Search Results (6,069)

Background/Objectives: A mechanistic understanding of transdermal drug delivery relies on accurately capturing the layered structure and barrier function of the skin. This study presents a four-layer numerical model that explicitly includes the donor compartment, stratum corneum (SC), viable skin (RS), and receptor compartment. Methods: The model is based on Fickian diffusion and incorporates interfacial partitioning and mass transfer resistance. It is implemented using the finite element method in MATLAB and calibrated through nonlinear least-squares optimization against experimental data from Franz diffusion cell studies using porcine skin. Validation was performed using receptor concentration profiles over time and final drug content in the SC and RS layers, assessed via tape stripping and residual skin analysis. Results: The model provided excellent agreement with experimental data. For diclofenac, the optimized partition coefficient at the SC–RS interface was close to unity, indicating minimal interfacial discontinuity and that a simplified three-layer model may be sufficient for this compound. Conclusions: The proposed four-layer framework provides a physiologically informed and generalizable platform for simulating transdermal drug delivery. It enables spatial resolution, mechanistic interpretation, and flexible adaptation to other drugs and formulations, particularly those with significant interfacial effects or limited lipophilicity. Full article

In modern livestock production, obtaining accurate body weight measurements for pigs is essential for feeding management and economic assessment, yet conventional weighing is laborious and can stress animals. To address these limitations, we developed a contactless image-based pipeline that first uses BiRefNet for high-precision background removal and YOLOv11-seg to extract the pig dorsal mask from top-view RGB images; from these masks we designed and extracted 17 representative phenotypic features (for example, dorsal area, convex hull area, major/minor axes, curvature metrics and Hu moments) and included camera height as a calibration input. We then compared eight machine-learning and deep-learning regressors to map features to body weight. The segmentation pipeline achieved mAP₅₀–₉₅ = 0.995 on the validation set, and the XGBoost regressor gave the best test performance (MAE = 3.9350 kg, RMSE = 5.2372 kg, R² = 0.9814). These results indicate the method provides accurate, low-cost and computationally efficient weight prediction from simple RGB images, supporting frequent, noninvasive monitoring and practical deployment in smart-farming settings. Full article

Polypharmacology in Amyotrophic lateral sclerosis (ALS) demands models that capture irreducible higher-order drug co-action. We introduce a categorical–topological pipeline that encodes regimens as truncated multicomplexes with a hypergraph–simplicial envelope; irreducible effects are identified by Möbius inversion, and CatMixNet predicts dose-response under monotone calibration while aligning multimodal omics via sheaf constraints. Under face-disjoint evaluation, omics fusion reduced RMSE from 0.164 to 0.149 (≈9%), increased PR-AUC from 0.38 to 0.44, and lowered calibration error to 2.6–3.1% with <10 dose-monotonicity violations per ${10}^3$ surfaces. Triad-irreducible signal strengthened (95th percentile ${\mathsf{\Delta }}^{★}=0.151$; antagonism retained at 24%). A risk-sensitive selector produced triads with toxicity headroom and projected ALSFRS-R slope gains of +0.04–0.05 points/month. Ablations confirmed the necessity of Möbius consistency, sheaf regularization, and monotone heads. Distilled monotone splines yielded compact titration charts with mean error 0.023. The framework supplies reproducible artifacts and actionable shortlists for iPSC and SOD1 validation. Full article

