Search Results (663)

Background: Reduced physical fitness in individuals with schizophrenia contributes to disability and health disparities, highlighting the need for valid and responsive field measures in exercise interventions. Objective: This study examined the long-term stability, construct validity, and responsiveness of the Six-Minute Walk Test (6MWT) in individuals with schizophrenia undergoing structured exercise interventions. Methods: This secondary analysis included 70 participants from two randomized controlled trials of 12-week Baduanjin or brisk walking interventions. The cohorts were pooled after confirming comparable baseline characteristics and no significant group-by-time interaction in 6MWT change. Baseline and post-intervention assessments included the 6MWT, the Timed Up-and-Go (TUG), the cognitive dual-task TUG (TUGcognitive), and the 30-Second Chair Stand Test (30CST). Long-term stability of 6MWT scores was assessed using the intraclass correlation coefficient (ICC), and measurement error was estimated using the standard error of measurement (SEM) and minimal detectable change at the 95% confidence level (MDC₉₅). Construct validity was examined using Spearman correlations (ρ), and responsiveness was examined using the standardized response mean (SRM) and Cohen’s d. Results: The 6MWT showed excellent long-term stability (ICC = 0.96, 95% CI: 0.93–0.97), with SEM = 13.41 m and MDC₉₅ = 37.19 m. Construct validity was supported by correlations with the TUG (ρ = −0.54, p < 0.001), 30CST (ρ = 0.45, p < 0.001), and TUGcognitive (ρ = −0.36, p = 0.002). Responsiveness was large (SRM = 1.08, 95% CI: 0.84–1.31), with a complementary effect size of d = 0.32 (95% CI = 0.24 to 0.42), and 24.29% of participants exceeded the MDC₉₅ threshold. Conclusions: The 6MWT demonstrated appropriate long-term stability, construct validity, and responsiveness as a measure of functional exercise capacity in schizophrenia, supporting its use for evaluating exercise outcomes in psychiatric rehabilitation. Because the present psychometric analysis did not include a non-exercise comparison group, the responsiveness estimates reflect within-intervention change and should not be interpreted as causal treatment effects of exercise. Larger studies are needed to identify responder characteristics and trajectories of changes in test performance. Full article

This study aimed to examine within-subject changes associated with a 12-week multicomponent training program combined with cognitive stimulation in cognitive–motor performance in older women, and to evaluate changes in cognitive status and health-related quality of life (HRQoL). Thirty-three participants aged 70.24 (4.32) years were assessed at baseline, 6 weeks, and 12 weeks. Cognitive–motor performance was evaluated using a Go/No-Go task, including reaction time (RT), omission errors, and commission errors. Cognitive status was assessed using the Memory, Fluency, and Orientation (MEFO) score, and HRQoL was evaluated with the 36-Item Short Form Health Survey (SF-36). Mixed-effects models showed a significant reduction in RT (Δ = 51.5 ms, p < 0.001; d = 0.50) and omission errors (OR = 0.04, p < 0.001), and an increase in commission errors (OR = 2.06, p = 0.008). Cognitive status improved significantly (Δ = 0.45, p = 0.021; d = 0.34), whereas HRQoL improved only in the general health dimension (Δ = 6.82, p = 0.004; d = 0.53). Overall, the intervention was associated with a reorganization of Go/No-Go performance, characterized by faster responding, but increased commission errors, indicating no improvement in motor inhibition. These findings suggest that this multidomain approach is feasible and warrants evaluation in randomized controlled trials. Full article

