Search Results (29,021)

Early and reliable detection of skin cancer is critical for improving patient outcomes and minimizing diagnostic uncertainty in dermatological practice. This study proposes an interpretable hybrid framework that integrates ConvMixer-based deep feature extraction with gradient boosting classifiers to perform multi-class skin lesion classification on the publicly available PAD-UFES-20 dataset. The dataset contains 2298 dermoscopic and clinical images with associated patient metadata (age, gender, and anatomical site), enabling a joint evaluation of demographic and anatomical factors influencing model performance. After data augmentation, normalization, and class balancing using Borderline-SMOTE, Image embeddings extracted via ConvMixer were integrated with patient metadata and subsequently classified using CatBoost, XGBoost, and LightGBM. Among these, CatBoost achieved the highest macro-AUC of 0.94 and macro-F1 of 0.88, with a melanoma sensitivity of 0.91, while maintaining good calibration (Brier score = 0.06). Grad-CAM and SHAP analyses confirmed that the model’s attention and feature importance correspond to clinically relevant lesion regions and attributes. The results highlight that age and body-region imbalances in the PAD-UFES-20 dataset modestly influence predictive behavior, emphasizing the importance of balanced sampling and stratified validation. Overall, the proposed ConvMixer–CatBoost framework provides a compact, explainable, and generalizable solution for AI-assisted skin cancer classification. Full article

Background/Objectives: Anterior disc displacement (ADD) is a common temporomandibular joint (TMJ) disorder and may progress to internal derangements. Although arthroscopic discopexy (minimally invasive disc repositioning with fixation) has been adopted, suggested techniques and pooled outcomes have not been comprehensively synthesized. The aim of the present study was to summarize the effectiveness of arthroscopic discopexy in treating patients with disc-related TMJ disorders. Methods: This systematic review and meta-analysis followed Cochrane guidance and PRISMA 2020 protocol. Four databases were searched through 1 September 2025. A total of 26 studies were included in this review. Nine studies met the eligibility criteria for meta-analysis and were pooled. The remaining 17 studies were narratively described, focusing on surgical characteristics. Continuous outcomes (MIO, pain score (VAS 0–10)) were pooled as mean differences (MD) with 95% confidence intervals (CIs), and joint sounds were synthesized as dichotomous outcomes. Analyses and heterogeneity were performed in RevMan 5.4. Certainty was graded with GRADE. (PROSPERO: CRD420251145229). Results: 1086 TMJs were analyzed. Arthroscopic discopexy significantly improved MIO, pain, and joint sounds at all time points. The MD of MIO was 10.58 mm (95% CI: 4.46 to 16.70; p ≤ 0.001), 9.83 mm (95% CI: 4.09 to 15.57; p ≤ 0.001), and 13.06 mm (95% CI: 4.40 to 21.72; p ≤ 0.001), respectively. The MD of the pain score was −4.36 (95% CI: −6.89 to −1.82; p ≤ 0.001), −3.91 (95% CI: −6.23 to −1.59; p ≤ 0.001), and −4.56 (95% CI: −7.81 to −1.31; p < 0.01), respectively. At 12 months, joint sounds were less frequent than preoperatively (OR = 0.07; 95% CI: 0.01 to 0.37; p < 0.01). Overall, the certainty of evidence according to the GRADE approach was rated as low. Therefore, the results should be interpreted with caution, as high heterogeneity was observed across the three follow-up time points and the included studies were observational. Conclusions: These findings underscore the significance of arthroscopic discopexy in enhancing TMJ function and alleviating symptoms. Current evidence, characterized by a low risk of bias and low certainty, supports the advantage of arthroscopic discopexy. Due to the observational evidence base and heterogeneity, high-quality randomized trials conducted under standardized treatment protocols and with longer follow-up are needed. Full article

