Search Results (1,875)

The durability of the carbon fiber-reinforced polymer (CFRP)–concrete interface is a critical indicator for assessing the service life of composite structures in cold regions. This study systematically investigates the normal bond behavior under coupled deicing salt and freeze–thaw cycles through single-sided salt-frost tests on 126 specimens. The influence of surface roughness, number of freeze–thaw cycles, concrete strength grade, and CFRP material type was systematically evaluated. The results demonstrate that bond behavior is positively correlated with surface roughness, with the f₂ interface exhibiting optimal performance and increasing the ultimate capacity by up to 76.61% compared to the smooth interface. CFRP cloth showed superior bond retention compared to CFRP plates, which experienced a bond strength loss rate up to 26.90% higher than cloth specimens after six cycles. A critical performance threshold was identified between six and eight cycles, where the failure mode transitioned from cohesive adhesive failure to brittle interfacial debonding. Concrete matrix strength had a negligible effect compared to the dominant environmental damage. A two-parameter prediction model based on cycle count and roughness was established with high accuracy. SEM analysis confirmed that epoxy resin cracking, fiber–matrix debonding, and microcrack propagation in the concrete surface layer were the fundamental causes of macroscopic mechanical degradation. These findings provide a theoretical foundation for optimizing interface treatment and predicting the structural integrity of CFRP-strengthened systems in salt-frost regions. Full article

Topological semimetals with nontrivial band structures host a variety of unconventional transport phenomena and have attracted significant attention in condensed matter physics. SnTaS₂, a recently proposed topological nodal-line superconductor with a centrosymmetric layered structure, provides an ideal platform to explore the interplay between topology and electronic transport. Here, we report a comprehensive study of the normal-state magnetotransport and magneto-thermoelectric properties of SnTaS₂ single crystals. We observed large magnetoresistance and nonlinear Hall resistivity at low temperatures, which can be well described by a two-band model, indicating the coexistence of electron and hole carriers. The Seebeck and Nernst coefficients were found to exhibit pronounced and nonmonotonic magnetic field dependences at low temperatures, consistent with multiband transport behavior. Moreover, clear quantum oscillations with a single frequency are detected in both electrical and thermoelectric measurements. Analysis of the oscillations reveals a small effective mass and a nontrivial Berry phase, suggesting that the corresponding Fermi surface arises from a topologically nontrivial band. These findings shed light on the normal-state electronic structure of SnTaS₂ and highlight the important role of topological bands in shaping its transport properties. Full article

Polyester yarn bundle geogrids are widely used materials in flexible retaining structures due to their high toughness and high-strength mechanical properties. To investigate the mechanical characteristics and the interfacial mechanical properties of these geogrids, a series of pull-out tests were conducted under different pull-out rates and filling water contents. Based on the test results, a DEM-FDM coupled numerical model for pull-out behavior was established to analyze the pull-out deformation behavior of the geogrids. Combined with the theoretical analysis of the load-bearing characteristics of the geogrids, the reinforcement mechanism of polyester yarn bundle geogrids was revealed. The results show that there exists a critical pull-out rate of 1 mm/min that maximizes the pull-out resistance; the interface friction angle decreases with an increase in pull-out rate, while the interface cohesion shows an opposite trend. The filling water content presents a more significant weakening effect on the soil–geogrid interface strength under low stress, resulting in a strain-softening type of pull-out curve. Unlike fine-ribbed plastic geogrids, the sliding frictional resistance of polyester yarn bundle geogrids accounts for 80% of the total pull-out resistance during the pull-out process. The mechanical interlocking force, which arises from the bulges on the mid-section of transverse ribs and the downward bending of longitudinal rib edges, is subject to dynamic changes in the course of the pull-out process. The geogrid exhibits overall shear failure under low normal stress (${\sigma }_n<$ 200 kPa) and penetration shear failure under high normal stress (${\sigma }_n\ge$ 200 kPa). In practical engineering installation, polyester yarn bundle geogrids should be placed as parallel as possible to maximize the frictional resistance with filled soil and should take care of the geogrid joints for enhanced durability of the geogrids. Full article

