Search Results (591)

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

Background and Objectives: This methodology-focused narrative review synthesizes the eye-tracking literature in bipolar disorder (BD) to evaluate the utility of such technology in identifying potential state or trait biomarkers for BD. Methods: A PubMed and Scopus search was performed from database inception to August 2026. Eligible studies included adults with BD or individuals at high-risk for BD compared with healthy controls (HCs) that used at least one quantitative eye-tracking paradigm and reported extractable quantitative outcomes. Studies were grouped into eight paradigms: free-viewing task (FVT), antisaccade (AS), prosaccade (PS), memory-guided saccade (MGS), smooth pursuit eye movements (SPEM), vergence, binocular rivalry (BR), and eye-blink rate (EBR). Findings were synthesized narratively based on study designs and outcome measures. Results: 24 studies met inclusion criteria. FVT and PS were the most frequently represented paradigms (n = 8 each), followed by AS (n = 7), BR (n = 4), and SPEM (n = 3); MGS, vergence, and EBR were each represented by one report. Findings from FVT, AS, and PS were inconsistent; the study designs, stimulus types, mood states during the study, and outcome measures of studies with these paradigms were diverse. BR alternation rate was consistently slower in BD across three studies. SPEM deficits were more closely associated with psychotic features. Evidence from single studies of MGS, vergence, and EBR limits their interpretations. Conclusions: Eye-tracking paradigms are promising tools for studying cognitive, affective, perceptual, sensorimotor, and reward-related presentations of BD. However, the field needs standardized protocols that include eye-tracking paradigms, mood states, symptom severity, outcome measures, study samples, and comparison groups to examine the utility of eye-tracking technology in BD. Full article

Structural analysis using X-rays and electron beams has long provided the average arrangement of atoms and molecules—that is, “structural information”—with high precision. By contrast, static measurements cannot directly yield dynamic information on how a material changes over time; instead, information on motion is convolved into a single numerical value such as the B-factor (atomic displacement parameter). Taking this limitation as its starting point, this review surveys the recent trend of introducing a time axis into measurements to observe material dynamics directly. First, we outline the technological foundations that have made the transition from static to time-resolved measurement possible. It rests on the dramatic shortening of exposure times, enabled by the increased brilliance of X-ray and electron sources and by advances in detection technology such as direct photon-counting detectors. Next, we survey dynamic measurement techniques, including time-resolved X-ray crystallography, coherent X-ray scattering, neutron scattering, and time-resolved electron microscopy. We also point out the essential limitation that most of them still return ensemble or volume averages. Building on this, we systematically describe diffracted X-ray tracking (DXT), diffracted X-ray blinking (DXB), small-angle X-ray blinking (SAXB), transmitted X-ray blinking (TXB), and electron-beam molecular dynamics (EBMD), which use gold nanocrystals and gold nanoparticles as motion probes. We distinguish throughout between methods that follow individual objects—DXT and EBMD, which yield trajectories of single labeled molecules or single particles—and methods that analyze intensity fluctuations arising from many contributors within one pixel or illuminated volume—DXB, SAXB and TXB. The latter are not single-molecule measurements; rather, they replace a global ensemble average by a spatially localized statistical one, retaining local heterogeneity that a bulk measurement would average away. Finally, we discuss the implementation and prospects of the large-volume data analysis—principal component analysis, Bayesian inference, machine learning, and autonomous measurement—needed to handle the explosively increasing amount of information that the time axis introduces. We close with the outlook that time-resolved measurement incorporating AI and big-data analysis will become established as a new measurement platform that complements and extends conventional static structural analysis. Full article

