Search Results (1,725)

Generalized anxiety disorder (GAD) is characterized by persistent, excessive, and difficult-to-control worry. The Contrast Avoidance Model (CAM) proposes that individuals with GAD use worry to sustain negative emotional arousal, thereby avoiding sharp negative emotional contrasts that would otherwise follow unexpected adverse events. A virtual reality (VR) protocol was developed to simulate such contrasts by alternating guided relaxation with brief anxiety-inducing scenarios (skyline plank, crowded elevator, and loose dog encounter). This study evaluated the usability and feasibility of this protocol in 20 subclinical adults aged 18–45 who met a screening threshold of GAD-7 ≥ 5, using a Meta Quest 3 headset and Polar H10 heart rate sensor. Exposure segments produced a significant decrease in RMSSD (β = −0.185, p < 0.001), consistent with reduced parasympathetic activity during exposure, whereas heart rate did not differ significantly between conditions. Subjectively, exposure increased SUDS (β = 2.23, p < 0.001) and SAM arousal (β = 1.95, p < 0.001), and decreased SAM valence (β = −2.68, p < 0.001) and dominance (β = −1.70, p = 0.005). Presence scores, cybersickness ratings, and qualitative feedback supported the usability of the protocol and identified concrete design refinements. These results support the feasibility of the protocol and provide a foundation for future controlled clinical evaluation. Full article

Underwater Wireless Sensor Networks (UWSNs) play a vital role in ocean monitoring and exploration. However, harsh underwater conditions and frequent topology changes caused by node and sink mobility pose significant challenges for reliable routing. Conventional routing protocols that depend on global route reconstruction and static paths generate excessive control overhead and degrade performance in large-scale underwater environments. In this paper, we propose an energy-efficient virtual cell-based mobile-sink adaptive routing (VC-MAR) protocol for UWSNs. The sensing field is logically partitioned into a three-dimensional grid of virtual cells, where a cell-gateway is elected in each cell to construct a low-overhead routing backbone. To support sink mobility, VC-MAR introduces a localized route-adjustment mechanism that updates only the affected backbone segments rather than reconstructing the entire routing structure. By confining routing updates to neighboring cells influenced by sink movement, the proposed protocol significantly reduces control packet exchanges while ensuring stable and reliable data delivery. Simulation results show that the proposed VC-MAR improves the packet delivery ratio by up to 20% and reduces routing control overhead by about 34% compared with traditional grid-based routing methods. These results confirm the suitability of VC-MAR for dynamic and realistic underwater sensing scenarios. Full article

Article discusses Digital Twins (DTs) as a solution for artificial intelligence (AI)-based modeling and metrology in Industry 5.0 and Industry 6.0 manufacturing systems. DTs enable the creation of real-time virtual replicas of physical assets, processes, and systems, increasing transparency, prediction, and optimization in manufacturing environments. By integrating AI, machine learning (ML), and advanced sensor data, DT support adaptive, self-learning production models capable of responding to dynamic operating conditions. In metrology, DTs improve measurement accuracy, traceability, and quality assurance by continuously synchronizing data between the physical and virtual domains. This technology improves process simulation, predictive maintenance, and fault detection, reducing downtime and operating costs. Furthermore, DTs facilitate human-centric production by enabling collaborative decision-making between intelligent systems and skilled workers. Their role in sustainable production is significant, supporting energy optimization, waste reduction, and lifecycle performance analysis. In Industry 6.0, DTs go beyond cyber-physical integration to encompass cognitive intelligence, ethical automation, and autonomous optimization. However, challenges remain in data interoperability, cybersecurity, model scalability, and real-time computational performance. DTs represent a revolutionary framework for the development of intelligent, resilient, and precise manufacturing ecosystems in next-generation industrial systems. Full article

