Search Results (63,814)

Logistics parks are increasingly acting as coordination hubs in cross-border agricultural supply chains (CASCs), yet evidence on how park-enabled governance mechanisms translate into sustainability remains limited. This study examines the drivers of CASC sustainability within the context of logistics parks in Henan, China, and assesses whether the dominant park type conditions these effects. A total of 385 valid questionnaire responses were analyzed using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA). SEM results show that symbiotic environment cultivation is the strongest predictor of sustainability, while interface mediation efficiency and safety also significantly support cross-border circulation. The moderating role of dominant park type is supported only for the interface and sustainability link. fsQCA further identifies three equifinal configurations leading to high sustainability, indicating that strong environmental cultivation and interface efficiency can compensate for weaker elements under certain combinations. These findings clarify how logistics parks enable economic, environmental, and social value creation in CASCs and provide actionable levers for park management and policy design. Full article

Automatic sprinkler systems are widely used for fire protection in various buildings, with deluge valves serving as the core component of these systems. Traditional deluge valves employ a diaphragm-type design (Zoning Sprinkler Fire Monitor, ZSFM), which is prone to significant safety hazards such as corrosion and damage due to uneven pressure distribution on the diaphragm. This study modified a 150 mm diameter ZSFM to a non-pressure diaphragm type, establishing and validating a CFD model of the internal flow field. Based on the original structure, six drag reduction optimization cases are designed. Among these, case 5 exhibits the minimum inlet-to-outlet pressure drop of 0.050 MPa under rated operating conditions, meeting and significantly exceeding the fire protection industry standard (≤0.08 MPa). Full article

Given the rising global demand for environmentally sustainable energy sources, solar photovoltaic (PV) power generation has emerged as a pivotal component of the energy transition. In PV systems, power conversion efficiency is degraded and operational lifespan reduced due to the presence of defective modules. Consequently, achieving accurate and efficient defect detection during PV module manufacturing is critical to ensuring product quality and reliability. To address this challenge, we propose MambaVSS-YOLOv11n, an electroluminescence (EL) image-based multi-defect detection method for PV modules. Our study utilizes a dataset containing six types of defects—Broken Gate, Cold Solder Joint, Black Spot, Scratch, Microcrack, and Suction Mark—to construct 692 labeled EL images of defective PV modules. The model integrates the Vision State Space (VSS) module from Mamba and optimizes the C3k2 Bottleneck structure to enhance fine-grained feature extraction, while employing Space-to-Depth Convolutional (SPD-Conv) Layer for downsampling to improve computational efficiency. Additionally, to address YOLOv11n’s limited generalization capability for small objects and complex backgrounds, we adopt the Inner Mask Distance Penalized Intersection over the Union (Inner-MDPIoU) loss function, which enhances detection accuracy and mitigates the impact of low-quality samples. Experimental results demonstrate that compared to YOLOv11n, MambaVSS-YOLOv11n reduces the number of parameters by 18.1%, while improving mAP@0.5 to 0.869 and mAP@0.5:0.95 to 0.637. This achieves model lightweighting while enhancing detection performance. These findings indicate that the model is well-suited for real-time defect detection in PV module production lines, providing PV manufacturers with a lightweight yet accurate and reliable solution for PV module defect inspection. Full article

Accurate prediction of asphalt mixture density is critical for quality control in pavement engineering. This study develops a novel dielectric-based predictive framework by applying the Lichtenecker–Rother (L-R) dielectric mixing theory to asphalt composites. The model’s key microstructural parameter, the geometric arrangement factor c, was optimized to 0.3 using a combined experimental dataset: laboratory measurements on AC (asphalt concrete) mixtures produced in this study, supplemented with published data from open-graded friction course (OGFC), stone mastic asphalt (SMA), and asphalt mixture (AM) types reported in the literature. The resulting model, termed the Geometric Arrangement Optimization (GAO) model, was systematically compared against three established dielectric models: the complex refractive index method (CRIM), the Rayleigh mixing model, and the Bottcher-type model adapted by Leng et al. (denoted ALL). Validation on a total of 34 sets of laboratory specimens showed that GAO achieved the highest prediction accuracy, with a mean relative error of 1.83% and a coefficient of determination R² of 0.91. When tested on eight independent field cores, GAO maintained reliable performance, yielding a mean relative error of 3.01%. These results indicate that the GAO model provides a physically grounded and practically applicable approach for asphalt mixture density estimation, contributing a useful tool for pavement performance evaluation and quality assurance. Full article

