suggesting coordinated genomic regulation. Tissue transcriptome analysis demonstrated strong sex- and tissue-biased expression patterns, with wnt5a predominantly expressed in ovaries and wnt5b specifically upregulated in pseudo-male testes. Functional assays revealed that knockdown of wnt5a or wnt5b induced testis-specific genes (sox9b, tesk1) and suppressed ovarian markers (foxl2, cyp19a1a), indicating antagonistic regulatory roles in gonadal fate determination. Promoter analysis identified yy1a as a selective repressor of wnt5b, but not wnt5a, providing a mechanistic basis for paralog divergence. Furthermore, pull-down combined with LC–MS/MS analysis showed that WNT5b interacts with proteins enriched in ribosome biogenesis and ubiquitin-mediated proteolysis, suggesting a role in translational regulation and protein turnover during spermatogenesis. Together, these findings establish WNT5 signaling—particularly wnt5b—as a key driver of testicular development in C. semilaevis and provide new insights into the molecular mechanisms underlying sex differentiation and sex reversal in flatfish. Full article

The Jurassic reservoirs in the Fukang Sag of the Junggar Basin exhibit heterogeneous overpressure. As the mechanisms underlying overpressure generation remain poorly constrained, this poses challenges for accurate pre-drilling-pressure prediction and hinders a comprehensive understanding of hydrocarbon accumulation processes. Through integrated analysis of measured pressure, mud weight, and well-logging curves, this study delineates distinct overpressure characteristics in sandstones and identifies the well-logging response to overpressure in mudstones. By coupling the loading-unloading response with the analysis of geological conditions conducive to overpressure, we differentiate the overpressure-generating mechanisms between sandstones and mudstones and assess their implications for deep petroleum exploration. The study reveals significant vertical heterogeneity in pressure regimes, with sandstones exhibiting pressure coefficients ranging from 1.2 to 1.8, locally exceeding 2.1. Strong overpressure preferentially develops in isolated sand bodies linked to deep source kitchens via oil-source faults. The logging response of overpressured mudstones shows high acoustic transit time, high neutron, and low resistivity, deviating from the normal compaction trend, yet demonstrates progressive density increases attributable to chemical compaction processes. Overpressure points with pressure coefficients between 1.2 and 1.4 align with the loading curve dominated by disequilibrium compaction. The overpressure with a pressure coefficient exceeding 1.4 correlates with abrupt unloading responses indicative of fault-transferred overpressure in sandstones. Our results highlight that overpressured fluid migration via faults is a critical process in hydrocarbon migration, with large-magnitude overpressured reservoirs being readily formed near oil-source faults. Multi-overpressure mechanisms create a complex pore-pressure distribution in deep layers, challenging conventional pressure-prediction models. These insights advance predictive models for pore pressure and provide a robust framework for optimizing exploration strategies in the Fukang Sag. Full article

The reverse circulation pneumatic down-the-hole (DTH) drilling system employs percussive drilling to achieve high efficiency and strong adaptability across diverse rock formations. However, its cutting removal efficiency remains suboptimal. To enhance reverse circulation performance, a comprehensive understanding of airflow and solid particle dynamics at the borehole bottom is essential. This study investigates rock cutting transportation and distribution under varying drilling parameters and evaluates reverse circulation flow ratio using a Computational Fluid Dynamics (CFD) multiphase flow model, coupled with finite volume analysis of the reverse circulation bit. Simulation results reveal that increasing the input gas flow rate (Q), reducing the equivalent particle diameter (D), and minimizing the borehole enlargement ratio (E) significantly improve cutting removal efficiency, with optimal values identified for each parameter. Additionally, solid volume fraction contours at the borehole bottom indicate that the arrangement of spherical teeth influences the flow field. Optimal values for rock cutting density (ρ), rate of penetration (ROP), and rotational speed (N) were also determined to maximize reverse circulation flow ratio. The Genetic Algorithm–Least Squares Support Vector Machine (GA-LSSVM) method was used to train the response surface data and construct a predictive model, which was then further optimized using Particle Swarm Optimization (PSO) to determine accurate parameter settings. These findings provide operational insights into optimizing drilling parameters to advance efficient drilling performance. Full article