Gene expression-based tumor classification aims to distinguish tumor types based on gene expression profiles. This task is difficult due to the high dimensionality of gene expression data and limited sample sizes. Most datasets contain tens of thousands of genes but only a small number of samples. As a result, selecting informative genes is necessary to improve classification performance and model interpretability. Many existing gene selection methods fail to produce stable and consistent results, especially when training data are limited. To address this, we propose a multi-task ensemble strategy that combines repeated sampling with joint feature selection and classification. The method generates multiple training subsets and applies multi-task logistic regression with ${\mathcal{\ell }}_{2,1}$ group sparsity regularization to select a subset of genes that appears consistently across tasks. This promotes stability and reduces redundancy. The framework supports integration with standard classifiers such as logistic regression and support vector machines. It performs both gene selection and classification in a single process. We evaluate the method on simulated and real gene expression datasets. The results show that it outperforms several baseline methods in classification accuracy and the consistency of selected genes. Full article

Virtual reality (VR) offers built-in wearable sensor-based tracking capabilities. Current research focusses on position and orientation error, with limited results on more clinically relevant metrics, such as joint angles. This leads us to our first objective, to characterize the accuracy of upper extremity VR motion capture. Since the intent is for clinical translation, our second objective is to compare the errors across people identified as healthy controls and people who had experienced a spinal cord injury (SCI). Spatially and temporally synced VR and infrared motion capture data were collected during a variety of custom VR Beat Saber levels. Error values were found with infrared motion capture as the ground truth. The median RMSE was found to be below 7° for shoulder horizontal adduction and elbow flexion and 5° for shoulder elevation and wrist joint metrics. The percentage median error for the range of motion was found to be below 30%, 15%, and 5% for the frontal wrist, sagittal wrist, and all other joints, respectively. Larger standard deviations suggest that repetitions are needed to obtain reliable measurements. No statistical difference in any error metric was found between the control cohort and SCI cohort, providing evidence for clinical translation for post-SCI treatment. Full article

Additive manufacturing technologies are no longer limited to rapid prototyping but are increasingly used for low-volume production of functional end-use components. Among advanced AM techniques, HP Multi-Jet Fusion (MJF) stands out for its high precision and efficiency. Polyamides, thanks to their balanced mechanical and thermal properties, are commonly used as building materials in this technology. However, these materials are notoriously difficult to bond with conventional adhesives. This study investigates the shear strength of bonded joints made from two frequently used MJF materials—PA12 and glass-bead-filled PA12—using four different industrial adhesives. Experimental procedures were conducted according to ASTM standards. Specimens for single-lap-shear tests were fabricated on an HP MJF 4200 series printer, bonded using a custom jig, and tested on a Zwick-Roell Z250 electro-mechanical testing machine. Surface roughness of the adherends was measured with a 3D optical microscope to assess its influence on bonding performance. The polyurethane-based adhesive (3M Scotch-Weld DP620NS) demonstrated superior performance with maximum shear strengths of 5.0 ± 0.35 MPa for PA12 and 4.4 ± 0.03 MPa for PA12GB, representing 30% and 17% higher strength, respectively, compared to epoxy-based alternatives. The hybrid cyanoacrylate–epoxy adhesive (Loctite HY4090) was the only system showing improved performance with glass-bead-reinforced substrate (16.5% increase from PA12 to PA12GB). Statistical analysis confirmed significant differences between adhesive types (F_3,24 = 31.37, p < 0.001), with adhesive selection accounting for 65.7% of total performance variance. In addition to the experimental work, a finite element-based numerical simulation was performed to analyze the distribution of shear and peel stresses across the adhesive layer using Siemens Simcenter 3D 2406 software with the NX Nastran solver. The numerical results were compared with analytical predictions from the Volkersen and Goland–Reissner models. Full article