Markerless video-based motion capture is a fast-developing, low-burden and versatile alternative to marker-based stereophotogrammetry, yet independent validation evidence for several commercial solutions is still scarce. This study validates the markerless software CapturyStudio (The Captury GmbH, Saarbrücken, Germany) for the reconstruction of upper- and lower-limb joint kinematics, comparing it with a validated Xsens (Movella, Henderson, NV, USA) inertial measurement unit system. Ten healthy subjects (five females and five males) performed two standardized clinical tasks, a Reach-To-Grasp gesture for the upper limb and a Timed-Up and Go test for the lower limb, recorded simultaneously with eight BTS SMART EVO-DX 2 (BTS Bioengineering S.p.A., Garbagnate Milanese, Italy) cameras operating in markerless mode and a 17-sensor Xsens system. Elbow, shoulder, knee and hip flexion–extension angles were reconstructed from three-dimensional anatomical keypoints provided by CapturyStudio and compared with the Xsens angles through root mean square error in absolute and percentage terms, range-of-motion accuracy, intraclass correlation coefficient (ICC), Spearman’s coefficient (ρ), Bland–Altman analysis and non-parametric Wilcoxon Rank-Sum tests. CapturyStudio reproduced the temporal pattern of all four angles faithfully (ICC ≥ 0.87; ρ ≥ 0.89), with median discrepancies of about 12° for the elbow and below 8° for shoulder, knee and hip and with the best agreement for the upper limb; the main weakness was a systematic overestimation of hip range of motion. Since both systems are indirect measurement techniques, these values represent the discrepancy between two methods and an upper bound on the error of the markerless system rather than its absolute accuracy. Their magnitude is comparable to the changes regarded as clinically meaningful in goniometric assessment, so the system is presently suited to the analysis of movement patterns rather than to the measurement of absolute joint angles. The study is to be read as a technical comparison of two measurement systems, delimiting the conditions under which future clinical, rehabilitation and sports applications may be pursued. Full article

Periodic manual oversight is difficult to scale for large portfolios of funded research projects. Progress delays, budget irregularities, and superficial reporting often go undetected until final acceptance. Many conventional detection methods also generate false positives when contextually supported schedule adjustments resemble anomalous patterns. We present the Intelligent Project Monitoring System (IPMS), which couples a feature-decoupled multi-task LSTM-Attention network with an Adaptive Isolation Forest. The LSTM-Attention component models project workflows through finite state machines and predicts milestone deviations. The Adaptive Isolation Forest then flags records after a context gate screens cases meeting the study’s legacy legitimate-deviation criteria before final alerting. A multi-head attention module tracks how execution performance evolves over the project lifecycle, and the system includes a loss-ratio signal for candidate-shift review and feedback-gated controlled recalibration; its response was evaluated only under one researcher-designed synthetic global policy-change injection. On a real-world dataset from a provincial management platform, in which approximately 7% of legacy-labeled records carried an anomalous reference label, IPMS achieved an AUC of 0.924 and a false-positive rate of 4.2% against the available legacy binary reference labels, a 76.9% relative reduction in observed FPR compared with standard Isolation Forest. Milestone deviation prediction reached an MAE of 1.85 days, 34.2% lower than standard LSTM. Execution profiling achieved an MAE of 0.082. Removing the deviation filter alone degraded F1 by 12.3%, and removing multi-scale fusion increased the miss rate for long-duration stalls by 23%. Full article

Response inhibition is often summarized as a single individual score, although performance may change within a session as temporal demand and repeated exposure vary. We examined whether change in No-Go motor performance differed across stimulus-rate conditions and was associated with affective temperament in 53 physically active participants, predominantly young adults. Participants completed three blocks of an elbow-based Go/No-Go task; four 50-stimulus series were administered at 40, 50, 60, and 70 bpm in randomized order. The analysis included 636 series-level observations and 6360 No-Go trials, including 3185 commission errors (50.1%). Commission-error occurrence was given primary interpretive priority; conditional error duration was considered complementary, and the pragmatically weighted integrated error index was treated as a secondary composite. Error burden differed across stimulus-rate conditions, and rate-by-block interactions emerged for commission-error occurrence (trial-level GEE; Wald χ²(6) = 25.96, p < 0.001), conditional error duration (Wald χ²(6) = 14.96, p = 0.021), and the integrated index (Wald χ²(6) = 30.40, p < 0.001). Direct contrasts confirmed larger Block-1-to-Block-3 reductions at 50–70 bpm than at 40 bpm. A secondary exploratory participant-level analysis suggested that higher irritable temperament was associated with a more negative Block-3-minus-Block-1 change (β = −0.82, 95% CI −1.39 to −0.26, FDR-adjusted p = 0.020), although the corresponding moderation did not survive global correction across all temperament tests. Overall, the data indicate rate-associated differences in within-session No-Go motor performance; temperament-related findings remain exploratory. Full article

