Search Results (749)

Quantum machine learning (QML) is emerging as a promising paradigm at the intersection of quantum computing and artificial intelligence, yet its security under adversarial conditions remains insufficiently understood. This scoping review aims to systematically map empirical research on adversarial robustness in QML and to identify dominant threat models, defense strategies, evaluation approaches, practical constraints, and future research directions. Following PRISMA-ScR guidelines, four major databases were searched, resulting in 53 eligible empirical studies published between 2020 and 2026. The findings show that most research concentrates on input-level evasion attacks, particularly adversarial examples, and primarily evaluates robustness in classification-oriented models such as variational quantum circuits and quantum neural networks. Defense strategies are largely adapted from classical adversarial training and noise-based mitigation, with limited deployment on real quantum hardware. Robustness assessment is predominantly empirical, relying on accuracy degradation and attack success rate, while formal certification methods remain less common. The literature also highlights substantial constraints related to hardware limitations, NISQ noise, computational cost, and dataset scale. Overall, the evidence indicates that adversarial robustness research in QML is expanding but remains methodologically concentrated, underscoring the need for standardized benchmarking, scalable defenses, and hardware-validated robustness evaluation frameworks. Full article

Introduction: Circuit resistance training is widely used to enhance physical performance. However, the acute-performance- and fatigue-related effects of exercise order and volume in circuit training, particularly between upper and lower limbs, remain unclear. Objectives: This study examined acute velocity-based responses to different exercise orders and volumes during full-body circuit resistance training. Methods: Thirty resistance-trained adults completed four circuit protocols: alternating exercises with maximal repetitions per exercise (A1), grouped exercises with maximal repetitions per exercise (G1), alternating exercises with 50% of maximal repetitions in the first round (A2), and grouped exercises with 50% of maximal repetitions in the first round (G2). Mean propulsive velocity (MPV) in the bench press and squat at 60% 1RM was assessed before and after each circuit. Results: A significant main effect of Time was observed for both bench press and squat MPV (p < 0.001), with no Intervention × Time interactions. Alternating configurations showed larger effect sizes, indicating greater velocity loss. Under equal volume, upper limbs exhibited greater performance decline than lower limbs. Conclusions: Although exercise order did not result in statistically significant differences, alternating configurations induced a greater magnitude of fatigue-related performance decline than grouped configurations, particularly in upper-body exercises. Full article

The convergence of sensing and processing is a critical frontier in the development of energy-efficient spiking edge intelligence. This paper presents a novel hardware implementation of a sensory neuron evolving from the leaky integrate-and-fire (LIF) model by coupling a volatile memristor with an LC tank circuit. The proposed memristor–resistor–inductor–capacitor (MRLC) neuron embeds electromagnetic sensing directly into neuronal dynamics, enabling direct transduction of proximity information into spike trains. We demonstrate that the circuit functions as a metal-sensitive proximity sensor with spiking output in both simulation and physical experiments. Moreover, the MRLC neuron exhibits rich dynamical regimes, including regular spiking, bursting with 2–5 spikes per burst, and quasi-chaotic behavior, as well as sensing memory provided by hysteresis-like multistability, which is a notable advancement over simple rate-encoding LIF neurons. Full article

The application of quantum machine learning (QML) to security-relevant problems has attracted growing attention, yet its practical behavior in realistic workloads remains insufficiently characterized. This paper investigates the feasibility and limitations of variational quantum classifiers (VQCs) for identifying dummy power traces in side-channel analysis (SCA). A controlled benchmarking framework is developed to evaluate training stability, sensitivity to key design parameters, and resource–performance trade-offs under realistic constraints. To move beyond idealized simulation, hardware-relevant factors, including finite measurement budgets and device noise, are incorporated, and inference robustness under degraded operating conditions is assessed. The results show that VQCs can capture meaningful discriminative patterns in structured side-channel data, although robustness and performance depend strongly on encoding strategy, circuit depth, and measurement conditions. These findings provide an empirical assessment of the potential and limitations of QML for side-channel security and offer practical guidance for future research. Full article