Sex-related differences in lower-limb biomechanics and neuromuscular strategies during rope jumping remain underexplored, particularly in combat-sport athletes. This study investigated leg stiffness and muscle activation in ten female (22.8 ± 0.8 years) and ten male (22.9 ± 1.4 years) Muay Thai athletes. Participants performed rope skipping under three conditions: dominant leg, non-dominant leg, and double leg at 2.2 Hz. Ground reaction forces were recorded at 1000 Hz, center of mass displacement at 200 Hz, and electromyographic activity of the vastus lateralis, biceps femoris, tibialis anterior, and medial gastrocnemius at 3000 Hz. Vertical stiffness (K_vert) was calculated as the ratio of peak vertical force to displacement. Results showed no significant sex differences in peak ground reaction force (e.g., dominant leg: females 2.83 ± 0.42 vs. males 3.22 ± 0.57 kN; double leg: females 4.04 ± 0.83 vs. males 4.35 ± 0.73 kN; p > 0.05), vertical stiffness (females 17.02 ± 3.66 vs. males 16.21 ± 4.09 kN/m; p > 0.05), contact time (females 0.280 ± 0.03 vs. males 0.275 ± 0.05 s; p > 0.05), or flight time (females 0.205 ± 0.03 vs. males 0.245 ± 0.05 s; p > 0.05). In contrast, females exhibited significantly higher co-activation ratios during unilateral skipping, including BF/VL (0.76 ± 0.18 vs. 0.63 ± 0.10; p < 0.05) and TA/MG (0.38 ± 0.11 vs. 0.29 ± 0.07; p < 0.05), suggesting a neuromuscular strategy to enhance joint stability. These findings highlight rope jumping as a practical drill that can promote neuromuscular control and stability in Muay Thai training. Full article

Enhancing winter wheat yield in early spring relies on optimal soil temperature (ST) conditions and robust root systems, particularly in cold and dry areas. However, the long-term combined effects of conservation tillage and plastic film mulching (PFM) on the crop root system during early spring (the period of rejuvenation and jointing) remain unstudied. This study is based on a 22-year field experiment involving two long-term conservation tillage methods: mouldboard plowing with crop residue incorporation (MC) and no-tillage with crop residue cover (NC). The main treatments were further divided by applying black (B) and white (W) plastic films to each, resulting in MC with black (MCB) and white (MCW), and NC with black (NCB) and white (NCW) films. ST was recorded at depths of 0–40 cm during the afternoon, evening, and morning, while root characteristics (RCs) were measured at the peak flowering stage at depths of 0–60 cm, and crop yield and attributes were recorded at harvest during the 2023–2024 cropping season. Compared with MC and NC, MCB and MCW increased afternoon ST by 2.5 °C and 0.94 °C, and evening ST by 1.94 °C and 1.87 °C, while NCB and NCW decreased ST. MCB and MCW also increased accumulated ST during overwintering (131–161 °C) under the tilled system. PFM on MC increased the root length and weight densities by 10–17% and 25–32%, respectively; NCB and NCW decreased RCs by 8–15.2% across the soil depth. Additionally, afternoon and evening STs at 5–20 cm positively correlated with RCs and yield attributes (r > 0.84), whereas morning ST and a 40 cm depth were negatively correlated (r < −0.77). Under tilled conditions, both MCB and MCW substantially increased grain yield (10–12%) and biomass (31–38%) compared with MC. In contrast, NCB and NCW showed no yield and biomass advantage and even reductions (16–12% and 14–3%, respectively) compared with NC. FPM improved STs, RCs, and yield under tilled conditions but not in no-till systems, highlighting the need for supplementary practices to optimize ST in no-till systems. Full article

With the accelerating digital transformation of modern society, numerous data center (DC) agents are connected to the distribution power networks (DPNs) via microgrid and engaging in fierce market competition. To address the asymmetric operational risks faced by each data center agent, particularly those arising from market volatility and equipment failures, a novel cooperative game-theoretic approach is proposed in this paper. Firstly, a cooperative operation framework for the microgrid-integrated data centers (MDCs) system is established from two dimensions: joint task allocation across MDCs on the computing side and energy sharing among MDCs on the power side. Moreover, an optimal operating model for MDCs is established, which integrates the task allocation model that takes into account the task processing capacity of MDCs. Then, a cooperative operation model for the MDCs system based on Nash game theory is developed, and a joint solution framework for task allocation and the cooperative operation model is designed. Finally, the proposed cooperative game-theoretic approach is validated in a test system. The results show that the proposed approach ensures the reliable operation of the DPN while avoiding asymmetric operation risks among MDCs. It enhances the stability and security of distributed data processing. Furthermore, the Nash game-theoretic model achieves a symmetric distribution of profits and risks across MDCs, eliminating individual biases and maximizing the overall benefits of the cooperative alliance. Full article