Coprime arrays are attractive for direction-of-arrival (DOA) estimation because they can generate a large virtual aperture from a limited number of physical sensors. Their performance, however, deteriorates markedly when coherent sources coexist with unknown nonuniform sensor noise. To cope with this difficulty, this paper develops a structured DOA estimation scheme that integrates difference-coarray lag averaging, Toeplitz positive semidefinite covariance reconstruction, Hankel-based low-rank refinement, and forward–backward spatial smoothing. The sample covariance of the physical coprime array is first mapped into the coarray domain, where repeated lags are averaged, and missing lags are treated by a mask, rather than by zero padding. A Hermitian Toeplitz positive semidefinite virtual covariance matrix is then recovered in the lag domain with redundancy-aware weighting. To further enhance robustness under source coherence, the reconstructed covariance sequence is refined through a Hankel-structured low-rank restoration step. The recovered virtual covariance is finally processed by forward–backward spatial smoothing, and DOAs are obtained from the MUSIC spectrum. Simulation results under coherent-source and unknown nonuniform-noise scenarios show that the proposed method yields a lower estimation error than representative baselines, preserves clear spectral separation in multi-source cases, and maintains reliable two-source resolution under different angular separations. Additional experiments further examine RMSE trends with respect to SNR, snapshots, source number, and computational costs. Full article

Electrolytic-plasma hardening of steel components requires reliable non-contact temperature monitoring, but traditional pyrometry is complicated by the variable emissivity of steel and the intense radiation of the plasma envelope. This work presents an approach that repurposes a compact multispectral AS7341 sensor into a virtual temperature sensor based on physically grounded spectral feature engineering and regularized machine learning. The use of logarithmic ratios of the near-infrared channel (940 nm) to the visible channels suppresses the plasma contribution and linearizes Wien’s radiation law. On a controlled dataset of 20 cycles, this increases the Pearson correlation with the peak temperature from r = 0.498 (raw NIR channel) to r = 0.781 for the log(NIR/Clear) feature. Current is identified as a confounding variable; normalizing the NIR/Clear ratio by the cycle-averaged current (r = 0.761) ensures correct signal interpretation under varying process conditions. Two narrow channels–NIR (940 nm) and F8 (680 nm)–provide accuracy equivalent to the broadband Clear channel (r = 0.778 vs. 0.781), thus simplifying hardware implementation. Ridge regression using three weakly correlated features (log(NIR/Clear), cycle duration, and initial temperature) achieves a mean absolute error of 91.4 °C under leave-one-out cross-validation (LOOCV) and 85.5 °C on an independent current-group test (R² = 0.536). Independent verification by scanning electron microscopy and Vickers microhardness on 30KhGSA steel confirms reliable separation of the three thermal regimes: underheating (<800 °C, 280–320 HV), optimal quenching (800–900 °C, 620–680 HV, fine-needle martensite), and overheating (>900 °C, 540–590 HV). The proposed set of spectral features provides a physically justified basis for a low-cost industrial temperature sensor for electrolytic-plasma processing. Full article

Background: Accurate three-dimensional positioning of dental implants is critical for ensuring biomechanical stability, prosthetic passivity, and long-term clinical success. While computer-assisted navigation systems achieve high precision, their complexity and cost often limit accessibility. This study presents the development and preclinical experimental validation of a low-cost prototype designed to enhance angular accuracy in dental implant placement within a controlled 3D haptic simulation environment. Methods: A preclinical experimental design was implemented using a 3D haptic simulator (Virteasy, Montpellier, France). The prototype incorporated high-precision inertial measurement units (IMUs) and an Extended Kalman Filter (EKF) for real-time angular feedback. Ninety-seven simulated implant placements were performed—both freehand and with prototype assistance—under identical virtual conditions by a single experienced operator. Angular deviations in mesiodistal and buccolingual planes were recorded, combined into a composite 3D index, and analyzed using paired t-tests and linear mixed-effects models. The study was conducted in a controlled simulation environment, which does not fully replicate clinical conditions. Results: The prototype significantly reduced angular deviation from 13.49° to 2.99° in the mesiodistal plane (−77.8%) and from 13.56° to 5.59° in the buccolingual plane (−58.8%), achieving an overall 67% improvement in three-dimensional orientation (p < 0.001; Cohen’s d = 1.47). Agreement with an optical reference system (OptiTrack) was excellent (bias = +0.36°, RMSE = 0.39°). Intra-operator reliability exceeded 0.95 (ICC), confirming strong reproducibility and measurement stability. Conclusions: The proposed inertial sensor-based prototype achieved angular accuracy within the range reported for computer-guided systems while maintaining advantages of portability, low cost, and usability. Its integration into haptic simulators provides a valid tool for both educational and preclinical applications, offering real-time feedback that enhances spatial perception and psychomotor learning. Future clinical studies should validate its performance in cadaveric and patient-based contexts to determine its practical impact on surgical precision and implant success. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article