Image restoration lies between camera acquisition and geometric estimation, but pixel improvements may not transfer to motion estimates. We evaluated directional-kernel Richardson–Lucy (RL) deblurring under kernel-length and direction controls. The restoration analysis covered 3071 paired GoPro, RealBlur-J, and RealBlur-R images. An exploratory feature analysis used a fixed 155-image subset with Oriented FAST and Rotated BRIEF (ORB), scale-invariant feature transform (SIFT), two geometry models, ten random directions, NAFNet, and Restormer. A separate task analysis used ten red–green–blue plus depth (RGB-D) sequences from the Technical University of Munich (TUM) benchmark, synthetic 20 ms exposures, and fixed RGB-D perspective-n-point odometry. Estimated directions contained information relative to random angles, yet the tested global RL branches remained below Blur Input on average. Changes in sequence-mean absolute trajectory error (ATE) RMSE ranged from +0.004 to +0.051 m for ORB and from −0.008 to +0.036 m for SIFT. Seeds were averaged within each sequence before inference. No tested branch achieved a robust ATE improvement across both detectors. Pixel, raw-feature, normalized-feature, geometry-state, and trajectory endpoints produced different method rankings. These findings motivate endpoint-specific evaluation. The task experiment does not validate naturally blurred long-exposure video, a deployed simultaneous localization and mapping system, or sensor hardware. Full article

Background/Objectives: This study aimed to examine the acute effects of a dual-task intervention combining moderate-intensity cycling with a food-related Go/No-Go task on inhibitory control, subjective appetite, and frontal electroencephalogram (EEG) activity in healthy adults. Methods: Forty healthy adults were randomly assigned to an experimental group or a control group in a single-blind design. The experimental group performed cycling combined with a food-related Go/No-Go task, whereas the control group performed only the cognitive task. Outcomes were assessed before and after the intervention, and Group × Time effects were analyzed using repeated-measures ANOVA with correction for multiple comparisons. Results: No significant Group × Time interactions were observed for the primary outcomes, including EEG alpha and theta power (ηp² = 0.004 and 0.040, respectively) and Stroop reaction time and accuracy (ηp² = 0.076 and 0.062, respectively). Subjective appetite decreased in the experimental group and increased in the control group, showing a significant interaction at the uncorrected level (p = 0.017, ηp² = 0.142); however, this interaction did not remain significant after correction for multiple comparisons. The exploratory DEBQ outcomes showed no significant Group × Time interactions. Conclusions: In this single-session study, the intervention did not demonstrate clear intervention-specific benefits across the cognitive, EEG, or eating-related outcomes. Nevertheless, the contrasting descriptive pattern in subjective appetite warrants further investigation in larger studies involving repeated interventions and clinical populations. Full article