Tiles are commonly used as architectural finishing materials, but are prone to debonding defects due to construction and environmental factors in engineering applications. Therefore, effective detection of tile debonding holds significant engineering relevance. This study proposes a tile debonding detection method based on impact sound signal features and a dual-branch convolutional neural network. The sound signals collected through tapping are transformed into two types of two-dimensional feature maps using Mel-frequency cepstral coefficients (MFCCs) and continuous wavelet transform (CWT), which are then fed in parallel into the dual-branch convolutional neural network for feature extraction and fusion. Finally, tile debonding classification is performed in the classifier module. Experimental results show that the proposed model achieves a classification accuracy of 98.5% under laboratory conditions. Moreover, it demonstrates strong robustness under varying noise levels and sound pressure conditions, maintaining an accuracy of 82% in a 75 dB human voice noise environment. Field validation in real-world engineering environments yields an accuracy of 91.5%. These findings indicate that the proposed method, which combines MFCC and CWT features with a dual-branch convolutional neural network architecture, enables high-precision identification of tile debonding defects. Full article

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

Numerical simulations of (1) two aerosol types such as organic carbon (i.e., spherical) and dust (i.e., non-spherical) particles, and (2) their mixtures are carried out. Optical and microphysical parameters of these aerosols in our simulations are provided by MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2). The inversion routine is performed with TiARA (Tikhonov Advanced Regularization Algorithm) using the Lorenz–Mie (i.e., spherical) light-scattering model in unsupervised and automated, i.e., autonomous mode. The results of our numerical simulations show that the accuracy of the inversion results for the aerosol mixtures from synthetic optical data perturbed by ±10% random error is comparable to the accuracy observed for the inversion results of the “pure” spherical particles. In particular, the retrieval uncertainties of effective radius, and number, surface-area, and volume concentrations of these mixtures are ±30%, ±10%, between –50% and +100% and ±30%, respectively. However, we need to apply a modified version of the gradient correlation method (GCM) to stabilize the inversion results. The results of this study will form the baseline for future work, where we plan to apply TiARA to optical data products obtained from real lidar observations in the framework of the SCC (Single Calculus Chain) of EARLINET (European Aerosol Research Lidar Network). Full article

The growing prevalence of antibiotic-resistant bacterial infections poses a serious burden on healthcare systems worldwide. Endolysins are promising candidates for a new type of antibiotic due to their strong bacteriolytic activity. However, important limitations, including reduced activity and short persistence in the bloodstream, must still be addressed. We evaluated the key physicochemical and biological factors limiting the activity and stability of the endolysins Cpl-1 and Pal in blood. The analysis included ionic composition and strength, pH, bystander proteins, physiological temperature, and proteolytic activity. Our results indicate that the aforementioned factors significantly affect Cpl-1 and Pal, suggesting that physiological conditions in human circulation markedly restrict the anti-bacterial potential of endolysins. To overcome these limitations, we designed a set of Cpl-1 and Pal variants with modified amino acid compositions aimed at increasing their resistance to such physiological constraints. One variant demonstrated improved performance in an ex vivo mouse model and lacked a cleavage site for blood proteases. Full article

