Search Results (2,024)

The Gonghe–Yushu Expressway (GYE) traverses the degrading permafrost region of the Qinghai–Xizang Plateau, where climate warming has resulted in widespread water ponding, posing significant engineering challenges. However, the spatiotemporal dynamics of this water accumulation and its impacts on permafrost embankment stability remain inadequately understood. This study integrates high-resolution unmanned aerial vehicle (UAV) remote sensing with ground-penetrating radar (GPR) to characterize the spatial patterns of water ponding and to quantify the spatial distribution, seasonal dynamics, and hydrothermal effects of roadside water on permafrost sections of the GYE. UAV-derived point cloud models, optical 3D models, and thermal infrared imagery reveal that approximately one-third of the 228 km study section of GYE exhibits water accumulation, predominantly occurring near the embankment toe in flat terrain or poorly drained areas. Seasonal monitoring showed a nearly 90% reduction in waterlogged areas from summer to winter, closely corresponding to climatic variations. Statistical analysis demonstrated significantly higher embankment distress rates in waterlogged areas (14.3%) compared to non-waterlogged areas (5.7%), indicating a strong correlation between surface water and accelerated permafrost degradation. Thermal analysis confirmed that waterlogged zones act as persistent heat sources, intensifying permafrost thaw and consequent embankment instability. GPR surveys identified notable subsurface disturbances beneath waterlogged sections, including a significant lowering of the permafrost table under the embankment and evidence of soil loosening due to hydrothermal erosion. These findings provide valuable insights into the spatiotemporal evolution of water accumulation along transportation corridors and inform the development of climate-adaptive strategies to mitigate water-induced risks in degrading permafrost regions. Full article

Electrical resistivity tomography (ERT) is one of the most commonly used geophysical methods for imaging the distribution of electrical resistivity in the subsurface. It is often employed to characterise heterogeneity in karst regions and locate cavities and conduits below the surface. The resistivity contrast between the host rock and the cavity depends on the material filling the cavity. Air has a high electrical resistivity and should therefore produce strong reflections and refractions off cavity walls. However, cavities are not always easily detectable. A decrease in resistivity contrast at the interface between rock and air may result from different physical conditions relating to pore saturation, fracturing and stress near the cavity walls. Our first goal is to understand how extensive fracturing and hydrogeological conditions in the first subsurface layers can affect electric current flow in the presence of a karst tunnel. We use the commercial Res2Dmod software 3.0 to simulate an ERT on several ground models. The results, which are based on hydrogeological models, are presented for several conditions of a karst conduit: empty; full of water within a homogeneous background; and below the groundwater level in the presence of extensive fractures in the shallow layer above it. Full article

Understanding spatial variability in crop yields across fields is critical for developing precision agricultural strategies that optimize productivity while reducing negative environmental impacts. This variability often arises from a complex interplay of topographic features, soil characteristics, and hydrological conditions. This study investigates the influence of hydro-topographic factors on corn and soybean yield variability from 2016 to 2023 at the well-managed experimental sites in Beltsville, Maryland. A high-resolution surface digital elevation model (DEM) and subsurface DEM derived from ground-penetrating radar (GPR) were used to quantify topographic factors (elevation, slope, and aspect) and hydrological factors (surface flow accumulation, depth from the surface to the subsurface-restricting layer, and distance from each crop pixel to the nearest subsurface flow pathway). Topographic variables alone explained yield variation, with a relative root mean square error (RRMSE) of 23.7% (r² = 0.38). Adding hydrological variables reduced the error to 15.3% (r² = 0.73), and further combining with remote sensing data improved the explanatory power to an RRMSE of 10.0% (r² = 0.87). Notably, even without subsurface data, incorporating surface-derived flow accumulation reduced the RRMSE to 18.4% (r² = 0.62), which is especially important for large-scale cropland applications where subsurface data are often unavailable. Annual spatial yield variation maps were generated using hydro-topographic variables, enabling the identification of long-term persistent yield regions (LTRs), which served as stable references to reduce spatial anomalies and enhance model robustness. In addition, by combining remote sensing data with interannual meteorological variables, prediction models were evaluated with and without hydro-topographic inputs. The inclusion of hydro-topographic variables improved spatial characterization and enhanced prediction accuracy, reducing error by an average of 4.5% across multiple model combinations. These findings highlight the critical role of hydro-topography in explaining spatial yield variation for corn and soybean and support the development of precise, site-specific management strategies to enhance productivity and resource efficiency. Full article