Machine learning (ML) has become an increasingly important tool in concrete engineering which has significantly altered the method of prediction and optimization of concrete properties, enabling more efficient, accurate, and sustainable processes. However, the inherent variability of concrete is a significant challenge to the generalization and performance of ML models. This study is a review that explores the effect of the variability of concrete material on the reliability and accuracy of predictions by ML. To explain the influence of these sources of variability on mechanical and durability related behaviors, the paper groups the sources of variability into four major groups, namely composition, microstructure, curing conditions, and environmental factors. A broad range of machine learning paradigms—including supervised learning, unsupervised learning, reinforcement learning (RL), and hybrid physics-informed approaches—is examined with respect to their robustness against data heterogeneity and distributional shifts. The weaknesses and advantages of the two types of algorithms are highlighted with regard to forecasting fresh and hardened concrete properties and the optimization of the mix design. Based on this synthesis, the review identifies key unresolved challenges, including the lack of standardized multi-source datasets, limited transferability of models across experimental settings, and insufficient reporting of preprocessing and normalization practices. Full article

Digital technologies that continuously quantify animal behavior, physiology, and production offer significant potential for the early identification of health and welfare disorders of dairy cows. In this study, a multimodal artificial intelligence (AI) framework is proposed for real-time health monitoring of dairy cows through the integration of physiological, behavioral, production, and thermal imaging data, targeting veterinarian-confirmed udder, leg, and hoof infections. Predictions are generated at the cow-day level by aggregating multimodal measurements collected during daily milking events. The dataset comprised 88 lactating cows, including veterinarian-confirmed udder, leg, and hoof infections grouped under a single ‘sick’ label. To prevent information leakage, model evaluation was performed using a cow-level data split, ensuring that data from the same animal did not appear in both training and testing sets. The system is designed to detect early deviations from normal health trajectories prior to the appearance of overt clinical symptoms. All measurements, with the exception of the intra-ruminal bolus sensor, were obtained non-invasively within a commercial dairy farm equipped with automated milking and monitoring infrastructure. A key novelty of this work is the simultaneous integration of data from three independent sources: an automated milking system, a thermal imaging camera, and an intra-ruminal bolus sensor. A hybrid deep learning architecture is introduced that combines the core components of established models, including U-Net, O-Net, and ResNet, to exploit their complementary strengths for the analysis of dairy cow health states. The proposed multimodal approach achieved an overall accuracy of 91.62% and an AUC of 0.94 and improved classification performance by up to 3% compared with single-modality models, demonstrating enhanced robustness and sensitivity to early-stage disease. Full article

This study proposes a data-driven framework to predict the rock mass-specific fracture energy distributions using microstructural descriptors extracted from SEM-EDS automated characterization images. Ore textures were encoded through unsupervised k-means clustering to identify six representative mineralogical patterns. The resulting cluster proportions were then used as input features for supervised machine learning models, which seek to estimate the parameters of the log-normal distribution (median and standard deviation) adjusted to the experimental fracture energy data. Both models (XGBoost and decision tree regressor) were validated through Leave-One-Out cross-validation and showed high accuracy ($R^2$ of 0.80 and 0.91, respectively) and predict over 85% of the energy distributions matched the experimental ones according to Kolmogorov–Smirnov and Cramér–von Mises tests. The proposed method outperforms traditional empirical approaches by incorporating mineralogical variability and predicting the complete distribution of fracture behavior, representing a step toward more efficient, texture-aware comminution practices. Full article

This article presents a detailed study of the two-dimensional quaternion fractional Fourier transform (2D QFRFT) and investigates its role in the probabilistic analysis of quaternion-valued signals. The 2D formulation is constructed by applying fractional Fourier transforms independently along each spatial dimension, thereby extending classical 2D Fourier and fractional Fourier frameworks to the quaternion domain. Key analytical properties of the 2D QFRFT, including linearity, shift behavior, differentiation, convolution, and energy relations, are summarized based on existing results in the literature. Furthermore, the transform is employed to define and analyze fundamental probabilistic quantities, such as expected value and normalized probability distributions, within the 2D quaternion fractional transform domain. These results provide a systematic 2D extension of existing quaternion transform-based probabilistic models and offer a clear theoretical foundation for the representation and analysis of 2D quaternion-valued signals in non-commutative settings. Full article

