Search Results (426)

Purpose: Predicting colorectal cancer liver metastasis (CRLM) is essential for prognostic assessment. This study aims to develop and validate an interpretable multimodal machine learning framework based on multiparametric MRI for predicting CRLM, and to enhance the clinical interpretability of the model through SHapley Additive exPlanations (SHAP) analysis and deep learning visualization. Methods: This multicenter retrospective study included 463 patients with pathologically confirmed colorectal cancer from two institutions, divided into training (n = 256), internal testing (n = 111), and external validation (n = 96) sets. Radiomics features were extracted from manually segmented regions on axial T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI). Deep learning features were obtained from a pretrained ResNet101 network using the same MRI inputs. A least absolute shrinkage and selection operator (LASSO) logistic regression classifier was developed for clinical, radiomics, deep learning, and combined models. Model performance was evaluated by AUC, sensitivity, specificity, and F1-score. SHAP was used to assess feature contributions, and Grad-CAM was applied to visualize deep feature attention. Results: The combined model integrating features across the three modalities achieved the highest performance across all datasets, with AUCs of 0.889 (training), 0.838 (internal test), and 0.822 (external validation), outperforming single-modality models. Decision curve analysis (DCA) revealed enhanced clinical net benefit from the integrated model, while calibration curves confirmed its good predictive consistency. SHAP analysis revealed that radiomic features related to T2WI texture (e.g., LargeDependenceLowGrayLevelEmphasis) and clinical biomarkers (e.g., CA19-9) were among the most predictive for CRLM. Grad-CAM visualizations confirmed that the deep learning model focused on tumor regions consistent with radiological interpretation. Conclusions: This study presents a robust and interpretable multiparametric MRI-based model for noninvasively predicting liver metastasis in colorectal cancer patients. By integrating handcrafted radiomics and deep learning features, and enhancing transparency through SHAP and Grad-CAM, the model provides both high predictive performance and clinically meaningful explanations. These findings highlight its potential value as a decision-support tool for individualized risk assessment and treatment planning in the management of colorectal cancer. Full article

Intravenous infusion is an important clinical medical intervention, and its safety is critical to patient recovery. To mitigate the elevated risk of complications (e.g., air embolism) arising from delayed response to infusion endpoints, this paper designs a flexible double pole capacitive (FPB) sensor, which includes a main pole plate, an adaptive pole plate, and a back shielding electrode. The sensor establishes a mapping between residual liquid volume in the infusion bottle and its equivalent capacitance, enabling a non-contact adaptive monitoring system. The system enables precise quantification of residual liquid levels, suppressing baseline drift induced by environmental temperature/humidity fluctuations and container variations via an adaptive algorithm, without requiring manual calibration, and overcomes the limitations of traditional rigid sensors when adapting to curved containers. Experimental results showed that the system achieved an overall sensitivity of 753.5 $\mathrm{f}\mathrm{F}/\mathrm{m}\mathrm{m}$, main pole plate linearity of 1.99%, and adaptive pole plate linearity of 0.53% across different test subjects, linearity of 0.53% across different test subjects, with liquid level resolution accuracy reaching 1 mm. These results validate the system’s ultra-high resolution (1 mm) and robust adaptability. Full article

As a typical reforested region of the Loess Plateau, the Caijiachuan watershed plays a vital role in ecological security and resource management. This study evaluates the spatiotemporal variations in key ecosystem services—namely soil retention, water yield, carbon storage, and habitat quality—between 2002 and 2024 using the InVEST model, calibrated with field-measured rainfall, carbon density, and high-resolution land use data derived from integrated remote sensing and field surveys. Statistical analyses based on the R language reveal dynamic trade-offs and synergies among these services. The results show that: (1) soil retention, carbon storage, and habitat quality have steadily improved, while water yield shows an overall upward trend with significant spatial heterogeneity; (2) a consistent and significant trade-off exists between carbon storage and water yield (average R² ≈ 0.28), while other ecosystem service interactions are relatively weak; (3) climatic variability, topographic heterogeneity (e.g., slope and elevation), and vegetation structure are key drivers of these trade-offs. This study provides scientific evidence to support ecological management and policy formulation in reforested areas of the Loess Plateau. Full article