To address the challenges of complex harmonic characteristics, multi-source coupling, and strong time variability in aggregated loads downstream of high-voltage substations, this paper proposes an Adaptive Multi-Task Gaussian Process Regression (AMT-GPR) method for harmonic modeling. First, field measurements from the medium-voltage side of a 500 kV substation are denoised and analyzed using Fourier transform to reveal the dynamic patterns and interdependencies of harmonic current magnitudes. Then, a multi-task GPR framework is constructed, incorporating task correlation modeling and adaptive kernel functions to capture inter-task coupling and differences in feature scales. Finally, a probabilistic harmonic model is developed based on multiple sets of measured data, and the modeling performance of AMT-GPR is compared with single-task GPR, conventional MT-GPR, and mainstream machine learning approaches including RBF, LS-SVM, and LSTM. Simulation results demonstrate that traditional harmonic modeling methods are insufficient to capture the dynamic behavior and uncertainty of aggregated loads and AMT-GPR maintains strong robustness under small-sample conditions, significantly reduces prediction errors, and yields narrower uncertainty intervals, outperforming the baseline models. These findings validate the effectiveness of the proposed method in modeling harmonics of aggregated loads in high-voltage substations and provide theoretical support for subsequent harmonic assessment and mitigation strategies. Full article

Historic masonry churches are highly vulnerable to structural degradation and seismic hazards due to their geometric complexity, material ageing, and lack of detailed construction records. The Church of Santos Juanes in Valencia, a monument of exceptional historical and architectural value, presents these challenges, intensified by centuries of transformations and partial loss of documentation. In this study, we develop a comprehensive methodology that integrates historical research, non-destructive testing (3D laser scanning with Leica Geosystems Cyclone v9.1.1; infrared thermography, commercial software; ground-penetrating radar with gprMax 2016 and GPR-SLICE v7.MT), and advanced finite element modelling (Angle v1). The integrated survey data enabled the creation of an accurate 3D geometric model, the detection of hidden construction elements, and the characterisation of subsoil stratigraphy. Structural simulations under static and seismic loading—considering soil–structure interaction—revealed the high global stiffness of the complex, the influence of the Baroque vault on load distribution, and localised vulnerabilities, particularly in the San Juan ‘O’ façade, which coincide with existing cracks confirmed by thermography. This methodological framework not only advances the diagnosis and conservation of Santos Juanes but also provides a replicable model for assessing and safeguarding other heritage buildings with similar typological and structural challenges. Full article

Inter-individual variability in response to androgenetic alopecia (AGA) therapies remains a therapeutic challenge. This study evaluated the clinical and mechanistic utility of a 26-SNP pharmacogenetic panel in guiding treatment decisions. By using a database containing data from 252 individuals stratified by genotype, overall response rates were high (85.6–91.0%), exceeding published benchmarks for minoxidil, finasteride, and dutasteride. SNP association analysis identified rs1042028 in SULT1A1 as a robust predictor of poor response across all three drugs (minoxidil: p = 2.4 × 10⁻⁸, OR = 0.09; dutasteride: p = 0.023, OR = 0.21; finasteride: p = 0.025, OR = 0.11). For dutasteride, the TT genotype of rs39848 in SRD5A1 was also associated with reduced efficacy (p = 0.018, OR = 0.02). SNP–SNP interaction analysis revealed significant epistatic effects between genes involved in prostaglandin signalling and oxidative stress response, including PTGFR × MUC1 (p = 5.38 × 10⁻⁶) and GPR44 × FUT2 (p = 9.4 × 10⁻⁵). Network enrichment analyses further supported drug-specific mechanistic clusters. Importantly, no statistically significant differences in response were observed between pharmacogenetically guided treatment groups (p > 0.1), suggesting successful genotype-based alignment. Together, these findings demonstrate that SNP-informed therapy can enhance efficacy, clarify drug mechanisms, and provide a foundation for precision treatment in AGA. Full article

The present study investigates the tensile properties of 3D-printed PA12 composites reinforced with short carbon fibers, focusing on the impact of printing parameters on material performance. We employed both experimental testing and machine learning-based predictive modeling to evaluate the influence of layer thickness, extrusion width, and raster angles on failure stress, failure strain, and stress–strain curves. Four machine learning models, including Gaussian process regression (GPR), gradient boosting regression (GBR), random forest (RF), and artificial neural network (ANN), were developed and trained on the experimental data. The results indicated that ANN and GPR models outperformed RF and GBR in predicting mechanical properties, with ANN demonstrating the highest accuracy across all tasks. A SHAP analysis was conducted to interpret the models, revealing that raster angles significantly influence failure stress predictions, while extrusion width predominantly affects failure strain predictions. The ability of the models to predict entire stress–strain curves provides a comprehensive understanding of the material’s mechanical behavior, which is crucial for applications requiring detailed material response data. This study highlights the potential of machine learning models, particularly ANN, in predicting the tensile properties of 3D-printed composites. The findings offer valuable insights for optimizing the 3D printing process to achieve desired material characteristics and pave the way for further research in integrating these predictive tools into additive manufacturing workflows for real-time optimization and quality control. Full article

