Search Results (181)

Background: Unilateral complex training has been proposed as an effective strategy to improve lower-limb neuromuscular function; however, its effects on isokinetic strength, inter-limb strength asymmetry, and athletic performance in competitive basketball players remain insufficiently investigated. This study examined the effects of an eight-week unilateral complex training program on lower-limb isokinetic strength, strength asymmetry, hamstring-to-quadriceps (H/Q) ratio, and athletic performance in young male basketball players. Methods: Twenty-nine competitive male basketball players (age: 19.14 ± 0.99 years) competing in the Turkish Basketball Youth League and U18 League were randomly assigned to an experimental group (n = 15) or a control group (n = 14). The experimental group performed unilateral complex training twice weekly for eight weeks in addition to regular basketball practice, whereas the control group continued routine basketball training only. Before and after the intervention, lower-limb isokinetic strength (60°·s⁻¹), bilateral strength asymmetry, H/Q ratio, countermovement jump (CMJ), Abalakov jump, 20-m sprint performance, and force-platform-derived jump variables were assessed. Data were analyzed using two-way mixed-design analysis of variance. Results: Significant Group × Time interactions were observed for right and left knee flexor strength and left knee extensor strength (all p < 0.05). Bilateral quadriceps and hamstring strength asymmetries were significantly reduced by approximately 47% and 63%, respectively, accompanied by an improvement in the left H/Q ratio (p < 0.05). The intervention also produced significant improvements in CMJ height, Abalakov jump performance, flight time, time to takeoff, and 20-m sprint performance (all p < 0.05), whereas no significant interaction was found for peak force (p > 0.05). Conclusions: Adding an eight-week unilateral complex training program to regular basketball training was associated with improvements in selected lower-limb isokinetic strength and athletic performance outcomes, reductions in measured inter-limb strength asymmetry indices, and a side-specific improvement in the left H/Q ratio in competitive young male basketball players. However, because the control group did not receive an additional time- and volume-matched training stimulus, these findings should be interpreted as the effects of adding the overall training program rather than as evidence of the specific effects of its unilateral or complex components. Full article

The U-shaped Disassembly Line Balancing Problem (UDLBP) is a challenging combinatorial optimization problem for which efficient solution approaches remain limited. This study proposes an efficient branch-bound-and-remember (BBR) algorithm that integrates a memory-based mechanism and U-shaped dominance rules to effectively reduce the search space. Specifically, a new branching method, an additional lower bounding method, and new dominance rules are developed to suit the UDLBP, and different search strategies are developed and explored. Extensive computational experiments are conducted on a comprehensive set of benchmark instances to evaluate the performance of the proposed approach. The results demonstrate that the proposed BBR algorithm can consistently identify the best-known solutions for the evaluated benchmark instances. Compared with constraint programming, mixed-integer linear programming, and several state-of-the-art metaheuristic algorithms, the proposed approach achieves competitive solution quality and computational efficiency, consistently matching the best-known solutions with an average recorded CPU time of 0.0199 s under the 500 s computational setting. These findings indicate that the proposed algorithm provides an efficient optimization framework for solving UDLBP, achieving high-quality solutions with substantially low computational cost. Full article

Low-precision floating-point computation has become central to efficient artificial intelligence, yet its behavior for compact transformer-based hyperspectral imaging (HSI) models remains underexplored. In this work, we present a controlled comparative study of fully quantized training (FQT) and post-training quantization (PTQ) for pixel-wise foreign plastic object (FPO) classification in poultry hyperspectral data. Using a fixed state-of-the-art spatial–spectral transformer backbone, a common mixed-precision strategy, and identical training and inference protocols, we evaluate FP32, FP16, BF16, FP8, and NVFP4 across predictive performance, model compression, training efficiency, and inference efficiency. The results show that mixed-precision FQT remains highly robust across the tested precision spectrum, with all reduced-precision configurations staying within 0.63 percentage points of the FP32 baseline in overall accuracy while consistently outperforming PTQ at matched precisions. Across the evaluated formats, BF16 provides the closest accuracy to FP32, whereas FP8 offers a particularly favorable balance between accuracy preservation and reduced precision, while model compression increases progressively to $3.69\times$ under NVFP4. The computational benefits, however, are strongly workload dependent. Native FP8/FP4 hardware support does not automatically improve training throughput for this compact model at moderate workloads, and the larger training batches required to better utilize low-precision hardware can degrade predictive performance. In contrast, large-batch inference can effectively exploit FP8 and NVFP4 without affecting predictive accuracy. An ablation study further shows that selective retention of numerically sensitive modules in FP32 is essential for stable ultra-low-precision operation. Overall, the findings demonstrate that low precision is a viable but workload-dependent design choice for compact HSI transformers, with FQT providing greater accuracy robustness than PTQ and FP8, offering a favorable overall accuracy–efficiency trade-off. Full article

