Search Results (141)

The COVID-19 pandemic highlighted the need for effective disinfection strategies in order to minimize human exposure and reduce the risk of contagion in indoor environments. Ultraviolet-C (UV-C) irradiation has proven to be an effective solution for inactivating a wide range of pathogens. However, traditional fixed UV-C systems suffer from limited coverage and lack operational flexibility. To address these limitations, this paper proposes an augmented reality (AR)-based teleoperation framework for an omnidirectional mobile robot equipped with a UV-C disinfection light. Unlike traditional toolchain integrations, our framework synergizes immersive spatial visualization of a reconstructed environment, operator-guided waypoint-based remote navigation, and real-time interaction with the disinfection payload in a single operational workflow. The system is implemented using a ROSMASTER X3 Plus robotic platform, which generates a three-dimensional representation of the environment through visual simultaneous localization and mapping using RTAB-Map. The result is a 3D map that is imported into the Unity game engine and deployed to a Meta Quest 3 head-mounted display, enabling immersive visualization and interaction. Communication between the AR interface and the robotic system is achieved via the ROS-TCP-Connection, allowing real-time data exchange and remote robot control. Through the AR interface, the operator can navigate the robot within the scanned environment and activate the UV-C light. Experimental validation conducted in a classroom demonstrates the feasibility of the proposed approach and shows measurable reductions in surface microbial load. These results indicate that our system-level integration of AR-assisted teleoperation with mobile UV-C robotics represents a feasible proof-of-concept for flexible, operator-guided disinfection of indoor spaces. Full article

Accurate quantification of mineralogical composition in carbonate rocks is essential for reservoir characterization in the oil industry, directly influencing petrophysical properties such as porosity and permeability. However, traditional methods such as X-ray diffraction (XRD) are destructive and provide limited spatial sampling. The aim of this study was to develop and validate a workflow for the continuous quantification of calcite and dolomite in drill cores from the Brazilian pre-salt oil province by integrating short-wave infrared (SWIR) hyperspectral imaging (HSI) and Machine-Learning algorithms. A total of 80 m of cores were evaluated using 170 XRD-validated samples to calibrate linear, nonlinear, and ensemble models. The results showed that the combination of Multiplicative Scatter Correction (MSC) preprocessing with Multilayer Perceptron (MLP) and Support Vector Regression (SVR) achieved the best performance, reaching an $R^2$ of 0.84. Explainable Artificial Intelligence (SHAP) confirmed the relevance of diagnostic bands between 2330 and 2360 nm, improving geological interpretability of the predictions. The proposed methodology provides a non-destructive and high-resolution alternative for mineralogical profiling, supporting the evaluation of complex reservoirs and decision-making in the oil and gas industry. Although the workflow was validated using a specific pre-salt dataset, future studies should assess its transferability to other carbonate reservoirs and broader geological settings. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

Deep learning has become central to semantic segmentation of three-dimensional medical images. However—despite many published models—their adoption in practice remains limited, as deployment often requires advanced programming skills and familiarity with specific machine learning frameworks. Thus, technical barriers restrict its use to specialized users. To address this, we present SegMed (version 1.0), an open-source, standalone desktop application that provides an end-to-end workflow for deep learning-based medical image segmentation. SegMed supports the loading and inspection of common medical image formats, as well as array-based formats. The application integrates standard preprocessing operations often used in the field and directly supports loading of pretrained segmentation models implemented in both PyTorch (version 2.X) and Keras (version 2.X) and those created using the Medical Open Network for AI framework (version 1.X). Models are automatically inspected to infer required configurations, such as input size and post-processing steps, enabling segmentation with minimal user intervention. Results can be exported as volumetric images or 3D surface meshes for downstream analysis, visualization, or special applications such as virtual reality. SegMed was tested using multiple publicly available pretrained models, demonstrating robustness and flexibility across diverse segmentation tasks. By abstracting low-level implementation details, SegMed lowers technical barriers, promotes reproducibility, and facilitates the integration of AI-assisted segmentation into medical imaging workflows. Full article

