Search Results (129)

Reinforced concrete structures, once considered very durable and capable of withstanding a variety of adverse environmental conditions, often suffer from premature reinforcement corrosion, compromising their safety and serviceability. Ensuring the safety of bridges and buildings requires effective, non-destructive inspection and monitoring techniques to assess the state of degradation without damaging the integrity of the asset. Although a wide range of non-destructive testing (NDT) methods is currently available, few are capable of identifying durability issues during the initial stages before the damage becomes critical. To address this gap, this paper describes an innovative laboratory experiment based on an integrated approach that combines Ground-Penetrating Radar (GPR) and electrochemical methods. This research represents an advanced step in our ongoing projects, merging geophysical and electrochemical expertise to enhance diagnostic precision. A reinforced cement mortar specimen was subjected to free corrosion via partial immersion in sodium chloride solutions of varying concentrations (1, 10, and 35 g/L), followed by an accelerated corrosion phase. The phenomenon was monitored simultaneously using GPR and electrochemical tests. Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. Specifically, the application of the Hilbert Transform to GPR signals allowed for a correlation between envelope amplitude variations and the electrochemical behavior of the rebars. These laboratory results highlighted that an integrated observation was useful to indirectly observe the evolution of the phenomenon of corrosion in the steel reinforcement embedded in the mortar specimens. Full article

Occupational noise-induced hearing loss (NIHL) is a common occupational disorder, yet non-invasive molecular indicators of chronic occupational noise exposure remain insufficiently characterized. Although the cochlear mechanisms behind NIHL have been extensively studied in experimental models, peripheral blood transcriptomic alterations in affected human populations are less well defined. In this exploratory study, we aimed to describe peripheral blood gene expression patterns associated with occupational NIHL and to generate candidate molecular signals for future validation. Peripheral blood RNA sequencing (RNA-seq) was performed in 11 male individuals with occupational bilateral sensorineural hearing loss and four noise-unexposed healthy male controls. Transcript abundance was quantified using a standardized RNA-seq workflow, and formal differential expression analysis was conducted on gene-level count data derived from Salmon quantification using DESeq2 with Benjamini–Hochberg correction. Through our analysis, we identified a limited set of differentially expressed genes, including upregulated interferon-associated transcripts, such as RSAD2, IFIT1, IFI44L, and CMPK2, host-defense-related genes, including DEFA1, DEFA3, and DEFA4, and immune-regulatory transcripts such as HLA-DRB1 and GPR15, together with downregulated non-coding RNAs including SNORD3A and SNORD3C. These findings suggest that occupational NIHL may be accompanied by detectable peripheral blood transcriptomic alterations, predominantly involving immune- and host-defense-related pathways. Given the limited cohort size and exploratory design, these genes represent preliminary candidates for validation in larger independent cohorts. Full article

This study proposes a stress field data-driven prediction method that combines a finite element thermo-mechanical coupling model with a multi-machine learning framework. This method takes the inversion of stress based on the shrinkage behavior of deposition layers as the core logic, extracts the node displacement shrinkage during the cooling to solidification process of the melt pool in the thermal coupling simulation as the key feature input, and constructs extreme gradient boosting (XGBoost), Gaussian process regression (GPR), and deep convolutional neural network (DCNN) models, respectively, to achieve accurate prediction of nodal effect stress and triaxial stress in the laser directed energy deposition (L-DED) node process. The experimental results show that the XGBoost algorithm performs the best in various stress prediction indicators, and its generated stress distribution cloud map is highly consistent with the thermal coupling simulation results, suggesting a strong correlation between deposition layer shrinkage behavior and the stress field under the investigated conditions. In addition, compared to traditional finite element simulations, this method significantly improves computational efficiency while ensuring prediction accuracy, providing a new approach for rapid assessment of residual stresses. Full article