Background: Middle-aged women with obesity frequently exhibit postural misalignment and impaired balance control, which may increase the risk of functional limitations and falls. This study aimed to compare the effects of Pilates circuit training and weight circuit training on body composition, pelvic alignment indices, and balance performance in obese middle-aged women. Methods: Eighteen women (body fat ≥ 30%) were randomized to either a Pilates circuit training group (PCG, n = 9) or a weight circuit training group (WCG, n = 9) in an exploratory comparative study. Both groups performed supervised exercise three times per week for eight weeks. Outcome measures included body composition, pelvic alignment indices, dynamic balance (Y-Balance Test), and static balance (BESS). Data were analyzed using a two-way mixed ANOVA to examine time, group, and interaction effects. Results: Both groups showed significant reductions in body weight (PCG: −3.09 kg; WCG: −2.00 kg), percentage body fat (PCG: −1.85%; WCG: −1.53%), and waist-to-hip ratio (PCG: −0.05; WCG: −0.04) (p < 0.01). Significant improvements in pelvic alignment indices were observed primarily in the PCG, whereas the WCG showed smaller changes. Dynamic and static balance improved in both groups, with greater improvements observed in the PCG. Conclusions: Both training modalities improved body composition and balance outcomes in obese middle-aged women. Pilates circuit training may be associated with greater improvements in pelvic alignment and balance; however, these findings should be interpreted cautiously due to the exploratory design and small sample size. Further adequately powered randomized controlled trials are required to confirm these findings. Full article

Spinal cord injury (SCI) induces persistent locomotor deficits that are closely associated with maladaptive structural plasticity of spinal neuronal circuits. Although motor rehabilitation improves functional outcomes, the cellular substrates underlying rehabilitation-induced recovery remain incompletely understood, particularly in relation to activity-dependent neuromodulation strategies. Here, we investigated how treadmill-based motor training (TMT) and its combination with bioluminescent optogenetic (BL-OG) stimulation of Hb9 (homebox 9)-positive motoneurons and excitatory interneurons selectively modulate microarchitectural plasticity in the injured rat spinal cord. At the level of gross locomotor assessment, Basso, Beattie and Bresnahan (BBB) scores were comparable between the BL-OG and SCI+TMT groups. Although no statistically significant differences in the total score in rung ladder were observed at 28 days post-injury, animals in the BL-OG group showed a tendency toward a higher ratio of successful hindlimb placements, indicating improved step accuracy. BL-OG stimulation was associated with a slightly greater attenuation of SCI-induced spine abnormalities compared to TMT alone, with significant differences between the experimental groups detected specifically in laminae VIII and IX. These lamina-specific alterations in dendritic integration and dendritic spine composition were accompanied by preservation of wisteria floribunda agglutinin WFA-positive perineuronal net (PNN) architecture. Against this background, reduced glypican-4 (GPC-4) expression and attenuated WFA/GPC-4 colocalization were observed in the SCI+BL-OG group relative to SCI in laminae VII–IX, consistent with activity-dependent modulation of PNN-associated synaptic organization in Hb9-positive neuronal populations. Together, these findings indicate that motor rehabilitation and bioluminescent optogenetic stimulation engage distinct but partially overlapping mechanisms of activity-dependent microarchitectural remodeling, preferentially targeting synaptic and perineuronal net-associated substrates rather than inducing large-scale circuit reorganization. Further studies are warranted to elucidate the mechanisms underlying these distinct plasticity profiles. Full article