Background/Objectives: Transcranial magnetic stimulation (TMS) initiates a cascade from intracranial electric-field exposure through neural recruitment and plasticity to distributed network responses. Neuronavigation improves the geometric reproducibility of delivery but does not guarantee equivalent cortical exposure or target engagement. This narrative review integrates these levels within an operational framework for precision TMS. Methods: Six domain-specific PubMed searches covering 1 January 1985 to 31 July 2026 were supplemented by Google Scholar and citation tracking. A documented rerun on 17 August 2026 yielded 6430 records (5617 unique after cross-query deduplication). Evidence was synthesized narratively; no quantitative synthesis or formal risk-of-bias assessment was performed. Results: Neuronavigation improves geometric precision by stabilizing target definition and coil pose, whereas individualized electric-field models estimate intracranial exposure. Neither establishes biological precision, which also depends on neuronal orientation, brain state, circuit architecture, medication, and behavior. Motor-system measures are not validated as universal biomarkers for nonmotor cortex, and no single validated biomarker captures TMS-induced plasticity. Convergent, controlled multimodal evidence may strengthen inference about target engagement; adaptive and closed-loop approaches remain experimental. Conclusions: Geometric delivery, modeled exposure, biological engagement, and durable functional or clinical benefit require separate validation. Spatial accuracy alone does not establish clinical value. Full article

Efficient parking space management in urban settings represents a growing challenge owing to the sustained increase in the vehicle fleet. This study presents a comparative evaluation of five object detection architectures —YOLOv8s, YOLOv11s, YOLOv12s, RT-DETR-L and Faster R-CNN—applied to real-time intelligent vehicle occupancy monitoring under variable lighting conditions. The models were trained via transfer learning on a custom dataset of 1463 source images (21,944 annotated instances; expanded to 3511 files and 52,664 instances through offline augmentation of the training subset; three classes: free, occupied and unavailable) captured on a university campus located in Juliaca (Puno region), Peru, at 3824 m a.s.l. under daytime and nighttime clear-sky conditions from a single fixed-camera viewpoint. Each architecture was evaluated in ten independent experiments. Six dataset partitioning schemes of increasing strictness—a random control (R0) plus five leakage-controlled partitions—were evaluated. Under the strictest scheme (D3), simultaneously disjoint in acquisition date and camera viewpoint and therefore the most rigorous generalization estimate obtained in this study, accuracy ranges from mAP@0.5:0.95 of 0.9325 for Faster R-CNN to 0.8763 for YOLOv11s. Under the random partitioning conventionally applied to fixed-camera datasets, the same five architectures fell within 0.0055 of one another, all above 0.985, and their ranking was essentially inverted (Spearman $\rho =-0.80$). The differences in computational efficiency across architectures were statistically significant ($H=47.06$, $p<0.001$). YOLOv8s was the fastest of the four non-dominated architectures under the disjoint partition and was selected in 73.3% of weightings, although it ranked fourth in accuracy; its recommendation therefore rests on computational efficiency under a real-time constraint, whereas deployments that prioritize accuracy are better served by Faster R-CNN. The integrated system YOLOv8s + ByteTrack + FastAPI + Next.js 14 achieved per-slot accuracies of 87.5% and 91.8% under daytime and nighttime clear-sky conditions, respectively, using 1395 observations collected in a single university parking lot. For YOLOv8s, the transition from random to disjoint partitioning costs 0.1085 in mAP@0.5:0.95 (0.9913 to 0.8828), indicating that the near-saturated performance obtained under random partitioning substantially reflects the memorization of a fixed spatial configuration rather than generalization. The results support the feasibility of single-stage CNN architectures for intelligent parking monitoring in high-altitude Andean university environments under the evaluated acquisition conditions. Full article