Occupancy detection is fundamental to the operational intelligence of smart buildings, driving critical functions in energy management, HVAC automation, and physical security. While modern Deep Learning (DL) models have achieved high accuracy in parsing complex environmental sensor data, they remain highly vulnerable to adversarial examples, imperceptibly perturbed inputs designed to deceive neural networks. These vulnerabilities pose severe real-world risks, ranging from energy sabotage, in which systems heat empty rooms, to critical security breaches in which intruders go undetected. To address this security gap, we propose ADS-Guard, a novel Adversarial Detection and Sanitization (ADS) framework rooted in sequence-to-sequence autoencoder purification. Unlike standard denoising techniques, ADS-Guard incorporates a latent consistency regularization mechanism that encourages alignment between clean and adversarial representations in the latent feature space. We evaluated ADS-Guard using a comprehensive experimental pipeline comprising five distinct DL architectures (LSTM, GRU, 1D-CNN, MLP, and Transformer) across three diverse datasets: (1) The UCI Occupancy dataset (20,699 samples) for standard binary detection; (2) Building59 dataset (7200 samples) for three-class occupancy-level classification (Low, Medium, High); and (3) Room Occupancy dataset (10,129 samples), representing a highly imbalanced binary occupancy-detection task. We evaluate ADS-Guard against both Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks across diverse occupancy datasets and model architectures. We further assess the framework under adaptive white-box attacks and compare its performance with FGSM-based and PGD-based adversarial training baselines. Our results demonstrate that adversarial attacks can substantially degrade occupancy-detection performance across datasets and model architectures. ADS-Guard consistently improves robustness relative to undefended models against both FGSM and PGD attacks, recovering a substantial portion of the lost performance in binary occupancy tasks and providing meaningful gains in the more challenging multi-class setting. Furthermore, ADS-Guard remains effective under stronger adaptive threat models while providing a practical retraining-free defense that can be integrated with existing occupancy-detection systems without modifying downstream classifiers. Full article

Background/Objectives: Neurofeedback (NFB) modulates electroencephalographic activity and has been proposed to enhance cognitive performance. This study investigated the effects of repeated sensorimotor rhythm (SMR; 12–15 Hz) neurofeedback training on selective auditory attention in healthy adults aged 25–40 years, utilizing both electrophysiological and behavioral assessments. Methods: A prospective, controlled factorial design with repeated measures was employed, including a sham NFB intervention to control for placebo effects. Electrophysiological evaluation measured the amplitudes and latencies of event-related potentials (N100, N200, and P300) during an auditory Go/No-Go task. Speech-in-noise perception, which demands a higher level of selective auditory attention, was further assessed using the QuickSIN test. Results: Electrophysiological results revealed that SMR training statistically significantly reduced latencies of the N200 component at the Cz and Pz electrodes, and the P300 component at the Fz, Cz, and Pz electrodes, in a dose-dependent manner. No significant amplitude effects were observed for any ERP component. Behavioral results demonstrated a linear improvement in speech perception in noise (QuickSIN test) in the experimental group, a task that indirectly engages auditory selective attention mechanisms, as a function of training sessions, with significant group differences favoring the experimental group. These effects were maintained at the one-month follow-up. Conclusions: These findings indicate that SMR-based NFB modulates neural activity and improves selective attention, supporting its potential as an effective intervention for improving attentional performance and auditory processing in healthy populations, with effects persisting at least one month post-training. Full article

Due to the absence of traffic signal control and the difficulty in accurately estimating the future movements of surrounding vehicles, autonomous vehicle decision-making faces challenges at unsignalized intersections. This study proposes a trajectory prediction-aided deep reinforcement learning framework. First, a composite prioritized replay mechanism is introduced into the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, jointly considering temporal-difference error and reward-based event severity to enhance critical-experience reuse. Second, a convolutional multi-layer long short-term memory (CM-LSTM) model predicts surrounding-vehicle trajectories through convolutional local-motion encoding and stacked LSTM temporal modeling, and the predicted trajectories are incorporated into the deep reinforcement learning state representation. A multi-objective reward function is designed to balance collision avoidance, passing efficiency, lane keeping, and task completion. In CARLA go-straight and left-turn tests, CLS-TD3 achieves success rates of 93.8% and 90.2%, collision rates of 2.5% and 4.2%, and average passing times of 5.18 s and 5.58 s. Compared with TD3, the success rates increase by 6.3 and 8.6 percentage points, while average passing times decrease by 18.8% and 20.5%. These results demonstrate that the proposed framework improves the safety and crossing efficiency of autonomous vehicle decision-making at unsignalized intersections. Full article

