Search Results (2,051)

Accurate estimation of State of Health (SOH) is pivotal for managing the lifecycle of lithium-ion batteries (LIBs) and ensuring safe and reliable operation in electric vehicles (EVs) and energy storage systems. While feature fusion methods show promise for battery health assessment, they often suffer from suboptimal integration strategies and limited utilization of complementary health indicators (HIs). In this study, we propose a Feature Accretion Method (FAM) that systematically integrates four carefully selected health indicators–voltage profiles, incremental capacity (IC), and polynomial coefficients derived from IC–voltage and capacity–voltage curves—via a progressive three-phase pipeline. Unlike single-indicator baselines or naïve feature concatenation methods, FAM couples’ progressive accretion with tuned ensemble learners to maximize predictive fidelity. Comprehensive validation using Gaussian Process Regression (GPR) and Random Forest (RF) on the CALCE and Oxford datasets yields state-of-the-art accuracy: on CALCE, RMSE = 0.09%, MAE = 0.07%, and R² = 0.9999; on Oxford, RMSE = 0.33%, MAE = 0.24%, and R² = 0.9962. These results represent significant improvements over existing feature fusion approaches, with up to 87% reduction in RMSE compared to state-of-the-art methods. These results indicate a practical pathway to deployable SOH estimation in battery management systems (BMS) for EV and energy storage applications. Full article

School buildings in hot-humid climates encounter considerable difficulties in balancing energy use and thermal comfort due to this environment, necessitating optimized design strategies to reduce energy consumption while enhancing occupant comfort. This study presents sustainable design strategies for educational structures in hot-humid regions, aiming to optimize energy efficiency and thermal comfort for environmental preservation and occupant welfare. The present work introduces a multi-objective optimization framework for window design in school buildings situated in hot-humid climates, targeting a balance between Energy Use Intensity (EUI) and Thermal Comfort Time Ratio (TCTR). Exploring multi-objective optimization through NSGA-II genetic algorithms, the study conducts Sobol sensitivity analysis for parameter assessment and applies Gaussian Process Regression (GPR) for effective model validation, identifying optimal window configurations that reduce energy consumption while enhancing thermal comfort. It finds that the Window-to-Wall Ratio (WWR) and Solar Heat Gain Coefficient (SHGC) are the most significant factors, with WWR and SHGC accounting for 28.1% and 23.7% of the variance in EUI and TCTR, respectively. The results reveal a non-linear trade-off between the objectives, with the Balanced Solution offering a practical compromise: a 6.7% decrease in energy use and a 14.3% enhancement in thermal comfort. The study examined various ranges of window parameters, including WWR (0.1–0.50), SC (0.20–0.80), K (1.0–2.5 W·m⁻²·K⁻¹), SHGC (0.1–0.4), Shading width (0.3–2.0 m), and Shading angle (0°–90°). The recommended compromise, known as the Balanced Solution, suggests optimal values as follows: WWR = 0.40, SC = 0.30, SHGC = 0.40, K = 1.2 W·m⁻²·K⁻¹, Shading width = 1.22 m, and Shading angle = 28°. The GPR model exhibited high predictive precision, with R² values of 0.91 for EUI and 0.95 for TCTR, underscoring the framework’s effectiveness. This research offers actionable insights for designing energy-efficient and comfortable school buildings in hot-humid climates, enriching sustainable architectural design knowledge. Full article

This study develops an innovative framework that utilizes real-time operational data to forecast electrical power output (EPO) in Combined Cycle Power Plants (CCPPs) by employing a temperature segmentation-based modeling approach. Instead of using a single general prediction model, which is commonly seen in the literature, three separate prediction models were created to explicitly capture the nonlinear effect of ambient temperature (AT) on efficiency (AT < 12 °C, 12 °C ≤ AT < 20 °C, AT ≥ 20 °C). Linear Ridge, Medium Tree, Rational Quadratic Gaussian Process Regression (GPR), Support Vector Machine (SVM) Kernel, and Neural Network methods were applied. In the modeling, the variables considered were AT, relative humidity (RH), atmospheric pressure (AP), and condenser vacuum (V). The highest performance was achieved with the Rational Quadratic GPR method. In this approach, the weighted average Mean Absolute Error (MAE) was found to be 2.225 with seasonal segmentation, while it was calculated as 2.417 in the non-segmented model. By applying seasonal prediction models, the hourly EPO prediction error was reduced by 192 kW, achieving a 99.77% average convergence of the predicted power output values to the actual values. This demonstrates the contribution of the proposed approach to enhancing operational efficiency. Full article

