Search Results (2,431)

Background/Objectives: Incomplete spinal cord injury (iSCI) represents a significant challenge in neurorehabilitation, with conventional limitations including recovery plateaus and declining patient motivation. Virtual reality (VR) and augmented reality (AR) have emerged as promising technologies to supplement traditional therapy through gamification and multisensory feedback. This systematic review and meta-analysis evaluates the effectiveness of VR and AR interventions for improving balance and locomotor function in patients with incomplete spinal cord injury. Methods: A systematic review was conducted following PRISMA guidelines, with searches in PubMed, Scopus, Web of Science, Science Direct, and Google Scholar. Randomized controlled trials and high-quality controlled studies evaluating VR/AR interventions in patients with iSCI (American Spinal Injury Association Impairment Scale [AIS] classifications B, C, or D) for a minimum of 3 weeks were included. A random-effects meta-analysis (Standardized Mean Difference, SMD; 95% Confidence Interval, CI) was conducted for the balance outcome. Results: Eight studies were included (n = 142 participants). The meta-analysis for balance (k = 5 studies) revealed a statistically significant improvement with a large effect size (SMD = 1.21, 95% CI: 0.04–2.38, p = 0.046). For locomotor function, a quantitative meta-analysis was not feasible due to a limited number of methodologically homogeneous studies; a qualitative synthesis of this evidence remained inconclusive. Substantial heterogeneity was observed in the balance analysis (I² = 81.5%). No serious adverse events related to VR/AR interventions were reported. Conclusions: VR/AR interventions show potential as an effective adjunctive therapy for improving balance in patients with iSCI, though the benefit should be interpreted with caution due to considerable variability between studies. The current evidence for locomotor function improvements is insufficient to draw conclusions, highlighting a critical need for more focused research. Substantial heterogeneity indicates that effectiveness may vary according to specific intervention characteristics, populations, and methodologies. Larger multicenter studies with standardized protocols are required to establish evidence-based clinical guidelines. Full article

Dynamic environments pose significant challenges for Visual Simultaneous Localization and Mapping (VSLAM), as moving objects can introduce outlier observations that severely degrade localization and mapping performance. To address this problem, we propose YSAG-VINS, a VSLAM algorithm specifically designed for dynamic scenes. The system integrates an enhanced YOLOv8 object detection network with an adaptive epipolar constraint strategy to effectively identify and suppress the impact of dynamic features. In particular, a lightweight YOLOv8n model augmented with ODConv and UIB modules is employed to balance detection accuracy with real-time efficiency. Based on semantic detection results, images are divided into static background and potentially dynamic regions, and the motion state of these regions is further verified using geometric constraints. Features belonging to truly dynamic objects are then removed to enhance robustness. Comprehensive experiments on multiple public datasets demonstrate that YSAG-VINS achieves superior pose estimation accuracy compared with VINS-Fusion, VDO-SLAM, and Dynamic-VINS. On three dynamic sequences of the KITTI dataset, the proposed method achieves average RMSE improvement rates of 48.62%, 12.18%, and 13.50%, respectively. These results confirm that YSAG-VINS provides robust and high-accuracy localization performance in dynamic environments, making it a promising solution for real-world applications such as autonomous driving, service robotics, and augmented reality. Full article

Background/Objectives: Integrating 3D visualization technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), into dental education may enhance students’ understanding of facial anatomy and clinical procedures. This study aimed to assess dental students’ perceptions of using MR for three-dimensional visualizations of impacted teeth. Methods: Cone-beam computed tomography (CBCT) scans of patients with impacted teeth were retrospectively selected from a university clinic database. The CBCT images were processed to adjust contrast for optimal visualization before being uploaded to MR goggles (HoloLens 2). A total of 114 final-year dental students participated, each manipulating the 3D images in space using the goggles. Following this, they completed a seven-question survey on a five-point Likert scale (1 = strongly agree, 5 = strongly disagree), evaluating image quality and the usefulness of 3D visualization. Results: The study group consisted of 29 males and 85 females (mean age = 24.11 years, SD = 1.48). The most favorable responses were for enhanced visualization of the impacted tooth’s position relative to adjacent structures and the inclusion of 3D image visualization as a teaching aid, which benefited students while learning and allowed them to better understand the course of the procedure for exposure/extraction of the impacted tooth, with median scores of 1, indicating a highly favorable opinion. A statistically significant relationship was found between the responses of females and males regarding the quality of the presented image using HoloLens 2 goggles. No significant correlation was found between participants with and without prior experience using VR/MR/AR. No significant correlation was found between age and responses. Conclusions: Students reported an improved understanding of the relationships between impacted teeth and adjacent structures, as well as potential benefits for clinical training. These findings demonstrate a high level of acceptance of MR technology among students; however, further research is required to objectively assess its effectiveness in enhancing learning outcomes. Full article