Indoor climbing gyms are high-occupancy settings, yet integrated indoor air quality (IAQ) studies that analyze objective exposure and occupant perception remain scarce. The novelty consists of combining user perception with multi-zone, high-resolution IAQ measurements. We investigated a climbing gym in Romania to (i) quantify particulate matter (PM₁, PM_2.5, PM₁₀) and carbon dioxide (CO₂), (ii) compare natural and mechanical ventilation under real operating conditions with per capita normalization, (iii) relate exposure to occupancy and user perception, and (iv) coupling continuous optical monitoring with 24 h gravimetric and morphological/chemical analyses (scanning electron microscopy, confocal microscopy, X-ray fluorescence, and inductively coupled plasma mass spectrometry). The gravimetric 24 h reference measurements (EN 12341:2014) showed that daily means for PM_2.5 and PM₁₀ were 1.9–2.0× and 2.3–2.8× higher than the WHO guideline values, which confirms persistent daily particulate loads. Mechanical ventilation reduced coarse PM and CO₂, but absolute PM remained elevated and fine fractions persisted. CO₂ revealed a near-uniform vertical mixing, confirming dilution but indicating that CO₂ is not a surrogate for particulate exposure. Survey responses from occupants revealed a gap between perception and reality: most of the users rated IAQ as good despite high PM. This study is among the few integrations of perception of IAQ for climbing gyms and the first comprehensive assessment in Romania, providing evidence-based recommendations on ventilation and filtration upgrades, chalk use management, and dust-reservoir control, thus creating sparkling interest for IAQ researchers, building services engineers, sports facilities operators, and policymakers. Full article

Flexible magnetic sensors have become a hot research topic due to their non-contact human–machine interaction capabilities in areas such as motion recognition and posture detection of intelligent robots, virtual reality (VR) space reconstruction, and the Internet of Things. This study proposes a flexible, low-power three-dimensional (3D) magneto-impedance (MI) sensor based on a planar FeSiB/PI/graphene microcoil/PI/FeSiB heterostructure. Through the magneto-impedance effect of soft magnetic materials and the magnetoresistance effect of graphene under the synergistic modulation of weak current excitation, this sensor can decouple the magnetic field components in the X, Y, and Z directions with a single measurement, thus guaranteeing the real-time detection capability of a 3D magnetic field. Experimental results show that the proposed 3D magnetic sensor possesses the obvious advantages, such as the low power consumption of 76 μW, high resolutions of 31, 36, and 6992 nT/Hz^1/2 in the X, Y, and Z directions, respectively. Additionally, the magnetic sensor exhibits excellent anti-bending performance and can adapt to complex mechanical deformation environments. These characteristics endow it with great application potential in the field of intelligent wearable devices and provide new ideas for the future flexible electronics technology. Full article

Diffractive deep neural networks (D²NNs) have garnered significant attention for their ultra-low energy consumption and parallel optical computing capabilities. However, their practical deployment is hindered by the “model–reality” gap caused by fabrication inaccuracy, device fluctuation, assembly misalignment, environmental perturbation, etc. Here, we propose a combined framework that integrates sharpness-aware minimization (SAM) and aberration-immune learning (AIL), enabling joint immunity against both stochastic noise and systematic deviations from theoretical model training. Specifically, we show that under multiple perturbations such as salt-and-pepper noise, Gaussian noise, and wavefront aberration, the SAM–AIL framework achieves significant classification accuracy improvements on MNIST and Fashion-MNIST compared to conventional offline training approaches. D²NN trained with the SAM–AIL scheme exhibited significant accuracy enhancement under moderate salt-and-pepper noise, Gaussian noise, X-axis, and Y-axis tilting perturbations, respectively. Our work provides an efficient solution for offline training and deploying high-robustness D²NNs on realistic physical systems that are resilient to a variety of imperfections, significantly enhancing model transferability and reliability for optical computing tasks. Full article

The anticipated transition from 5G to 6G is driven not by incremental performance demands but by a widening mismatch between emerging application requirements and the capabilities of existing cellular systems. Despite rapid progress across 3GPP Releases 15–20, the current literature lacks a unified analysis that connects these standardization milestones to the concrete technical gaps that 6G must resolve. This study addresses this omission through a cross-release, application-driven review that traces how the evolution from enhanced mobile broadband to intelligent, sensing integrated networks lays the foundation for three core 6G service pillars: immersive communication (IC), everything connected (EC), and high-precision positioning. By examining use cases such as holographic telepresence, cooperative drone swarms, and large-scale Extended Reality (XR) ecosystems, this study exposes the limitations of today’s spectrum strategies, network architectures, and device capabilities and identifies the performance thresholds of Tbps-level throughput, sub-10 cm localization, sub-ms latency, and 10 M/km² device density that next-generation systems must achieve. The novelty of this review lies in its synthesis of 3GPP advancements in XR, the non-terrestrial network (NTN), RedCap, ambient Internet of Things (IoT), and consideration of sustainability into a cohesive key performance indicator (KPI) framework that links future services to the required architectural and protocol innovations, including AI-native design and sub-THz operation. Positioned against global initiatives such as Hexa-X and the Next G Alliance, this paper argues that 6G represents a fundamental redesign of wireless communication advancement in 5G, driven by intelligence, adaptability, and long-term energy efficiency to satisfy diverse uses cases and requirements. Full article