Excessive maternal fructose intake contributes to the developmental programming of hypertension in offspring, partly via gut microbiota dysbiosis and oxidative stress. Fecal microbiota transplantation (FMT) may restore microbial balance and modulate short-chain fatty acid (SCFA) production. We investigated whether maternal FMT from healthy donors could prevent hypertension in offspring exposed to a high-fructose (HF) diet. Pregnant Sprague Dawley rats (n = 12) were fed normal chow (ND) or a 60% HF diet from mating to delivery. Cross-FMT was performed: HF dams received FMT from ND donors, and ND dams received FMT from HF donors. Male offspring (n = 8/group) were assigned to ND, HF, ND + HF-FMT, or HF + ND-FMT groups. Offspring of HF dams developed higher systolic blood pressure (+13 mmHg vs. ND, p < 0.05). Maternal FMT from ND donors reduced this elevation by ~8 mmHg (p < 0.05). Protective effects were accompanied by higher plasma butyrate, increased expression of SCFA receptors (GPR41, GPR43), reduced renal oxidative stress markers (8-OHdG), and distinct gut microbiota profiles. Maternal FMT generated four enterotypes in offspring, each associated with differential blood pressure outcomes. These findings suggest that maternal microbiota-targeted interventions, such as FMT, can mitigate hypertension of developmental origin by restoring gut microbial and metabolic homeostasis. Full article

The gut microbiome is strongly implicated in the development of obesity and type 2 diabetes mellitus (T2DM). A recent study demonstrated that 6-week oral supplementation of Anaerostipes rhamnosivorans (ARHAM) combined with the prebiotic myo-inositol (MI) reduced fasting glucose levels in mice. In the present study, we investigated the effects of a 13-week ARHAM-MI supplementation in high-fat diet-fed mice and examined the metabolic fate of MI, including its microbial conversion into short-chain fatty acids (SCFAs), using ¹³C-MI and stable isotope tracers in the cecum, portal vein, and peripheral blood. The results showed that the ARHAM-MI group gained less weight than the MI-only and placebo groups. Analysis of intestinal mRNA and stable isotope tracing revealed that MI is primarily absorbed in the upper gastrointestinal tract, whereas microbial conversion to SCFAs predominantly occurs in the cecum and is enhanced by ARHAM. ARHAM-MI mice also showed increased cecal Gpr43 mRNA expression, indicating enhanced SCFA-mediated signaling. Notably, SCFAs derived from MI displayed distinct distribution patterns: ¹³C-butyrate was detected exclusively in the cecum, ¹³C-propionate was present in the cecum and portal vein, whereas ¹³C-acetate was the only SCFA detected in peripheral blood. Collectively, ARHAM-MI co-supplementation confers modest metabolic benefits in high-fat diet-fed mice, underscoring the need to optimize the dosage and administration frequency of ARHAM-MI to enhance its therapeutic efficacy. Full article

We present the results of a Ground-Penetrating Radar (GPR) survey conducted at the archaeological site of Bisya and Salūt (Sultanate of Oman), aimed at assessing archaeological risk associated with the planned infrastructural development of the site. The survey employed a dual-frequency GPR system with a survey rugged cart to adapt to the varying conditions of the area. The survey was designed around a scale-adaptive grid strategy, across three sectors, combining medium- and low-definition acquisitions over broader areas to identify zones with low archaeological potential, with a high-density grid near previously excavated structures. Data interpretation was integrated with Geographic Information System (GIS)-based spatial mapping, allowing the definition of a parametric risk indicator for subsurface archaeological potential derived from radar facies characterisation and point-by-point anomaly analysis along GPR profiles. Within the area of higher density, the method successfully mapped buried alignments suggestive of anthropogenic features. The results confirmed the effectiveness of GPR as a predictive tool for archaeological prospection, particularly when combined with spatial analysis. Overall, this study highlights the feasibility of incorporating non-invasive methods into heritage protection strategies, contributing to the sustainable development and planning of archaeological landscapes. Full article