Reflective-mode photoplethysmography (PPG) potentially enables non-invasive physiological monitoring of heart rate and Peripheral Oxygen Saturation (SpO₂) from virtually any anatomical body site, but its performances are strongly affected by several parameters such as local perfusion, skin temperature, and microvascular bed and tissue optical properties. This study systematically evaluates the quality of reflective-mode PPG signals acquired at the finger, wrist, ear, nose, temple, upper lip, and lower lip, using two commercial PPG sensors. PPG signal quality was quantified via Skewness, Kurtosis, Perfusion Index, and Shannon entropy. Heart rate (HR) and pulse transit time (PTT) were also computed. Skewness and Perfusion Index were the most informative quality indices, revealing the finger as the site with the best signal quality and the wrist as the most challenging location. Several facial regions—including the lips, nose, and temple—showed signal quality comparable to the finger. HR estimation was most accurate using the GREEN wavelength, with the lower lip achieving the lowest error, followed by the upper lip and finger. PTT values reflected physiological differences in pulse propagation, being longest at the finger and wrist and shortest at the lips. These findings highlight the potential of non-conventional anatomical sites as alternatives to the finger and wrist for reflective-mode PPG acquisition. Full article

Long-duration animal trajectories are central to computational ethology, yet constructing large rodent cohorts remains costly, time-intensive, and constrained by animal-use considerations. We present a sensor-aware generative framework that separates latent behavioral dynamics from sensing-induced observation distortion to synthesize observed-domain trajectories that are behaviorally plausible while reproducing proxy-referenced observation distortions. The framework combines a run-level semi-Markov ethology model, occupancy calibration, and state-conditioned kinematic generation with a regime-dependent Ultra-Wideband observation channel that explicitly captures Line-of-Sight and Non-Line-of-Sight sensing conditions. Using four UWB sessions, this proof-of-concept study models three states—exploring, feeding, and burrowing—and evaluates realism through state occupancy, state-conditioned kinematic divergence, residual-domain agreement, and mean-squared displacement across time lags. We further assess whether sensor-aware conditioning improves robustness under LoS/NLoS domain shift in downstream trajectory classification. Sensor-aware conditioning yields stable mixed-domain performance with AUC = 0.995, whereas condition-agnostic baselines decline to AUC = 0.974 and AUC = 0.901. These results support the feasibility of sensor-aware in silico ethology as a proof-of-concept framework for controlled robustness studies and algorithm evaluation under proxy-referenced observation distortion. Because the present evaluation is based on four UWB sessions and uses a smoothed UWB-derived reference trajectory rather than independent ground truth, broader applications to synthetic-cohort generation, disease modeling, and statistical power-analysis workflows should be considered future directions requiring validation in larger datasets. Full article