A cooperative guidance law based on the experience-guided multi-agent proximal policy optimization (E-MAPPO) algorithm is proposed for multiple unmanned aerial vehicles (UAVs) to track dynamic points of interest in civilian applications, such as collaborative search and rescue and environmental monitoring. In multi-UAV cooperative tracking, accurate arrival-time coordination is important for improving collaborative task execution, but it remains challenging because of continuous action spaces, target maneuvering, uncertain time-to-go estimation, and inefficient exploration in multi-agent reinforcement learning. Specifically, a multi-UAV cooperative guidance environment is formulated, and the problem is modeled as a Markov decision process. To address the challenges of large action spaces and poor convergence in multi-agent reinforcement learning, an experience-guided MAPPO framework is introduced to enhance training efficiency and policy stability. Different from standard MAPPO, the proposed E-MAPPO introduces proportional-navigation-guided experience only during the early training stage to guide exploration, while the final policy is still optimized through the MAPPO objective. Subsequently, a composite reward function is designed by integrating distance-based heuristic terms with auxiliary guidance signals, thereby improving exploration efficiency and facilitating coordinated rendezvous and tracking of dynamic references. Comparative simulations with cooperative proportional navigation guidance (CPNG), sliding mode control (SMC), and standard MAPPO are conducted under different target motion scenarios. The results show that E-MAPPO reduces the average convergence step by 17.07% compared with MAPPO. In the straight-moving target scenario, E-MAPPO reduces the cooperative time error by 55.10% compared with CPNG and by 8.33% compared with MAPPO. In the S-type maneuvering target scenario, E-MAPPO reduces the cooperative time error by 55.81% compared with CPNG and by 9.52% compared with MAPPO. Monte Carlo experiments further verify its effectiveness and robustness. Additional robustness tests under Gaussian measurement noise, observation bias, and communication delay show that the proposed method maintains acceptable tracking accuracy and cooperative timing performance under different uncertainty conditions. In addition, the results indicate that the proposed method generalizes well to different types of maneuvering targets. Full article

Byzantine theological education often focuses on textual abstraction, frequently failing to convey the tradition’s embodied, spatial, and liturgical depths. This mixed-methods case study examined whether immersive virtual reality (IVR) could bridge this divide. A total of thirty (n = 30) graduate theology students were invited to navigate a bespoke reconstruction of a Byzantine church—modeled on the Chora Monastery—using Meta Quest 3 headsets (Qualcomm Snapdragon XR2 Gen 2, standalone inside-out tracking). Following a 25 min immersive session, participants completed usability assessments, semi-structured interviews, and observational documentation. Quantitative data revealed above-average system usability (M = 72.50, SD = 5.18), significantly exceeding the standard 68-point usability benchmark, t(29) = 4.760, p < 0.001, d = 0.869, and intuitive navigation even among IVR novices (63.3% of participants completed the guided task sequence with zero errors). Through thematic analysis, six key dimensions of the experience—spatial presence, theological meaning-making, navigational ease, interaction affordances, pedagogical limitations, and technical constraints—were identified. Participants described the sense of ‘immersive presence’ as transformative and reported that it helped them correct prior spatial misconceptions and deepened their affective engagement with the liturgy. However, they also identified significant pedagogical hurdles. The lack of collaborative features was described as “theologically incomplete,” while the absence of multisensory elements—specifically scent—created a noticeable liturgical void. Furthermore, participants highlighted an unresolved tension between the experience’s devotional immersion and the critical distance required for academic study. The findings suggest that if IVR can support theological education effectively, future digital architectures may need to go beyond technical functionality to prioritize communal presence, contemplative pacing, and scaffolded criticality. This single-case, non-comparative design does not test IVR’s efficacy relative to physical site visits or 360° video. It instead establishes baseline usability and experiential evidence to inform such comparisons in future research. Full article