The position of the center of mass directly affects the attitude control and flight safety of moving bodies such as unmanned aerial vehicles (UAVs). Therefore, high-precision measurement of the center of mass is required. Existing methods require changing the posture of the measured object multiple times. This introduces repeated positioning errors and suffers from poor equipment versatility. To address these issues, this paper proposes a static and dynamic combined measurement method for the center of mass based on multi-view vision. First, the relationship between the swing period and the pendulum length under the simple pendulum principle is analyzed. The basic principle of determining the direction of the center of mass using the line of gravity is also examined. Second, an under-constrained compound pendulum fixture is designed. A binocular vision system is used to track circular markers, perform FFT-based period verification, and fit the gravity line using singular value decomposition (SVD). Third, using a standard cubic iron block as the test object, the influence of pendulum length and swing angle on measurement accuracy is studied. Finally, experiments verify that the proposed method can obtain three-dimensional coordinates of the center of mass under a single suspension condition. The results show that with a pendulum length of 330 mm and an initial swing angle of 4°, the root mean square error of the center of mass measurement is 0.70 mm, and the maximum deviation over five repeated measurements is 1.45 mm. This method does not require repeated lifting or changes in posture. It can meet the need for in-situ, high-precision center of mass measurement of UAVs and other aircraft. Full article

Background/Objectives: Fetal Alcohol Spectrum Disorder (FASD) is a preventable neurodevelopmental condition associated with dysfunction of fronto-subcortical networks affecting attention, executive function, and behavioral regulation. Central auditory processing difficulties frequently persist despite preserved peripheral hearing, complicating clinical evaluation when behavioral testing is limited or unreliable, and objective markers that do not depend on behavioral cooperation are therefore needed. This exploratory observational case study investigated whether Frequency-Following Responses (FFRs) can capture measurable within-subject change in speech-evoked neural encoding in a pediatric patient with FASD. Methods: FFRs were recorded at two time points bracketing an eight-session period of non-invasive superficial neuromodulation (NESA^®) in an 8-year-old girl with FASD, using speech syllables (/da/, /ba/, /ga/) presented monaurally at 80 dB SPL. Two-tailed block-level Mann–Whitney U tests with rank-biserial correlations compared peak latencies, sustained pitch-tracking metrics (pitch strength, pitch error), onset stimulus–response correlation, and signal-to-noise ratio. Results: Sustained pitch-tracking metrics differed between time points, with increased pitch strength and reduced pitch error, whereas onset peak latencies, onset cross-correlation, and signal-to-noise ratio remained stable. Peak C, marking the transition to the sustained portion of the response, occurred earlier at the post-assessment in all six stimulus–ear combinations. The differences were therefore selective to sustained encoding rather than to onset timing or recording quality. Conclusions: FFR-derived pitch-encoding metrics detected measurable within-subject change, providing proof of concept for the feasibility of FFRs as a candidate objective tool for longitudinal monitoring of speech-evoked neural encoding in pediatric FASD. This single-case observation does not evaluate diagnostic accuracy, sensitivity, specificity, or discrimination from typically developing children, and therefore does not establish FFR as a validated diagnostic technique. The findings are hypothesis-generating and motivate controlled longitudinal studies. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article

Background: The increasing integration of Artificial Intelligence (AI) and Machine Learning (ML) into medical devices necessitates robust quality evaluation methods. However, existing approaches remain qualitative, sector-specific, or focused on isolated attributes, leaving a gap in quantifiable assessment for AI/ML-driven Software as a Medical Device (SaMD). Objective: This study introduces Q_AI/ML-SaMD, a novel hybrid metric that provides a comprehensive, quantifiable measure of AI/ML-SaMD quality by synthesizing health and information technology (IT) dimensions into a single composite, benchmark-ready score. Methods: The metric integrates key attributes from a systematic literature review, classified into Health and IT domains. Sub-metrics (Q_Health and Q_IT) use weighted sums, while the overall score employs a Weighted Geometric Mean with configurable parameters to penalize domain imbalances. Validation included (a) theoretical validation against four mathematical properties, (b) an illustrative example with sensitivity analysis, (c) expert-based validation with six specialists, and (d) an evidence-based case study on FDA-authorized IDx-DR using public regulatory and clinical documentation. Results: The illustrative example yielded a score of 29.7 (“Unsuitable”). Sensitivity analysis confirmed robustness across weight, score, and combined uncertainty perturbations, with classification unchanged. Expert validation showed 83.3% agreement. The IDx-DR case study produced a score of 82.3 (“Admissible”), correctly aligning with the device’s regulatory status and supporting external validity. Conclusions: The Q_AI/ML-SaMD metric provides a foundational, quantifiable framework for AI/ML-SaMD quality assessment, bridging qualitative regulatory principles and measurable outcomes. It offers a practical tool for developers, regulators, and clinicians to benchmark and track quality across the SaMD lifecycle. Full article