The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop health, optimizing resource management, and predicting yield. However, the development and validation of robust data-driven algorithms, from vegetation index analysis to complex deep learning models, are contingent upon the availability of high-quality, standardized, and publicly accessible datasets. This review systematically surveys and characterizes the current landscape of available datasets containing multispectral imagery acquired by UAVs in agricultural contexts. Following guidelines for reporting systematic reviews and meta-analyses (PRISMA methodology), 39 studies were selected and analyzed, categorizing them based on key attributes including spectral bands (e.g., RGB, Red Edge, Near-Infrared), spatial and temporal resolution, types of crops studied, presence of complementary ground-truth data (e.g., biomass, nitrogen content, yield maps), and the specific agricultural tasks they support (e.g., disease detection, weed mapping, water stress assessment). However, the review underscores a critical gap in standardization, with significant variability in data formats, annotation quality, and metadata completeness, which hampers reproducibility and comparative analysis. Furthermore, we identify a need for more datasets targeting specific challenges like early-stage disease identification and anomaly detection in complex crop canopies. Finally, we discuss future directions for the creation of more comprehensive, benchmark-ready open datasets that will be instrumental in accelerating research, fostering collaboration, and bridging the gap between algorithmic innovation and practical agricultural deployment. This work serves as a foundational guide for researchers and practitioners seeking suitable data for their work and contributes to the ongoing effort of standardizing open data practices in agricultural remote sensing. Full article

Creating a digital 3D model of museum artefacts has been a common practice for many years. Such models can be used for archiving, research, and marketing purposes, as well as to counteract various types of exclusion. A digital copy created using professional 3D scanners using 3D structured-light scanning (3D SLS) or terrestrial laser scanning technology requires expensive equipment, specialised software for postprocessing, and a trained team. The introduction of mobile phones with Light Detection and Ranging (LiDAR) sensors and the development of appropriate open-access software have enabled the use of phones to generate digital 3D models. This study compares the quality of 3D models created with 3D SLS and mobile LiDAR technologies using three identical small museum artefacts from the Silk Road area of the Samarkand State University museum in Uzbekistan. They were digitised in 2017 and 2025. The results indicate that digital 3D models generated with an iPhone 16 PRO MAX device using Scaniverse LiDAR software are incomplete and thus less versatile. Therefore, they cannot serve as archival models. Their accuracy and quality (mesh density, size, and texture quality), as well as the speed of generating 3D models, make them ideal for marketing purposes and digital tourism. Full article

Background/Objectives: Encephalitis and related acute encephalopathic syndromes represent severe neurological conditions with diverse etiologies and variable clinical outcomes. This study aimed to analyze nationwide hospitalization patterns for encephalitis-related diagnoses in Ecuador between 2018 and 2024. Methods: We used data from the Ecuadorian National Institute of Statistics and Census to estimate age-adjusted hospitalization and mortality rates according to ICD-10 codes. Binary and multinomial logistic regression models were employed to identify sociodemographic factors and diagnostic categories of encephalitis associated with hospitalization and in-hospital mortality. Results: A total of 1560 hospitalizations related to encephalitis-spectrum diagnoses were recorded, with an overall age-adjusted rate of 0.127 per 100,000 inhabitants and 6.0% in-hospital mortality. Unspecified encephalitis and encephalomyelitis were the most common diagnostic categories. Adolescents (10–19 years) were more frequently diagnosed with acute disseminated and bacterial meningoencephalitis, while patients aged ≥70 had higher odds of “other” encephalitis subtypes and the highest mortality risk (aOR = 0.265; 95% CI: 0.116–0.608). Indigenous individuals were more likely to be diagnosed with acute disseminated encephalitis, and Black individuals showed a higher risk for myelopathy associated with human T-cell lymphotropic virus type 1-associated myelopathy. Conclusions: Age and ethnicity significantly influence hospitalization due to encephalitis-related diagnoses in Ecuador. These findings provide epidemiological rates for a lower-middle–income country where the lack of precise diagnosis, age, and ethnicity contribute to the vulnerability of encephalitis. Full article

