Search Results (19,072)

Accurate diagnosis of knee joint injuries from magnetic resonance (MR) images is critical for patient care. Background/Objectives: While deep learning has advanced 3D MR image analysis, its reliance on extensive labeled datasets is a major hurdle for diverse knee pathologies. Few-shot learning (FSL) addresses this by enabling models to classify new conditions from minimal annotated examples, often leveraging knowledge from related tasks. However, creating robust 3D FSL frameworks for varied knee injuries remains challenging. Methods: We introduce MedNet-FS, a 3D FSL framework that effectively classifies knee injuries by utilizing domain-specific pre-trained weights and generalized end-to-end (GE2E) loss for discriminative embeddings. Results: MedNet-FS, with knee-MRI-specific pre-training, significantly outperformed models using generic or other medical pre-trained weights and approached supervised learning performance on internal datasets with limited samples (e.g., achieving an area under the curve (AUC) of 0.76 for ACL tear classification with k = 40 support samples on the MRNet dataset). External validation on the KneeMRI dataset revealed challenges in classifying partially torn ACL (AUC up to 0.58) but demonstrated promising performance for distinguishing intact versus fully ruptured ACLs (AUC 0.62 with k = 40). Conclusions: These findings demonstrate that tailored FSL strategies can substantially reduce data dependency in developing specialized medical imaging tools. This approach fosters rapid AI tool development for knee injuries and offers a scalable solution for data scarcity in other medical imaging domains, potentially democratizing AI-assisted diagnostics, particularly for rare conditions or in resource-limited settings. Full article

Ransomware, a significant cybersecurity threat, encrypts files and causes substantial damage, making early detection crucial yet challenging. This paper introduces a novel multi-phase framework for early ransomware detection, designed to enhance accuracy and minimize false positives. The framework addresses the limitations of existing methods by integrating operational data with situational and threat intelligence, enabling it to dynamically adapt to the evolving ransomware landscape. Key innovations include (1) data augmentation using a Bi-Gradual Minimax Generative Adversarial Network (BGM-GAN) to generate synthetic ransomware attack patterns, addressing data insufficiency; (2) Incremental Mutual Information Selection (IMIS) for dynamically selecting relevant features, adapting to evolving ransomware behaviors and reducing computational overhead; and (3) a Deep Belief Network (DBN) detection architecture, trained on the augmented data and optimized with Uncertainty-Aware Dynamic Early Stopping (UA-DES) to prevent overfitting. The model demonstrates a 4% improvement in detection accuracy (from 90% to 94%) through synthetic data generation and reduces false positives from 15.4% to 14%. The IMIS technique further increases accuracy to 96% while reducing false positives. The UA-DES optimization boosts accuracy to 98.6% and lowers false positives to 10%. Overall, this framework effectively addresses the challenges posed by evolving ransomware, significantly enhancing detection accuracy and reliability. Full article

This study presents an advanced energy demand management approach within residential microgrids using bipartite models for optimal demand response. The methodology relies on linear programming, specifically the Simplex algorithm, to optimize power distribution while minimizing costs. The model aims to reduce residential energy consumption by flattening the demand curve through demand response programs. Additionally, the Internet of Things (IoT) is integrated as a communication channel to ensure efficient energy management without compromising user comfort. The research evaluates energy resource allocation using bipartite graphs, modeling the generation of energy from renewable and conventional high-efficiency sources. Various case studies analyze scenarios with and without market constraints, assessing the impact of demand response at different levels (5%, 10%, 15%, and 20%). Results demonstrate a significant reduction in reliance on external grids, with optimized energy distribution leading to potential cost savings for consumers. The findings suggest that intelligent demand response strategies can enhance microgrid efficiency, supporting sustainability and reducing carbon footprints. Full article

Massive relief supplies inter-regional lateral transshipment (MRSIRLT) can significantly enhance the efficiency of disaster response, meet the needs of affected areas (AAs), and reduce deprivation costs. This paper develops an integrated allocation and intermodality optimization model (AIOM) to address the MRSIRLT challenge. A phased interactive framework incorporating adaptive differential evolution (JADE) and improved adaptive large neighborhood search (IALNS) is designed. Specifically, JADE is employed in the first stage to allocate the volume of massive relief supplies, aiming to minimize deprivation costs, while IALNS optimizes intermodal routing in the second stage to minimize the weighted sum of transportation time and cost. A case study based on a typhoon disaster in the Chinese region of Bohai Rim demonstrates and verifies the effectiveness and applicability of the proposed model and algorithm. The results and sensitivity analysis indicate that reducing loading and unloading times and improving transshipment efficiency can effectively decrease transfer time. Additionally, the weights assigned to total transfer time and costs can be balanced depending on demand satisfaction levels. Full article