Background/Objectives: Accurate preoperative TN staging is essential for guiding surgical and adjuvant treatment decisions in colorectal cancer (CRC), yet conventional imaging still faces limitations in reliably distinguishing early from advanced disease. This study aimed to evaluate whether CT-based radiomics combined with machine learning can noninvasively predict both tumor (T) and nodal (N) stages of CRC, and to identify which feature groups most contribute to each task. Methods: Fifty-three patients (55 tumors) with histologically confirmed CRC who underwent preoperative contrast-enhanced CT were retrospectively analyzed. A total of 107 radiomic features were extracted using PyRadiomics version 3.1.0, including shape, first-order, and texture features. Multiple preprocessing strategies—z-score normalization, PCA, and SMOTE—were tested across 11 machine learning classifiers. Results: For T staging, logistic regression using shape-based features achieved a mean sensitivity of 0.721, a specificity of 0.68, a balanced accuracy of 0.70, and an AUC of 0.751. For N staging, the AdaBoost model using texture-based features achieved a sensitivity of 0.742, a specificity of 0.622, a balanced accuracy of 0.682, and an AUC of 0.750. Shape features predominantly contributed to T prediction, while texture matrices drove N prediction, reflecting morphological and microstructural correlates of invasiveness and lymphatic dissemination. Conclusions: CT-based radiomics can quantitatively capture both morphological and textural patterns of tumor behavior, providing a noninvasive framework for preoperative TN staging in CRC. Full article

This study provides a detailed comparative analysis of a three-hybrid intrusion detection method aimed at strengthening network security through precise and adaptive threat identification. The proposed framework integrates an Autoencoder-Gaussian Mixture Model (AE-GMM) with two supervised learning techniques, XGBoost and Logistic Regression, combining deep feature extraction with interpretability and stable generalization. Although the downstream classifiers are trained in a supervised manner, the hybrid intrusion detection nature of the framework is preserved through unsupervised representation learning and probabilistic modeling in the AE-GMM stage. Two benchmark datasets were used for evaluation: NSL-KDD, representing traditional network behavior, and UNSW-NB15, reflecting modern and diverse traffic patterns. A consistent preprocessing pipeline was applied, including normalization, feature selection, and dimensionality reduction, to ensure fair comparison and efficient training. The experimental findings show that hybridizing deep learning with gradient-boosted and linear classifiers markedly enhances detection performance and resilience. The AE–GMM-XGBoost model achieved superior outcomes, reaching an F1-score above 0.94 ± 0.0021 and an AUC greater than 0.97 on both datasets, demonstrating high accuracy in distinguishing legitimate and malicious traffic. AE-GMM-Logistic Regression also achieved strong and balanced performance, recording an F1-score exceeding 0.91 ± 0.0020 with stable generalization across test conditions. Conversely, the standalone AE-GMM effectively captured deep latent patterns but exhibited lower recall, indicating limited sensitivity to subtle or emerging attacks. These results collectively confirm that integrating autoencoder-based representation learning with advanced supervised models significantly improves intrusion detection in complex network settings. The proposed framework therefore provides a solid and extensible basis for future research in explainable and federated intrusion detection, supporting the development of adaptive and proactive cybersecurity defenses. Full article