Early-stage stroke rehabilitation increasingly incorporates virtual reality (VR) systems to provide interactive motor training and positive reinforcement. However, the minimal voluntary plantar pressure activations generated during early recovery are often below the detection limits of conventional pressure-sensing platforms, restricting timely feedback. This study quantitatively evaluates the detectability of low-amplitude plantar micro-intent signals under varying sensor resolution and adaptive threshold conditions. Publicly available plantar pressure recordings from the PhysioNet Center for Verification and Evaluation of Stroke (CVES) database were used as physiological baseline signals. Micro-intent was modeled as short-duration half-sine pressure pulses with systematically varied amplitudes and integrated into low-load baseline segments. Sensor resolution was represented through controlled noise modeling to emulate low-, medium-, and high-resolution sensing scenarios. A sliding-window adaptive threshold detector was evaluated across multiple amplitudes and sensitivity stages. The detection probability, false positive rate, and minimum detectable amplitude (defined as ≥80% detection probability) were quantified. The results show that detection probability increases with signal amplitude and shifts toward lower amplitudes with improved sensor resolution and more sensitive threshold configurations. Higher-resolution sensing reduced the minimum detectable amplitude, while adaptive thresholding enabled earlier detection of weak plantar activations without substantial increases in false positives. These findings provide quantitative design guidance for pressure-sensing VR rehabilitation systems targeting early-stage motor recovery. Full article

In roll-to-roll (R2R) web processing systems, traction rollers impose precise velocity profiles on the moving web. Ideally, the web follows this trajectory without deviation, but slip can occur during rapid acceleration or deceleration, leading to tension loss and degraded product quality. Although slip can be detected directly using high-resolution encoders that track the actual web speed, such sensors are expensive and require machine downtime for installation, making them impractical for large-scale industrial deployment. To overcome this limitation, we developed a virtual slip sensor that estimates slip using existing machine signals only. A temporary encoder was used to collect ground-truth data, enabling the training of predictive models that eliminate the need for a permanent physical sensor. The proposed system employs an ensemble modeling approach: a CatBoost model captures low-slip behavior where data is abundant, while a linear model extrapolates to high-slip, out-of-distribution conditions. Targeted feature engineering ensures generalization across varying ramp times and web speeds. Despite being trained primarily on data containing limited slip, the models successfully generalized to scenarios with severe slip, demonstrating robust predictive performance. The ensemble reduces the regular CatBoost model’s MSE at 60 m/min by approximately 54% in the speed-based evaluation and by approximately 68% in the quantile-based evaluation while maintaining comparable performance in the low-speed regimes. The resulting virtual sensor enables continuous real-time slip monitoring, providing operators with timely insights to prevent quality degradation and operate at higher acceleration profiles to increase throughput, even on machines that have not previously experienced extreme slip. Full article

Remote exercise supervision and functional movement assessment require sensing pipelines that can capture body motion, interpret protocol progression, and provide meaningful feedback within the same runtime environment. This paper presents ORAMA, an integrated computer vision platform for the execution and remote monitoring of digital exercises and clinically oriented assessment protocols related to physical fitness, mobility, balance, and health. The system combines ZED 2i stereo capture and depth-aware body tracking with a protocol-driven software architecture that includes a computer-vision pipeline, an exercise and assessment engine, a real-time feedback layer, persistent session handling, structured output generation, and a chatbot-assisted interaction path. Unlike solutions that focus only on movement recognition, ORAMA organizes each task as an explicit executable protocol with calibration stages, state transitions, task-specific metrics, and live visual guidance. The paper analyzes the system architecture, reviews the surrounding literature on virtual coaching and rehabilitation-oriented computer vision, and demonstrates representative user-interface and runtime views for both assessment and exercise scenarios. The present work reports a prototype architecture and representative operational demonstrations, rather than a completed clinical validation or participant-based efficacy study. The resulting platform shows how markerless 3D body tracking can be embedded within a unified and interpretable environment for guided exercise, functional testing, and remote follow-up without requiring wearable sensors. Full article