Large-scale Virtual Power Plants (VPPs) are increasingly essential as Distributed Energy Resources (DERs) assume ancillary service duties once supplied by conventional generation, yet scaling a VPP exposes a persistent trilemma among economic efficiency, data privacy, and operational security. Centralized coordination can approach optimal revenue but requires collecting fine-grained DER operational data and creates a single point of compromise. Federated Learning (FL) mitigates raw data centralization by keeping measurements and experience local, but it introduces a fragile trust assumption that the aggregator will correctly and fairly combine model updates. This trust gap is acute in reinforcement learning-based VPP control because aggregation deviations, including selectively dropping updates, manipulating weights, replaying stale models, or injecting a replacement model, can silently bias the learned policy and degrade both profit and compliance. We propose a zero-knowledge federated reinforcement learning framework for trustless VPP coordination in which each DER trains a local deep reinforcement learning agent to solve a multi-objective dispatch problem that balances ancillary service revenue against battery degradation under operational and grid constraints, while the global aggregation step is made externally verifiable. In each round, participants bind membership via signed receipts and commit to their updates, and the aggregator produces a zk-SNARK, proving that the published global parameters equal the agreed aggregation rule applied to the receipt-bound set of committed updates under a fixed-point encoding with range constraints. Verification is lightweight and can be performed independently by each DER, removing the need to trust the aggregator for aggregation integrity without centralizing raw DER operational data or trajectories. The proposed design does not aim to hide model updates from the aggregator. Instead, it provides external verifiability of the aggregation computation while keeping raw measurements and local experience. We formalize the threat model and verifiable security properties for aggregation correctness and update inclusion, present a circuit construction with proof complexity characterized by model dimension and fleet size, and evaluate the approach in power and cyber co-simulation on the IEEE 33 bus feeder with ancillary service signals. Results show near-centralized economic performance under benign conditions and improved robustness to aggregator side deviations compared to standard federated reinforcement learning. Full article

Background/Objectives: Peripheral vestibular hypofunction (PVH) commonly causes dizziness, imbalance, gait disturbances, and reduced quality of life. Task-oriented circuit training (TOCT) is a rehabilitation approach in which patients perform structured, task-specific functional movements repetitively to improve real-life motor performance. TOCT integrates functional, multisensory, and repetitive exercises based on motor learning and neuroplasticity principles, potentially enhancing rehabilitation outcomes. This study aimed to investigate the effects of TOCT on dizziness, vertigo, balance, gait, disability, and quality of life in patients with PVH. Methods: In this single-blind, randomized controlled trial, 28 patients with PVH were randomly allocated to either a task-oriented circuit training (TOCT) group (n = 16) or a control group (n = 12). The control group performed a conventional home-based vestibular exercise program consisting of gaze stabilization and walking exercises. The TOCT group completed 25 task-specific stations, targeting gaze stabilization, balance, and gait, three times per week for four weeks. Outcomes were assessed at baseline and post-intervention using the Visual Analog Scale for dizziness and vertigo, the Sensory Organization Test for balance, spatiotemporal gait analysis, and the Dizziness Handicap Inventory (DHI) for disability and quality of life. Data were analyzed using two-way repeated-measures ANOVA, with the group × time interaction used to determine whether changes over time differed between the TOCT and control groups. Results: Significant time × group interactions favored TOCT for dizziness severity, vertigo severity, vestibular-related balance parameters, cadence during eyes-closed walking, and DHI total scores (p < 0.05). Within-group analyses demonstrated moderate-to-large improvements in all measured outcomes for the TOCT group, whereas the control group showed limited improvements in dizziness measures and minimal changes in balance, gait, and DHI scores. Conclusions: Task-oriented circuit training significantly improves dizziness, vertigo, balance, gait, disability, and overall quality of life in patients with PVH compared with conventional home-based vestibular exercises. Incorporating functional, multisensory, and task-specific activities within structured circuits may optimize vestibular rehabilitation outcomes. Full article

