Search Results (21,615)

As accessibility development in public buildings has gradually shifted from facility compliance toward experience- and performance-oriented evaluation, the quantitative assessment of indoor mobility experiences among blind users still lacks a systematic sensor-supported analytical framework. To address this gap, this study proposes an indoor accessibility assessment approach that integrates multi-sensor data acquisition with explainable machine learning, using a tactile museum as the experimental setting. Sixty-four participants with first-level blindness were recruited to complete a real-world directed walking task. A multimodal database was constructed by integrating objective data collected from an ultra-wideband (UWB) indoor positioning system, an intelligent gait analysis system, and video-based behavioral recording, including spatiotemporal trajectories, gait characteristics, and behavioral events, together with post-task accessibility satisfaction ratings. Based on this dataset, a random forest model was developed using the Overall Accessibility Satisfaction Score (OAS) as the response variable. SHAP, partial dependence analysis, and GAM smoothing were further applied to interpret the associations between key variables and predicted satisfaction. The results showed that walking distance, number of turns, self-reported collision perception, and selected gait indicators made relatively high contributions to the model interpretation, and these variables exhibited certain nonlinear associations with predicted satisfaction. These findings suggest that combining multi-source sensor-based behavioral measurement with explainable machine learning has potential for sensor-supported post-occupancy evaluation of indoor accessibility environments and can provide exploratory references for the quantitative assessment and optimization of accessibility in public buildings. Full article

For small-satellite imagery, traditional image points derived from the direct back-projection of laser altimetry points onto the imagery often exhibit deviations of tens of pixels or more, owing to the relatively limited attitude and orbit determination accuracy of small-satellite platforms. Moreover, the diversity of imaging sensors, variations in image resolution, and inherently weak image geometric configurations further complicate the accurate acquisition of image-space coordinates for laser altimetry points. To facilitate the application of laser altimetry data for geometric positioning across multi-satellite, multi-sensor, and multi-resolution small-satellite imagery, this study proposes a measurement method for laser altimetry points tailored to small-satellite images and establishes a combined geometric positioning model that integrates virtual control points, laser altimetry points, and image-matching tie points. The framework comprises four key procedural components: (1) an image-point-measurement strategy for laser altimetry points; (2) the construction of a laser altimetry data-assisted geometric positioning model for small-satellite imagery; (3) the solution of the geometric positioning model using a total least squares approach based on the partial-EIV (errors-in-variables) models; and (4) a comprehensive accuracy assessment conducted under multiple image-combination scenarios, including single-satellite single-stereo, single-satellite multi-stereo, dual-satellite single-stereo, and multi-satellite multi-stereo imagery configurations. Experimental validation is carried out using Jilin-1 small-satellite panchromatic images (KF01A, GF02A, and GF02B) acquired over the Henan region of China. The experimental results demonstrate that, with the laser altimetry point-measurement method and the combined geometric positioning model, the vertical positioning accuracy is substantially improved across all tested image-combination scenarios. These findings further confirm the capability in enhancing the vertical geometric positioning performance of stereoscopic small-satellite imagery characterized by multi-satellite platforms, multi-sensors, and multi-resolutions over terrain conditions similar to those tested. Full article