Off-grid photovoltaic (PV) hydrogen production systems must coordinate rapidly varying PV power, battery energy, and the operating states of multiple alkaline water electrolyzers. Inappropriate coordination may lead to PV curtailment, frequent unit switching, and persistent workload concentration on a small number of electrolyzers. This paper develops an efficiency- and load-balanced operation (ELBO) scheme as an improved rule-based supervisory strategy rather than an online optimization method. ELBO adopts a two-level decision structure. A planned number of online electrolyzers is first determined from the moving-average PV power and the reference power associated with high single-unit efficiency. This planned count is then corrected using real-time PV power, battery state of charge, and the previous electrolyzer states. The controller adjusts the powers of the online units before changing their number, uses the battery to bridge temporary power deficits, and distributes the remaining adjustable power under the operating and ramp-rate constraints. Five representative PV profiles selected from one year of measured data were used to compare ELBO with PV-following operation (PFO), multi-electrolyzer coordinated operation (MECO), and an offline mixed-integer linear programming (MILP) benchmark. ELBO produced 1328 kg of hydrogen, which was 8.85% and 6.07% higher than PFO and MECO, respectively. Its overall PV-to-hydrogen efficiency and PV utilization reached 65.2% and 94.9%, respectively, with 36 start–stop events. MILP produced 1345 kg of hydrogen, only 1.28% more than ELBO, but required the complete future PV sequence. Ablation analysis further shows that the planned-count layer, moving-average filtering, battery-supported retention, and load-balancing allocation contribute to different and complementary aspects of capacity matching, operating continuity, and workload distribution. The results indicate that the benefit of ELBO arises from the ordered coordination of these supervisory functions and that it provides a practical compromise between operating performance, workload distribution, information requirements, and computational complexity under the representative conditions considered. Full article

As data centers become major and schedulable loads of the new power system, connecting them to multiple microgrids offers a promising route to absorb local renewable energy through cross-domain coordination. However, when the microgrids belong to competing operators, coordinated scheduling forces each party to disclose its data-center load curve, storage state, and pricing strategy, which constitutes a core operational secret that no microgrid is willing to reveal. This paper develops a secure and privacy-preserving distributed scheduling scheme for data-center-integrated microgrids built on blockchain. A “data-stays-local, energy-crosses-centers” model is established that elevates privacy from an add-on feature to a first-order architectural constraint, defining a “three-no” principle and a two-layer architecture in which each microgrid optimizes its interior in plaintext and exposes only encrypted matchable factors. On this basis, a decentralized ciphertext scheduling-negotiation algorithm is designed on blockchain smart contracts, performing cross-microgrid matching under secure multi-party computation entirely in the encrypted domain, committing auditable encrypted digests on-chain, and dynamically allocating scheduling priority through an on-chain reputation mechanism. Case studies on a cluster of interconnected microgrids show that the proposed scheme attains cost and renewable accommodation within about three-tenths of a percent of the centralized optimum while reducing operational data-leakage risk from 96.7 percent to 3.8 percent, at the manageable expense of a few seconds of negotiation latency. Benchmarking against an exact mixed-integer solver on small-scale systems bounds the mean optimality gap of the decomposed scheme at 0.74 percent, with a worst case of 2.54 percent over sixty instances. Full article

