Search Results (5,064)

This study provides computational insights into the flexural strengthening of reinforced concrete (RC) T-beams in the negative moment region using steel-reinforced polymer cement mortar (PCM) overlays. A validated three-dimensional nonlinear finite element (FE) model was developed using the Advanced Tool for Engineering Nonlinear Analysis (ATENA) software (version 2023.0.0.22492) to simulate the behavior of beams retrofitted with 40 mm thick PCM layers embedded with 13 mm and 16 mm deformed bars. Model validation was performed against previously published experimental results reported by the authors, demonstrating excellent agreement, with normalized mean square error (NMSE) values expressed as fractions between 0.0001 and 0.0022, and experimental-to-numerical ultimate load ratios ranging from 0.99 to 1.01. Parametric analyses were then conducted to investigate the influence of key variables, concrete compressive strength, PCM overlay thickness, and longitudinal reinforcement ratio on the global flexural performance. The results revealed that increasing the overlay thickness raised the ultimate load capacity by up to 15.4% and improved energy absorption by 43%. Enhancing concrete strength led to gains of up to 12.5% in load capacity and 15.8% in stiffness. Variations in reinforcement ratio had the most significant impact, increasing peak load by up to a factor of 2.02 and improving energy absorption by up to a factor of 1.49. Despite these improvements, reductions in ductility were observed across all strengthening configurations, underscoring a strength–deformability trade-off critical for seismic applications. These findings affirm the efficacy of steel-reinforced PCM overlays and provide design-oriented insights for optimizing negative moment retrofitting strategies in RC bridge girders and continuous beam systems. Full article

Breast cancer poses a global health risk and requires precision and accessibility in diagnostic measures. Ultrasound imaging is vital for breast lesion identification due to its safety, cost-effectiveness, and real-time capabilities. This paper presents a new fuzzy system architecture that utilizes ultrasound-based radiomics features to classify breast cancers. In order to ensure uniformity and consistency in shape-based characteristics limited to tumors, we calculate parameters such as elongation, compactness, spherical disproportion, and volumetrics following IBSI recommendations. We employ a hierarchical fuzzy system tree to handle high-dimensional data space and to identify the most discriminative characteristics. The selected features are incorporated into a modular fuzzy logic design that promotes transparency and maintains an auditable decision history according to clinical interpretability. Our framework enables the more accurate classification of breast cancer while addressing the beliefs and values prevalent in clinical applications. Tested on an independent set of data, the model achieved high accuracy of 99.60%, with low overfitting and strong generalization. To enhance its generalizability, we validated it on an internal dataset, attaining a sensitivity of 93.65%, a specificity of 99.24%, an AUC of 0.996, and an 18% reduction in unnecessary biopsies, as demonstrated through decision curve analysis, demonstrating substantial clinical utility across various settings. The findings confirm the system’s ability to identify intricate radiomic patterns linked to cancer. Due to its computing efficiency, it may be executed in real time during routine screening. The proposed radiomics-based fuzzy classification framework may offer a clinically beneficial approach for differentiating benign from malignant breast lesions. Explainability is enhanced with user-friendly artifacts for clinicians, including ranking IF-THEN rules and counterfactuals, all of which were validated in usability trials that demonstrated increased trust among radiologists compared to other technologies. Enhanced differentiation in the classification of various lesion types will decrease unnecessary biopsies. This approach integrates radiomics features with transparent and interpretable fuzzy logic to deliver enhanced predictors and a comprehensible framework for users, including physicians, to facilitate decision-making. This approach advances precision medicine standards through the early detection of lesions using more specific and systematic diagnostic instruments. Full article

Effective electrochemical CO₂ reduction to liquid fuels requires that the local catalytic environment facilitates the desired reactivity, yet a microscopic understanding of this environment is difficult to achieve from experiment alone. In this work, a 3D reaction-diffusion model was developed to explore the effects of electrode surface area and local geometry on the performance of a heterogeneous catalyst that performs a two-step CO₂ reduction cascade reaction to CO and then CH₃OH under aqueous conditions. Kinetic parameters for the model were inspired by experimental results using a cobalt phthalocyanine (CoPc) catalyst. Three-dimensional architectures composed of arrays of square pillars with varying dimensions and either smooth or periodically modulated surfaces were tested, revealing the extent to which geometry modulates the performance of the cascade reactions. Although structural variations modulate local concentration gradients, we find that electrochemically active surface area predominantly governs the overall cascade reaction. Moreover, the results suggest that supersaturation of CO, with concentrations up to ten-fold higher than the equilibrium solubility limit, might be critical for more efficient conversion to CH₃OH. For any given geometry, the spatially averaged ratio of [CO] to [CO₂] is dictated by the electrochemically active surface area and determines the yield of CH₃OH. For a fixed surface area, geometries that spatially confine the electrolyte yield moderate local [CO] to [CO₂] ratios within small volumes. In contrast, less confining geometries result in a broader distribution of local ratios spread over larger volumes, with both configurations yielding the same spatially averaged [CO] to [CO₂] ratio. These insights provide valuable design principles—highlighting the critical importance of surface area and possibly CO supersaturation—for engineering advanced electrode architectures that leverage intermediate trapping and CO supersaturation to enhance overall performance in tandem CO₂ reduction systems. Full article