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that pedestrian crossing time (s) had the highest tendency to push the model towards class 1 (e.g., crossing illegally), while total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

This study investigated the technological properties and volatile flavor profiles of tallow from three anatomical regions of Wagyu cattle, omental fats (OF), perirenal fats (PF), and subcutaneous fats (SF), smelted at temperatures ranging from 100 to 160 °C. The objective was to provide a theoretical basis for the targeted utilization of Wagyu fats. Results showed that smelting temperature significantly affected oil yield, with the highest yield obtained at 160 °C for all regions. PF exhibited the greatest oil yield, followed by OF and SF. Physicochemical analyses indicated that OF had the highest degree of unsaturation, whereas PF demonstrated superior hardness and oxidative stability. Microstructural and spectroscopic analyses, Fourier-transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) were employed to characterize the samples, revealed that the compact protein structure of SF residues limited oil release, while the porous structures of OF and PF residues facilitated higher yields. With respect to flavor profiling, headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS) was employed to analyze volatile compounds, identified aldehydes as the dominant flavor contributors in OF and PF, imparting fatty and citrus notes, whereas SF was characterized by a distinct creamy aroma primarily due to γ-butyrolactone. These regional differences were further validated by principal component analysis (PCA). Overall, PF obtained the highest comprehensive quality score. The integrated evaluation underscores the potential for precision-based utilization of Wagyu tallow: PF and OF are recommended for applications demanding high yield and intense flavor, whereas SF, characterized by its distinctive creamy aroma, is more suitable for specialized or niche products. Full article

Marine data assets are increasingly recognized as important drivers of value creation in the blue economy, yet their valuation remains challenging due to difficulties in isolating data-related earnings in capital-intensive maritime enterprises. This study proposes a methodological valuation framework that integrates the multi-period excess earnings method with the Analytic Hierarchy Process (AHP) and the Fuzzy Comprehensive Evaluation (FCE) approach, incorporating both financial and non-financial dimensions. The framework follows a “total synergistic return–data contribution separation” logic to isolate data-related excess earnings and applies an AHP–FCE-based adjustment coefficient to account for data quality, application value, and risk. A representative container shipping enterprise is used as an illustrative application to demonstrate the implementation logic of the framework. The results indicate that marine data assets can constitute a non-negligible component of enterprise value under reasonable parameter settings, while sensitivity analysis highlights the influence of key parameters such as the data contribution coefficient and discount rate. The proposed framework provides a transparent methodological reference for marine data asset valuation and supports sustainability-oriented research and practice in the blue economy. Full article

Tocosh, a traditional Peruvian fermented potato product, is known for its health-promoting properties, including its antioxidant, anti-inflammatory, probiotic, and antibiotic effects, which have popularized its consumption, particularly in rural areas. To gain a better understanding of its antimicrobial properties, this study aimed to perform a comprehensive whole-genome analysis and functional assessment of the Bacillus velezensis TCSH0001 strain isolated from tocosh. The isolate was identified through whole-genome sequencing using the MinION nanopore platform. AntiSMASH analysis revealed nine biosynthetic gene clusters (BGCs) potentially responsible for producing secondary metabolites with antibiotic potential. Notably, seven BGCs showed a 100% similarity to known clusters involved in the biosynthesis of polyketide synthases (PKSs) and non-ribosomal peptides (NRPSs), including difficidin, bacillibactin, bacilysin, macrolactin H, bacillaene, fengycin, and bacillomycin D. In vitro analysis revealed antimicrobial activity against S. aureus strains. In addition, RT-qPCR indicated that the expression of the baeJ (bacillaene), bmyA (bacillomycin D), and pks2A (macrolactin H) occurs predominantly during the exponential growth phase. Our results suggest that this B. velezensis strain has the capacity to produce a diverse array of bioactive compounds, supporting the traditional use of tocosh as a natural antimicrobial agent, and revealing the potential of the strain as a high NRPS producer. Full article