The exhaust gas temperature (EGT) of the gas generator is a critical indicator for the health management system of a marine gas turbine engine. Therefore, EGT prediction can not only support predictive maintenance decision-making but also serves as a reliable virtual sensor for EGT measurement. However, the engine EGT exhibits strongly nonlinear coupling relationships with other gas path variables, which causes challenges for data-driven prediction. Graph neural networks (GNNs) are particularly effective in capturing the coupling relationships among gas path sensor variables. However, conventional static graph structures fail to characterize the varying coupling strengths under different operating conditions. In this study, a thermodynamic knowledge-driven graph attention network (TKD-GAT) method is proposed for accurate and robust EGT prediction. First, a physics-guided graph topology is constructed based on the gas turbine thermodynamic equations. Subsequently, a multi-head attention mechanism is introduced to generate edge weights that capture the varying thermodynamic coupling strengths under different operation conditions. The proposed model is evaluated on a real-world LM2500 gas turbine, which is widely used in modern propulsion systems of commercial and military ships. The ablation study confirms that the thermodynamic knowledge-driven graph topology and the attention mechanism-based edge weights are both necessary to enhance the EGT prediction performance. The TKD-GAT model shows the best performance with an RMSE of 0.446% and an R² of 0.971 compared with state-of-the-art models. The paired t-test and effect size measurement (Cohen’s d) statistically confirm the significance of performance improvements. The statistical results from multiple independent experiments prove the stability of the TKD-GAT model. Additionally, the model achieves a competitive computational cost despite the integration of a physics-guided graph topology and attention mechanisms. Crucially, an interpretability analysis confirms that the learned attention weights adhere to thermodynamic principles under different operation conditions. The proposed TKD-GAT model provides an effective solution for EGT prediction in health management systems. Full article

Biodegradable magnesium implants offer significant clinical promise, but their safe use requires reliable real-time in vivo monitoring of coating integrity. Existing methods lack sufficient sensitivity and temporal resolution to detect degradation at early stages, and there are no computational tools able to predict the success of a given sensor design before animal experiments. In the present paper, we present BioElectroSynth—a digital simulator of an implantable zero-resistance ammetry (ZRA) corrosion sensor in a mouse model. The simulator combines electrochemical noise, cardiac and muscular bioelectric interference, and instrumental limitations into a unified model, enabling virtual experiments, which mimic the complexity of the in vivo system. Using Monte Carlo analysis, we establish that a 2% breach in a chitosan coating on an AZ91 magnesium alloy electrode is statistically detectable from approximately 30 recordings of 30 s each, and quantify how electrode area, its location, sampling rate, and coating quality jointly determine detection sensitivity. The framework provides the first quantitative tool for predicting in vivo experiment feasibility from standard in vitro electrochemical data alone. By identifying instrument and design configurations that are statistically underpowered before any animal use, the approach directly supports the 3R principles of humane research. Full article

Inherently, there exists a significant security hole in sensor networks. The majority of sensors are not high-end Internet of Things (IoT) devices with sufficient computing resources. Connected sensors (physical nodes in real networks) are allocated to logical nodes and managed remotely by a supervisor in a virtual network. Data acquired by sensors are then collected by a data center on which artificial intelligence operates. If an adversary spoofs a logical node (e.g., an account in a transport layer security (TLS) session) of a vulnerable sensor on the network, then it can manipulate data input to artificial intelligence. Artificial intelligence cannot verify the integrity of the data input for learning. It is difficult to stop data poisoning with no countermeasures against session spoofing. To avoid session spoofing, physical and logical nodes must be linked seamlessly. One might think this can be achieved by utilizing Hardware Root-of-Trust (HRoT) based on a Physically Unclonable Function (PUF). However, a PUF is based on an expensive System-on-a-Chip (SoC), which has been specifically designed for high-end devices, like expensive smartphones. Many sensors (low-end and middle-end IoT devices) can hardly be protected with existing PUFs. Since the number of IoT devices with a PUF is insufficient to cover the entirety of IoT devices, an attacker can find a vulnerable IoT device with no PUF to perform session spoofing. This is the problem of numbers. To resolve it, we propose Physical Cyber Authentication (PCA). A Blockchain account (a logical node in a TLS session) is anchored to an integrated circuit (IC) chip inside a sensor, allowing Blockchain to manage sensor networks, which provides necessary data to artificial intelligence, thus forming a Blockchain of sensors. Full article