The architecture of contemporary museums often emphasizes visual aesthetics, such as large volumes, open-plan layouts, and highly reflective finishes, resulting in acoustic challenges, such as excessive reverberation, poor speech intelligibility, elevated background noise, and reduced privacy. This study quantified the impact of surface—specific absorption treatments on acoustic metrics across eight gallery spaces. Room impulse responses calibrated virtual models, which simulated nine absorption scenarios (low, medium, and high on ceilings, floors, and walls) and evaluated reverberation time (T₂₀), speech transmission index (STI), clarity (C₅₀), distraction distance (r_D), Spatial Decay Rate of Speech (D_2,S), and Speech Level at 4 m (L_p,A,S,4m). The results indicate that going from concrete to a wooden floor yields the most rapid T₂₀ reductions (up to −1.75 s), ceiling treatments deliver the greatest STI and C₅₀ gains (e.g., STI increases of +0.16), and high-absorption walls maximize privacy metrics (D_2,S and L_p,A,S,4m). A linear regression model further predicted the STI from T₂₀, total absorption (Sabins), and room volume, with an 84.9% conditional R², enabling ±0.03 accuracy without specialized testing. These findings provide empirically derived, surface-specific “first-move” guidelines for architects and acousticians, underscoring the necessity of integrating acoustics early in museum design to balance auditory and visual objectives and enhance the visitor experience. Full article

This study presents a novel methodology for determining zonal electricity generation and capacity requirements corresponding to forecasted annual production in an integrated power system (IPS). The proposed model combines the statistical analysis of historical daily load patterns with a calibration technique to translate forecast total demand into zonal powers (base, semi-peak and peak). A representative reference daily electrical load graph (ELG) is selected from retrospective data using least squares criteria, and a calibration factor α = W_x/W_ie scales its zonal outputs to match the forecasted annual generation W_x. The innovation lies in this combination of historical ELG identification and calibration for accurate zonal power prediction. Applying the model to Ukrainian IPS data yields high accuracy: a zonal power error below 1.02% and a generation error below 0.39%. Key contributions include explicitly stating the research questions and hypotheses, providing a schematic procedural description and discussing model limitations (e.g., treatment of renewable variability and omission of meteorological/astronomical factors). Future work is outlined to incorporate unforeseen factors (e.g., post-war demand shifts, electric vehicle adoption) into the forecasting framework. Full article

Suyukou Gully, located on the eastern slope of the Helan Mountains in northwest China, is a typical debris-flow-prone catchment characterized by a steep terrain, fractured bedrock, and abundant loose colluvial material. The area is subject to intense short-duration convective rainfall events, which often trigger destructive debris flows that threaten the Suyukou Scenic Area. To investigate the dynamics and risks associated with such events, this study employed the FLO-2D two-dimensional numerical model to simulate debris flow propagation, deposition, and hazard distribution under four rainfall return periods (10-, 20-, 50-, and 100-year scenarios). The modeling framework integrated high-resolution digital elevation data (original 5 m DEM resampled to 20 m grid), land-use classification, rainfall design intensities derived from regional storm atlases, and detailed field-based sediment characterization. Rheological and hydraulic parameters, including Manning’s roughness coefficient, yield stress, dynamic viscosity, and volume concentration, were calibrated using post-event geomorphic surveys and empirical formulations. The model was validated against field-observed deposition limits and flow depths, achieving a spatial accuracy within 350 m. Results show that the debris flow mobility and hazard intensity increased significantly with rainfall magnitude. Under the 100-year scenario, the peak discharge reached 1195.88 m³/s, with a maximum flow depth of 20.15 m and velocities exceeding 8.85 m·s⁻¹, while the runout distance surpassed 5.1 km. Hazard zoning based on the depth–velocity (H × V) product indicated that over 76% of the affected area falls within the high-hazard zone. A vulnerability assessment incorporated exposure factors such as tourism infrastructure and population density, and a matrix-based risk classification revealed that 2.4% of the area is classified as high-risk, while 74.3% lies within the moderate-risk category. This study also proposed mitigation strategies, including structural measures (e.g., check dams and channel straightening) and non-structural approaches (e.g., early warning systems and land-use regulation). Overall, the research demonstrates the effectiveness of physically based modeling combined with field observations and a GIS analysis in understanding debris flow hazards and supports informed risk management and disaster preparedness in mountainous tourist regions. Full article