Historical chemical production sites often harbor irregularly distributed solid waste landfills, posing significant environmental risks. Traditional drilling methods, while accurate, are inefficient for comprehensive characterization due to high costs and spatial limitations. This study aims to develop an integrated geophysical drilling approach to accurately delineate the spatial distribution and volume of landfilled solid waste (predominantly organic pollutants) at two decommissioned chemical plant sites (total area: 8954 m²). Methods: We combined (1) geophysical surveys (transient electromagnetic (TEM, 50 profiles, 2936 points), high-density resistivity (HDR, 2 profiles, 192 points), and ground-penetrating radar (GPR, 22 profiles, 1072.1 m)) and (2) systematic drilling verification (136 boreholes, ≤10 m × 10 m density). Anomalies were interpreted through integrating geophysical responses, historical records, and borehole validation. Spatial modeling was conducted using Kriging interpolation in EVS software. The results show that (1) the anomalies exhibited a “sparse multi-point distribution” across zones A2 (primary waste concentration), A4, and A6, which were differentiated into solid waste, foundations, contaminated soil, voids, and cracks; (2) drilling confirmed solid waste at nine locations (A2: “multi-point, small-quantity” residues; A6: contaminated clay layers with garbage) with irregular thicknesses (0.2–1.3 m); (3) TEM identified diagnostic medium–high-resistivity anomalies (e.g., 28–37 m in A4L3), while GPR detected 17 shallow anomalies (only one validated as waste); and (4) the total waste volume was quantified as 266.9 m³. The methodology reduced the field effort by ∼35% versus drilling-only approaches, resolved geophysical limitations (e.g., HDR’s volume effect overestimating the thickness), and provided a validated framework for efficient characterization of complex historical landfills. Full article

Hyperbolic reflection features from ground-penetrating radar (GPR) data have been recognized as essential indicators for detecting hidden defects in the asphalt pavement. Computer vision and deep learning algorithms have been developed to detect and enhance the hyperbolic features of hidden defects. However, migrating existing hyperbolic feature detection methods using raw GPR data results in inaccurate predictions. Pre-processing raw GPR data using straightforward image processing methods could enhance the reflection features for fast and accurate hyperbolic detection during real-time GPR measurements. This study proposed accessible and straightforward image processing methods as GPR data preprocessing steps (such as the Sobel edge detector and histogram equalization) to assist existing computer vision algorithms for reflection feature enhancement during the GPR survey. Field tests were conducted, and several image processing methods with existing standard image processing libraries were applied. The proposed regions of the identified hyperbola signal-to-noise ratio (RIHSNR) were used to quantify the enhancement performance of hyperbolic feature detectability. Applying Sobel edge detection and Otsu’s thresholding to GPR data significantly improves detection accuracy and speed: mAP@0.5 rises from 0.65 to 0.85 for Faster R-CNN and from 0.72 to 0.88 for CBAM-YOLOv8 using the proposed computer vision methods as preprocessing steps. At the same time, inference time drops to 30 ms and 25 ms, respectively. Full article

Carcinogenesis is a complex process characterized by the uncontrolled proliferation of abnormal cells, influenced by environmental, genetic, and epigenetic factors. Chronic inflammation is undoubtedly one of the key contributors to carcinogenesis. Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) due to persistent inflammation resulting from continuous immune system activation and excessive immune cell recruitment. IBD is also linked to certain nutritional deficiencies, primarily due to dietary modifications necessitated by the disease’s pathophysiology. Consequently, individualized nutritional supplementation appears to be a rational approach to addressing these deficiencies. The use of functional foods, including anti-inflammatory nutraceuticals, in individuals with IBD may play a crucial role in modulating cellular pathways that inhibit the release of inflammatory mediators. Thus, the regulation of the aryl hydrocarbon receptor (AhR) and G protein-coupled receptor 35 (GPR35) through dietary ligands appears to be of significant importance not only in the treatment of IBD and maintenance of remission but also in the prevention of tumorigenic transformation, particularly in genetically predisposed individuals. This narrative review was conducted using PubMed, Scopus, and Web of Science databases. The search covered literature published between January 2000 and June 2024. Keywords included ‘inflammatory bowel disease’, ‘colorectal cancer’, ‘AhR’, ‘aryl hydrocarbon receptor’, ‘GPR35’, ‘cytochrome P450’, ‘nutraceuticals’, ‘probiotics’, and ‘superfoods’. Only English-language articles were included. The selection focused on studies investigating mechanistic pathways and the role of dietary ligands in AhR and GPR35 activation in IBD and CRC. The SANRA guidelines for narrative reviews were followed to ensure transparency and minimize bias. Full article