High-performance concrete (HPC) is an essential construction material used for modern buildings and infrastructure assets, recognized for its exceptional strength, durability, and performance under harsh situations. Nonetheless, the HPC production process frequently correlates with elevated carbon emissions, principally attributable to the high quantity of cement utilized, which significantly influences its carbon footprint. In this study, data-driven modeling and optimization strategies are employed to minimize the carbon footprint of high-performance concretes while keeping their performance properties. Starting from an experimental dataset, artificial neural networks (ANNs), ensemble techniques (ETs), and Gaussian process regression (GPR) are employed to yield predictive models for compressive strength of HPC mixes. The model’s input variables are the various components of HPC: cement, water, superplasticizer, fly ash, blast furnace slag, and coarse and fine aggregates. Models are trained using a dataset of 356 records. Results proved that the GPR-based model exhibits excellent accuracy with a determination coefficient of 0.90. The prediction model is used in a double objective optimization task formulated to identify mix configurations that allow for high mechanical performance aligned with a reduced carbon emission. The multi-objective optimization task is undertaken using genetic algorithms (GAs). Promising results are obtained when the machine learning prediction model is associated with GA optimization to identify strong yet sustainable mix configurations. Full article

Accurate detection of reinforcing bars (rebars) in concrete structures using ground penetrating radar (GPR) is crucial for effective structural evaluation but remains challenging, particularly when asphalt overlays compromise signal clarity. This study evaluates the performance of deep learning-based rebar detection using the You Only Look Once version 8 (YOLOv8) object detection model across three GPR datasets categorized as clear, interfering, and blurry. Models trained on each category were applied across varying conditions to assess generalization and robustness. A filtering algorithm was introduced to eliminate redundant and overlapping detections, thereby significantly improving the accuracy of YOLOv8-based predictions. The YOLOv8 approach outperforms traditional analytical techniques, especially under noisy or complex scenarios. In blurry GPR images where analytical methods fail, the filtered YOLOv8 model accurately detects rebar with a count that closely matches the ground truth. Across different datasets, the YOLOv8 approach demonstrates improved consistency in both location and quantity estimation, with filtered predictions correcting substantial over-detection seen in raw outputs. The study presents a practical framework for applying deep learning to GPR data, enhancing the reliability of rebar detection under diverse field testing and evaluation conditions. The findings highlight the importance of developing tailored training datasets and post-processing strategies when deploying AI tools for in-service bridge inspections. Full article

This article presents a preliminary study on the potential application of artificial intelligence methods for assessing the durability of HTS tapes in superconducting fault current limiters (SFCLs). Despite their importance for the selectivity and reliability of power networks, these devices remain at the prototype testing stage, and the phenomena occurring in HTS tapes during their operation—particularly the degradation of tapes due to cyclic transitions into the resistive state—are difficult to model owing to their highly non-linear and dynamic nature. A concept of an engineering decision support system (EDSS) has been proposed, which, based on macroscopically measurable parameters (dissipated energy and the number of transitions), aims to enable the prediction of tape parameter degradation. Within the scope of the study, five approaches were tested and compared: Gaussian process regression (GPR) with various kernel functions, k-nearest neighbours (k-NN) regression, the random forest (RF) algorithm, piecewise cubic hermite interpolating polynomial (PCHIP) interpolation, and polynomial approximation. All models were trained on a limited set of experimental data. Despite the quantitative limitations and simplicity of the adopted methods, the results indicate that even simple GPR models can support the detection of HTS tape degradation in scenarios where direct measurement of the critical current is not feasible. This work constitutes a first step towards the construction of a complete EDSS and outlines directions for further research, including the need to expand the dataset, improve validation, analyse uncertainty, and incorporate physical constraints into the models. Full article