Background: Demonstrating durable viral suppression after switching to dolutegravir/lamivudine in clinical practice solidifies its use. Methods: This was a prospective cohort (DUALING) study conducted in 24 Dutch HIV treatment centers. HIV-RNA-suppressed cases undergoing triple-drug antiretroviral therapy without prior virological failure or resistance who switched to dolutegravir/lamivudine (cases) were 1:2 matched to controls, who remained on triple-drug antiretroviral therapy. Matching was stratified by dolutegravir use in the triple-drug antiretroviral therapy, and further by age, sex, HIV acquisition route, CD4⁺T-cell nadir, and HIV-RNA zenith. The primary endpoint was the treatment failure rate at 2 years, determined using intention-to-treat and on-treatment analyses with a 5% noninferiority margin. Results: The 2040 mostly male (84.3%) participants included 390 cases of dolutegravir-based triple-drug regimens with 680 controls, and 290 cases of non-dolutegravir-based triple-drug regimens with 580 controls. In the dolutegravir-based cases and controls, treatment failure occurred in 12.6% and 23.3% of patients in the intention-to-treat analysis (difference: −10.7%, 95%CI: −15.3% to −6.1%) and 2.6% and 2.4% of patients in the on-treatment analysis (difference: +0.2%, 95%CI −1.9% to +2.3%). The treatment failure risk in non-dolutegravir-based cases and controls was 15.2% and 19.9% in the intention-to-treat analysis (difference: −4.7, 95%CI: −10.0% to +0.6%) and 1.2% and 1.9% in the on-treatment analysis (difference +0.7%, 95%CI −2.6% to +1.1%). Therapy modifications unrelated to virological failure explained the higher treatment failure rate in the intention-to-treat analysis. In dolutegravir/lamivudine cases, a shorter time of prior triple-drug antiretroviral therapy, age < 50 years, and non-Western origin were associated with treatment failure in the multivariable analysis. Viral blips occurred in 6.8% of cases and 5.1% of controls. In the post hoc analysis, discontinuing tenofovir disoproxil fumarate-based triple-drug antiretroviral therapy led to weight gain in people with (+2.7 kg) and without (+2.3 kg) prior dolutegravir use. Conclusions: In this nationwide clinical practice study, switching to dolutegravir/lamivudine was noninferior to continuing triple-drug antiretroviral therapy after 2 years of follow-up. Full article

Dental caries remains one of the most prevalent yet preventable non-communicable diseases worldwide, disproportionately affecting populations with limited access to dental care and persistent socioeconomic inequalities. Early-stage lesions frequently remain undetected because of their asymptomatic nature, inadequate screening infrastructure, and the absence of continuous monitoring technologies, resulting in preventable complications and increased healthcare costs. To address these challenges, this study proposes an Internet of Things (IoT)-enabled intraoral wearable sensing device (I-OWSD) for continuous, quantitative, real-time monitoring of biomarkers associated with caries progression. The proposed framework integrates intraoral wearable sensing, cloud-based telemedicine services, and artificial intelligence (AI)-assisted analytics to support preventive oral healthcare and remote clinical decision-making. Two primary contributions are presented. First, a fractional-order delay-type model (FODM) based on the Caputo–Fabrizio derivative is proposed to capture the memory-dependent and nonlocal dynamics of caries progression. Mathematical analysis establishes the model’s non-negativity, boundedness, existence, uniqueness, and stability properties. Second, a biocompatible intraoral sensor interface is designed to enable continuous data acquisition and secure wireless communication with digital health platforms. Simulation results based on the proposed FODM suggest that, under an estimated adoption rate of 67.49%, the I-OWSD framework could reduce caries prevalence by approximately 15% while improving opportunities for early intervention and preventive care. The findings demonstrate the potential of combining fractional-order modelling, wearable sensing, and AI-driven teledentistry to advance continuous oral health monitoring and preventive dental care. Full article