This study investigates the nonlinear and regime-dependent relationship between climate policy uncertainty (CPU) and sovereign credit default swap (CDS) spreads across a panel of developed and emerging economies from February 2010 to March 2025. Utilizing the Quantile-on-Quantile Regression (QQR) and Multivariate QQR (MQQR) frameworks, we capture the heterogeneous effects of CPU under varying market states and assess the marginal role of global risk factors, including geopolitical risk (GPR), economic policy uncertainty (EPU), and market volatility (VIX). The findings indicate that in developed markets, CPU exerts a nonlinear impact that intensifies during periods of heightened sovereign risk, while in low-risk regimes, its effect is often muted or reversed. In contrast, emerging economies exhibit more volatile and state-contingent responses, with CPU exerting stronger effects in calm conditions but diminishing in explanatory power once global risks are taken into account. These dynamics highlight the importance of institutional credibility and financial integration in moderating CPU-driven credit risk. Full article

Background: Understanding the genetic mechanisms and identifying potential therapeutic targets are essential for clarifying Autism Spectrum Disorder (ASD) etiology and improving treatments. This study aims to bridge the gap between basic transcriptomic discoveries and clinical applications in ASD research. Methods: Differentially expressed genes (DEGs) of GSE18123 datase were identified. A protein–protein interaction (PPI) network was constructed. Functional enrichment analysis was performed to link genetic loci to relevant biological pathways. Connectivity Map (CMap) analysis was used to predict potential drugs. Furthermore, immune infiltration correlation analysis explored associations between key genes and immune cell subpopulations. Diagnostic performance of top genes was evaluated by receiver operating characteristic (ROC) analysis. Results: The functional enrichment analysis successfully revealed relevant biological processes associated with ASD, while the CMap analysis predicted potential drugs that were consistent with some clinical trial results. Random forest analysis selected ten key feature genes (SHANK3, NLRP3, SERAC1, TUBB2A, MGAT4C, TFAP2A, EVC, GABRE, TRAK1, and GPR161) with the highest importance scores for autism prediction. Immune infiltration analysis showed significant correlations in genes and multiple immune cell types, demonstrating complex pleiotropic associations within the immune microenvironment. ROC curve analysis indicated that most top genes had strong discriminatory power in differentiating ASD from controls, particularly MGAT4C (AUC = 0.730), highlighting its potential as a robust biomarker. Conclusions: This study effectively bridges the basic transcriptomic discoveries and clinical applications in ASD research. The findings contribute to a better understanding of the etiology of ASD and provide potential therapeutic leads. Future research could focus on validating these potential drugs in clinical studies, as well as further exploring the biological functions of the identified genes to develop more targeted and effective treatments for ASD. Full article

Atopic dermatitis (AD) and autoimmune diseases exhibit epidemiological comorbidity, yet the shared genetic architecture remains incompletely understood. We investigated the genetic overlap between AD and three autoimmune disorders including inflammatory bowel disease (IBD), rheumatoid arthritis (RA), and vitiligo, leveraging genome-wide association data. Despite modest evidence for global genetic correlations, we found 113 independent pleiotropic loci shared among AD and autoimmune diseases, with 11 displaying a concordant effect across all 3 pairwise comparisons. Gene-set and tissue enrichment analyses evidenced the inflammatory background of pleiotropic associations. Multi-trait colocalization analysis prioritized 22 loci, linking the tissue-specific expression of DOK2, GPR132, RERE, RERE-AS1, SUOX, TNFRSF11A, and TRAF1 pleiotropic genes with AD risk. Mendelian randomization revealed no causal effect of genetic liability to AD on autoimmune diseases. Nevertheless, genetic liability to IBD increased AD risk, while vitiligo exhibited a protective effect post outlier correction. Our findings provide mechanistic insights into the multimorbidity of atopic dermatitis (AD) and autoimmune diseases, offering additional evidence for the pleiotropic genetic architecture of AD that contributes to systemic immune dysregulation across multiple organ systems. Full article