This article examines how DressGO, a fashion game developed by DRESSX on Roblox, organizes immersive fashion commerce for youth-oriented audiences through persuasive and gamified interface design. Rather than treating purchase as a discrete transactional event, the study examines how monetization cues are embedded in progression loops, cosmetic status, and habitual return within a platform-mediated retail environment. Methodologically, the article adopts a qualitative single-case study and interface analysis based on a primary corpus of 12 screenshots selected from an initial pool of 34 screenshots collected across six gameplay sessions in January 2026, complemented by observational notes and contextual documentary triangulation and supplementary verification material for Robux/payment-route visibility. The analysis identifies four operational mechanisms: staged unboxing as a sensory gateway to acquisition, daily rewards and quantified tasks as retention infrastructure, rankings and rarity displays as social comparison cues, and accelerators, probability boosters, and waiting timers as conversion pressure mechanisms. The findings indicate that the interface integrates virtual fashion consumption into ordinary play and social visibility, while monetization cues operate through playful aesthetics, repetition, scarcity cues, and low-friction prompts embedded in progression systems. The article contributes to immersive commerce research by examining how gamification, interface design, and symbolic fashion value converge in a youth-oriented virtual retail environment. It further argues that randomized access, temporal friction, and comparative visibility should be understood not only as engagement features, but also as matters of digital fairness, platform trust, and responsible interface design. Full article

Quantifying metabolic cost during short transitional movements is challenging because conventional metabolic measurements have limited temporal resolution. This proof-of-concept study examined whether model-derived metabolic cost during the sit-to-walk (STW) transition could be exploratorily stratified using interpretable ensemble learning. Forty-nine healthy adults completed the STW phase of the Timed Up and Go task with synchronized three-dimensional kinematics, ground reaction forces, and eight-channel surface electromyography. Individually scaled OpenSim gait2392 models and the Umberger metabolic model were used to estimate metabolic power from seat-off to the end of the first complete gait cycle. Window-averaged metabolic power was stratified into low-, medium-, and high-cost levels. Window-level biomechanical features were extracted from kinematic, kinetic, and muscle-state time series. Seven classifiers were trained using a subject-level 7:3 train–test split and stratified five-fold cross-validation within the training set, and their probability outputs were integrated through TOPSIS-weighted classifier fusion. SHapley Additive exPlanations were used for class-specific feature attribution. The fused ensemble achieved an AUC of 0.870, F1 score of 0.703, accuracy of 0.705, and specificity of 0.853 on the independent test set. Discrimination was stronger for the low- and high-cost levels than for the medium-cost level. SHAP-based attribution highlighted force-related changes and knee-angle variability and amplitude measures as prediction-relevant biomechanical features. These findings support a model-derived, interpretable workflow for extending STW assessment from task performance to task cost, while indicating the need for further validation in larger and clinical datasets. Full article

Post-error slowing (PES) is commonly considered a behavioral marker of post-error adaptation. However, adaptive processes may also emerge through subtle modifications of motor preparation, particularly in combat sports such as taekwondo (TKD), where maintaining rapid motor execution is essential. This study examined post-error neuromuscular adjustments during a TKD-specific kicking task by comparing standard Go and post-error Go trials for changes in muscle onset latency, peak electromyographic amplitude, and co-contraction indices. Twenty-eight TKD athletes (14 novice and 14 advanced) performed a sport-specific Go/No-Go task while wearable wireless surface electromyography sensors recorded lower-limb neuromuscular activity from eight lower-limb muscles. Muscle onset latency, peak electromyographic amplitude, co-contraction indices, and reaction time were analyzed using linear mixed-effects models. Post-error Go trials showed significant alterations in muscle onset latency in posterior lower-limb muscles involved in propulsion and movement preparation (semitendinosus, biceps femoris, lateral gastrocnemius, and soleus), with muscle activation occurring closer to the foot take-off. No significant differences were observed in reaction time, peak electromyographic amplitude, or co-contraction indices, and expertise and age did not modulate these effects. These findings suggest that error-related motor adjustments may be expressed through changes in muscle activation timing rather than overt behavioral slowing. Full article