Large Language Models (LLMs) encounter significant challenges when applied in specialized domains that require precise and localized information. This problem is particularly critical in regulatory sectors, such as the animal health sector in Brazil, where professionals depend on complex and constantly updated legal norms to perform their work. The generic knowledge encapsulated in traditional LLMs is often insufficient to provide reliable support in these contexts, which can lead to inaccurate or outdated responses. To address this gap, this work presents a practical implementation of a Retrieval-Augmented Generation (RAG) system. We detail the integration of this system with the Plataforma de Defesa Sanitária Animal do Rio Grande do Sul (PDSA-RS), a real platform used for animal production certification. Our solution connects an LLM to an external knowledge base containing specific Brazilian legislation, allowing the model to retrieve relevant legal texts in real time to generate its responses. The principal objective is to demonstrate how this approach can produce accurate and contextually grounded answers for professionals in the veterinary field, assisting in decision-making processes for sanitary certification. Full article

Understanding spatial relationships between objects in images is crucial for robotic navigation, augmented reality systems, and autonomous driving applications, among others. However, existing vision-language benchmarks often overlook explicit spatial reasoning, limiting progress in this area. We attribute this limitation in part to existing open datasets and evaluation metrics, which tend to overlook spatial details. To address this gap, we make three contributions: First, we greatly extend the COCO dataset with annotations of spatial relations, providing a resource for spatially aware image captioning and visual question answering. Second, we propose a new evaluation framework encompassing metrics that assess image captions’ spatial accuracy at both the sentence and dataset levels. And third, we conduct a benchmark study of various vision encoder–text decoder transformer architectures for image captioning using the introduced dataset and metrics. Results reveal that current models capture spatial information only partially, underscoring the challenges of spatially grounded caption generation. Full article

In multidisciplinary design optimization (MDO) of high-end equipment, parameter uncertainty significantly undermines performance robustness. Existing methods are limited in convergence efficiency and in controlling uncertainty propagation. To address this gap, we propose a multidisciplinary robust collaborative optimization method under parameter uncertainty (MRCO-PU). The approach augments traditional Collaborative Optimization (CO) with a collaborative optimization method based on weight distribution difference information (CO-WDDI) to accelerate cross-disciplinary convergence. It also integrates a double-layer EI–Kriging robust optimization model to enhance robustness under complex coupling and small-sample conditions. The MRCO-PU method targets single-objective, strongly coupled, multi-constraint MDO problems with high per-evaluation cost. The method was validated on a mathematical case and on a cantilever roadheader cutting-head case. In the mathematical case, the robust feasibility of the constraints increased from 0.49 to 1.00. In the engineering case, the specific energy consumption decreased by 6.3% under the premise of fully satisfying the robust feasibility of the constraints, leading to operational cost minimization under uncertainty. This work provides an effective approach to multidisciplinary robust optimization for high-end equipment. Full article