Ground-penetrating radar surveys on structures have a wide range of applications, and they are very useful in solving engineering problems: from detecting reinforcement, studying concrete characteristics, unfilled joints, analyzing brick elements, detecting water content in building bodies, and evaluating structural deformation. They generally pursued small investigation areas with measurements made in direct contact with target structures and for small depths. Detecting deep piles presents specific challenges, and surveys conducted from the ground level may be unsuccessful. To reach great depths, medium-low frequencies must be used, but this choice results in lower resolution. Furthermore, the pile signals may be masked when they are located beneath massive reinforced foundations, which act as an electromagnetic shield. Finally, GPR equipment looks for differences in the dielectric of the material, and the signals recorded by the GPR will be very weak when the differences in the physical properties of the investigated media are modest. From these weak signals, it is difficult to identify information on the differences in the subsurface media. In this paper, we are illustrating an exploration on plinth foundations, supported by drilled piles, submerged in soil, extensive, deep and uninformed. Pulse GPR prospecting was performed in common-offset and single-fold, bistatic configuration, exploiting the exposed faces of an excavation around the foundation. In addition, three velocity tests were conducted, including two in common mid-point and one in zero-offset transillumination, in order to explore the range of variation in relative dielectric permittivity in the investigated media. Thanks to the innovative survey on the excavation faces, it is possible to perform profiles perpendicular to the strike direction of the interface. The electromagnetic backscattering analysis approach allowed us to extract the weighted average frequency attribute section. In it, anomalies emerge in the presence of drilled piles with four piles with an estimated diameter of 80 cm. Full article

An accurate perception of upper-level wind fields is essential for improving civil aviation safety and route optimization. However, the sparsity of observational data and the structural complexity of wind fields make reconstruction highly challenging. To address this, we propose QuadMamba-WindNet (QMW-Net), a structure-enhanced deep neural network that integrates a hierarchical state-space modeling framework with a learnable quad-tree-based regional partitioning mechanism, enabling multi-scale adaptive encoding and efficient dynamic modeling. The model is trained end-to-end on ERA5 reanalysis data and validated with simulated flight trajectory observation masks, allowing the reconstruction of complete horizontal wind fields at target altitude levels. Experimental results show that QMW-Net achieves a mean absolute error (MAE) of 1.62 m/s and a mean relative error (MRE) of 6.68% for wind speed reconstruction at 300 hPa, with a mean directional error of 4.85° and an $R^2$ of 0.93, demonstrating high accuracy and stable error convergence. Compared with Physics-Informed Neural Networks (PINNs) and Gaussian Process Regression (GPR), QMW-Net delivers superior predictive performance and generalization across multiple test sets. The proposed model provides refined wind field support for civil aviation forecasting and trajectory planning, and shows potential for broader applications in high-dynamic flight environments and atmospheric sensing. Full article

Ground-penetrating radar (GPR) is an important geophysical technique in subsurface detection. However, traditional numerical simulation methods such as finite-difference time-domain (FDTD) face challenges in accurately simulating complex heterogeneous mediums in real-world scenarios due to the difficulty of obtaining precise medium distribution information and high computational costs. Meanwhile, deep learning methods require excessive prior information, which limits their application. To address these issues, this paper proposes a novel two-step forward modeling strategy for GPR data of metal pipes. The first step employs the proposed Polarization Self-Attention Image Translation network (PSA-ITnet) for image translation, which is inspired by the process where a neural network model “understands” image content and “rewrites” it according to specified rules. It converts scene layout images (cross-sectional schematics depicting geometric details such as the size and spatial distribution of underground buried metal pipes and their surrounding medium) into simulated clutter-free GPR B-scan images. By integrating the polarized self-attention (PSA) mechanism into the Unet generator, PSA-ITnet can capture long-range dependencies, enhancing its understanding of the longitudinal time-delay property in GPR B-scan images. which is crucial for accurately generating hyperbolic signatures of metal pipes in simulated data. The second step uses the Polarization Self-Attention Style Transfer network (PSA-STnet) for style transfer, which transforms the simulated clutter-free images into data matching the distribution and characteristics of a real-world underground heterogeneous medium under unsupervised conditions while retaining target information. This step bridges the gap between ideal simulations and actual GPR data. Simulation experiments confirm that PSA-ITnet outperforms traditional methods in image translation, and PSA-STnet shows superiority in style transfer. Real-world experiments in a complex bridge support structure scenario further verify the method’s practicability and robustness. Compared to FDTD, the proposed strategy is capable of generating GPR data matching real-world subsurface heterogeneous medium distributions from scene layout models, significantly reducing time costs and providing an efficient solution for GPR data simulation and analysis. Full article