Sea-level rise (SLR) and regional precipitation pattern change cause island subsurface freshwater, typically shaped like a thin lens, to be at higher risk of contamination from seawater intrusion (SWI). Installing a cutoff wall is considered a feasible strategy for protecting coastal fresh groundwater from SWI. However, the performance of the cutoff wall in managing freshwater lens (FWL) development and mitigating SWI into island aquifers under SLR and aquifer recharge (RCH) fluctuations remains inadequately quantified. This study investigates how water table elevation (WTE), FWL depth, thickness, and SWI extent, measured by aquifer salt mass and freshwater volume, in an island aquifer equipped with cutoff walls, respond to SLR and RCH fluctuations. It focuses on a two-dimensional, variable-density island groundwater simulation model based on hydrogeological conditions of San Salvador Island, Bahamas. The results demonstrate that RCH critically influences cutoff wall effectiveness for FWL development and SWI mitigation, with higher RCH amplifying gains in WTE, FWL metrics, freshwater storage, and aquifer salt removal, but this influence diminishes with wall depth increasing. SLR elevates WTE in a stable manner associated with its magnitude but negligibly affects the cutoff wall performance in FWL enhancement and SWI mitigation. Under simultaneous SLR and RCH fluctuations, SLR can offset the WTE reduction caused by reduced RCH, but the joint effects of SLR and RCH on FWL metrics, freshwater storage and aquifer salt removal align with their individual impacts. Moreover, cutoff walls are more efficient in low-RCH settings, yielding greater relative improvements in FWL development and SWI mitigation per unit wall depth increase. Full article

China possesses abundant marine fishery resources, which play a vital role in the national economy. Achieving rapid and high-precision classification of underwater targets in complex aquatic environments is of significant importance for enhancing aquaculture intelligence and operational efficiency. To address the challenges of insufficient feature extraction and inefficient classifier parameter optimization in underwater image classification, this study proposes a classification method integrating local binary patterns (LBP), kernel principal component analysis (KPCA), and an improved sparrow search algorithm (SSA). The method first extracts image texture features using LBP and then applies KPCA for nonlinear dimensionality reduction. Subsequently, three optimization strategies—dynamic weighting, boundary contraction, and adaptive mutation—are introduced to enhance SSA, which is then employed to optimize the core parameters of the Support Vector Machine (SVM). Experiments were conducted on an underwater image dataset containing four types of targets: sea urchins, fish, rocks, and scallops. The results demonstrate that, compared with the traditional KPCA-SVM method, the integration of LBP features and the improved SSA increases classification accuracy from 55% to 94.37%, validating the effectiveness of the proposed approach in extracting underwater image features and optimizing classifier parameters. This provides technical support for improving the feasibility of automatic underwater target recognition in aquaculture applications. Full article

Given that Acer sutchuenense Franch., an endangered maple endemic to China, severely threatened by habitat degradation and climate fluctuations, understanding its spatiotemporal dynamics is crucial for formulating conservation strategies. Herein, climatic, topographic and soil variables were employed to simulate historical, present, and future distribution patterns of A. sutchuenense using the optimized MaxEnt model. Our results indicated that Mean Temperature of Driest Quarter (Bio9) and Temperature Seasonality (Bio4) were the key environmental drivers. Since the Last Interglacial, A. sutchuenense had experienced a continuously reduction in its suitable area, though the mountains surrounding the Sichuan Basin functioned as vital glacial shelters. Although the potential suitable habitat was distributed in a ring shape, A. sutchuenense occurs only on the east and west sides of the Sichuan Basin, probably due to the terrain complexity and limited dispersal ability. In the future, A. sutchuenense faces a westward contraction and a migration lag behind climate velocity due to dispersal constraints. Overall, we recommend a multi-dimensional conservation framework that prioritizes in situ conservation in core refugia, urgently establishes ecological corridors to facilitate eastward migration under climate change, implements ex situ conservation through germplasm collection for vulnerable southwestern populations, and enhances long-term monitoring to ensure species persistence. Full article