In offshore wind farms, modular multilevel converters (MMCs) may operate under a deloading condition to accommodate wind-speed volatility and dispatch constraints. Here, deloading is defined as transmitted power < 0.2 pu (scenario S2, low-power non-reversal). Under this condition, submodule capacitor-voltage fault signatures are weak and exhibit strong operating-point-dependent drift, which degrades conventional threshold-based or offline-trained methods. We propose a lightweight switch-level IGBT open-circuit fault localization framework for deloaded MMCs. Wavelet packet decomposition is used to extract time–frequency energy features, and principal component analysis reduces feature dimensionality for lightweight deployment. An enhanced XGBoost model further integrates severity-index weighting to alleviate class imbalance and incremental learning to adapt to condition drift induced by wind-power fluctuations. MATLAB2024b/Simulink results show 99.6% accuracy in S2 with less than 2 ms inference latency, and robust performance in extended scenarios including partial-power operation and power reversal. Full article

The network traffic of 3D parallel training in large-scale deep learning, featuring burstiness, hot-spots, and periodic large-bandwidth patterns, severely challenges network efficiency, necessitating a high-performance and flexible optical network solution. To address this, this paper proposes Mercury, a hybrid optical network based on physical optical components: its optical timeslot switching (OTS) subnet uses an arrayed waveguide grating router (AWGR) and tunable lasers for dynamic traffic, while the optical circuit switching (OCS) subnet relies on wavelength selective switches (WSSs) for low-latency high-bandwidth transmission, which is coordinated by selective valiant load balancing (S-VLB) and most efficient path configuration (MEPC) mechanisms. Validated via simulations and FPGA-based testbed experiments, Mercury outperforms the Sirius network by reducing epoch training time (e.g., 179s with five jobs) and relieving OTS congestion through offloading large flows to OCS. This work demonstrates that Mercury provides a flexible, high-performance physical optical solution for 3D parallel training of large-scale deep learning models. Full article

Designing noise-robust parameterized quantum circuits (PQCs) is a central challenge in the noisy intermediate-scale quantum (NISQ) regime. Existing quantum architecture search methods rely on training large SuperCircuits and evaluating SubCircuits under noisy execution, resulting in high computational cost and architecture assessments that depend on task-specific optimization and device noise. In this work, we propose a training-free quantum architecture search framework based on information-theoretic expressibility measures rather than performance-based estimators. We empirically show that noise-free KL-divergence-based expressibility exhibits a consistent monotonic association with noisy task loss across diverse circuit architectures and realistic hardware noise models. Leveraging this relationship, we introduce an expressibility-guided evolutionary search that requires neither SuperCircuit training nor noisy execution during the search phase. Since expressibility is evaluated independently of hardware noise, the method is inherently device-agnostic, enabling architectures to be reused across multiple quantum devices without re-running the search. Experiments using IBM-derived Qiskit noise models demonstrate that the proposed approach achieves competitive performance compared to SuperCircuit-based baselines, while substantially reducing computational cost. These results establish expressibility as an effective information-theoretic surrogate for ranking PQC architectures under realistic noise. Full article

As well as the off-line phenomenon between the pantograph strip and the contact wire that occurs frequently, the current collection quality of trains is potential under threat. Pantograph arcing can bring about overvoltage and harmonics in the traction circuit, which can seriously threaten the construction’s strength and efficiency of current collection. Meanwhile, the electrified railway might meet very complex environments, including the various routes under different air pressures. When the train runs in a medium- or low-pressure area, the reduction in air pressure may result in significant differences in the dynamic evolution characteristics of pantograph arcing. So it is necessary to carry out a detailed study on the influence of pantograph arcing on the current collection of electrified trains in a low-pressure environment. In this paper, we proposed an improved pantograph arcing model suitable for medium-to-low-pressure regions, with the pressure parameters taken into consideration. Furthermore, we examined the influence of pantograph arcing under medium-to-low-pressure environments on the traction power supply system. The arcing dynamics, including the arc duration, the current zero-crossing, and the arcing-released energy at different air pressures were compared. The overvoltage and the harmonic distribution of the traction drive system were also analyzed. This work may be helpful for the design and maintenance of electrified railways under medium-to-low-pressure environments. Full article