Reliable multisensor perception is a key requirement for practical waste sorting, yet many low-cost sensor configurations cannot rely on hardware synchronization or carefully controlled acquisition. We present a pilot-scale multisensor waste-sorting testbed that combines an unsynchronized RGB camera with a long-wave infrared (LWIR) camera for object classification on a continuously moving conveyor, and introduce ThermalRGBTrash, a new paired RGB–LWIR video dataset for this task. To enable fusion under asynchronous acquisition, we develop a fully software-based registration pipeline that combines SuperPoint–SuperGlue matching with an adaptive sliding-window strategy designed to recover from long-wave infrared sensor artifacts, including non-uniformity correction events. Across 281,439 matched frame pairs from 19 paired videos, the registration pipeline achieves a mean spatial alignment error of 2.27 pixels and matches 99.98% of attempted frame pairs. We then detect and segment objects with Mask R-CNN, track them across the conveyor, and classify each tracklet using frozen DINOv2 self-supervised Vision Transformer (ViT-L/14) features with a lightweight multilayer perceptron head. RGB and LWIR representations are combined through late fusion. On 550 tracklets under video-disjoint 10-fold cross-validation, the fused pipeline reaches a macro F1 score of 0.924, outperforming RGB alone (0.886) and LWIR alone (0.856). On a mixed-class test set of 351 tracklets reserved exclusively for final evaluation, fusion reaches a macro F1 score of 0.947. The fusion advantage persists across multiple backbone and pretraining choices, while ablation studies support the chosen temporal sampling and pooling design. These results show that accurate RGB–LWIR object classification is achievable without synchronization hardware, and establish ThermalRGBTrash as a benchmark for future work on practical multisensor perception in conveyor-based waste sorting. Full article

Background: Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system, affecting more than 2.8 million individuals worldwide. Automated segmentation of white matter lesions on fluid-attenuated inversion recovery (FLAIR) magnetic resonance images (MRI) is essential for reproducible diagnosis and treatment monitoring, yet remains challenging due to extreme class imbalance, high lesion load variability, and poor contrast at lesion boundaries. Method: We propose a 2D U-Net with a ResNet50 encoder that exploits ImageNet-pretrained representations through a novel pseudo-RGB input strategy: three consecutive FLAIR slices centred on the target slice are stacked channel-wise to form a three-channel input, recovering inter-slice spatial context while enabling direct reuse of pretrained convolutional weights without modality-specific pretraining. A two-phase transfer-learning protocol first optimises only the decoder with the encoder frozen, then fine-tunes the upper encoder blocks at a reduced learning rate. Test-time augmentation (TTA) averaging over horizontal-flip and vertical-flip transformations further improves prediction robustness. Results: Evaluation on the held-out test set of the MSLesSeg2024 benchmark (12 patients, approximately 1650 axial slices) shows that the proposed model achieves a Dice similarity coefficient (DSC) of 0.714 (95% confidence interval (CI): 0.6845–0.7194), intersection-over-union (IoU) of 0.6571 (95% CI: 0.6272–0.6632), and area under the receiver operating characteristic (ROC) curve (AUC) of 0.9628 (95% CI: 0.9422–0.9793). Critically, the model records the lowest false-negative pixel count per slice (FNV = 33.6 px/slice) across all ablation conditions, indicating superior sensitivity to lesion tissue; this is a property of direct clinical relevance for MS monitoring, where missed lesions carry the greatest diagnostic risk. A patient-matched comparison against a 3D nnU-Net baseline shows statistically comparable DSC (0.714 vs. 0.726; paired Wilcoxon $p=0.68$–$0.79$, not significant) alongside a substantially higher pixel-level AUC for the proposed model (0.963 vs. 0.773) and a true volumetric Hausdorff distance gap smaller than an earlier estimate (12.5 mm vs. 10.7 mm), showing an honest mixed-strengths result rather than an outright improvement. A supplementary ablation further shows that replicating a single FLAIR slice across all three channels significantly outperforms the pseudo-RGB adjacent-slice encoding ($p<0.001$), indicating that the anticipated inter-slice-context benefit did not materialise here (see Discussion section). Cross-dataset evaluation on the independent MSSEG 2016 benchmark confirms generalisability: zero-shot transfer achieves DSC = 0.6562, recovering to DSC = 0.7046 after brief fine-tuning (30 epochs), within 1.5 percentage points of in-domain performance. Conclusions: The present study reveals that an optimized, lightweight 2D pipeline can rival the segmentation overlap of context-aware 3D baselines on specific datasets, doing so with a significantly reduced computational footprint. Given its strong pixel-wise discrimination, this methodology offers an effective and practical tool for routine automated MS lesion assessment in clinical settings. Full article