The present study investigates the influence of the configurations of anchor bolts and stiffeners on the monotonic response under moment conditions in the major axis and compression force of partial-strength exposed column base plate connections in order to ameliorate their response, limiting the number of welded details. Parametric finite element simulations were performed based on models calibrated against experimental results available from the recent literature. The results show the efficiency of the investigated configurations, namely, (i) the presence of rib stiffener results in high stiffness and strength with a reduction in ductility; (ii) the linear pattern of anchor bolts (e.g., rectangular distribution) is characterized by the limited contribution of the outer anchor bolts to the overall resistance of the connection; (iii) the trapezoidal pattern of the anchor bolts exhibit a better mechanical performance as well as their efficiency; and (iv) the increase in compression force influences the mechanical response of the base connection with an increase in both resistance and rigidity until the column is stable against the moment–axial force interaction. Full article

The reducing streamflow is a major concern in the Yilong Lake Basin (YLB), which supplies water for agriculture and the growing population in the basin and to maintain the health of the regional ecosystem. The YLB has experienced remarkable land use/land cover change (LUCC) and climate change (CC) in recent years. To understand the drivers of the streamflow change in this basin, the effects of the land use change and climate variation on the temporal flow variability were studied using the Soil and Water Assessment Tool (SWAT). The calibration and validation results indicated that the SWAT simulated the streamflow well. Then the streamflow responses to the land use change between 2010 and 2020 and climate change with future climate projections (SSP245, SSP370, and SSP585) were evaluated. Results showed that the LUCC in the YLB caused a marginal decline in the annual streamflow at the whole basin scale but significantly altered rainfall–runoff relationships and intra-annual discharge patterns; e.g., monthly streamflows decreased by up to 3% in the dry season under the surface modification, with subbasins of the YLB exhibiting divergent responses attributed to spatial heterogeneity in land surface transitions. Under future climate scenarios, streamflow projections revealed general declining trends with significant uncertainties, particularly under high-emission pathways, e.g., SSP370 and SSP585, in which the streamflow could be projected to reduce by up to 5.9% in the mid-future (2031–2045). In addition, droughts were expected to intensify, exacerbating seasonal water stress in the future. It suggests that integrated water governance should synergize climate-resilient land use policies with adaptive infrastructure to address regional water resources challenges. Full article

Coarse-grained beaches consisting of gravel, pebbles, and cobbles play a crucial role in coastal protection. On the Croatian Adriatic coast, there are artificial gravel pocket beaches created for recreational and protective purposes. However, these beaches are subject to constant morphological changes due to natural forces and human intervention. This study investigates the morphodynamics of artificial gravel pocket beaches, focusing on berm formation and crest build-up processes characteristic for low to moderate wave conditions. Despite mimicking natural formations, artificial beaches require regular maintenance due to sediment shifts dominantly caused by wave action and storm surges. Structure-from-Motion (SfM) photogrammetry and UAV-based surveys were used to monitor morphological changes on the artificial gravel pocket beach Ploče (City of Rijeka). The XBeach-Gravel model, originally adapted to simulate the effects of high-energy waves, was calibrated and validated to analyze low to moderate wave dynamics on gravel pocket beaches. The calibration includes adjustments to the inertia coefficient (ci), which influences sediment transport by shear stress at the bottom; the angle of repose (ϕ), which controls avalanching and influences sediment transport on sloping beds; and the bedload transport calibration coefficient (γ), which scales the transport rates linearly. By calibrating XBeach-G for low to moderate wave conditions, this research improves the accuracy of the model for the cases of morphological responses “berm formation” and “crest build-up”. Full article