The accurate forecasting of CO${}_2$ emissions from power stations is critical for effective climate policy and the transition to sustainable energy systems. However, the complexity of power generation processes and the high dimensionality of operational data present significant challenges to traditional modeling approaches. This paper introduces a novel multi-stage framework that integrates advanced feature selection with explainable machine learning (XAI) to deliver high-accuracy forecasts of power station CO${}_2$ emissions while maintaining full model transparency. The proposed methodology comprises a three-stage feature selection process—combining filter, wrapper, and embedded methods—to systematically identify the most influential emission drivers from a large set of potential variables. The selected features are then used to train a suite of machine learning models, including XGBoost, Random Forest, LSTM, and SVR. The best-performing model, XGBoost, achieved a Root Mean Square Error (RMSE) of 28.5, a Mean Absolute Error (MAE) of 19.8, and a coefficient of determination (R²) of 0.96 on a real-world dataset. To address the “black-box” nature of these models, we employ SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret the model’s predictions, providing granular insights into the key factors driving emissions. The results demonstrate that the proposed framework not only outperforms state-of-the-art forecasting models but also offers a clear, interpretable, and actionable tool for policymakers and plant operators to support CO${}_2$ reduction strategies. The novelty of this work lies in its unique combination of a multi-stage feature selection pipeline and a comprehensive XAI-based analysis, providing a robust and transparent solution for a critical environmental challenge. Full article

This research investigated the feasibility of 3D-printed external fixator (EF) rings made from carbon fiber reinforced polyamide 6 (PA6-CF) as an alternative to the conventional metallic counterpart. The study integrated tensile testing with digital image correlation (DIC) in as-printed and cold plasma-sterilized conditions, finite-element analysis (FEA) under wire loading, topology optimization for material and energy reduction, and evaluation of printability limits for large PA6-CF rings. The average Young’s modulus was 4.76 GPa and the maximum tensile strength was 60.5 MPa for as-printed samples, decreasing by 6.4% and 10.4% after sterilization, respectively. Using these properties as model inputs, FEA predicted safety factors larger than 1.42 for all configurations under 1000 N wire pretension, while topology optimization targeted up to 50% mass reduction without compromising ring stiffness. The study also revealed challenges in the printability of PA6-CF for large and thin components, including dimensional contraction, significant warping and moisture-induced defects, requiring an experienced 3D printer operator. Full article

Background: Toe motor control contributes to postural stability and walking, yet clinical assessments have focused on toe-grip strength. The kinematics of selective toe extension under conditions requiring non-target toes to remain in contact with the ground remain poorly quantified. The aim of the present study was to characterize the kinematics and reliability of selective toe extension tasks using three-dimensional motion capture and to compare young and older adults. Methods: A total of 40 participants (20 young adults and 20 older adults) performed three tasks twice: all-toe extension, selective hallux extension, and selective four-toe extension (toes 2–5), with non-target toes required to remain in contact with the ground during selective tasks. Extension angles of the hallux, second, and fifth toes were quantified, and toe-grip strength was measured. Reliability was assessed using the intraclass correlation coefficient (ICC(1,2)). Toe angles were analyzed using two-way analysis of variance (group × condition, including resting and task conditions). Results: Toe angles demonstrated moderate to excellent reliability (ICC(1,2) = 0.81–0.95; 95% CI: 0.637–0.974). Compared with all-toe extension, both selective tasks showed reduced extension ranges, indicating an incomplete extension phenomenon in both groups. Significant group × condition interactions were observed for the hallux and second toes. During selective tasks, older adults exhibited greater unintended extension of non-target toes. Toe-grip strength was significantly lower in older adults (p < 0.001, Cohen’s d = 2.51). Conclusions: Selective toe extension tasks provide reliable kinematic indices of inter-toe motor control by quantifying incomplete extension and associated movements. Older adults showed greater associated movements under ground-contact constraints, suggesting age-related declines in motor coordination and possible reductions in toe flexor strength. Full article