Surface water systems are increasingly exposed to multiple pressures generated by climate variability, intensified water resource exploitation, and evolving geopolitical dynamics. This study provides a novel contribution by identifying critical threshold effects and non-linear interactions that influence nitrate concentrations through an integrated information systems framework. It develops an integrated information-system-based analytical framework that combines hydrological, climatic, geopolitical, and strategic indicators to shape the broader contextual framework within which hydrological and climatic pressures operate, rather than serving as direct predictors. Considering the nitrate concentration in rivers as a key parameter of water quality, the paper goes beyond univariate analysis of nitrite concentration, examining its relationship with four explanatory variables: the Water Exploitation Index Plus (WEI+), the number of heat stress days (Heat_Stress), the Geopolitical Risk Index (GPR), and a proxy variable representing the presence of strategic infrastructure (Nuclear_State) using a Reduced Error Pruning Tree (REPTree) decision tree algorithm with 10-fold cross-validation. The results indicate that climatic stress emerges as the primary predictor, with a critical threshold of approximately 7.83 heat stress days, beyond which nitrate concentrations increase significantly. Under conditions of high climatic stress and intensive water exploitation (WEI+ ≥ 67.39), predicted nitrate levels exceed 20 mg/L and can reach extreme values of up to 58.82 mg/L. In contrast, low hydrological pressure (WEI+ < 0.39) combined with moderate climatic stress is associated with very low nitrate concentrations, around 2.75 mg/L. The model demonstrates strong predictive performance, with a correlation coefficient of 0.976, a Mean Absolute Error (MAE) of 0.593, a Root Mean Squared Error (RMSE) of 2.046, and a Receiver Operating Characteristic (ROC) area exceeding 0.94 for classification tasks. While geopolitical and strategic variables do not act as direct predictors, they contribute to shaping the contextual framework influencing water resource management and environmental vulnerability. Overall, the study highlights the non-linear and systemic nature of water quality dynamics and demonstrates the effectiveness of decision tree-based models within integrated information systems for supporting environmental monitoring and decision-making under conditions of climate stress and geopolitical uncertainty. Full article

Kynurenic acid (KYNA), a metabolite of the L-kynurenine pathway of L-tryptophan degradation, is an endogenous blocker of glutamate ionotropic excitatory amino acid (EAA) receptors and nicotinic acetylcholine receptors (nAChRs). KYNA plays a significant role in various neuropsychiatric disorders and the aging process. Some researchers have suggested that KYNA may contribute to memory impairment. In this study, we examined the impact of L-kynurenine (a KYNA substrate) and the anti-dementia drugs D-cycloserine and Cerebrolysin on kynurenine aminotransferase (KAT) activity, an enzyme forming KYNA, in liver homogenates of Helix pomatia snails. Furthermore, a memory model was established using these snails, wherein tentacle shortening served as an indicator of learning activity. In vitro experiments on Helix pomatia demonstrated the significant impact of L-kynurenine and anti-dementia drugs on KYNA synthesis. KYNA levels increased significantly in the presence of L-kynurenine in liver homogenate. However, KYNA formation decreased when anti-dementia drugs, including Cerebrolysin or D-cycloserine, were administered to the snails’ liver homogenate. L-kynurenine has been shown to impair the learning process in vivo in snails, but an anti-dementia drug has been demonstrated to reverse this effect. Significant inhibition of tentacle lowering was observed in response to L-kynurenine treatment, which corresponded with elevated KYNA levels in the central nervous system. Administering D-cycloserine or Cerebrolysin alongside L-kynurenine reversed its effects. The Helix pomatia memory model is a valuable tool for studying learning and memory formation in various conditions and in the presence of different pharmacological agents. A drug or natural extract that blocks KYNA synthesis has the ability to increase tentacle lowering and could be considered an anti-dementia agent. Furthermore, this metabolite may also protect against aging and delay damage to the central nervous system related to memory. Full article