Accurate lens distortion correction is important for calibration, registration, image stitching, and 3D reconstruction, especially in low-data device-specific settings where disposable or specialised cameras cannot provide large calibration datasets. We address distortion correction for cameras with highly irregular or non-stationary distortion fields, where fixed polynomial models and generic learning-based rectification methods can struggle. We propose a framework based on Deep Gaussian Processes (DGPs) to model the non-linear mapping required for undistortion. The key motivation is that conventional single-layer GPs with stationary kernels must use one global notion of smoothness, whereas DGPs can represent spatially varying behaviour through composed latent mappings while preserving per-pixel predictive uncertainty. This uncertainty can be used to identify or downweight unreliable corrected regions in downstream tasks. We evaluate the method on three real camera datasets with increasing distortion complexity. The full structured acquisitions contain 512 horizontal and 512 vertical line images per camera. These are not thousands of natural calibration images, but they yield up to 29,532, 11,311, and 31,686 detected intersection correspondences for the RPI, Theta, and Pillcam datasets, respectively. This distinction is important for cameras where acquiring many independent images is impractical. The results are assessed using qualitative rectification, uncertainty maps, normalised collinearity errors, and total training time. Polynomial calibration remains strongest for the regular radial RPI distortion, while DGP and DGP2 models show lower normalised collinearity-error distributions than the standard GP and lightweight MLP baselines on the more distorted Theta and Pillcam datasets. For the full datasets, total DGP/DGP2 training times ranged from 2383.50 s to 10092.50 s, reflecting the additional computational cost of probabilistic non-stationary modelling. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly embedded within diagnostic imaging workflows, reshaping clinical decision-making, health system governance, and regulatory oversight. While technical advances in radiological AI have accelerated, governance mechanisms have struggled to keep pace with issues of bias, transparency, accountability, and lifecycle oversight. This study examines ethical, regulatory, and implementation challenges in AI-enabled diagnostic imaging, building on prior reviews that have often emphasised technical performance by integrating ethical risk domains with governance responses across the AI lifecycle. Methods: This study presents a PRISMA-ScR-informed systematic survey of 156 sources, including peer-reviewed publications, regulatory documents, policy reports, and professional guidance materials (2018–2025), synthesised through thematic analysis and lifecycle mapping spanning data acquisition, model development, deployment, monitoring, and continuous learning. Results: Drawing on both thematic insights derived from the reviewed literature and established ethical and regulatory frameworks, we propose a literature-derived conceptual ethical-governance framework organised around five pillars: equity and bias mitigation, explainability and transparency, accountability and oversight, privacy-preserving infrastructure, and adaptive regulatory alignment. Although illustrated through the Australian healthcare context, the framework is designed to be transferable to federated and multi-jurisdictional health systems. This review further identifies trust quantification as an underdeveloped but essential dimension of clinical AI governance, emphasising the need to integrate measurable indicators such as calibration, clinician–AI concordance, and patient acceptance into lifecycle-based evaluation. Conclusions: By bridging technical, ethical, and policy perspectives, this review proposes a structured conceptual governance framework to support safe, equitable, and trustworthy AI integration in digital health systems. Full article

Effective fault detection is essential for maximizing energy production and ensuring the safe operation of wind turbines. However, supervised AI models for Supervisory Control and Data Acquisition (SCADA)-based condition monitoring are often limited by the lack of reliably labeled datasets. To address this issue, this manuscript proposes an Automated Labeling Procedure (ALP) that generates a structured and reliable labeled dataset from initially unlabeled wind turbine SCADA data. The proposed ALP integrates initial labeling, preprocessing, feature selection, class balancing, model-based validation, selective relabeling, and iterative retraining. A documented gearbox-changeout interval serves as the initial fault-related reference period. A Grid Search-optimized Decision Tree (GS-DT) is employed as the main validation model, while Random Forest and XGBoost are used for comparison. The main contribution is a novel misclassification-guided refinement loop in which disagreement between provisional labels and model predictions is analyzed using their SCADA values, timestamps, and relation to the fault reference interval before any selective relabeling is performed. The results show that the ALP reduces the labeling task to a small set of disputed samples requiring manual verification, instead of reviewing the entire dataset. Through iterative relabeling and retraining, dataset consistency improved and results became stable across models. Overall, the findings demonstrate the suitability of the refined dataset for subsequent wind turbine fault-detection applications. Full article