Background: The choice of mechanical prosthesis for mitral valve replacement (MVR) is critical, yet data comparing long-term outcomes across different valve types are still needed. This study aimed to compare the long-term clinical and echocardiographic outcomes of four distinct mechanical mitral valve prostheses. Methods: We retrospectively analyzed 431 patients who underwent mechanical MVR between 2009 and 2022 with one of four valve types: Carbomedics (n = 112), Bicarbon (n = 176), ATS (n = 89), or On-X (n = 54). A competing risk regression model was used to identify predictors of a composite endpoint (valve thrombosis, reoperation, stroke, pulmonary embolism, and major bleeding), accounting for all-cause mortality. Longitudinal echocardiographic data were analyzed using linear mixed-effects models. Results: The median follow-up was 62 months. The cumulative incidence of the composite endpoint at 10 years was 14% for the On-X valve, 12% for the Bicarbon valve, 9.5% for the Carbomedics valve, and 7% for the ATS valve. After adjusting for confounders, the type of valve prosthesis was not significantly associated with the composite endpoint. Significant predictors of adverse events included coronary artery disease (Sub-distribution Hazard Ratio [SHR] 2.70, p = 0.023), peripheral artery disease (SHR 6.29, p = 0.007), and smaller valve size (SHR 0.87, p = 0.037). No significant difference in overall survival was observed between the groups (log-rank p = 0.904). All valve types were associated with favorable LV remodeling. The Carbomedics group showed the greatest reduction in left ventricular end-diastolic diameter, likely reflecting regression to the mean given the larger baseline ventricular dimensions in this group. Conclusions: The type of mechanical mitral valve did not significantly influence long-term thromboembolic and bleeding events or overall survival. Patient-specific factors and valve size were the primary determinants of adverse outcomes. The observed differences in ventricular remodeling may warrant further investigation. Full article

Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts. Full article

The silkworm, Bombyx mori, is a model organism with significant agricultural and economic importance, but it is threatened by Bombyx mori nucleopolyhedrovirus (BmNPV). A crucial chaperone, heat shock protein 90 (HSP90), can also facilitate the proliferation of viruses, and our previous quantitative acetylome analysis revealed that lysines 550 and 567 in the carboxyl-terminal domain (CTD) of Bombyx mori HSP90 (BmHSP90) were significantly deacetylated following BmNPV infection, but the underlying mechanism remained unknown. In this study, deacetylation-mimetic (K to R) mutants of BmHSP90 exhibited increased dimerization and chaperone activity compared with the wild-type. In addition, the mutants also exhibited higher affinity for actin, promoting F-actin polymerization. Collectively, these changes facilitated BmNPV replication and progeny virion production. This study reveals that the deacetylation of BmHSP90 at K550 and K567 mediates crucial host–virus interactions, providing novel insights into potential antiviral strategies. Full article

In modern organizations, IT Service Management (ITSM) relies on the efficient handling of large volumes of unstructured textual data, such as support tickets and incident reports. This study investigates the automated classification of IT support requests as a data-driven decision-support task within a real-world enterprise ITSM context, addressing challenges posed by multilingual content and severe class imbalance. We propose an applied machine-learning and natural language processing (NLP) pipeline combining text cleaning, stratified data splitting, and supervised model training under realistic evaluation conditions. Multiple classification models were evaluated on historical enterprise ticket data, including a Logistic Regression baseline and transformer-based architectures (multilingual BERT and XLM-RoBERTa). Model validation distinguishes between deployment-oriented evaluation on naturally imbalanced data and diagnostic analysis using training-time class balancing to examine minority-class behavior. Results indicate that Logistic Regression performs reliably for high-frequency, well-defined request categories, while transformer-based models achieve consistently higher macro-averaged F1-scores and improved recognition of semantically complex and underrepresented classes. Training-time oversampling increases sensitivity to minority request types without improving overall accuracy on unbalanced test data, highlighting the importance of metric selection in ITSM evaluation. The findings provide an applied empirical comparison of established text-classification models in ITSM, incorporating both predictive performance and computational efficiency considerations, and offer practical guidance for supporting IT support agents during ticket triage and automated request classification. Full article