This paper addresses a fundamental challenge in statistical radar detection systems: optimizing the trade-off between the probability of a false alarm ($P_{FA}$) and the probability of a miss ($P_M$). These two metrics are inversely related and critical for performance evaluation. Traditional detection approaches often enhance one aspect at the expense of the other, limiting their practical applicability. To overcome this limitation, a fuzzy hypothesis testing framework is introduced that improves decision making under uncertainty by incorporating both crisp and fuzzy data representations. The methodology is divided into three phases. In the first phase, we reduce the probability of false alarm $P_{FA}$ while maintaining a constant probability of miss $P_M$ using crisp data characterized by deterministic values and classical statistical thresholds. In the second phase, the inverse scenario is considered: minimizing $P_M$ while keeping $P_{FA}$ fixed. This is achieved through parameter tuning and refined threshold calibration. In the third phase, a strategy is developed to simultaneously enhance both $P_{FA}$ and $P_M$, despite their inverse correlation, by adopting adaptive decision rules. To further strengthen system adaptability, fuzzy data are introduced, which effectively model imprecision and ambiguity. This enhances robustness, particularly in scenarios where rapid and accurate classification is essential. The proposed methods are validated through both real and synthetic simulations of radar measurements, demonstrating their ability to enhance detection reliability across diverse conditions. The findings confirm the applicability of fuzzy hypothesis testing for modern radar systems in both civilian and military contexts, providing a statistically sound and operationally applicable approach for reducing detection errors and optimizing system performance. Full article

Accurate identification of active-layer compositions in organic photovoltaic (OPV) devices often relies on invasive techniques such as electrical measurements or material extraction, which risk damaging the device. In this study, we propose a non-invasive classification approach based on simulated full-device absorption spectra. To account for fabrication-related variability, the active-layer thickness varied by over ±15% around the optimal value, creating a realistic and diverse training dataset. A multilayer perceptron (MLP) neural network was applied with various activation functions, optimization algorithms, and data split ratios. The optimized model achieved classification accuracies exceeding 99% on both training and testing sets, with minimal sensitivity to random initialization or data partitioning. These results demonstrate the potential of applying deep learning to spectral data for reliable, non-destructive OPV composition classification, paving the way for integration into automated manufacturing diagnostics and quality control workflows. Full article

Earth fill dams are susceptible to internal erosion and instability when founded over cavity-prone formations such as gypsum or karstic limestone. Subsurface voids can significantly compromise dam performance, particularly under seismic loading, by altering seepage paths, raising pore pressures, and inducing structural deformation. This study examines the influence of cavity presence, location, shape, and size on the behavior of zoned earth dams. A 1:25 scale physical model was tested on a uniaxial shake table under varying seismic intensities, and seepage behavior was observed under steady-state conditions. Numerical simulations using SEEP/W and QUAKE/W in GeoStudio complemented the experimental work. Results revealed that upstream and double-cavity configurations caused the greatest deformation, including crest displacements of up to 0.030 m and upstream subsidence of ~7 cm under 0.47 g shaking. Pore pressures increased markedly near cavities, with peaks exceeding 2.7 kPa. Irregularly shaped and larger cavities further amplified these effects and led to dynamic factors of safety falling below 0.6. In contrast, downstream cavities produced minimal impact. The excellent agreement between experimental and numerical results validates the modeling approach. Overall, the findings highlight that cavity geometry and location are critical determinants of dam safety under both static and seismic conditions. Full article

Hydraulic tomography (HT) based on the successive linear estimator (SLE) offers the high-resolution characterization of aquifer heterogeneity but conventionally requires prolonged pumping to achieve steady-state conditions, limiting its applicability in contamination-sensitive or low-permeability settings. This study bridged theoretical and practical gaps (1) by identifying spatial periodicity (hole effect) as the mechanism underlying divergences in steady-state cross-correlation patterns between random finite element method (RFEM) and first-order analysis, modeled via an oscillatory covariance function, and (2) by validating a novel short-term sampling strategy for SLE-based HT using field experiments at the University of Göttingen test site. Utilizing early-time drawdown data, we reconstructed spatially congruent distributions of hydraulic conductivity, specific storage, and hydraulic diffusivity after rigorous wavelet denoising. The results demonstrate that the short-term sampling strategy achieves accuracy comparable to that of long-term sampling strategy in characterizing aquifer heterogeneity. Critically, by decoupling SLE from steady-state requirements, this approach minimizes groundwater disturbance and time costs, expanding HT’s feasibility to challenging environments. Full article