Multi-label remote sensing scene classification requires identifying multiple land-cover categories from a single high-resolution aerial image. Existing methods strengthen visual features or model label dependencies, yet they apply a fixed calibration strategy regardless of the underlying label-density regime, leading to over-prediction on dense scenes or under-correction on sparse scenes. We propose Density-Adaptive CDG Calibration (DARC), a lightweight framework that explicitly conditions calibration on dataset label density. DARC comprises three modules: (1) Label-density Driven Profile Selection (DDP) automatically routes the calibration path based on training-set density statistics; (2) Label-token Correlative-Discriminative Graph Mixing (CDM) injects both co-occurrence and exclusivity relations into label semantic tokens through positive and negative graph propagation; (3) Density-aware Gated Calibration (DCM) applies cardinality-controlled gating for dense labels and EMA-stabilized graph calibration for sparse labels. Experiments on AID-ML and UCM-ML demonstrate that DARC achieves 90.12% and 89.05% sample-F1, respectively, outperforming six competitive baselines including SFIN, ASL, C-Tran, ML-Decoder, SPIN, and Two-Way Loss by 1.65–3.87%, while introducing only 1.42% additional parameters. Cross-regime routing analysis confirms that no single fixed strategy matches DARC’s adaptive approach, and sensitivity analysis shows the routing is robust across a wide threshold range. Ablation studies validate the necessity of each component, and visualization analyses demonstrate that the learned label graphs capture interpretable semantic patterns. Full article

Background: Multimorbidity is a growing global health challenge in aging populations, yet its progression across physical, psychological, and cognitive domains and its contribution to health inequalities remain unclear. We compared longitudinal patterns of multimorbidity in China and the United States (US) and examined the socioeconomic and healthcare-related factors associated with these patterns and their functional consequences. Methods: This longitudinal cohort study included adults aged ≥45 years from China (2011–2020) and the US (2012–2020), matched 1:1 by baseline age and sex. Multimorbidity was classified into eight domains spanning physical, psychological, and cognitive conditions and their combinations. Longitudinal changes across five survey waves were assessed. Multinomial logistic mixed models examined socioeconomic and healthcare-related correlations, and Cox models estimated associations with subsequent limitations in activities of daily living (ADL) limitations and instrumental activities of daily living (IADL) limitations. Results: A total of 7064 participants from China (mean age 60.15 ± 7.93 years; 45.4% male) and 7064 matched participants from the US were included. Multimorbidity patterns were more complex in the US at baseline, but progression toward cross-domain multimorbidity occurred in both countries and was more pronounced in China. Psychological conditions occupied a central position in the development of more complex multimorbidity patterns. Higher educational and household wealth were consistently associated with lower odds of cognitive-related and cross-domain multimorbidity in both countries, whereas associations with healthcare-related factors varied across settings. Cross-domain multimorbidity was more strongly associated with functional limitations than within-domain multimorbidity. In particular, physical–psychological–cognitive multimorbidity was associated with substantially higher risks of ADL limitations (hazard ratio (HR)  =  5.70, 95% confidence interval (CI)  =  4.87–6.67 in China; HR  =  7.72, 95% CI  =  5.90–10.11 in the US) and IADL limitations (HR  =  4.14, 95% CI  =  3.64–4.71; HR  =  5.84, 95% CI  =  4.50–7.59, respectively). Conclusions: Multimorbidity increasingly spans physical, psychological, and cognitive domains in both China and the US. Psychological conditions appeared to bridge physical and cognitive conditions in more complex multimorbidity patterns. Integrated care models incorporating psychological health and strategies addressing socioeconomic inequalities may help reduce the burden of multimorbidity and related functional decline. Full article