This study presents a novel approach combining near-infrared (NIR) spectroscopy with multivariate calibration to develop simplified yet robust regression models for evaluating the quality of various edible oils. Using a reduced number of NIR wavelengths selected via the stepwise decorrelation method (SELECT) and ordinary least squares (OLS) regression, the models quantify pigments (carotenoids and chlorophyll), antioxidant activity, and key sensory attributes (rancid, fruity green, fruity ripe, bitter, and pungent) in nine extra virgin olive oil (EVOO) varieties. The dataset also includes low-quality olive oils (e.g., refined and pomace oils, supplemented or not with hydroxytyrosol) and sunflower oils, both before and after deep-frying. SELECT improves model performance by identifying key wavelengths—up to 30 out of 700—and achieves high correlation coefficients (R = 0.86–0.96) with low standard errors. The number of latent variables ranges from 26 to 30, demonstrating adaptability to different oil properties. The best models yield low leave-one-out (LOO) prediction errors, confirming their accuracy (e.g., 1.36 mg/kg for carotenoids and 0.88 for rancidity). These results demonstrate that SELECT–OLS regression combined with NIR spectroscopy provides a fast, cost-effective, and reliable method for assessing oil quality under diverse processing conditions, including deep-frying, making it highly suitable for quality control in the edible oils industry. Full article

A high-performance liquid chromatography with evaporative light scattering detection (HPLC-ELSD) method was developed and validated for analyzing fermentable and reducing sugars in brewing matrices. The method exhibited detection limits of 2.5–12.5 mg/L and quantification limits of 12.0–30.0 mg/L. Linearity was achieved for all sugars, fitted with a quadratic calibration model (R² = 0.9998). Precision metrics revealed relative standard deviations (RSDs) below 2% for repeatability and below 6% for intermediate precision. Recovery rates between 86 and 119% confirmed robustness and minimal matrix interference. Application to brewing samples highlighted variability in sugar profiles, with sucrose concentrations in wort ranging from 3.5 to 22.0 g/L and maltose and maltotriose in finished beers between 0.80 and 1.50 g/L and 1.10–2.50 g/L, respectively. Batch variability analysis showed that brewing conditions had a greater impact on sugar concentrations than malt batch origin, with maltose variation reaching 34.6%. This HPLC-ELSD method provides a robust and reliable tool for sugar analysis in brewing, offering valuable insights into fermentation dynamics and batch consistency. Its application to industrial contexts underscores its potential for improving quality control and optimizing brewing processes. Full article

Background: Pre-eclampsia is a significant hypertensive disorder affecting 2–8% of pregnancies globally, significantly contributing to maternal/perinatal deaths. Early identification of at-risk patients is crucial for reducing these mortalities, yet first-trimester screening remains inaccessible in many low-resource settings. This study aims to develop a second-trimester risk stratification model based on clinical parameters to assist in managing pre-eclampsia in diverse healthcare contexts. Methods: This retrospective cohort study analyzed medical records from 700 pregnancies (350 with preeclampsia, 350 controls) between January 2021 and August 2024 at a tertiary medical center in western Romania. Sample size was calculated to achieve 90% power with α = 0.05 for detecting clinically significant differences between groups. Data analysis focused on clinical variables such as maternal age, hypertension, diabetes, and socioeconomic factors. A scoring model was developed using logistic regression and validated for predictive accuracy using ROC curve analysis, with AUC as the primary metric. Calibration was assessed using the Hosmer–Lemeshow test. Results: The risk stratification model demonstrated an AUC of 0.91 (95% CI: 0.88–0.94), indicating high discriminative capability. The model showed good calibration (p = 0.78). Sensitivity was 74.4%, and specificity reached 97.8%. Patients were categorized into low (0–4 points), moderate (5–7 points), and high-risk (≥8 points) groups based on optimized cut-off values. High-risk patients showed significantly higher rates of adverse outcomes, including eclampsia (12.3% vs. 0% in low-risk, p < 0.001) and HELLP syndrome (8.7% vs. 0.5% in low-risk, p < 0.001). Neonates born to high-risk mothers had lower birth weight (mean difference: 486 g, p < 0.001), smaller head circumference (mean difference: 2.3 cm, p < 0.001), and lower APGAR scores (median difference: 2 points, p < 0.001). Conclusions: This novel model offers a practical second-trimester risk assessment tool that leverages routine clinical data available after 20 weeks of gestation. It facilitates targeted care and resource allocation, particularly benefiting settings lacking early screening access. Implementation of risk-stratified management protocols could significantly improve maternal and neonatal outcomes in diverse healthcare environments. Full article