Accurate dynamic parameters are required for model-based control of lower-limb exoskeletons, but limited excitation, transmission friction, and assembly-dependent uncertainty can degrade conventional estimates. This study examines a two-stage method that combines recursive least squares (RLS) with an adaptive grey wolf optimizer (AGWO). Offline RLS tracks the base-parameter trajectory and expands its post-convergence extrema to construct a finite search space; a non-smooth friction severity index then modulates the GWO convergence schedule. The method was evaluated on a pedestal-mounted, single-degree-of-freedom hip mechanism using a 5 s calibration trajectory and a separate 7 s validation trajectory. Deterministic least squares (LS) and bound-constrained least squares (BCLS) were compared with standard PSO, RLS–PSO, RLS–GA, RLS–GWO, and RLS–AGWO. Each stochastic method used a population of 30, with 80 iterations (2400 fitness evaluations) and 30 independent seeds. On the independent trajectory, BCLS obtained an RMSE of 0.1152 Nm. Median validation RMSEs were 0.1152, 0.1152, 0.1562, and 0.1516 Nm for RLS–PSO, RLS–GA, RLS–GWO, and RLS–AGWO, respectively. Thus, the adaptive schedule improved median GWO error by 3.0%, but deterministic BCLS was both more accurate and faster for the present linear-in-parameters model. AGWO is therefore not mathematically necessary for the current convex objective; its potential advantage should be tested with genuinely nonlinear friction parameterizations. The conclusions remain limited to a single-axis pedestal experiment and do not establish performance during human-worn gait. Full article

Telemedicine in emergency and remote care relays medical images from ambulances and rural clinics to a hospital edge-computing server over a congested wireless uplink. Existing work prices such transmissions per bit or per quality-of-experience; neither metric captures the clinical value of a medical transmission. Diagnostic utility vanishes below a modality-specific acceptability floor rather than degrading gracefully, the deadline is determined by triage acuity rather than by the network, and a missed finding is far costlier than a false alarm. A per-bit clearing price therefore disadvantages the node that has expended local compute to produce a compact, diagnostically sufficient stream. We propose SemAuc, a triage-aware truthful mechanism for medical-image admission over a rate-splitting uplink, in which the shared semantic knowledge base rides the common stream, and case-specific residuals ride private streams. SemAuc filters tiers below the diagnostic floor and beyond the clinical deadline, reserves a regulated-price lane for life-threatening cases, and allocates remaining capacity through a single-parameter contestable auction whose bid-independent pre-selection step satisfies the conditions of Myerson’s lemma. The contestable lane is dominant-strategy truthful, individually rational, near-linear in the number of nodes, and achieves a constant-factor density-greedy welfare guarantee; the clinical lanes follow from triage policy without disturbing these properties. Diagnostic value is grounded by an offline kernel fitted on BraTS and CheXpert. On a Rayleigh-faded uplink at two hundred contending nodes, SemAuc preserves the high-acuity diagnostic service-level objective where bit-centric benchmarks fail, and tracks the offline optimum. Full article