Background & Objective: Stroke remains a leading cause of mortality and long-term disability worldwide, demanding rapid and accurate diagnosis to improve patient outcomes. Computed tomography (CT) scans are widely used in emergency settings due to their speed, availability, and cost-effectiveness. This study proposes an artificial intelligence (AI)-based framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from the Ministry of Health of the Republic of Turkey. Methods: We adopted MaxViT, a state-of-the-art Vision Transformer (ViT)-based architecture, as the primary deep learning model for stroke classification. Additional transformer variants, including Vision Transformer (ViT), Transformer-in-Transformer (TNT), and ConvNeXt, were evaluated for comparison. To improve model generalization and handle class imbalance, classical data augmentation techniques were applied. Furthermore, explainable AI (XAI) was integrated using Grad-CAM++ to provide visual insights into model decisions. Results: The MaxViT model with augmentation achieved the highest performance, reaching an accuracy and F1-score of 98.00%, outperforming the baseline Vision Transformer and other evaluated models. Grad-CAM++ visualizations confirmed that the proposed framework effectively identified stroke-related regions, enhancing transparency and clinical trust. Conclusions: This research contributes to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and improving access to timely and optimal stroke diagnosis in emergency departments. Full article

Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and manual discourse analysis are often limited by subjectivity, high labor costs, and poor scalability. These challenges underscore the need for automated, objective tools to support pedagogical assessment. This study explores and compares the use of Transformer-based language models for the automatic classification of teaching behaviors from real classroom transcriptions. A dataset of over 1300 utterances was compiled and annotated according to the teaching styles proposed in the circumplex approach (Autonomy Support, Structure, Control, and Chaos), along with an additional category for messages in which no style could be identified (Unidentified Style). To address class imbalance and enhance linguistic variability, data augmentation techniques were applied. Eight pretrained BERT-based Transformer architectures were evaluated, including several pretraining strategies and architectural structures. BETO achieved the highest performance, with an accuracy of 0.78, a macro-averaged F1-score of 0.72, and a weighted F1-score of 0.77. It showed strength in identifying challenging utterances labeled as Chaos and Autonomy Support. Furthermore, other BERT-based models purely trained with a Spanish text corpus like DistilBERT also present competitive performance, achieving accuracy metrics over 0.73 and and F1-score of 0.68. These results demonstrate the potential of leveraging Transformer-based models for objective and scalable teacher behavior classification. The findings support the feasibility of leveraging pretrained language models to develop scalable, AI-driven systems for classroom behavior classification and pedagogical feedback. Full article

This Scoping Review (ScR) synthesizes advances in architectural eye-tracking (ET) research published between 2010 and 2024. Drawing on 75 peer-reviewed studies that met clear inclusion criteria, it monitors the field’s rapid expansion, from only 20 experiments before 2018, to more than 45 new investigations in the three years thereafter, situating these developments within the longer historical evolution of ET hardware and analytical paradigms. The review maps 13 recurrent areas of application, focusing on design evaluation, wayfinding and spatial navigation, end-user experience, and architectural education. Across these domains, ET reliably reveals where occupants focus, for how long, and in what sequence, providing objective evidence that complements designer intuition and conventional post-occupancy surveys. Experts and novices might display distinct gaze signatures; for example, architects spend longer fixating on contextual and structural cues, whereas lay users dwell on decorative details, highlighting possible pedagogical opportunities. Despite these benefits, persistent challenges include data loss in dynamic or outdoor settings, calibration drift, single-user hardware constraints, and the need to triangulate gaze metrics with cognitive or affective measures. Future research directions emphasize integrating ET with virtual or augmented reality (VR) (AR) to validate design interactively, improving mobile tracking accuracy, and establishing shared datasets to enable replication and meta-analysis. Overall, the study demonstrates that ET is maturing into an indispensable, evidence-based lens for creating more intuitive, legible, and human-centered architecture. Full article