Seconds count differently for people in danger. We present a real-time streaming pipeline for audio-based detection of hazardous life events affecting life and property. The system operates online rather than as a retrospective analysis tool. Its objective is to reduce the latency between the occurrence of a crime, conflict, or accident and the corresponding response by authorities. The key idea is to map reality as perceived by audio into a written story and question the text via a large language model. The method integrates streaming, zero-shot algorithms in an online decoding mode that convert sound into short, interpretable tokens, which are processed by a lightweight language model. CLAP text–audio prompting identifies agitation, panic, and distress cues, combined with conversational dynamics derived from speaker diarization. Lexical information is obtained through streaming automatic speech recognition, while general audio events are detected by a streaming version of Audio Spectrogram Transformer tagger. Prosodic features are incorporated using pitch- and energy-based rules derived from robust F0 tracking and periodicity measures. The system uses a large language model configured for online decoding and outputs binary (YES/NO) life-threatening risk decisions every two seconds, along with a brief justification and a final session-level verdict. The system emphasizes interpretability and accountability. We evaluate it on a subset of the X-Violence dataset, comprising only real-world videos. We release code, prompts, decision policies, evaluation splits, and example logs to enable the community to replicate, critique, and extend our blueprint. Full article

The aim of this review was to harmonize major consensus statements (European Working Group on Sarcopenia in Older People 2; Asian Working Group for Sarcopenia 2019; Foundation for the National Institutes of Health Sarcopenia Project operational criteria) into a stage- and setting-stratified algorithm. It maps diagnostic strata to dose-defined resistance and combined training, integrates multimodal and technology-enabled options (whole-body electrical muscle stimulation, whole-body vibration, virtual reality, AI-assisted telerehabilitation) with safety cues, and embeds nutrition (≥1.2 g/kg/day protein, vitamin D, key micronutrients) and education to sustain adherence. Sarcopenia is a consequential geriatric syndrome linked to falls, loss of independence, hospitalization, mortality, and psychosocial burden, yet translation to practice is hindered by heterogeneous definitions, diagnostics, and treatment guidance. Literature searches via PubMed/MEDLINE, EBSCO, SciELO, and Google Scholar (January 2000 to August 2025) yielded 354 records; after screening and deduplication, 132 peer-reviewed studies were included. We summarize tools for screening, strength, muscle mass, and function (e.g., Sarcopenia Five-Item Questionnaire, grip strength, dual-energy X-ray absorptiometry, gait speed) and identify resistance exercise as the cornerstone, with aerobic, balance, and flexibility training adding functional and metabolic benefits. Clinic-ready tables and figures operationalize a stepwise program across primary to severe sarcopenia and across acute or iatrogenic to community settings. Early screening plus structured, exercise-centered care, augmented by targeted nutrition and education, offers pragmatic, scalable benefits. Full article

This review is a brief overview of the current status and the potential role of artificial intelligence (AI) in interventional radiology (IR). The literature published in the last decades was reviewed and the technical developments in terms of radiomics, virtual reality, robotics, fusion imaging, cone-beam computed tomography (CBCT) and Imaging Guidance Software were analyzed. The evidence shows that AI significatively improves pre-procedural planning, intra-procedural navigation, and post-procedural assessment. Radiomics extracts features from optical images of personalized treatment strategies. Virtual reality offers innovative tools especially for training and procedural simulation. Robotic systems, combined with AI, could enhance precision and reproducibility of IR procedures while reducing operator exposure to X-ray. Fusion imaging and CBCT, augmented by AI software, improve real-time guidance and procedural outcomes. Full article

In recent years, gaze estimation has received a lot of interest in areas including human–computer interface, virtual reality, and user engagement analysis. Despite significant advances in convolutional neural network (CNN) techniques, directly and effectively predicting the point of gaze (PoG) in unconstrained situations remains a difficult task. This study proposes a gaze point estimation network (L1fcs-Net) that combines facial features with positional features derived from a two-dimensional array obtained by projecting the face relative to the screen. Our approach incorporates a Face-grid branch to enhance the network’s ability to extract features such as the relative position and distance of the face to the screen. Additionally, independent fully connected layers regress x and y coordinates separately, enabling the model to better capture gaze movement characteristics in both horizontal and vertical directions. Furthermore, we employ a multi-loss approach, balancing classification and regression losses to reduce gaze point prediction errors and improve overall gaze performance. To evaluate our model, we conducted experiments on the MPIIFaceGaz dataset, which was collected under unconstrained settings. The proposed model achieves state-of-the-art performance on this dataset with a gaze point prediction error of 2.05 cm, demonstrating its superior capability in gaze estimation. Full article