Fingerplate expansion joints are commonly used in bridges to accommodate large movements in bridge decks, often due to thermal expansion or contraction. Although these joints are designed to last the bridge’s lifetime, they have experienced premature degradation under high-volume vehicular loads. Damage to these joints can compromise structural integrity and endanger public safety. To address this, a series of experimental fatigue tests were conducted to simulate cyclic vehicular loading, with the goal of identifying the controlling failure modes and refining design practices for fingerplate expansion joints. The study involved constructing fingerplate joint specimens based on standard Missouri Department of Transportation (MoDOT) designs, incorporating three design variables: fingerplate thickness, flange stiffeners, and concrete embedment. Additionally, two optimized designs were developed and tested under both fatigue and static loading conditions. Two distinct failure types were observed in the specimens. Specimens with flange stiffeners experienced fatigue failure, characterized by crack propagation through the back weld of the fingerplate to the supporting beam. In contrast, specimens without flange stiffeners failed due to serviceability issues, as they could not sustain the required load before reaching the maximum allowable deformation, leading to buckling of the supporting beam’s top flange. The optimized designs showed no fatigue degradation and exhibited increased ultimate strengths compared to the standard MoDOT designs. Overall, a thicker fingerplate improved the stiffness and fatigue performance of the expansion joint, while bolted connections effectively eliminated the crack propagation fatigue failure observed in many specimens and in the field. Full article

Compared with pure pumped storage, hybrid pumped storage plants (HPSPs) face more complex challenges in electricity markets, such as multi-time-scale decision-making and coupled market mechanisms. Existing mid- to long-term curve decomposition strategies often lead to deviations from actual spot prices and compressed bidding space, limiting profitability and sustainable development. To address this, this study introduces Contracts for Difference (CfDs) to enhance revenue and operational flexibility. A bi-level optimization model is developed for joint participation in spot and frequency regulation markets under CfDs: the upper level maximizes HPSP revenue through capacity allocation and bidding, while the lower level maximizes social welfare via joint energy and ancillary service market clearing. The model is solved using a commercial solver and NSGA-II. Simulations show that CfDs increase spot market revenue by 33.2% and improve bidding alignment with price fluctuations, demonstrating strong market adaptability. Full article

With the rapid advancement of cloud computing, edge computing, and the Internet of Things, traditional routing protocols such as OSPF—which rely solely on network topology and link state while neglecting computing power resource status—struggle to meet the network-computing synergy demands of the Computing Power Network (CPN). Existing reinforcement learning-based routing approaches, despite incorporating deep strategies, still suffer from issues such as resource imbalance. To address this, this study proposes a reinforcement learning-based computing-aware routing path selection method—the Computing-Aware Routing-Reinforcement Learning (CAR-RL) algorithm. This achieves coordination between network and computing power resources through multi-factor joint computation of “computational power + network”. The algorithm constructs a multi-factor weighted Markov Decision Process (MDP) to select the optimal computing-aware routing path by real-time perception of network traffic and computing power status. Experiments conducted on the GN4–3N network topology using Mininet and K8S simulations demonstrate that compared to algorithms such as Q-Learning, DDPG, and CEDRL, the CAR-RL algorithm achieves performance improvements of 24.7%, 35.6%, and 23.1%, respectively, in average packet loss rate, average latency, and average throughput. This research not only provides a reference technical implementation path for computing-aware routing selection and optimisation in computing power networks but also advances the efficient integration of network and computing power resources. Full article

In an era of intensifying global technological competition and systemic disruptions, the resilience of metropolitan innovation networks has emerged as a cornerstone of sustainable regional development. Based on joint invention patents, this study employs a multi-method analytical framework integrating social network analysis, network motif analysis, a random walk algorithm, and the Exponential Random Graph Model (ERGM) to trace the evolution of resilience across node, structural, and community levels in the Shanghai Metropolitan Area (2011–2020). Our findings reveal a significant trajectory of strengthening resilience, marked not only by a shift from a monocentric to a polycentric structure at the node level but also by a qualitative change in collaborative patterns at the structural level, and enhanced integration at the community level. ERGM analysis identifies policy coordination and industrial upgrading as the most potent drivers of this evolution, with a pivotal finding being that digital connectivity, measured by information proximity, has superseded geographic proximity in facilitating collaboration. This study develops and applies a multi-scale resilience framework, while also extending proximity theory by highlighting the growing importance of policy and information dimensions over geographic distance. It offers actionable insights for building resilient innovation ecosystems in policy-driven metropolitan regions. Full article

