Soccer requires rapid tactical decision-making, perceptual-cognitive processing, and coordinated physical execution, making it a relevant application domain for virtual reality (VR). VR systems integrate head-mounted displays, inertial measurement units, eye-tracking sensors, and electroencephalographic interfaces to support training, assessment, rehabilitation, and engagement. However, evidence remains fragmented across populations, study designs, sensing configurations, and research purposes, limiting conclusions about effectiveness and real-world application. Following PRISMA 2020, this systematic review synthesized 20 empirical studies published between January 2020 and 16 August 2026 involving school, academy, collegiate, and selected indirect adult soccer populations. Studies were classified by sensor modality, tracking configuration, evidence purpose, and outcome domain. Controlled intervention studies reported possible short-term improvements in selected sensorimotor, tactical, perceptual-cognitive, technical, and motivational outcomes. In contrast, cross-sectional and profiling studies showed that some VR tasks distinguished players by expertise, age, or competitive level; these findings support assessment or discriminative validity but do not demonstrate that VR training improves performance. Rehabilitation and injury-related evidence was limited and partly derived from adult or clinical populations, whereas engagement studies suggested potential benefits for motivation, attention, and participation. Six-degrees-of-freedom HMDs, 360° projection systems, eye tracking, and EEG generated different types of evidence, but no study directly compared hardware configurations within the same sample. The main contribution of this review is an integrated evidence-purpose and sensor-based synthesis that distinguishes training effectiveness from assessment validity across student-soccer applications. Overall, evidence remains promising but preliminary because of small and mixed populations, methodological heterogeneity, incomplete technical reporting, possible publication and language bias, short follow-up, and limited transfer to full-match performance. Standardized sensor reporting, controlled interventions, and longitudinal transfer assessments are required. Protocol registration: OSF.
Jaejun Park, Zainab Ghazanfar, Saba Ghazanfar Ali et al.· Italian National Conference...· 0 citations
: The exponential growth of cloud data centers necessitates highly efficient resource allocation and task migration strategies. However, multi-dimensional memory fragmentation severely limits the efficacy of standard scheduling algorithms under heavy-tailed, real-world workloads. This paper proposes Fuzzy-SLW, a hybrid swarm-intelligence architecture that integrates a Mamdani fuzzy-inference pre-filter with a distributed Spark Lion-Whale Optimization (SLWO) core via Apache Spark. The fuzzy pre-filter mathematically prunes the search space using non-compressible hardware constraints, while the Spark execution model resolves the traditional serial bottleneck of swarm intelligence. Evaluated within a discrete-event environment utilizing the Google Cluster Trace (2019), Fuzzy-SLW demonstrates a greater than 240% relative improvement ( + 42.2 percentage points) in virtual machine utilization over load-scattering metaheuristics and avoids the premature policy convergence observed in Deep-DQN baselines. For large-population offline optimization configurations ( P ≥ 5000 individuals), the distributed architecture achieves a 5.85 times sub-linear Amdahl speedup; below this population threshold, including the P = 20 configuration used for online, per-task scheduling, thread-pool context-switching overhead dominates and distributed partitioning does not improve wall-clock latency. The results empirically quantify the necessary tradeoff between aggressive hardware consolidation and Service Level Agreement preservation, establishing Fuzzy-SLW as a scalable solution for power-constrained hyper-scale environments.
Nidhi Chauhan, Navneet Kaur, Jawad Khan et al.· Computers, Materials & C...· 0 citations
Sparse-view computed tomography (CT) reconstruction aims to recover high-quality CT volumes from a limited number of X-ray projection images, thereby reducing radiation exposure during image acquisition. However, this problem is inherently ill-posed because each projection provides only indirect line-integral supervision, and different attenuation distributions can explain similar sparse measurements. Existing analytic and iterative methods often suffer from streak artifacts and unstable solutions, while supervised learning-based methods require paired training data and may generalize poorly across anatomical regions or acquisition settings. Neural Radiance Field (NeRF)-based methods have recently shown promise by representing the attenuation field as a continuous coordinate-based function optimized directly from projection images. Nevertheless, these methods mainly enforce projection consistency and do not explicitly use volume-domain uncertainty to guide subsequent reconstruction. In this work, we propose EpiC-NeRF, a CT-specific closed-loop framework that actively feeds estimated epistemic uncertainty back into sparse-view reconstruction. EpiC-NeRF adapts evidential uncertainty estimation and aggregation to the X-ray CT line-integral formulation and maintains the resulting spatial uncertainty in a persistent three-dimensional Epistemic Grid Map. The accumulated uncertainty is used by Epistemic-Adaptive Layer Normalization to modulate intermediate features and by dual active sampling to guide ray- and point-level sample allocation. The newly estimated uncertainty then updates the grid map and guides subsequent optimization iterations, forming a unified feedback loop between uncertainty estimation and CT reconstruction. Experiments on four CT volume datasets demonstrate that EpiC-NeRF achieves improved reconstruction fidelity over existing analytic, iterative, and neural implicit reconstruction methods.
Donghyuk Choo, Haill An, Younhyun Jung· Mathematics· 0 citations