Objectives: The physical and physiological characteristics of padel players are essential for appropriate training load prescription; however, this area remains underexplored. Therefore, this hypothesis-driven study aimed to analyse physical and physiological differences in male padel players according to playing side. A secondary objective was to observe the influence of training volume on these parameters. Methods: Fourteen high-level male players competing in professional circuits or top-level regional competitions participated in this cross-sectional study using directional (one-tailed) testing. Results: Vertical jump performance differed significantly between the countermovement jump (CMJ) and the Abalakov jump (ABK) (p < 0.001), with lower values in the CMJ (40.98 cm) compared with the ABK (46.96 cm). Isometric handgrip strength showed significant inter-limb differences (p < 0.001), with greater force in the dominant hand (49.08 kg) than in the non-dominant hand (44.22 kg). Mean completion time in the agility t-test was 10.40 s (95% CI: 10.06–10.74 s). The Yo-Yo Intermittent Recovery Test showed a mean distance of 404.28 m, corresponding to an estimated VO₂max of 50.79 mL·kg⁻¹·min⁻¹. Playing side significantly affected Yo-Yo performance and estimated VO₂max (p = 0.036), with higher values in left-side players. Although no significant differences were found in handgrip strength according to playing side. As expected, weekly training frequency did not significantly influence any variable. Conclusions: These findings help characterise the physical and physiological profile of high-level padel players and provide practical reference values to support training prescription and performance monitoring. Full article

Chemical monitoring of pollutants and hazardous materials in water supply systems traditionally depends on centralized laboratories, advanced instrumentation, and trained personnel, limiting accessibility and preventing real-time, on-site analysis. This work presents an alternative cost-effective, field-deployable approach that uses genetically engineered bioluminescent bioreporters, encapsulated in self-sufficient alginate capsules and integrated with an optoelectronic detection circuit, to detect and quantify target materials in water. We have developed a scalable single-channel prototype featuring four sensing tracks—two for sample measurement, one for clean water, and one for a standard reference solution. The latter employs the standard ratio (SR) method to ensure robust quantification, compensating for batch variability and environmental effects. System characterization showed high uniformity across tracks. Validation with nalidixic acid (NA) demonstrated reliable quantitative performance, with a blind test estimation of 5.6 mg/L for a true concentration of 5 mg/L, well within the calibration error range. Additional sensitivity testing confirmed detection of mitomycin C (MMC) at concentrations as low as 50 µg/L. Overall, the results highlight the potential of bacterial chemical sensing as a practical and scalable tool for real-time, in situ water quality monitoring networks. Full article

Stator short-circuit faults (SSCFs) account for a significant portion of induction motor failures, yet their early detection remains a challenge in industrial environments where labeled fault data is scarce and installing additional sensors is often impractical. This paper proposes a novel, data-driven fault detection and diagnosis framework grounded in the Residual Information Value (RIV) principle to overcome reliability limitations of traditional spectral and residual energy methods. By redefining fault detection as a statistical test of independence between control inputs (voltages) and current residuals, the proposed method identifies incipient faults as model drifts without relying on prior knowledge of fault distributions. A key contribution of this work is the seamless integration of the diagnostic scheme into standard Variable Speed Drives (VSDs): the healthy nominal model (a Multilayer Perceptron) is trained exclusively using data from the drive’s existing self-commissioning routine, eliminating the need for manual data collection or complex physical parameter identification. Experimental validation on an industrial test bench demonstrates that the framework achieves superior diagnostic performance compared to traditional baselines, providing higher statistical separability and a reduced false alarm rate. The system can detect 1% incipient faults in approximately 61 ms while accurately identifying the faulty phase. The results confirm that the proposed RIV-based strategy offers a robust, non-intrusive, and industry-ready solution for predictive maintenance that effectively balances high-speed detection with enhanced statistical reliability. Full article