This study presents a quasi-static optimization framework for the pressure-based control of a multi-segment soft continuum manipulator. The proposed method circumvents traditional curvature and length-based modeling by directly identifying the quasi-static input–output relationship between actuator pressures and the 6-DoF end-effector pose. Experimental data were collected using a high-frequency electromagnetic tracking system under monotonic pressurization to minimize hysteresis effects. Transfer functions were identified for each coordinate–actuator pair using the System Identification Toolbox in MATLAB, and optimal actuator pressures were computed analytically by solving a constrained quadratic program via a manual active-set method. The resulting control strategy achieved sub-millimeter positioning error while minimizing the number of actuators engaged. The approach is computationally efficient, sensor-minimal, and fully implementable in open-loop settings. Despite certain limitations due to sensor nonlinearity and actuator hysteresis, the method provides a robust foundation for feedforward control and the real-time deployment of soft robots in quasi-static tasks. Full article

To reduce carbon emission levels and improve the low-carbon performance and economic efficiency of Integrated Energy Systems (IESs), this paper introduces oxy-fuel combustion technology to transform traditional units and proposes a low-carbon economic dispatch method. Considering the stepwise carbon trading mechanism, it provides new ideas for promoting energy conservation, emission reduction, and economic operation of integrated energy systems from both technical and policy perspectives. Firstly, the basic principles and energy flow characteristics of oxy-fuel combustion technology are studied, and a model including an air separation unit, an oxygen storage tank, and carbon capture equipment is constructed. Secondly, a two-stage power-to-gas (P2G) model is established to build a joint operation framework for oxy-fuel combustion and P2G. On this basis, a stepwise carbon trading mechanism is introduced to further constrain the carbon emissions of the system, and a low-carbon economic dispatch model with the objective of minimizing the total system operation cost is established. Finally, multiple scenarios are set up for simulation analysis, which verifies that the proposed low-carbon economic optimal dispatch strategy can effectively reduce the system operation cost by approximately 21.4% and improve the system’s carbon emission level with a total carbon emission reduction of about 38.3%. Meanwhile, the introduction of the stepwise carbon trading mechanism reduces the total cost by 12.3% and carbon emissions by 2010.19 tons, increasing the carbon trading revenue. Full article

As energy efficiency becomes a significant performance indicator in mobile machines, power losses are recognized as an important criterion in the design and optimization of these systems. This paper analyses the loads and power loss due to friction in the revolute joints of the manipulator drive mechanisms during all phases of the loader manipulation task, based on dynamic simulations of the loader model with different variants of Z-kinematics manipulator drive mechanisms, using the MSC ADAMS 2020 software. The analysis is based on a general dynamic mathematical model of the loader, which enables the assessment of the influence of the parameters of the manipulator mechanisms on the functional, structural, and tribological characteristics of the revolute joints within the manipulator’s kinematic chain. Based on the analysis results, a minimum power loss criterion was defined as part of a multi-criteria optimal synthesis procedure for the manipulator drive mechanisms, with the objective of maximizing energy efficiency by minimizing power loss caused by friction in the revolute joints of the manipulator drive mechanisms. Full article

Radio frequency fingerprint identification (RFFI) leverages the unique characteristics of radio signals resulting from inherent hardware imperfections for identification, making it essential for applications in telecommunications, cybersecurity, and surveillance. Despite the advancements brought by deep learning in enhancing RFFI accuracy, challenges persist in model deployment, particularly when transferring RFFI models across different receivers. Variations in receiver hardware can lead to significant performance declines due to shifts in data distribution. This paper introduces the source-free cross-receiver RFFI (SCRFFI) problem, which centers on adapting pre-trained RF fingerprinting models to new receivers without needing access to original training data from other devices, addressing concerns of data privacy and transmission limitations. We propose a novel approach called contrastive source-free cross-receiver network (CSCNet), which employs contrastive learning to facilitate model adaptation using only unlabeled data from the deployed receiver. By incorporating a three-pronged loss function strategy—minimizing information entropy loss, implementing pseudo-label self-supervised loss, and leveraging contrastive learning loss—CSCNet effectively captures the relationships between signal samples, enhancing recognition accuracy and robustness, thereby directly mitigating the impact of receiver variations and the absence of source data. Our theoretical analysis provides a solid foundation for the generalization performance of SCRFFI, which is corroborated by extensive experiments on real-world datasets, where under realistic noise and channel conditions, that CSCNet significantly improves recognition accuracy and robustness, achieving an average improvement of at least 13% over existing methods and, notably, a 47% increase in specific challenging cross-receiver adaptation tasks. Full article