The dynamic approach integrates Global Positioning System and K-band range-rate (KRR) observations to enable precise orbit determination (POD) and gravity field recovery. However, background model uncertainties and temporal aliasing introduce frequency-dependent noise into the post-fit KRR residuals, thereby degrading overall solution accuracy. To mitigate these effects, empirical signals are typically modeled using either dynamic (DYN) or kinematic (KIN) parameterization strategies. Nevertheless, the combined use of DYN and KIN parameterizations remains largely unassessed, and their potential synergistic impact on POD and gravity field recovery merits systematic evaluation. This study evaluates the individual and joint impacts of DYN and KIN (DYN+KIN) on The Gravity Recovery and Climate Experiment (GRACE) Follow-On orbit accuracy and monthly gravity field recovery using nearly one year of 2019 data (excluding February due to severe data gaps). The refined solutions act as empirical temporal filters, effectively suppressing low-frequency components in KRR residuals, particularly below 1-cycle-per-revolution. Relative to nominal ambiguity-fixed reduced-dynamic orbits, the refined solutions mainly enhance the cross-track component, with DYN+KIN showing the largest improvement, while along-track precision experiences only minor (sub-millimeter) degradation. Overall three-dimensional orbit accuracy improves from 3.8 cm to 3.0 cm (DYN), 2.8 cm (KIN), and 2.8 cm (DYN+KIN). In terms of gravity field recovery, the DYN+KIN solution begins to exhibit more pronounced deviations from the other solutions beyond degree and order 30. Over oceanic regions, residual mass anomaly analysis shows that the DYN+KIN solution is associated with an approximately 16% higher noise level compared to the individual DYN and KIN strategies, which exhibit modest noise reductions relative to the nominal solution. The DYN+KIN also exhibits a dampened ~160-day periodicity in the temporal evolution of low-degree coefficients (e.g., C_2,0), likely due to spectral overlap between empirical parameter frequencies and low-degree gravity signal components. These results indicate that over-parameterization introduces spectral redundancy and absorbs geophysical signals, underscoring the need to balance parameter flexibility and signal fidelity in gravity recovery strategies. Full article

Rapid Internet of Things (IoT) expansion creates security risks due to resource limits and evolving botnets. While Graph Neural Networks (GNNs) offer accuracy, their computational demands hinder real-time edge deployment. This study presents IoTGuard, based on a ‘Hetero-MLP’ architecture. The model replaces costly message passing with 8-dimensional categorical embeddings to capture protocol semantics. To avoid topology overfitting, L3 identifiers were excluded, relying on 13 L4 attributes selected via Pearson correlation. Evaluations on the NF-BoT-IoT-v2 dataset (37.7 M samples) demonstrate a 12.17 KB (INT8) footprint via post-training quantization. This represents a 1.9× size reduction, enabling independent operation on ARM Cortex-M7 platforms (Arm Ltd., Cambridge, UK) at 37,093 requests per second. The framework achieves a DDoS F1-score of 0.9943 with a false-positive rate of 0.0054. Comparative analysis confirms that while Random Forest is accurate, Hetero-MLP reduces parameters by 25.4× versus standard GAT models. The proposed approach balances detection depth with edge constraints, offering scalable critical infrastructure protection. Full article

Two accelerated magnetic resonance imaging (MRI) protocols for pediatric brain imaging, GOBrain and Deep Resolve Swift Brain, developed by Siemens Healthineers (Erlangen, Germany), were evaluated in a series of clinically relevant pediatric cases at 3 Tesla. Pediatric patients are particularly prone to motion, may be uncooperative, and often require sedation, especially in emergency settings. Consequently, there is a persistent clinical demand for fast brain MRI protocols that provide diagnostically sufficient image quality while minimizing examination time. Contemporary turbo spin-echo (TSE)-based clinical protocols commonly integrate parallel imaging (PI) and simultaneous multi-slice (SMS) techniques to achieve substantial reductions in scan time. Recent advances in three-dimensional volumetric encoding, compressed sensing, and deep learning (DL)-based reconstruction have further mitigated geometry-factor-related noise amplification, enabling higher acceleration factors (GOBrain). In parallel, echo-planar imaging (EPI) has emerged as a promising approach for ultrafast multi-contrast imaging. To overcome the limitations of single-shot EPI, a multi-shot EPI-based brain MRI protocol combined with the DL-based reconstruction method Deep Resolve Swift Brain has been developed. This approach leverages the efficiency of EPI while improving image quality. Using these accelerated protocols, a comprehensive diagnostic multi-contrast brain MRI examination, particularly suited to triage and emergency imaging, can be completed in minutes. This case overview, including therapy-related leukencephalopathy in acute lymphoblastic leukemia (ALL), a brain abscess, traumatic diffuse axonal injury (DAI), a posterior circulation infarction due to vertebral artery dissection, leuokostasis syndrome, and a posterior fossa tumor with obstructive hydrocephalus, demonstrates the potential clinical feasibility of both protocols in pediatric neuroimaging. Both protocols position them as supplementary options alongside established imaging protocols, while dedicated high-resolution protocols might remain necessary for subtle pathological findings, such as focal cortical dysplasia, and for neuronavigation until larger comparative studies are available. Full article