In this study, the structure of Walnut green husk polysaccharides (WGHP) and their effects on immune checkpoint inhibitor induced colitis (ICIIC) and intestinal microbiota in mice were studied. The results showed that WGHP was composed of mannose (Man) 0.56%, rhamnose (Rha) 6.81%, galacturonic acid (GalA) 53.52%, glucose (Glc) 8.93%, galactose (Gal) 13.94%, arabinose (Ara) 15.88% and fucose (Fuc) 0.35%. The results of animal experiments showed that the intake of WGHP could not only effectively improve the phenotype of ICIIC in mice, but also significantly regulate the composition of intestinal flora and the content of short-chain fatty acids in mice, such as regulating the ratio of Firmicutes/Bacterotoides, Lachnospiraceae NK4A136 group, Lactobacillus, and increasing the content of butyric acid, acetic acid, and isobutyric acid to restore intestinal homeostasis. In addition, WGHP improves inflammation in mouse ICIIC by inhibiting the secretion of pro-inflammatory cytokines TNF-α and IL-1β, thereby activating the GPR43/PD-1/PD-L1 signaling pathway. Therefore, WGHP can be used as a functional polysaccharide for the prevention of ICIIC. Full article

Computed tomography (CT) images are stored at a 12-bit depth. However, many deep learning libraries and pre-trained models are designed for 8-bit images, requiring an intermediate compression step before restoring the original 12-bit physical range. This process causes information loss and can compromise image reliability. This study investigated the impact of two CT resampling methods (8-bit compression; 12-bit decompression) on dose calculation and image quality. Ten total marrow and lymphoid irradiation patients were selected. CT scans were resampled using linear and non-linear look-up tables (l_LUT/nl_LUT). Original and resampled CTs were evaluated considering: (i) Hounsfield unit (HU) root mean squared error (RMSE); (ii) dose-volume histogram (DVH) statistics for target volume and several organs; (iii) 3D gamma passing rate (GPR) with a 1%/1.25 mm criterion; (iv) lymph nodes contouring and diagnostic quality (scale 1–5). The RMSE for l_LUT vs. nl_LUT was 7 ± 1 vs. 10 ± 1 HU. Maximum differences in DVH statistics were 0.4%, with a 3D-GPR = 100% for all cases. CTs resampled with l_LUT exhibited evident brain pixelation (score = 1), whereas nl_LUT matched the original CT quality (score = 4). Both LUTs were acceptable for lymph nodes delineation. The nl_LUT optimized the CT resampling process, providing a more efficient method for possible deep learning applications in synthetic CT generation. Full article

Ground penetrating radar (GPR) data inversion, especially in parallel-layered homogeneous media with multiple subsurface targets, still faces challenges in accurately reconstructing geometric structures due to weak reflections and complex target–medium interactions. To address these limitations, this paper proposes a novel multi-scale inversion framework named GPRGAN-SCSE (Ground Penetrating Radar Generative Adversarial Network with Spatial-Channel Squeeze and Excitation). Built upon the Pix2Pix Generative Adversarial Network (Pix2PixGAN), the proposed model incorporates a Spatial-Channel Squeeze and Excitation (SCSE) module into a residual U-Net generator to adaptively enhance target features embedded in layered media. Furthermore, a tri-scale discriminator ensemble is designed to enforce structural consistency and suppress layer-induced artifacts. The network is optimized using a composite loss integrating adversarial loss, L1 loss, and gradient difference loss to jointly improve structural continuity and boundary sharpness. Experiments conducted on a simulation dataset of parallel-layered homogeneous media with multiple targets demonstrate that GPRGAN-SCSE substantially outperforms existing inversion networks. The proposed method reduces the MAE by 63.8% and achieves a Structural Similarity Index (SSIM) of 99.96%, effectively improving the clarity of subsurface edges and the fidelity of geometric contours. These results confirm that the proposed framework provides a robust and high-precision solution for non-destructive subsurface imaging under layered media conditions. Full article