When methane (natural gas) fuel gas turbines switch to hydrogen–ammonia co-fuel, their thermodynamic performance undergoes significant changes. To expand the operating range of heavy-duty gas turbines when using hydrogen–ammonia co-fuel and to improve their thermodynamic performance, based on the 255.6 MW heavy-duty gas turbine at the Banshan Power Plant in Hangzhou, China, a simulation model was established. A strategy based on changing the angle of the compressor inlet guide vanes (IGVs) was proposed. The thermodynamic performance, turbine stage supercritical flow velocity, and flow matching characteristics of gas turbines were studied under different hydrogen–ammonia mixing ratios. The results indicate that the developed model can accurately predict the performance of the gas turbine under rated operating conditions, yielding a rated output power of 254.59 MW and an efficiency of 36.33%, with relative errors of −0.4% and −1.54% compared with the design values, respectively. When hydrogen–ammonia blended fuel is employed, the outlet Mach numbers of the second- and third-stage turbine stators exceed the safety limit unity. Reducing the IGV angle effectively decreases the turbine stator outlet Mach number and improves operational safety, although a slight reduction in gas turbine efficiency is observed. As the ammonia volumetric fraction in the blended fuel increases, the gas turbine output power increases while the efficiency decreases slightly, accompanied by a reduction in turbine stator outlet pressure and an increase in outlet temperature. Further investigation shows that, after IGV regulation, the combustor outlet pressure, gas turbine power output, and efficiency all increase. Under a fixed IGV opening condition, the gas turbine efficiency gradually decreases with increasing ammonia volumetric fraction. Under off-design fuel flow conditions, increasing the relative fuel flow leads to higher combustor outlet pressure and temperature, whereas increasing the ammonia volumetric fraction causes a slight reduction in these parameters. This research can provide theoretical support for the optimal design and operation of gas turbines using hydrogen–ammonia mixed fuel. Full article

In the context of urban stock renewal and metropolitanization, radial rail transit corridors in megacities need to transition from singular transportation functions to composite development carriers. This study proposes a hybrid multi-criteria decision-making (MCDM) model combining the Interval-Valued Intuitionistic Fuzzy Analytic Hierarchy Process (IVIF-AHP) with the entropy weight method, featuring an adaptive weighting mechanism that balances subjective expert judgment and objective data patterns. Novel corridor-scale indicators (i.e., hub functional matching, jobs–housing proximity, and gradient stability) are introduced, alongside a three-stage renewal pathway identification method. A case study of five Shanghai corridors (Lines 5, 9, 11, 16, and 17) reveals that jobs–housing spatial proximity is the primary vitality dimension (weight 0.244), while functional mix exhibits a pattern that identifies it as a common shortcoming across all corridors. The five corridors are classified into three vitality tiers, with five typical syndromes diagnosed. A sensitivity analysis confirms the robustness of corridor rankings to weighting and normalization choices, but reveals their sensitivity to the jobs–housing proximity threshold, which validates the 10 km standard. The proposed model offers quantitative diagnostics and differentiated renewal strategies, providing planning references for corridor renewal. Full article

Wayfinding problems in complex teaching buildings can reduce destination-finding efficiency and increase users’ mental load, especially when many rooms have similar layouts and are distinguished mainly by numbering systems. Signage is commonly used to support indoor wayfinding, but its effectiveness depends on whether the sign design and placement match the spatial layout and users’ interpretation of directional information. Taking the Third Teaching Building at Beijing University of Technology as a case study, this paper investigates classroom-related wayfinding difficulties and evaluates a practical supplementary signage strategy. First, questionnaire surveys were conducted among students and teachers who had entered the building to identify common wayfinding problems and signage-related causes. Then, based on user preferences, supplementary floor signage was designed to provide directional arrows and classroom-related textual information. A field experiment was conducted on a selected route to Room 301, comparing an original signage condition with a mixed-signage condition in which the supplementary floor sign was added while the existing ceiling-mounted signs were retained. The mixed-signage condition reduced the mean wayfinding time from 163.75 s to 133.80 s, corresponding to an 18.29% reduction. Post-experiment responses also indicated fewer hesitation- or detour-related difficulties under the mixed-signage condition. These findings suggest that supplementary floor signage placed at confusing decision points can be a feasible and low-cost retrofit measure for improving classroom wayfinding in complex teaching buildings. Full article