Background/Objectives: Objective and reproducible evaluation of glabellar lines remains challenging, as current clinical assessments rely largely on subjective rating scales and two-dimensional photography, which lack depth information. This study aimed to assess the clinical utility of a laser-based three-dimensional (3D) imaging approach for objective quantitative evaluation of glabellar lines in adults undergoing botulinum toxin treatment. Methods: A laser-based 3D imaging system was used to quantitatively measure glabellar line morphology. System accuracy for area, perimeter, volume, and depth was evaluated using standardized physical models. In a prospective observational study, 31 adults with moderate-to-severe glabellar lines undergoing routine botulinum toxin treatment were assessed at baseline, day 7, and week 4. Quantitative 3D measurements were compared with clinician- and participant-reported severity scores, as well as patient satisfaction and Global Rating of Outcome (GRO) scores. Results: The 3D imaging measurements demonstrated high geometric measurement precision, with errors ≤2% for area, perimeter, and volume, and ≤0.5 mm for depth. Significant reductions in wrinkle depth were observed after treatment. Quantitative 3D measurements showed moderate correlations with clinician-reported scores (r = 0.53–0.54) and participant-reported scores (r = 0.59–0.66). Improvement rates derived from 3D measurements were positively correlated with patient satisfaction and GRO scores. Conclusions: Laser-based 3D imaging provides an objective and quantitative approach for evaluating glabellar lines and treatment response to botulinum toxin. This method may complement conventional clinical assessments and support further validation in larger clinical studies. Full article

Axial flow fans are widely used in high-speed train cooling and ventilation systems, where both static efficiency and noise reduction are critical performance requirements. In this study, the effects of hub geometry variation on the aerodynamic and acoustic characteristics of an axial flow fan are numerically investigated through three-dimensional simulations. Five fan configurations with different hub angles are analyzed under identical operating conditions. Steady aerodynamic performance is first evaluated using the Reynolds-averaged Navier–Stokes (RANS) approach with the k-ω shear stress transport (SST) turbulence model. The unsteady flow field is then resolved using large eddy simulation (LES) to capture the vortex structures and blade surface pressure fluctuations responsible for noise generation. The far-field aerodynamic noise is predicted based on the Ffowcs Williams–Hawkings (FW–H) acoustic analogy, and both tonal and broadband noise characteristics are analyzed using multiple virtual microphones. The results show that reducing the hub angle leads to improved aerodynamic performance at lower volumetric flow rates. Meanwhile, a reduction in tonal noise at the blade-passing frequency (BPF) and broadband noise at higher frequencies is observed. The findings demonstrate that appropriate hub angle design provides an effective approach for the simultaneous improvement of static efficiency and the reduction of aerodynamic noise of axial-flow fans used in high-speed train applications. Full article

An integrated nonlinear dynamic model was developed to investigate resonance in a four-cylinder engine mounted on a viscoelastic foundation. A coupled lumped-parameter formulation captures vertical and torsional responses under unbalanced inertial forces, combustion torque, and stochastic base excitation. Time-domain simulations show that at low rotational speeds the vertical displacement reaches transient amplitudes before converging to periodic oscillations, whereas higher excitation speeds reduce steady-state amplitudes. Torsional motion exhibits initial angles near 0.05 rad that decay below 0.01 rad in steady state, with further reduction at higher speeds. Frequency-domain analysis indicates that vibration energy is concentrated in engine-order harmonics between approximately 8 and 50 Hz, while components above 60 Hz are strongly attenuated, yielding a dynamic range exceeding 50 dB. Finite element modal analysis identifies the first four structural modes between 18 Hz and 666 Hz, revealing an increasingly dominant overall translational mode and a localized directional behavior at higher frequencies. A high-dimensional kernel density spectrogram integrates modal and spectral features to map resonance regions. Results indicate that increasing rotational excitation enhances inertial stiffening, systematically reduces displacement amplitudes, and preserves bounded periodic dynamics without instability. Full article