Purpose: Collagen membranes are widely used biomaterials in periodontal and implant dentistry and can be combined with hyaluronic acid (HA). Although collagen membranes are expected to exhibit bioactive properties and support fibroblast infiltration, their specific impact on fibroblast behavior remains unclear. Methods: To investigate this, human gingival fibroblasts were seeded on collagen matrices—mucoderm^®, a collagen fleece derived from dermis, and Jason^® membrane derived from pericardium—with or without lyophilized HA. Subsequent bulk RNA sequencing was used to assess transcriptional responses. Results: Both mucoderm^® and the collagen fleece caused significant transcriptional changes compared with fibroblasts grown on standard tissue culture surfaces and Jason^® membrane. These changes included upregulation of CEMIP, STC1, and TM4SF1, and downregulation of ADM2, PSAT1, and GPR1. Notably, the collagen fleece increased expression of extracellular matrix-related genes including CCN1, CCN2, COL1A1, POSTN, SPARC, TAGLN, FBN2, CCDC80, and CREB3L1 relative to mucoderm^®. Additionally, the expression of proteases MMP3 and MMP10, along with detoxification-related genes MT1E, MT2A, HMOX1, and NQO1, was relatively decreased. HA coating elevated IL24 expression in mucoderm^®, but no similar effect was observed in the collagen fleece. Conclusions: These findings demonstrate that collagen membranes can influence the transcriptome of gingival fibroblasts and suggest that collagen fleece has a stronger effect on extracellular matrix formation than mucoderm^®. Furthermore, HA coating does not consistently alter fibroblast responses. Full article

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Electrical discharge machining (EDM) is widely used for machining hard and difficult-to-cut materials; however, the complex and nonlinear nature of the process makes the accurate prediction of key performance indicators challenging, particularly when only limited experimental data are available. In this study, a combined Taguchi design and Gaussian process regression (GPR) modeling framework is proposed to predict the surface roughness (Ra), material removal rate (MRR), and overcut (OC) in die-sinking EDM. An L18 Taguchi orthogonal array was employed to efficiently design experiments involving discharge current, pulse duration, and electrode material. GPR models with an automatic relevance determination (ARD) radial basis function kernel were developed to capture nonlinear relationships and varying parameter relevance. Model performance was evaluated using strict leave-one-out cross-validation (LOOCV). The developed GPR models achieved low prediction errors, with RMSE (MAE) values of 0.54 µm (0.41 µm) for R_a, 1.56 mm³/min (1.21 mm³/min) for MRR, and 0.0065 mm (0.0055 mm) for OC, corresponding to approximately 9.8%, 5.4%, and 5.9% of the respective response ranges. These results confirm stable and reliable predictive accuracy within the investigated parameter domain. Based on the validated surrogate models, multi-objective optimization was performed to identify Pareto-optimal process conditions, revealing graphite electrodes as the dominant choice within the feasible operating region. The proposed approach demonstrates that accurate and robust prediction of EDM performance can be achieved even with compact experimental datasets, providing a practical tool for process analysis and optimization. Full article

Foliar application of fertilizers and bioactive compounds helps viticulture adapt to climate change, while biochar (BC) derived from grapevine pruning residues (GPRs) represents a versatile material that further contributes to climate change mitigation. In this study, the foliar application impact of seven different formulations on the chemical composition and quality of must and wine of Malvazija istarska (Vitis vinifera L.) was investigated. The suspensions contained various combinations of BC, urea, and amino acids. BC increased the pH of the solutions in which it was present due to its alkaline nature, thereby influencing the uptake of nutrients and other compounds. Treatments C (control) and A (amino acids) led to the highest amount of yeast-assimilable nitrogen (YAN) (170 and 172 mg N/L). The amino acid profile of the must differed from the typical composition, with glutamine identified as the predominant compound. The combination of BC with urea and amino acids was associated with a higher sugar concentration in the must compared to the application of BC alone, ranging from 208 to 223 g/L. Combining BC with other components led to wines that received superior sensory evaluation scores compared to both C and B. BC alone did not influence must or wine quality. However, its application in combination with other components makes it a suitable carrier for such compounds. Due to its benefits, easy and cheap production, foliar application of BC suspensions with fertilizers can become a standard operation in viticulture and contribute to sustainable fertilization. Full article