Ground penetrating radar (GPR) is a non-destructive testing (NDT) method increasingly used for evaluating concrete structures by identifying internal flaws and embedded objects. This study presents a structured image-based methodology for interpreting GPR B-scan data using a practical flowchart designed to aid in distinguishing common subsurface anomalies. The methodology was validated through a laboratory experiment involving four concrete slabs embedded with simulated defects, including corroded rebar, hollow pipes, polystyrene sheets (to represent delamination), and hollow containers (to represent voids). Scans were performed using a commercially available device, and the resulting radargrams were analyzed based on signal reflection patterns. The proposed approach successfully identified rebar positions, spacing, and depths, as well as low-dielectric anomalies such as voids and polystyrene inclusions. Some limitations were noted in detecting non-metallic materials with weak dielectric contrast, such as hollow pipes. Overall, the findings demonstrate the reliability and adaptability of the proposed method in improving the interpretation of GPR data for structural diagnostics. The proposed methodology achieved a detection accuracy of approximately 90% across all embedded features, which demonstrates improved interpretability compared to traditional manual GPR assessments, typically ranging between 70 and 80% in similar laboratory conditions. Full article

Background: Short-chain fatty acids (SCFAs) are produced by the colon microbiome and bind to specific G-protein coupled receptors GPR 41 and GPR 43. Leptin and glucagon-like peptide 1 (GLP-1) are produced mainly in the intestinal lumen as a result of SCFAs binding to their receptors at this level. Inflammatory bowel diseases (IBD) such as Crohn’s disease (CD) and ulcerative colitis (UC), and their major complication, colorectal cancer (CRC), can disturb the dynamics of the colonic microenvironment thus influencing SCFAs production and effects. Our study aimed to investigate serum levels of SCFAs and SCFAs-mediated production of circulating leptin, GLP-1, and Nesfatin-1 in patients with IBD and CRC. Methods: A total of 88 subjects (29 with CRC, 29 with IBD, and 30 controls) were included in this pilot study. Serum SCFAs, leptin, Nesfatin-1, and GLP-1 levels were analyzed. Results: Nesfatin-1 levels were significantly higher in CRC patients (p < 0.05) compared to IBD and controls. Leptin levels were positively correlated with Nesfatin-1 levels in CRC, IBD, and control groups (CRC: R² = 0.6585, p < 0.01; IBD: R² = 0.2984, p < 0.01; Control: R² = 0.2087, p < 0.05). Serum SCFAs levels were negatively correlated with GLP-1 levels in CRC and IBD (CRC: R² = 0.3324, p < 0.01; IBD: R² = 0.1756, p < 0.05) and negatively correlated with Nesfatin-1 levels in CRC (R² = 0.2375, p < 0.05). Conclusions: These findings suggest that alterations in gut microenvironment may influence systemic metabolic regulators involved in appetite control and inflammation, potentially influencing IBD and CRC pathogenesis. This is the first study to evaluate the relationships between Nesfatin-1, leptin, GLP-1, and SCFAs in CRC and IBD patients; further research is needed to clarify their mechanistic links and therapeutic potential. Full article

Obesity is an epidemic that currently impacts many nations. The persistence of this disease is shaped by both genetic and epigenetic factors that extend beyond calorie balance. Research in the past year has revealed that epigenetic and cellular memory within adipose tissue can predispose individuals to weight regain after initial fat loss, as shown by studies indicating persistent transcriptional and chromatin changes even after fat mass reduction. Independent studies also demonstrate long-lasting metabolic shifts, such as those triggered by glucose-dependent insulinotropic polypeptide receptor (GIPR)-induced thermogenesis and sarcolipin (SLN) stabilization that also support a form of “metabolic memory” that is associated with sustained weight loss. At the neural level, rare variants in synaptic genes like BSN (Bassoon presynaptic cytomatrix protein), a presynaptic scaffold protein, and APBA1 (amyloid beta precursor protein binding family A member 1), a neuronal adaptor involved in vesicular trafficking, disrupt communication in feeding circuits, elevating obesity risk and illustrating how synaptic integrity influences food intake regulation. Similarly, the spatial compartmentalization of metabolic signaling within neuronal cilia is emerging as crucial, with cilia-localized receptors G protein-coupled receptor 75 (GPR75) and G protein-coupled receptor 45 (GPR45) exerting opposing effects on energy balance and satiety. Meanwhile, genome-wide association studies (GWAS) have advanced through larger, more diverse cohorts and better integration of environmental and biological data. These studies have identified novel obesity-related loci and demonstrated the value of polygenic risk scores (PRS) in predicting treatment responses. For example, genetic variants in GLP-1R (glucagon-like peptide-1 receptor) and GIPR (glucose-dependent insulinotropic polypeptide receptor) may modulate the effectiveness of incretin-based therapies, while PRS for satiation can help match individuals to the most appropriate anti-obesity medications. This review focuses on studies in the last two years that highlight how advances in obesity genetics are driving a shift toward more personalized and mechanism-based treatment strategies. Full article

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article