Background/Objectives: Psilocybin-assisted therapy has progressed from early proof-of-concept work to a substantially expanded evidence base, with four pivotal studies published in 2025–2026 that were not incorporated into earlier reviews. This review synthesizes the pharmacological and neurobiological foundations of psilocybin and appraises clinical evidence across major depressive disorder (MDD), treatment-resistant depression (TRD), post-traumatic stress disorder (PTSD), cancer-related distress, and substance use disorders, emphasizing long-term durability, expanding indications, and methodological limitations. Methods: A narrative review was conducted using a targeted search of PubMed/MEDLINE, Embase, Scopus, and Web of Science (January 2000–April 2026), emphasizing 2025–2026 publications. Predefined eligibility principles prioritized randomized controlled trials, long-term follow-up studies, and systematic reviews; formal PRISMA procedures were not applied, consistent with the narrative design. Results: Four 2025–2026 studies extend the evidence base: a 52-week follow-up confirming dose-dependent maintenance of antidepressant benefit after a single 25 mg psilocybin session; the first pilot study in Veterans with severe TRD, reporting a 60% response rate at three weeks; the first safety trial in PTSD, where symptom improvement tracked self-transcendent experience intensity but worsened with session anxiety; and a living review of 15 randomized trials confirming a meaningful antidepressant effect while identifying functional unblinding as a substantial threat to effect estimates. Conclusions: Evidence supports psilocybin-assisted therapy as mechanistically distinct and clinically promising, most strongly in MDD and TRD, with preliminary support in PTSD and Veterans. Functional unblinding, small open-label designs, narrow safety populations, and absent SSRI-integration protocols constrain the current conclusions. Full article

Agrivoltaic (APV) systems enable the simultaneous production of food and renewable electricity. They create spatially heterogeneous environments that influence crop productivity. The quantitative relationships linking cumulative photosynthetically active radiation (PAR) with wheat productivity remain poorly studied. The objective of this study was to quantify the spatial distribution of cumulative PAR beneath a commercial single-axis tracking APV system and determine its relationship with wheat flowering, physiological responses, and grain yield. Wheat was cultivated across a 19-row transect between photovoltaic arrays at the Bar-Ilan University Agrivoltaic Research Farm, Israel. Cumulative PAR was measured separately for flowering (88 days after sowing, DAS) and physiological maturity (158 DAS). Physiological traits (plant height, SPAD chlorophyll index, and leaf nitrogen concentration), flowering, grain yield, and yield loss were quantified along the radiation gradient. Cumulative PAR varied among the 19 rows from 347 to 1917 mol m⁻² at flowering and from 1570 to 4451 mol m⁻² at maturity, while corresponding PAR losses ranged from 82.2% to 1.6% and 65.2% to 1.3%, respectively. Flowering increased from 33% in the most shaded row to 100% in the central rows and exhibited a strong quadratic relationship with cumulative PAR (R² = 0.916; r = 0.913; p < 0.001). Plant height increased with increasing cumulative PAR, whereas SPAD and leaf nitrogen were greatest in the shaded edge rows, indicating physiological acclimation to reduced irradiance. Grain yield ranged from 2.96 to 6.04 t ha⁻¹, corresponding to 46.2% yield loss to a 9.8% yield gain relative to the open-field reference. Grain yield was strongly associated with cumulative PAR (R² = 0.811; r = 0.862; p < 0.001), while grain-yield loss closely followed PAR loss (R² = 0.842; r = −0.883; p < 0.001). The results demonstrate that cumulative seasonal PAR is the principal environmental variable governing wheat development and productivity beneath tracking APV systems. The predictive equations developed here provide a practical framework for designing agrivoltaic systems that maximize crop productivity while maintaining efficient photovoltaic electricity generation. Full article