Background and Objectives: This study aimed to evaluate the predictive value of systemic inflammatory response markers—namely, the Systemic Inflammatory Response Index (SIRI), Aggregate Index of Systemic Inflammation (AISI), and Monocyte-to-Lymphocyte Ratio (MLR)—in determining the occurrence of major complications following radical cystectomy. Materials and Methods: A retrospective analysis was conducted on 200 patients who underwent open radical cystectomy with ileal conduit diversion. Demographic, clinical, and laboratory variables, including albumin, creatinine, eGFR, smoking, and ASA score, were collected. SIRI, AISI, and MLR were calculated from preoperative blood counts. Major complications and their subtypes (infectious, wound, cardiopulmonary, thrombotic, and anastomotic) were adjudicated independently. Statistical analyses included multivariable logistic regression, ROC curves, calibration (Hosmer–Lemeshow, intercept, slope, and plots), bootstrap resampling (B = 2000), linearity checks (restricted cubic splines and Box–Tidwell), incremental value metrics (ΔAUC, IDI, and NRI), and decision-curve analysis (DCA). Results: Major complications occurred in 57 patients (28.5%). SIRI values were significantly higher in patients with major complications (median 2.12 vs. 1.63, p = 0.006), whereas AISI and MLR did not differ. SIRI remained an independent predictor in multivariable analysis (OR 1.37, 95% CI 1.01–1.86, p = 0.045). An AUC of 0.624 (95% CI 0.538–0.709) with a negative predictive value of 83.3% was observed for SIRI. The baseline clinical model yielded an AUC of 0.648, and an AUC of 0.672 was obtained when SIRI was added (ΔAUC = +0.024, 95% CI −0.022–0.071, p = 0.16). Calibration was excellent (intercept = 0.07, slope = 1.08), and superior net benefit was demonstrated for the SIRI-augmented model within threshold probabilities of 0.15–0.45 in DCA. A statistically significant improvement in IDI (0.024, p = 0.024) was identified, while NRI was positive but not significant. Subtype analyses indicated that the strongest associations of SIRI were with infectious and wound complications. Conclusions: SIRI was found to be an independent predictor of major complications after open radical cystectomy. Although gains in discrimination were modest, incremental analyses demonstrated improved calibration and net clinical benefit when SIRI was incorporated into a clinical model. External validation is required before translation into clinical practice. Full article

Conventional projection systems typically require a fixed spatial configuration relative to the projection surface, with strict control over distance and angle. In contrast, UAV-mounted projectors overcome these constraints, enabling dynamic, large-scale projections onto non-planar and complex environments. However, such flexible scenarios introduce a key challenge: severe geometric distortions caused by intricate surface geometry and continuous camera–projector motion. To address this, we propose a novel image registration method based on global dense matching, which estimates the real-time optical flow field between the input projection image and the target surface. The estimated flow is used to pre-warp the image, ensuring that the projected content appears geometrically consistent across arbitrary, deformable surfaces. The core idea of our method lies in reformulating the geometric distortion correction task as a global feature matching problem, effectively reducing 3D spatial deformation into a 2D dense correspondence learning process. To support learning and evaluation, we construct a hybrid dataset that covers a wide range of projection scenarios, including diverse lighting conditions, object geometries, and projection contents. Extensive simulation and real-world experiments show that our method achieves superior accuracy and robustness in correcting geometric distortions in dynamic UAV projection, significantly enhancing visual fidelity in complex environments. This approach provides a practical solution for real-time, high-quality projection in UAV-based augmented reality, outdoor display, and aerial information delivery systems. Full article

Background/Objectives: With a 17.9 million annual mortality rate, cardiovascular disease is the leading global cause of death. As such, early detection and disease diagnosis are critical for effective treatment and symptom management. Cardiac auscultation, the process of listening to the heartbeat, often provides the first indication of underlying cardiac conditions. This practice allows for the identification of heart murmurs caused by turbulent blood flow. In this exploratory research paper, we propose an AI model to streamline this process to improve diagnostic accuracy and efficiency. Methods: We utilized data from the 2022 George Moody PhysioNet Heart Sound Classification Challenge, comprising phonocardiogram recordings of individuals under 21 years of age in Northeast Brazil. Only patients who had recordings from all four heart valves were included in our dataset. Audio files were synchronized across all recordings and converted to Mel spectrograms before being passed into a pre-trained Vision Transformer, and finally a MiniROCKET model. Additionally, data augmentation was conducted on audio files and spectrograms to generate new data, extending our total sample size from 928 spectrograms to 14,848. Results: Compared to the existing methods in the literature, our model yielded significantly enhanced quality assessment metrics, including Weighted Accuracy, Sensitivity, and F-Score, and resulted in a fast evaluation speed of 0.02 s per patient. Conclusions: The implementation of our method for the detection of heart murmurs can supplement physician diagnosis and contribute to earlier detection of underlying cardiovascular conditions, fast diagnosis times, increased scalability, and enhanced adaptability. Full article