Objectives: Employing 18F-FDG PET/CT radiomic properties both within and surrounding tumors, in conjunction with clinical attributes, to precisely differentiate among several pathological subtypes of non-small-cell lung cancer (NSCLC). Approaches: The study comprised 222 patients who received 18F-FDG PET/CT scans from January 2015 to December 2020 and were later diagnosed with NSCLC, encompassing 169 cases of lung adenocarcinoma (LUAD) and 53 cases of lung squamous cell carcinoma (LUSC). They were arbitrarily allocated into a training group and a validation group in a 7:3 ratio. Radiomics feature extraction was conducted on 18F-FDG PET/CT images of primary tumors and adjacent tumor regions with LIFE-x (5.2.0). A multivariate logistic regression analysis was employed to develop a nomogram for differentiating lung adenocarcinoma (LUAD) from lung squamous cell carcinoma (LUSC). The clinical efficacy of each model was assessed and contrasted utilizing accuracy (Acc), sensitivity (Sen), specificity (Spe), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). Outcomes: The nomogram model that integrates 18F-FDG PET/CT radiomics features with clinical characteristics showed superior efficacy in differentiating adenocarcinoma from squamous cell carcinoma in NSCLC patients, surpassing models based only on PET or CT radiomics. The validation set exhibited an Area under curve (AUC) of 0.880, an Acc of 0.929, a Sen of 0.808, and a Spe of 0.962. This model exhibits the most superior overall performance in DCA. Conclusions: A nomogram model integrating radiomic features derived from 18F-FDG PET/CT images of tumors and adjacent tissues with clinical characteristics can effectively differentiate between LUAD and LUSC. Full article

Information popularity prediction is a critical problem in social network analysis. With the increasing prevalence of social platforms, accurate prediction of the diffusion process has become increasingly important. Existing methods mainly rely on graph neural networks to model structural relationships, but they are often insufficient in capturing the complex interplay between temporal evolution and local cascade structures, especially in real-world scenarios involving sparse or rapidly changing cascades. To address this issue, we propose the Cascading Dynamic attention-calibrated Graph Convolutional Network, named CasDacGCN. It enhances prediction performance through spatiotemporal feature fusion and adaptive representation learning. The model integrates snapshot-level local encoding, global temporal modeling, cross-attention mechanisms, and a hypernetwork-based sample-wise calibration strategy, enabling flexible modeling of multi-scale diffusion patterns. Results from experiments demonstrate that the proposed model consistently surpasses existing approaches on two real-world datasets, validating its effectiveness in popularity prediction tasks. Full article

This paper presents a bistatic 60 GHz frequency-modulated continuous-wave (FMCW) radar system for non-contact heart rate monitoring, utilizing high-gain Fabry–Perot cavity (FPC) antennas at both the transmitter and receiver. Each FPC antenna integrates a partially reflective surface (PRS) and a metallic ground plane to form a resonant cavity. Compared to conventional patch arrays of the same aperture, the FPC antenna improves the antenna gain from 4.1 dBi to 8.1 dBi at the transmitter and from 3.9 dBi to 7.8 dBi at the receiver, resulting in an overall link budget enhancement of approximately 7.9 dB. This dual high-gain configuration theoretically increases the maximum detection range by a factor of 2.48. The proposed radar system was implemented and experimentally validated under indoor conditions using both calibration targets and human participants. Active measurement results confirm that the bistatic radar equipped with FPC antennas extends the reliable heart rate detection distance by approximately 2.27 times compared to a conventional system, closely matching the theoretical prediction. These results confirm the practicality and effectiveness of FPC antennas in extending both the range and reliability of millimeter-wave vital sign detection systems. Full article

Accurate detection of fetal distress from cardiotocography (CTG) is clinically critical but remains subjective and error-prone. In this research, we present a leakage-safe Calibrated Global Logit Fusion (CGLF) framework that couples TabNet’s sparse, attention-based feature selection with XGBoost’s gradient-boosted rules and fuses their class probabilities through global logit blending followed by per-class vector temperature calibration. Class imbalance is addressed with SMOTE–Tomek for TabNet and one XGBoost stream (XGB–A), and class-weighted training for a second stream (XGB–B). To prevent information leakage, all preprocessing, resampling, and weighting are fitted only on the training split within each outer fold. Out-of-fold (OOF) predictions from the outer-train split are then used to optimize blend weights and fit calibration parameters, which are subsequently applied once to the corresponding held-out outer-test fold. Our calibration-guided logit fusion (CGLF) matches top-tier discrimination on the public Fetal Health dataset while producing more reliable probability estimates than strong standalone baselines. Under nested cross-validation, CGLF delivers comparable AUROC and overall accuracy to the best tree-based model, with visibly improved calibration and slightly lower balanced accuracy in some splits. We also provide interpretability and overfitting checks via TabNet sparsity, feature stability analysis, and sufficiency (k95) curves. Finally, threshold tuning under a balanced-accuracy floor preserves sensitivity to pathological cases, aligning operating points with risk-aware obstetric decision support. Overall, CGLF is a calibration-centric, leakage-controlled CTG pipeline that is interpretable and suited to threshold-based clinical deployment. Full article