Optimal lower limb biomechanics are crucial for movement efficiency and injury prevention in football players. The single-leg squat serves as a valuable assessment tool for neuromuscular control, providing insight into movement patterns and potential imbalances. Deficits in hip strength, ankle mobility, and foot alignment can significantly influence biomechanics, leading to compensatory movements and increased joint stress. Identifying and addressing these factors through targeted training can enhance performance and reduce the risk of injury. This study aimed to examine the relationship between hip and ankle/foot mobility, strength, and alignment with hip kinematics during the single-leg squat in football players. A cross-sectional study assessed 25 professional football players. Measurements included isometric strength of the hip abductors and external rotators, ankle dorsiflexion range of motion, and shank–forefoot alignment. Hip kinematics during the single-leg squat were analyzed using Inertial Measurement Units, and Pearson’s correlation coefficient was applied (p < 0.05). Shank–forefoot alignment showed moderate to strong correlations with contralateral pelvic drop (r = 0.44; p = 0.035), hip adduction (r = 0.42; p = 0.036), and hip internal rotation (r = 0.51; p = 0.009) during the single-leg squat. These findings highlight the importance of foot alignment in movement control, reinforcing its relevance for injury prevention strategies in football players. Full article

In this study, the stress–strain constitutive models of Cu-Cu joints in hybrid bonding after primary and secondary annealing were determined using nanoindentation experiments and finite element inverse analysis, and the correlation mechanism between the microstructure and macroscopic mechanical properties in hybrid bonding Cu-Cu joints during secondary annealing was revealed. The 350–400 °C secondary annealing facilitates recrystallization–grain growth, increasing grain size from 0.62 μm after primary annealing to 0.71 μm, accompanied by a 12% reduction in kernel average misorientation (KAM) values. This process enhances interface non-planarization and optimizes bonding quality. Concurrently, the secondary annealed Cu-Cu joints exhibit a softening effect, manifested by decreasing trends in elastic modulus (131.02 → 118.98 GPa), hardness (1.78 → 1.51 GPa), and yield strength (70.52 → 56.12 MPa), primarily attributed to the Hall–Petch effect and residual stress release. Notably, the yield strength of secondary annealed Cu-Cu joints demonstrates 31.0% and 68.5% enhancements compared to TSV-Cu (42.83 MPa) and bulk Cu (33.3 MPa), respectively. Full article

Robot-based rehabilitation emerges as a promise to enhance mobility and improve the rehabilitation outcomes in children with cerebral palsy. The study aimed to evaluate the preliminary effects of a robot-based therapy program with Atlas-2030 on spatiotemporal gait parameters, pelvis kinematics, gross-motor function, quality of life, and joint range-of-motion in children with cerebral palsy receiving care at a specialized rehabilitation center. This is a single-arm, institution-based, quantitative, longitudinal, pilot study with repeated measures. Sixteen sessions of a robot-based therapy program with the Atlas-2030 wearable exoskeleton were applied to all the children from APAC-IAP in Mexico City with cerebral palsy. Pre-intervention, after eight and sixteen sessions, the GMFM-66, the CP QoL-Child, and gait analysis were performed. The results suggest that an Atlas-2030 robot-based therapy program combined with therapeutic stimulation exhibited better scores on the modified Ashworth scale: hip flexors and extensors: 2.0(1.0), knee flexors and extensors: 2.0(2.9), p > 0.0167, and experience enhanced range of motion in hip flexion: 122.5(5) deg, and extension: 11(5) deg and knee extension: 0(5) deg, p < 0.0167, pelvis rotation approached zero on both sides (left: −1.99(14.04, right: 2.22(13.43), p > 0.0167) reducing the difference in laterality, inducing physiological muscle activation patterns, and higher scores in quality of life regarding well-being and acceptance: 17(1.0) and emotional well-being and self-esteem: 14.5 (1.0), p > 0.0167. The limitations of this study include the following: recruitment from a single specialty care center, the absence of a control group, and the adjusted significance level of p < 0.0167, which may lead to false negatives. Full article