Self-powered monitoring systems capable of scavenging ambient mechanical energy are a highly desirable solution to eliminate the reliance on batteries and grid power in remote and distributed atmospheric sensing networks. However, the widespread adoption of such systems is severely hindered by the insufficient output power density of current energy harvesters, which struggle to simultaneously drive environmental sensors, data acquisition units, and wireless transmission modules. In this work, we report a highly integrated hybrid power generation system that couples a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to efficiently harvest low-frequency mechanical energy from the surroundings. Through systematic structural optimization and synergistic matching of the two transduction mechanisms, the device achieves an outstanding volumetric power density of 129.9 W·m⁻³, which represents one of the highest values ever reported for hybrid nanogenerators targeting self-powered environmental applications. The output characteristics of both the TENG and EMG units under varying load impedances are thoroughly characterized, revealing the optimal operating points for maximum power extraction. A tailored power management module, consisting of rectification, energy storage, and regulation circuits, is designed to convert the irregular alternating output into a stable direct-current supply. To demonstrate the practical viability of the system, we construct a complete self-powered atmospheric environment monitoring node, which integrates multiple environmental sensors, a data acquisition module, and a wireless transmission module. Driven exclusively by the hybrid TENG–EMG generator under ambient mechanical excitation, the node successfully performs real-time sensing, signal processing, and remote data communication without any external power input. This work not only provides a record-high power density among hybrid generators for environmental monitoring, but also establishes a feasible pathway toward maintenance-free, widely distributed, and truly autonomous atmospheric sensing networks. The presented strategy of maximizing volumetric power density through hybrid design and impedance engineering can be readily extended to other self-powered systems. Full article

Background/Objectives: Keratoconus (KC) is a chronic disease that causes progressive corneal thinning and steepening, thereby negatively impacting visual acuity. Although corneal topography and keratometry are the primary measures to diagnose KC, access to these methods can be limited by various factors. To address these limitations, this study evaluates a novel low-cost deep-learning algorithm that infers keratometric categories from smartphone-assisted Placido ring photographs. Methods: Development utilized 1240 healthy control eye images and 188 K1-labeled KC images for pretraining, without using their K1 labels. A Variational Autoencoder with KL divergence regularization (AutoEncoderKL) was trained on this pool; its encoder generated latent features for KC images (n = 535). A held-out set (n = 70) with Pentacam keratometry was labeled by K1 into <40 D, 40–47 D, and >47 D. An ensemble classifier chosen via grid search and cross-validation used the encoder features. Performance was assessed for accuracy, precision, recall, and F1-score. Results: The model achieved 91% accuracy across all classes. Precision of the model was 0.77 (<40 D), 0.98 (40–47 D), and 0.86 (>47 D); recall was 0.83, 0.91, and 1.00; and F1-scores were 0.80, 0.94, and 0.92, respectively. Notably, the model achieved perfect recall for the >47 D K1 category. Conclusions: A smartphone-assisted Placido ring imaging approach was able to predict K1-based keratometric categories without requiring tomographic or keratometric measurements as model inputs at inference. These findings provide preliminary proof-of-concept for the potential use of smartphone-assisted Placido ring images as a low-cost approach for K1-based stratification. Larger externally validated studies across different sites, devices, operators, printed Placido discs, acquisition conditions, and patient populations are required before clinical utility can be assessed. Full article

Background: We present a miniature (30 × 34 mm) 64-channel data acquisition headstage optimized for high-density surface electromyography. Methods: The headstage is made up of a multi-channel ASIC analogue front-end utilizing only MOS transistors, fabricated in 350 nm CMOS technology (IC die dimensions 6.9 × 1.8 mm), combined with an off-the-shelf multi-channel current-input ADC (DDC264, Texas Instruments). The ASIC analogue front-end employs MOS-based capacitors for both processing and AC-coupling. Results: The combination of these two sub-circuits enables the simultaneous recording of 64 channels at a typical sampling rate of 4 KHz with a maximum analogue bandwidth of 0.5–1500 Hz and a resolution of 20-bits. Typical input-referred-noise, determined by the analogue front-end, is 3.5 μV_RMS for a surface EMG bandwidth of interest of 20–500 Hz. This two-chip solution results in a power consumption of 5 mW per channel. Analogue performance variability of the custom ASIC was characterized across a dataset of 960-channels (15 dies) from two fabrication runs. Conclusions: This work practically demonstrates the viability of using both a MOS-only analogue front-end and commercially available off-shelf high-performance back-end hardware already developed for medical imaging applications to record high-density surface biosignals. The aforementioned techniques can be employed to reduce the size and cost for systems or wearable devices; facilitating the translation of high-density bio-acquisition setups from the research environment to more affordable commercial products. Full article