Ceramsite foam concrete (CFC), recognized for its lightweight, thermal insulation, and eco-friendly properties, is a promising material for composite structures. The interfacial shear bond behavior between CFC and normal concrete (NC) critically governs the structural integrity of CFC-NC systems. This study investigates the interfacial shear bond strength through direct double shear tests on twelve cubic specimens with controlled interface roughness and casting intervals. Quantitative analysis reveals that increased roughness enhances shear strength by up to 28.6~59.5%, while prolonged casting intervals reduce strength by 22.3~34.6%. Notably, excessive roughness shifts failure modes from interfacial debonding to material failure within CFC, where shear bond strength becomes governed by CFC’s compressive strength. A rigid–plastic model is developed to characterize the shear bond behavior of CFC-NC interface and demonstrates 96% accuracy in predicting experimental results. The findings provide useful insights for improving CFC-NC composite design in engineering applications. Full article

This study proposes a multi-level performance margin modeling and belief reliability framework for redundant systems. Starting from system performance, a “performance–margin–reliability” linkage is established by defining the performance and margin of multi-level redundant systems and deriving performance, margin, and metric equations that account for failures. For complex redundant systems, a hierarchical Behavior Interaction Priority (BIP) modeling approach is developed to explicitly represent the normal and failure states of atomic component models. The effects of redundant components on the overall system are transformed into variations of performance parameters, enabling quantitative analysis of redundancy mechanisms. This paper proposes a boundary search algorithm for pruning optimization, which breaks through the computational bottleneck of non-analytic threshold sets in high-dimensional topological spaces. A case study on a power supply system with multi-level structural redundancy is conducted. Based on the proposed method, a performance-margin model of the redundant power supply system is constructed, critical states are analyzed, and system reliability is calculated. The results verify the effectiveness of the proposed margin-equation formulation and solution algorithm, offering practical guidance for reliability design of redundant systems. Full article

The treatment of depression is an uphill battle due to the low efficiency and delayed clinical response of antidepressants and the fact that most of them cause numerous side effects. Psychobiotics, probiotics that affect brain function and confer mental health benefits, emerged as a promising ally showing protective effects against depressive- and anxiety-like behaviors in various animal models of depression. There is rapidly accumulating evidence that psychobiotics show protective effects at the molecular level as well, affecting several pathophysiological processes implicated in depression. This narrative review summarizes preclinical insights into molecular changes related to the hypothalamic-pituitary-adrenal (HPA) axis, peripheral inflammation, neuroinflammation, neurotransmission and tryptophan metabolism underlying psychobiotic-driven mitigation of depressive and anxiety symptoms in stress-based, corticosterone-induced and inflammation-induced animal models of depression. Research evidence indicates that psychobiotics normalize the activity of the HPA axis, decrease levels of inflammatory mediators in the intestine, circulation, and brain, normalize the levels of neurotransmitters and their receptors, and regulate tryptophan metabolism in various animal models of depression. The main setbacks in this field are the extensive diversity of studied probiotic strains, which are often insufficiently characterized, and the lack of mechanistic studies in animal models. However, despite these challenges, further study of psychobiotics in the pursuit of supportive therapies for depressive disorders is firmly grounded. Full article

This study introduces an integrated Life Cycle Assessment–Multi-Criteria Decision Analysis–Nash Equilibrium (LCA–MCDA–NE) framework to assess the feasibility of hydrogen energy storage (HES) in Philippine island grids. It starts with a cradle-to-gate LCA of hydrogen production across various electricity mix scenarios, from diesel-dominated Small Power Utilities Group (SPUG) systems to high-renewable configurations, quantifying greenhouse gas emissions. These impacts are normalized and integrated into an MCDA framework that considers four stakeholder perspectives: Regulatory (PRF), Developer (DF), Scientific (SF), and Local Social (LSF). Attribute utilities for Maintainability, Energy Efficiency, Geographic–Climatic Suitability, and Regulatory Compliance inform a 2 × 2 strategic game where net utility gain (Δ) and switching costs (C₁, C₂) influence adoption behavior. The findings indicate that the baseline Nash Equilibrium favors non-adoption due to limited utility gains and high switching barriers. However, enhancements in Maintainability and reduced costs can shift this equilibrium toward adoption. The LCA results show that meaningful decarbonization occurs only when low-carbon generation exceeds 60% of the electricity mix. This integrated framework highlights that successful HES deployment in remote grids relies on stakeholder coordination, reduced risks, and access to low-carbon electricity, offering a replicable model for emerging economies. Full article