In post-disaster relief operations, emergency fleets typically consist of vehicles with varying load capacities, travel speeds, and operating costs. These heterogeneous vehicles are prone to unexpected breakdowns during delivery, which can severely disrupt supply chains and delay urgent aid. Existing scheduling approaches, however, rarely account for fleet heterogeneity, real-time breakdowns, and the trade-off between delivery speed and cost within a unified framework. This paper addresses this gap by formulating the dynamic scheduling of heterogeneous emergency fleets as a two-stage mixed-integer programming model, where total transportation time and cost are simultaneously minimized. The key algorithmic contribution is a fuzzy robust adaptive multi-objective hybrid algorithm (FR-AMOHA) with three interconnected design components. First, a fuzzy evaluation-based pre-matching strategy uses entropy-weighted multi-criteria assessment to generate high-quality initial solutions. Second, a failure-resilient rescheduling module freezes system state upon breakdown detection and selects recovery plans via multi-dimensional resilience scoring to prevent cascading failures. Third, a Pareto-guided adaptive neighborhood search dynamically adjusts operator selection to balance time and cost optimization. Tests on 40 real-world instances with 50 to 1000 demand nodes show that FR-AMOHA achieves optimal inverted generational distance values on 17 out of 40 instances, improves hypervolume by 15% to 35% on average compared with other metaheuristics, and keeps computation times between 40 and 250 s, which is within acceptable limits for emergency decision-making. FR-AMOHA outperforms Gurobi and six leading metaheuristics in solution quality with comparable computational cost. Full article

Spinel NiCo₂O₄ has emerged as a promising microwave absorption material due to its unique crystal structure and abundant defect sites. Nevertheless, its low intrinsic electrical conductivity leads to insufficient conductive loss and unsatisfactory high-frequency impedance matching, severely limiting the simultaneous realization of strong electromagnetic attenuation and broad absorption bandwidth. Fe³⁺ doping is an effective modification strategy for NiCo₂O₄ by virtue of its matched ionic radius and dual modulation capability for dielectric and magnetic properties. Herein, pristine and Fe-doped NiCo₂O₄ absorbers with different doping contents (4%, 6%, 8%) were fabricated via a hydrothermal–calcination route, and the correlation between Fe doping concentration, microstructure, electronic structure, electromagnetic properties, and microwave absorption performance was systematically investigated. Benefiting from moderate 6% Fe doping, the optimized F6 sample exhibits a refined porous nano-agglomerate structure, which provides abundant heterogeneous interfaces and pore channels for electromagnetic wave scattering and attenuation. The introduced oxygen vacancies and balanced Ni²⁺/Ni³⁺, Co²⁺/Co³⁺, and Fe²⁺/Fe³⁺ mixed-valence states effectively strengthen interfacial and dipole polarization, while the optimized electrical conductivity and magnetic properties synergistically boost conductive and magnetic losses. Owing to the dual-loss synergism and superior impedance matching (58% proportion of Δ < 0.4), the F6 sample achieves an excellent minimum reflection loss of −62.7 dB at 2.2 mm and a wide effective absorption bandwidth of 4.6 GHz. This work clarifies the intrinsic structure–performance mechanism of Fe-doped NiCo₂O₄, providing a reliable and feasible strategy for the design and preparation of high-performance spinel-type microwave-absorbing materials. Full article

Few studies have examined robot voice, appearance, and ambient sound within a single framework to understand the user experience. To address this gap, this study explores how these three factors influence the user experience, specifically focusing on the appearance-voice matching effect, user likability, and user satisfaction when robots serve as museum docents, thereby offering design-oriented strategic insights for robot deployment in museums. Audio recordings of museum docents were collected and clustered into four categories. Accordingly, four distinct voice models were synthesized, and subjective user perceptions of these models were evaluated. Subsequently, three different agents, including an iPad, a self-designed robot prototype and a Yanshee robot, were employed as experimental platforms. These agents were each equipped with different voice models to conduct the experiments. The experimental data were analyzed by repeated measures ANOVA and mixed-model analysis. The results indicate that robot appearance significantly affects the appearance-voice matching effect, while its voice moderates this relationship in a museum scenario. Robot voice primarily drives user likability and satisfaction. In contrast, robot appearance shapes the two dimensions differently. Appropriate ambient sound significantly enhances the perceived appearance-voice congruence, and significantly affects naturalness and liveliness within user likability, as well as the fun of the experience and pleasantness of use within user satisfaction. The congruence can positively predict user likability and satisfaction, but this predictive effect has boundaries. These findings can provide references from the perspective of design strategies for human–robot interaction in museums. Full article