The supervisory control and data acquisition (SCADA) data are increasingly used for wind turbine anomaly detection, but purely data-driven methods may be limited by weak physical interpretability, class imbalance, and reduced generalization under changing wind-farm operating conditions. Although the Extreme Gradient Boosting (XGBoost) is effective for structured nonlinear classification, its use in SCADA-based anomaly detection remains affected by label quality, probability calibration, and cross-farm transferability. This paper validates a physics-informed XGBoost decision layer using residual-based indicators, including power-curve residuals, gearbox and generator thermal residuals, rotor-speed variance, active-power ratio, and wind-speed fluctuation. Comprehensive Anomaly Detection Benchmark for Wind Turbine SCADA Data (CARE) logbook labels are used as the reference labels, while 2σ, 3σ, and 4σ residual thresholds are evaluated as competing rule-based detectors. The decision layer is trained and internally tested using event-grouped chronological splits from Wind Farm A and externally evaluated on unseen Wind Farms B and C. The results show physically interpretable anomaly detection behavior, although performance varies across validation settings. Under external Farm A to Farm B/C transfer, XGBoost achieved row-level F1-scores of 0.6296 and 0.6551, respectively. Shapley additive explanations (SHAPs) link anomaly predictions mainly to thermal, power-conversion, and operating-context features. The findings support the proposed decision layer as an interpretable benchmark-validation framework, while showing that additional maintenance-log validation is required before definitive component-level fault-diagnosis claims can be made. Full article

Urban freshwater ecosystems affected by eutrophication and recurrent algal blooms require monitoring approaches capable of representing optical complexity and spatial heterogeneity. This study evaluated an integrated workflow combining proximal in situ hyperspectral sensing, radiometric calibration, spectral filtering, predictor-band selection, data transformation, and machine-learning regression to estimate chlorophyll-a (chl-a) in a tropical eutrophic urban reservoir. Monthly field campaigns were conducted from September 2022 to February 2023, with simultaneous chl-a measurements and hyperspectral image acquisition. After preprocessing, noise removal, and exclusion of anomalous spectra, 82 matched hyperspectral–chl-a observations were retained for model development. Predictor bands were selected using Pearson correlation and F-test analysis, identifying five relevant wavelengths: 530, 535, 682, 687, and 732 nm. Multiple Linear Regression, Random Forest Regressor, Support Vector Regressor, and XGBoost Regressor were tested under different data transformations. The Support Vector Regressor with logarithmic transformation achieved the best performance, with R² = 0.86 and RMSE = 6.89 µg L⁻¹. The selected wavelengths correspond to spectral regions associated with green reflectance, red chl-a absorption, and red-edge/NIR responses in productive waters. The results indicate that proximal hyperspectral sensing combined with machine learning can support chl-a estimation in optically complex urban reservoirs and provide complementary information for eutrophication monitoring and bloom-management strategies. Full article