People with Parkinson’s disease (PD) experience mobility impairments that impact daily functioning, yet conventional clinical assessments provide limited insight into real-world mobility. This study evaluated motor-state classification and the concurrent validity of mobility metrics derived from augmented-reality (AR) glasses against a markerless motion capture system (Theia3D) during gamified AR exercises. Fifteen participants with PD completed five gamified AR exercises measured with both systems. Motor-state segments included straight walking, turning, squatting, and sit-to-stand/stand-to-sit transfers, from which the following mobility metrics were derived: step length, gait speed, cadence, transfer and squat durations, squat depth, turn duration, and peak turn angular velocity. We found excellent between-systems consistency for head position (X, Y, Z) and yaw-angle time series (ICC_(c,1) > 0.932). The AR-based motor-state classification showed high accuracy, with F1-scores of 0.947–1.000. Absolute agreement with Theia3D was excellent for all mobility metrics (ICC_(A,1) > 0.904), except for cadence during straight walking and peak angular velocity during turns, which were good and moderate (ICC_(A,1) = 0.890, ICC_(A,1) = 0.477, respectively). These results indicate that motor states and associated mobility metrics can be accurately derived during gamified AR exercises, verified in a controlled laboratory environment in people with mild to moderate PD, a necessary first step towards unobtrusive derivation of mobility metrics during in-clinic and at-home AR neurorehabilitation exercise programs. Full article

The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to enhance mobility and connectivity, creating a smarter and reliable autonomous transportation landscape. The UAV-assisted C-V2X networks enable hyper-reliable and low-latency vehicular communications for 6G applications including augmented reality, immersive reality and virtual reality, real-time holographic mapping support, and futuristic infotainment services. This paper presents a Markov chain model to study a third-generation partnership project (3GPP)-specified C-V2X network communicating with a flying UAV for task offloading in a Federated Learning (FL) environment. We evaluate the impact of various factors such as model update frequency, queue backlog, and UAV energy consumption on different types of communication latency. Additionally, we examine the end-to-end latency in the FL environment against the latency in conventional data offloading. This is achieved by considering cooperative perception messages (CPMs) that are triggered by random events and basic safety messages (BSMs) that are periodically transmitted. Simulation results demonstrate that optimizing the transmission intervals results in a lower average delay. Also, for both scenarios, the optimal policy aims to optimize the available UAV energy consumption, minimize the cumulative queuing backlog, and maximize the UAV’s available battery power utilization. We also find that the queuing delay can be controlled by adjusting the optimal policy and the value function in the relative value iteration (RVI). Moreover, the communication latency in an FL environment is comparable to that in the gross data offloading environment based on Kullback–Leibler (KL) divergence. Full article

This paper reveals profound obstacles to sustainable supply chain integration in Industry X.0 in the United Arab Emirates (UAE) by utilizing a hybrid Fuzzy Delphi-TOPSIS approach and enriching the viewpoints of 102 experts in oil/gas (45%), logistics (30%), government (15%), and academia (10%). The top obstacles are a lack of favorable leadership (Fuzzy Delphi Threshold (FDT), FDT = 0.82) and insufficiency of sustainability professionals (FDT = 0.82), with strategy prioritization training (Rank 1, Closeness Coefficient Index (cci) cci = 0.1255) and employee engagement (Rank 2, cci = 0.1499) being among the most important solutions as opposed to technological solutions. Most importantly, AI-related technologies had a low ranking of seventh place because of their lack of implementation, which proves that human capital enhancement is always prioritized before technological adaptation. The oil/gas industry values AI with respect to regulatory compliance commitments to emissions monitoring, whereas SMEs accentuate the problem of training because of the limited resources available to them, which also indicates the societal relevance of the concept of AI to social entrepreneurship and the blockchain-based transparency and access to green technologies. This study contributes (1) a decision-oriented framework bridging the traditional 2050 vision of the UAE and the realities it faces day to day, (2) empirical insights into the need for cultural principals within governance so as to prevent the so-called paperwork syndrome, and (3) a theoretical advancement that sees AI as an enhancer of human-centric methodologies. The conclusions provide policymakers with knowledge of the importance of the ability to contextualize investments in organizational culture prior to technology implementation in order to provide effective sustainability transitions. Full article