As one kind of ‘probable human carcinogen’ (Group 2B) compound classified by the International Agency for Research on Cancer, 3-MCPD is mainly formed during the thermal processing of food. Tedious pretreatment techniques are needed for the existing analytical methods to quantify 3-MCPD. Hence, a nondestructive sensing technique that offers low noise interference and high quantitative precision must be developed to address this problem. Following this, Fourier transform infrared spectroscopy association with an convolutional neural network (CNN) model was employed in this investigation for the nondestructive quantitative measurement of 3-MCPD in oil samples. Before building the CNN model, NL-SGS-D₂ was utilized to enhance the feature extraction capability of model by eliminating the background noise. Under the optimal hyperparameter settings, calibration model achieved a determination coefficient (R²_C) of 0.9982 and root mean square error (RMSEC) of 0.0181 during validation, along with a 16% performance enhancement enabled by the stepwise hybrid preprocessing strategy. The LODs (0.36 μg/g) and LOQs (1.10 μg/g) of the proposed method met the requirements for 3-MCPD detection in oil samples by the Commission Regulation issued of EU. The method proposed by CNN model with hybrid preprocessing was superior to the traditional model, and contributed to the quality monitoring of edible oil processing industry. Full article

Anxiety and stress-related disorders affect all ages in all geographical areas. As high anxiety and chronic stress result in the modulation of mitochondrial pathways, intensive research is being carried out on pharmaceutical interventions that alleviate pertinent symptomatology. Therefore, innovative approaches being currently pursued include substances that target mitochondria bearing an antioxidant moiety. In this study, a newly synthesized antioxidant consisting of triphenylphosphine (TPP), a six-carbon alkyl spacer, and hydroxytyrosol (HT) was administered orally to mice via drinking water. Cerebellum and liver samples were collected and analyzed using ultra-high-performance liquid chromatography-tandem triple quadrupole mass spectrometry (UHPLC-MS/MS) to assess the levels of TPP-HT in the respective tissues to evaluate in vivo administration efficacy. Sample preparation included extraction with appropriate solvents and a preconcentration step to achieve the required sensitivity. Both methods were validated in terms of selectivity, linearity, accuracy, and limits of detection and quantification. Additionally, a workflow for evaluating and statistically summarizing multiple fortified calibration curves was devised. TPP-HT penetrates the blood–brain barrier (BBB), with a level of 11.5 ng g⁻¹ quantified in the cerebellum, whereas a level of 4.8 ng g⁻¹ was detected in the liver, highlighting the plausibility of orally administering TPP-HT to achieve mitochondrial targeting. Full article

This study explores the design and performance of an underwater magnetic sensor network (UMSN) tailored for intrusion detection in complex environments such as riverbeds and areas with dense vegetation. The system utilizes wireless sensor network (WSN) principles and integrates AMR-based magnetic sensors (e.g., LSM303AGR) with MEMS-based accelerometers to provide accurate and high-resolution magnetic field measurements. Extensive calibration techniques were employed to correct hard-iron and soft-iron distortions, ensuring reliable performance in fluctuating environmental conditions. Field tests included both controlled setups and real-world scenarios, such as detecting intrusions across river sections, shorelines, and coordinated land-water activities. The results showed detection rates consistently above 90%, with response times averaging 2.5 s and a maximum detection range of 5 m. The system also performed well under adverse weather conditions, including fog and rain, demonstrating its adaptability. The findings underline the potential of UMSN as a scalable and cost-efficient solution for monitoring sensitive areas. By addressing the limitations of traditional surveillance systems, this research offers a practical framework for enhancing security in critical regions, laying the groundwork for future developments in magnetic sensor technology. Full article