Background: Maternal identity is the perception and recognition of a woman as a mother. Within this emerging identity, the maternal role takes on special importance as a manifestation of the set of responsibilities that a woman assumes in the care and upbringing of her baby. Respectful professional accompaniment during the period of maternal role acquisition is key to perinatal mental health and secure bonding with the baby. The main objective of this systematic review with narrative synthesis was to analyze the effects of psychological support programs aimed at maternal role acquisition during the transition to motherhood. Methods: Studies with experimental and quasi-experimental designs addressing maternal role acquisition in pregnant or postpartum women were included. A systematic search was conducted in PsycINFO, MEDLINE, PubMed and SCOPUS from inception to March 2025 following PRISMA recommendations. Due to the heterogeneity in study designs, interventions and outcome measures, a narrative synthesis was performed instead of a meta-analysis. Results: A total of 11 studies were extracted with a total sample of 1244 women, including five randomized controlled trials and six quasi-experimental studies. Psychological support programs focusing on maternal role acquisition generally showed improvements in maternal identity construction, self-efficacy and maternal competence, although not all findings reached statistical significance. In addition, several studies reported reductions in postnatal depressive symptoms, as well as improvements in subjective well-being and maternal role perception. Conclusions: results suggest that psychological support programs targeting maternal role acquisition may represent a promising approach for supporting perinatal mental health. However, the evidence should be interpreted with caution due to methodological limitations and heterogeneity across studies. In fact, most included studies were conducted in Eastern cultural contexts (Iran, China), limiting generalizability to Western populations without further adaptation and validation. Additionally, incomplete reporting of standardized effect sizes and precision measures across studies limits the quantitative interpretation of the findings. This review was not prospectively registered, and title/abstract screening was conducted by a single reviewer, increasing the risk of selection bias. Further research using rigorous and standardized designs is needed to clarify the effectiveness and generalizability of these interventions. Full article

Chillers serve as the core high-energy-consuming equipment in heating, ventilation, and air conditioning (HVAC) systems. During long-term continuous operation, they tend to suffer gradual subtle degradation, with a performance deviation less than 5%. Conventional fault diagnosis methods rely on manual threshold judgment or labeled fault data, which fail to realize accurate early warning signals. In addition, existing algorithms lack multi-dimensional baseline comparisons to verify their practical engineering performance. To address these limitations, this paper proposes an unsupervised health assessment method combining an LSTM autoencoder and Euclidean distance (LSTM-AE-ED). A multi-gradient fault time-series dataset is generated via a MATLAB R2022b/Simscape mechanism model verified by both summer field measurements and refrigeration pressure-enthalpy cycles, which resolves the practical engineering challenges of scarce on-site fault samples and potential equipment damage caused by actual fault tests. The proposed model is trained solely on healthy time-series data. It extracts dynamic coupling characteristics of chillers through LSTM, constructs a dimensionless health index based on Euclidean distance in feature space, and introduces the standard deviation of health index to improve evaluation stability. Baseline comparisons with vanilla AE and single-layer LSTM are carried out. Experimental results demonstrate that the proposed method achieves an identification accuracy of 96.3% and exhibits high sensitivity to mild degradation of four typical faults, adapting to dynamic multi-working-condition scenarios. This approach requires no additional acquisition devices for derived parameters such as power consumption and COP; online assessment can be realized merely with standard temperature, pressure, and flow sensors equipped on chillers. With lightweight inference performance, it is suitable for edge monitoring terminals of chillers in data centers, providing a low-cost and practical quantitative technical scheme for predictive maintenance and hierarchical early warning signals of refrigeration equipment. Full article

Salt stress severely restricts plant growth and reduces crop yield. Strigolactones (SLs) are carotenoid-derived phytohormones involved in the regulation of plant salt tolerance. Salt stress can modulate the expression of SL biosynthetic and signaling genes, thereby affecting SL accumulation and signaling responses. SLs also interact with abscisic acid (ABA), reactive oxygen species (ROS), and other signaling molecules to coordinate downstream stress responses. At the physiological level, SLs alleviate salt stress by maintaining Na⁺/K⁺ homeostasis, enhancing osmotic adjustment and antioxidant defense, and reducing damage to the photosynthetic system. In addition, SLs can enhance plant resource acquisition and adaptive capacity under salt stress by regulating root architecture and promoting hyphal branching of arbuscular mycorrhizal fungi (AMF). This review focuses on SL-mediated regulation of plant salt tolerance at the molecular and physiological levels and further summarizes exogenous SL application strategies for alleviating salt stress, as well as research progress on key genes in the SL pathway for the genetic improvement of salt tolerance. Clarifying the potential of SLs in regulating plant responses to salt stress could provide new insights into sustainable crop production in saline-alkali environments. Full article