The four buckets phase demodulation method is a widely used sinusoidal modulation and demodulation technique in interferometry. Strict calibration is essential to minimize nonlinear errors in subsequent measurements. The core of the algorithm calibration lies in determining the initial phase value of the modulation signal that matches the modulation depth while overcoming the influence of system phase delay. Currently, there are few systematic calibration methods specifically designed for optical fiber interferometry. This paper proposes a self-calibration method based on triangular wave mixing for four buckets phase demodulation in fiber optic interferometric probes, which efficiently achieves self-calibration of the phase demodulation while the measured object remains stationary. Simulations and experimental validations were conducted, demonstrating that the optimal initial phase value of 0.62 rad during phase demodulation can be accurately identified under static conditions. The calibrated phase value was then applied to the displacement measurement, where the target displacement was effectively detected, resulting in a root mean square (RMS) error of 3.0337 nm and an average error of 2.4479 nm. Full article

Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R²) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management. Full article

The global shift towards renewable energy sources necessitates efficient methods for assessing solar potential in urban areas. Rooftop photovoltaic (PV) systems present a sustainable solution for decentralized energy production; however, their effectiveness is influenced by structural and environmental factors, including roof slope, azimuth, and shading. This study aims to develop and validate a UAV-based methodology for assessing rooftop solar potential in urban areas. The authors propose a low-cost, innovative tool that utilizes a commercial unmanned aerial vehicle (UAV), specifically the DJI Air 3, combined with advanced photogrammetry and 3D modeling techniques to analyze rooftop characteristics relevant to PV installations. The methodology includes UAV-based data collection, image processing to generate high-resolution 3D models, calibration and validation against reference objects, and the estimation of solar potential based on rooftop characteristics and solar irradiance data using the proposed Model Analysis Tool (MAT). MAT is a novel solution introduced and described for the first time in this study, representing an original computational framework for the geometric and energetic analysis of rooftops. The innovative aspect of this study lies in combining consumer-grade UAVs with automated photogrammetry and the MAT, creating a low-cost yet accurate framework for rooftop solar assessment that reduces reliance on high-end surveying methods. By being presented in this study for the first time, MAT expands the methodological toolkit for solar potential evaluation, offering new opportunities for urban energy research and practice. The comparison of PVGIS and MAT shows that MAT consistently predicts higher daily energy yields, ranging from 9 to 12.5% across three datasets. The outcomes of this study contribute to facilitating the broader adoption of solar energy, thereby supporting sustainable energy transitions and climate neutrality goals in the face of increasing urban energy demands. Full article

Background: Critically ill patients in the intensive care unit (ICU) are characterized by complex comorbidities and a high risk of short-term mortality. Traditional severity scoring systems rely on physiological and laboratory variables but lack direct integration of electrocardiogram (ECG) data. This study aimed to construct an interpretable machine learning (ML) model combining ECG-derived and clinical variables to predict 28-day mortality in ICU patients. Methods: A retrospective cohort analysis was performed with data from the MIMIC-IV v2.2 database. The primary outcome was 28-day mortality. An ECG-based risk score was generated from the first ECG after ICU admission using a deep residual convolutional neural network. Feature selection was guided by XGBoost importance ranking, SHapley Additive exPlanations, and clinical relevance. A three-variable model comprising ECG score, APS-III score, and age (termed the E3A score) was developed and evaluated across four ML algorithms. We evaluated model performance by calculating the AUC of ROC curves, examining calibration, and applying decision curve analysis. Results: A total of 18,256 ICU patients were included, with 2412 deaths within 28 days. The ECG score was significantly higher in non-survivors than in survivors (median [IQR]: 24.4 [15.6–33.4] vs. 13.5 [7.2–22.1], p < 0.001). Logistic regression demonstrated the best discrimination for the E3A score, achieving an AUC of 0.806 (95% CI: 0.784–0.826) for the test set and 0.804 (95% CI: 0.772–0.835) for the validation set. Conclusions: Integrating ECG-derived features with clinical variables improves prognostic accuracy for 28-day mortality prediction in ICU patients, supporting early risk stratification in critical care. Full article

Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes. Full article

The present study aimed to establish a physiologically based toxicokinetic (PBTK) model to investigate the absorption, retention, and transport of inhaled nano-sized titanium dioxide (TiO₂-NPs) particles in rats, thereby providing a basis for understanding the absorption, distribution, and elimination mechanisms of TiO₂-NPs in various organs. A detailed respiratory module and the Hill coefficient equation were adopted in the PBTK model. Calibration and validation of the model were conducted using the only two available inhalation biodistribution datasets for TiO₂-NPs found in the literature, encompassing different doses and exposure conditions. The overall fit with both datasets was acceptable with R² value of 0.95 in respiratory system and 0.88 in the secondary organs. The sensitivity analysis indicated that the alveolar–interstitial transfer rate (K_{alv_inter}) and tissue–blood distribution coefficients (P_lu, P_li, P_ki) significantly influenced the retention of TiO₂-NPs in pulmonary regions and distribution to secondary organs, with these parameters exhibiting time-dependent behavior. The PBTK model demonstrates a good predictive performance for TiO₂-NPs content in all rat organs, with simulated values consistently ranging within 0.5- to 2-fold of the measured data. In last, we developed a PBTK model that can well predict the in vivo distribution of inhaled TiO₂-NPs and provided a novel computational tool for cross-species extrapolation of human inhalation exposure and subsequent biodistribution. Full article

Reliable toxicity assessments are essential for protecting biological processes in wastewater treatment plants (WWTPs). This study focuses on quantifying the measurement uncertainty of the ISO 8192:2007 method, which determines the inhibition of oxygen consumption in activated sludge. Using the GUM guideline and Monte Carlo Simulation (MCS), up to 29 uncertainty contributions were evaluated in terms of oxygen consumption rate and percentage inhibition. The results reveal that temperature tolerance, measurement interval, and oxygen probe accuracy are dominant contributors, accounting for over 90% of the total uncertainty. The GUM results for oxygen consumption rates were validated by Monte Carlo Simulation, confirming their reliability. The percentage inhibitions showed asymmetric distributions and were underestimated by the GUM method, especially at lower toxicant concentrations. This highlights the necessity of simulation-based approaches for asymmetric systems. Notably, the consideration of correlations in the GUM analysis had minimal impact on outcomes. The findings emphasize the need for the precise recording of measurement time intervals, temperature control, the regular calibration of oxygen probes, and repeat measurements at low toxicant concentrations. Overall, this study enhances the robustness of ISO 8192:2007-based toxicity testing and provides practical guidance for reducing measurement uncertainty. Full article

This study examines the design and implementation of a sensor developed to measure total pressure in supersonic flow conditions using nitrogen as the working fluid. Using a combination of absolute and differential pressure sensors, the total pressure distribution downstream of a nozzle—where normal shock waves are generated—was characterized across a range of low-pressure regimes. The experimental results were employed to validate and calibrate computational fluid dynamics (CFD) models, particularly within pressure ranges approaching the limits of continuum mechanics. The validated analyses enabled a more detailed examination of shock-wave behavior under near-continuum conditions, with direct relevance to the operational environment of differentially pumped chambers in Environmental Scanning Electron Microscopy (ESEM). Furthermore, an entropy increase across the normal shock wave at low pressures was quantified, attributed to the extended molecular mean free path and local deviations from thermodynamic equilibrium. Full article