In response to the challenge of multi-objective optimal scheduling and efficient solution of hydropower stations under large-scale renewable energy integration, this study develops a multi-objective optimization model with the dual goals of maximizing total power generation and minimizing the variance of residual load. Four complementarity evaluation indicators are used to analyze the wind–solar complementarity characteristics. Building upon this foundation, Hyper-dominance Evolutionary Algorithm (HEA)—capable of efficiently solving high-dimensional problems—is introduced for the first time in the context of wind–solar–hydropower integrated scheduling. The case study results show that the HEA performs better than the benchmark algorithms, with the best mean Hypervolume and Inverted Generational Distance Plus across nine Walking Fish Group (WFG) series test functions. For the hydro-wind-solar scheduling problem, HEA obtains Pareto frontier solutions with both maximum power generation and minimal residual load variance, thus effectively solving the multi-objective scheduling problem of the hydropower system. This work provides a valuable reference for modeling and efficiently solving the multi-objective scheduling problem of hydropower in the context of emerging power systems. This work provides a valuable reference for the modeling and efficient solution of hydropower multi-objective scheduling problems in the context of emerging power systems. Full article

Route planning under uncertain traffic conditions requires accounting for not only expected travel times but also the risk of late arrivals. This study proposes a mean-upper partial moment (MUPM) framework for pathfinding that explicitly considers travel time unreliability. The framework incorporates a benchmark travel time to measure the upper partial moment (UPM), capturing both the probability and severity of delays. By adjusting a risk parameter ($\theta$), the model reflects different traveler risk preferences and unifies several existing reliability measures, including on-time arrival probability, late arrival penalty, and semi-variance. A bi-objective model is formulated to simultaneously minimize mean travel time and UPM. Theoretical analysis shows that the MUPM framework is consistent with the expected utility theory (EUT) and stochastic dominance theory (SDT), providing a behavioral foundation for the model. To efficiently solve the model, an SDT-based label-correcting algorithm is adapted, with a pre-screening step to reduce unnecessary pairwise path comparisons. Numerical experiments using GPS probe vehicle data from Louisville, Kentucky, USA, demonstrate that varying $\theta$ values lead to different non-dominated paths. Lower $\theta$ values emphasize frequent small delays but may overlook excessive delays, while higher $\theta$ values effectively capture the tail risk, aligning with the behavior of risk-averse travelers. The MUPM framework provides a flexible, behaviorally grounded, and computationally scalable approach to pathfinding under uncertainty. It holds strong potential for applications in traveler information systems, transportation planning, and network resilience analysis. Full article

Background: Childhood obesity is a significant public health issue linked to poor diet, low physical activity, and limited access to supportive environments. Green schoolyards may promote physical activity and improve health outcomes. This study evaluated the impact of the Green Healthy Primary School of the Future (GHPSF) intervention—greening schoolyards—on children’s BMI z-scores, waist circumference, and hip circumference over 18 months, and compared these effects to those observed in the earlier Healthy Primary School of the Future (HPSF) initiative. Methods: This longitudinal quasi-experimental study included two intervention and two control schools in Limburg, a province both in the Netherlands and Belgium. Children aged 8–12 years (n = 159) were assessed at baseline, 12 months, and 18 months for anthropometric outcomes. Linear mixed models were used to estimate intervention effects over time, adjusting for sex, age, country, and socioeconomic background. Standardized effect sizes (ESs) were calculated. Results: The intervention group showed a greater reduction in BMI z-scores at 12 months (ES = −0.15, p = 0.084), though this was not statistically significant. Waist circumference increased in both groups, but less so in the intervention group, at 12 months (ES = −0.23, p = 0.057) and 18 months (ES = −0.13, p = 0.235). Hip circumference and waist–hip ratio changes were minimal and non-significant. GHPSF effect sizes were comparable to or greater than those from the HPSF initiative. Conclusions: Though not statistically significant, trends suggest that greening schoolyards may support favorable changes in anthropometric outcomes. Further research with larger samples and longer follow-up is recommended. Full article