Metabolites, once viewed mainly as energy substrates or structural precursors, are now increasingly recognized as key extracellular signaling mediators that regulate diverse physiological processes. This review synthesizes and systematizes current knowledge on metabolite-mediated signaling through G-protein-coupled receptors (GPCRs) in teleosts and, importantly, highlights new conceptual links between specific metabolite–GPCR axes and key physiological functions relevant to aquaculture. By integrating evidence across metabolite–GPCRs axes, including succinate–SUCNR1, aromatic amino acids (tryptophan and phenylalanine)–GPR142, basic amino acids (L-arginine)–GPRC6A, and lactate–GPR81. We clarify how metabolite–receptor interactions have the potential to modulate glucose homeostasis, immune responses, energy metabolism, and stress coping. A major contribution of this review is illustrating how metabolites act not only as nutrients but also as extracellular signaling molecules governing core physiological processes via GPCRs. Particularly from an evolutionary perspective, compared with peptide-activated GPCRs, metabolite-sensing GPCRs are relatively conserved across different species, suggesting that relevant findings from biomedical research could be translated to aquaculture applications. Therefore, understanding GPCR-mediated metabolite sensing provides a molecular foundation for improving nutrient formulation, developing functional feeds, and designing selective breeding strategies in precision aquaculture. Full article

River-training structures such as spur dikes are frequently used in the field of river engineering, which play a critical role in flow regulation and stabilization of the riverbank. However, previous studies lack a precise prediction of factors inducing scour and turbulence phenomena, such as tip velocity, for optimal design of the spur dikes. This study addresses a key gap in previous research by predicting tip velocity around spur dikes using advanced and interpretable machine learning models while simultaneously evaluating the influence of key geometric and hydraulic parameters. For this purpose, the current study utilized advanced artificial intelligence (AI) techniques like Gaussian Process Regression (GPR), Categorical Boosting (CatBoost), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), optimized with Particle Swarm Optimization (PSO), to predict tip velocity in the vicinity of the spur dike. In this paper, a small dataset of 69 laboratory-scale experimental trials was collected; therefore, the chosen AI models were selected for their ability to handle such limited data points. In this study, the input parameters included Froude number (Fr), separation length to spur dike length ratio (L/l), and incidence angle (β), while the output parameter was tip velocity. The selected four AI models were trained on 70%, 15%, and 15% of the data for the training, testing, and validation phases, respectively. SHapley Additive exPlanations (SHAP) analysis was used to observe the influence of the critical parameters on the tip velocity. The results demonstrated the superior performance of GPR, followed by the CatBoost model, compared to other models. GPR and CatBoost show greater values of coefficient of determination (R²) (GPR R² = 0.972 and CatBoost R² = 0.970) and lower values of root mean square error (RMSE) (GPR RMSE = 0.0107 and CatBoost RMSE = 0.0236). The result of the heatmap and SHAP analysis indicated a greater influence of Fr and L/l and a lower impact of β on the tip velocity. The results of this study recommend the utilization of GPR and CatBoost for precise and robust performance of the hydrodynamic phenomenon around the spur dikes, supporting scour mitigation strategies in river engineering. Full article

Subarachnoid hemorrhage (SAH) is a life-threatening cerebrovascular event with high mortality and long-term morbidity. While clinical grading scales such as Hunt and Hess or the World Federation of Neurological Surgeons (WFNS) score aid in prognosis, their accuracy implies a neurological assessment that can be confounded in sedated patients, highlighting the need for objective biomarkers. Biomarkers offer an alternative approach for risk stratification. This review examines the prognostic value of the glucose/potassium ratio (GPR) in patients with aneurysmal SAH and its potential integration into future predictive models. A literature review of retrospective studies assessing the association between GPR and clinical outcomes in SAH was conducted. Evidence on the pathophysiological basis of stress-induced hyperglycemia and hypokalemia in SAH is presented, along with findings from five key clinical studies evaluating GPR in relation to mortality, vasospasm, delayed cerebral ischemia, and functional outcomes. Elevated GPR levels were consistently associated with poor short- and long-term outcomes in SAH patients. Studies reported significant correlations between GPR and 30-day mortality, poor Glasgow Outcome Scale (GOS) scores, increased incidence of cerebral vasospasm, and higher rates of rebleeding. The optimal GPR cutoff for predicting adverse outcomes was greater than 37 mg/dL, with multivariate analyses confirming GPR as an independent prognostic factor. GPR is a promising, cost-effective biomarker that integrates two stress-response parameters (glucose and potassium), both of which are independently associated with SAH prognosis. Its incorporation into future predictive models may enhance early risk stratification and guide clinical decision-making. Further prospective studies are warranted to validate its utility and standardize its clinical application. Full article