Purpose: Upper extremity motor impairments following stroke substantially limit independence in daily living. Hand-tracking virtual reality (VR) rehabilitation systems may support intensive task-oriented practice while enhancing motivation; however, evidence integrating objective kinematic indicators with usability and patient-reported outcomes for controller-free consumer-grade VR remains limited. The primary aim of this single-arm pilot study was to evaluate the feasibility, safety (tolerability), and usability of a hand-tracking VR rehabilitation system delivered as an adjunct to conventional physiotherapy in individuals with stroke; describing its preliminary in-game kinematic profile and exploring participants’ experiences through open-ended feedback were secondary aims. The study was explicitly not designed or powered to test clinical efficacy. Materials and Methods: Ten individuals with stroke completed a 20-session (8-week) single-arm pilot intervention; all participants concurrently received standard hospital-based physiotherapy (median 3 sessions/week, ∼45 min/session). Early-phase (sessions 1–5) and late-phase (sessions 16–20) within-subject performance were compared using the Wilcoxon signed-rank test with Holm–Bonferroni correction across four pre-specified primary outcomes. Movement smoothness was assessed using the Spectral Arc Length (SPARC), usability was evaluated using the System Usability Scale (SUS), and cybersickness was monitored with the Simulator Sickness Questionnaire (SSQ). Results: Sixteen patients were screened, of whom fourteen started the intervention and ten completed the 8-week per-protocol program (intervention completion rate, 10/14 = 71.4%; per-protocol session adherence among the ten completers, 200/200 = 100%; no SSQ-defined adverse events). Within-subject comparisons showed a $+10.6$-point increase in success rate (adjusted $p=0.020$), a $+0.10$ m/s increase in mean movement speed (adjusted $p=0.020$), a $-341$ ms reduction in pause duration (adjusted $p=0.022$), and a $+27.0$-point Hodges–Lehmann median-difference increase in SS-QOL (95% CI $14.0$–$41.0$; participant-level median $\mathsf{\Delta }$$+18.5$; adjusted $p=0.020$). The mean SUS score was $78.5\pm 5.4$, indicating “good” usability. Thematic analysis of post-intervention open-ended feedback identified three themes—motivation and engagement, the value of feedback, and design and comfort suggestions—that converged with the high adherence and good usability ratings. Correlations between VR-derived kinematic change and clinical change were non-significant trends ($p\ge 0.12$). Conclusions: A controller-free hand-tracking VR system was found to be feasible, well tolerated, and rated as having good usability when delivered as an adjunct to conventional therapy. Because the study used a single-arm design, included only ten participants, and did not control for the confounding effect of concurrent conventional physiotherapy or natural recovery, the observed within-subject changes cannot be causally attributed to the VR intervention and should be interpreted as exploratory feasibility signals. Adequately powered randomized controlled trials with stroke-specific clinical scales (e.g., FMA-UE, ARAT) are required before clinical efficacy can be claimed. Full article

Melt-pool depth governs interlayer bonding and porosity in laser powder bed fusion and underpins part qualification, yet predicting it reliably remains difficult. Fast conduction models reach useful accuracy only after the absorptivity is fitted to the depths they are meant to predict, and inverse analyses have been used the same way, to recover a calibrated parameter rather than to test the model. Here, the absorptivity is fixed independently instead, a measured coupling for IN718 and, for IN625 and 316L, a published closed-form relation never fitted to the present depths. This converts a moving-source conduction model from an object of calibration into one of validation. The melt boundary is located by root-finding rather than on a grid, so no discretization error enters the diagnosis. Across 231 single tracks, the model reproduces conduction-regime depth and half-width to within a few percent and underpredicts increasingly once keyholing begins. Inverting each measured depth for the absorptivity conduction would require yielding a fitting-free diagnosis: no conduction-regime track demands a non-physical value, and the inferred value converges near 0.38 against inputs of 0.27 to 0.34, whereas every keyhole-classified track demands a value above unity. Because an inferred absorptivity also absorbs unmodeled transport, downward convection was emulated as an anisotropic effective diffusivity; at the enhancement reported for Marangoni flow, no keyhole track becomes explicable. A measured Ti-6Al-4V absorptivity rise of a factor 1.9 supports the mechanism. An enthalpy-indexed correction and data-driven baselines remain alloy-specific, whereas the physics-based model retains its advantage under cross-alloy extrapolation. All findings are for single tracks on bare plates. Full article