Background: Childhood obesity represents the most common nutritional and metabolic disorder in industrialized countries and constitutes a major public health concern. In Italy, 20–25% of school-aged children are overweight and 10–14% are obese, with marked regional variability. Excess adiposity in childhood is frequently associated with hypertension, dyslipidemia, insulin resistance, and non-alcoholic fatty liver disease (NAFLD), predisposing to future cardiovascular disease (CVD). Objective: To investigate anthropometric indicators of cardiometabolic risk in 810 children and adolescents aged 7–17 years who underwent assessment for competitive sports eligibility at the Sports Medicine Unit of Modena, evaluate baseline knowledge of cardiovascular health aligned with ESC, AAP (2023), and EASO guidelines. Methods: 810 children and adolescents aged 7–17 years undergoing competitive sports eligibility assessment at the Sports Medicine Unit of Modena underwent evaluation of BMI percentile, waist circumference (WC), waist-to-height ratio (WHtR), and blood pressure. Cardiovascular knowledge and lifestyle habits were assessed via a previously used questionnaire. Anthropometric parameters, blood pressure (BP), and lifestyle-related knowledge and behaviors were assessed using standardized procedures. Overweight and obesity were defined according to WHO BMI-for-age percentiles. Elevated BP was classified based on the 2017 American Academy of Pediatrics age-, sex-, and height-specific percentiles. Statistical analyses included descriptive statistics, group comparisons, chi-square tests with effect size estimation, correlation analyses, and multivariable logistic regression models. Results: Overall, 22% of participants were overweight and 14% obese. WHtR > 0.5 was observed in 28% of the sample and was more frequent among overweight/obese children (p < 0.001). Elevated BP was detected in 12% of participants with available measurements (n = 769) and was significantly associated with excess adiposity (χ² = 7.21, p < 0.01; Cramér’s V = 0.27). In multivariable logistic regression analyses adjusted for age and sex, WHtR > 0.5 (OR 2.14, 95% CI 1.32–3.47, p = 0.002) and higher sedentary time (OR 1.41 per additional daily hour, 95% CI 1.10–1.82, p = 0.006) were independently associated with elevated BP, whereas BMI percentile lost significance when WHtR was included in the model. Lifestyle knowledge scores were significantly lower among overweight and obese participants compared with normal-weight peers (p < 0.01). Conclusions: WHtR is a sensitive early marker of cardiometabolic risk, often identifying at-risk children missed by BMI alone. Baseline cardiovascular knowledge was suboptimal. The observed gaps in cardiovascular knowledge underscore the importance of integrating anthropometric screening with structured educational interventions to promote healthy lifestyles and long-term cardiovascular prevention. Full article

Prolonged violation behavior is characterized by sustained temporal presence, slow action changes, and similarity to normal behavior. Due to the complex construction environment, intelligent recognition algorithms face significant challenges. This paper proposes an improved YOLOv8-based model, DGEA-YOLOv8, to address these issues, using “playing with mobile phones” as a case study. The model integrates the DCNv3 module in the backbone to enhance behavior deformation adaptability and the GELAN module to improve lightweight performance and global perception in resource-limited environments. An ECA attention mechanism is added to enhance small target detection, while the ASPP module boosts multi-scale perception. ByteTrack is incorporated for continuous tracking of prolonged violation behavior in construction scenarios. Experimental results show that DGEA-YOLOv8 achieves 94.5% mAP50, a 2.95% improvement over the YOLOv8s baseline, with better data capture rates and lower ID change rates compared to algorithms like Deepsort and Strongsort. A construction-specific dataset of over 3000 images verifies the model’s effectiveness. From the perspective of data symmetry, the proposed model demonstrates strong capability in addressing asymmetric feature distributions and behavioral imbalance inherent in prolonged violations, restoring spatiotemporal consistency in detection. In conclusion, DGEA-YOLOv8 provides a precise, efficient, and adaptive solution for recognizing prolonged violation behaviors in construction sites. Full article