Achieving ultra-low-sulfur diesel is a crucial objective in modern fuel refining, driven by increasingly stringent environmental regulations. This study presents the development of a highly efficient oxidative–adsorptive desulfurization process utilizing a nanocatalyst synthesized from biowaste eggshell-extracted calcite. The oxidation reaction was conducted in a digital basket reactor (DBR), an advanced reactor system designed to enhance mass transfer and catalytic efficiency. To further augment the catalyst’s performance, the calcite was modified with eco-friendly MnO₂, while activated carbon was employed as an adsorbent to effectively capture oxidized sulfur compounds, ensuring compliance with ultra-low-sulfur fuel standards. The synthesized nanocatalyst underwent comprehensive physicochemical characterization using SEM, EDX, BET, and FTIR, confirming its high surface area, structural integrity, and superior catalytic activity. The MnO₂/P–calcite catalyst achieved a sulfur removal efficiency of 96.5% at 90 °C, 80 min, and 600 rpm, demonstrating excellent oxidative–adsorptive performance for real diesel fuel. The integration of this innovative nanocatalyst with the DBR system presents a sustainable, cost-effective, and industrially viable approach for deep desulfurization, offering significant advancements in clean fuel production and environmental sustainability. Full article

This article presents the results of a comprehensive investigation into geopolymer composites synthesized from fly ash, incorporating ground asphalt derived from reclaimed road pavement and quartz sand. The primary objective of this study was to elucidate the influence of mixture composition on the mechanical, physical, and microstructural characteristics of the developed materials. The innovative aspect of this research lies in the integration of two distinct filler types—mineral (quartz sand) and organic-mineral (milled asphalt)—within a single geopolymer matrix, while preserving key performance parameters required for engineering applications, including compressive and flexural strength, density, water absorption, and abrasion resistance. The experimental methodology encompassed the characterization of the raw materials by X-ray diffraction (XRD), chemical composition analysis via X-ray fluorescence (XRF), and assessment of particle size distribution. Additionally, the produced geopolymer materials underwent density determination, compressive and flexural strength measurements, abrasion testing, and mass water absorption evaluation. The chemical composition was further examined using XRF, and the surface morphology of the specimens was analyzed by scanning electron microscopy (SEM). The findings demonstrate that the incorporation of quartz sand enhances the density and mechanical strength of the composites, whereas the addition of recycled asphalt, despite causing a modest reduction in mechanical performance at elevated dosages, augments water resistance. Moreover, ternary composite material provide an optimal compromise between mechanical strength and durability under humid conditions. Overall, the results substantiate the feasibility of utilizing asphalt waste for the fabrication of functional and sustainable geopolymer materials suitable for construction applications. Full article

As sustainable tourism practices gain traction globally, immersive technologies such as augmented reality (AR) and virtual reality (VR) have emerged as effective tools to enrich visitor experiences while supporting heritage site preservation. Particularly within built cultural environments, these technologies facilitate non-invasive interpretation of architectural spaces, enabling sustainable interaction with fragile historical structures. Despite growing scholarly attention, existing research has primarily focused on the pre-adoption phase or the technical affordances of AR/VR, with limited understanding of user behavior in the post-adoption phase. To address this gap, this study integrates the Expectation Confirmation Model (ECM) with the experiential attributes of AR/VR-enabled heritage applications, proposing an integrated theoretical model to identify key determinants of tourists’ continuance intention. Based on 434 valid survey responses collected at the Terracotta Warriors Museum, a UNESCO World Heritage Site, and analyzed using structural equation modeling (SEM), the results reveal that perceived usefulness, perceived ease of use, satisfaction, and confirmation directly influence continuance intention, while visual appeal, entertainment, enjoyment, interactivity and confirmation exert indirect effects through mediating mechanisms. The findings contribute theoretically by extending ECM to the heritage tourism domain and empirically by providing robust evidence from a high-profile non-Western site. Practically, this study offers actionable implications for designing immersive experiences that enhance post-visit continuance intention and align with broader sustainability objectives. Full article