Search Results (19,534)

Background/Objectives: Children and adolescents with intellectual disabilities (ID) have an elevated burden of obesity and cardiometabolic risk, yet factors associated with high blood pressure (BP) in this group remain insufficiently described. This study assessed the prevalence of hypertension (HTN) and isolated systolic hypertension (ISH) at a single visit and examined anthropometric and body composition correlates of elevated BP in children with ID. Methods: A cross-sectional study was conducted among 461 children and adolescents with ID aged 7–18 y attending special education schools in southeastern Poland. Anthropometric indicators (BMI, waist circumference [WC], hip circumference [HC], and waist-to-height ratio [WHtR]) and body composition parameters (BF%, MM%, FFM%, TBW%) were measured using standardized procedures. BP was assessed three times during one visit, and the average of the second and third readings was used. Receiver operating characteristic (ROC) analyses were used for exploratory assessment of discriminatory performance of anthropometric and body composition parameters, and multivariable logistic regression examined associations with elevated BP (HTN + ISH). Results: Overall, 13.9% of participants had HTN and 10.4% had ISH (combined prevalence: 24.3%). Abdominal obesity was present in 39.5% of participants, and elevated HC in 28.2%, both more common in girls. Higher BP categories were associated with greater WC, HC, BMI, and BF%, and lower MM%, FFM%, and TBW% (p < 0.0001). HC showed the highest discriminatory accuracy for HTN + ISH (AUC = 0.844), followed by MM%, BF%, and FFM%, whereas WHtR demonstrated limited discriminatory performance in ROC analyses. In multivariable models, WHtR ≥ 0.5 was associated with increased odds of elevated BP (OR = 4.25), whereas higher TBW% (≥55.38%) was inversely associated with elevated BP (OR = 0.17) in the total sample; similar patterns were observed in sex- and age-stratified analyses. Conclusions: Children with ID show a high prevalence of elevated BP at a single visit, including HTN-range and ISH-range values. Anthropometric indicators, particularly HC and WHtR, and BIA-derived body composition parameters reflecting higher fat mass and lower lean tissue proportion were associated with elevated BP. These exploratory findings suggest that simple anthropometric and body composition measures may help identify individuals who warrant further BP assessment, although longitudinal studies with repeated measurements are required before clinical application. Full article

Accurate indoor localization in 5G remains challenging due to multipath propagation, signal blockage, and limited bandwidth in frequency range 1 (FR1). This study evaluates attention-based recurrent neural networks for two-dimensional user equipment (UE) localization using only positioning reference signal (PRS) magnitude data. We compare five models on the xG-Loc dataset (InF-DH scenario at 3.5 GHz, 5 MHz bandwidth): a simple GRU (M1), a deeper GRU with dropout (M2), a GRU optimized via Optuna (M3), a stacked GRU with multi-head attention (M4), and a bidirectional GRU with attention (M5). Model performance is quantified using the area above the cumulative distribution function (CDF) curve (AAC) metric, where lower values indicate better localization accuracy. Attention-based models significantly outperform baselines, and M4 achieves the lowest AAC of 6.71 (17% reduction versus M1’s 8.09), while M5 attains an AAC of 6.90. Statistical analysis confirms that M4 and M5 significantly outperform M3 (ANOVA, p < 0.000001). Optimal performance emerges with moderate numbers of time steps (TS ≈ 500 to 2500), with performance plateauing and degrading at higher values. These findings demonstrate that attention mechanisms substantially enhance 5G indoor localization accuracy using only PRS magnitudes, and that automated hyperparameter optimization improves model robustness. Full article

Three-dimensional (3D) woven composites have good impact resistance and are expected to become the fan casing material for the next generation of turbofan engines. Conducting research on the performance of woven composite plates under high-velocity impact of flaky projectiles is of great significance for the containment design of the fan casing. Based on the principle of energy conservation, an analytical model for the high-velocity impact of flaky projectiles on carbon fiber woven composite plates was established for three typical failure modes: shear plugging, fiber failure, and momentum transfer. A segmented solution method combining analytical and numerical calculations was developed for the model. The critical penetration velocity of the plate obtained by the analytical method at different roll angles of the projectile is in good agreement with the experimental results, which verifies the accuracy of the analytical model. Moreover, the analytical results indicate that the critical penetration velocity of the plate increases first and then decreases with the roll angle of the projectile. Further energy conversion analysis points out that shear plugging is the main form of energy dissipation for woven composite plates, and the energy dissipation of shear plugging at a roll angle of 30° is higher than that at 0° and 60°. This elucidates the mechanism by which the roll angle of the projectile affects the critical penetration velocity of the plate from the perspective of energy dissipation. Full article

Clonorchis sinensis and Metagonimus spp. are prevalent parasites in Korea, and accurate diagnosis is essential because treatment dosages differ between infections. However, their eggs are morphologically similar under light microscopy, making differentiation difficult and dependent on examiner expertise. To address this limitation, we evaluated an artificial intelligence (AI)-based automated microscope solution for the simultaneous detection and discrimination of both parasites. Microscopic images from 170 stool samples were analyzed using an AI system based on You Only Look Once version 5. The annotated dataset comprised 9455 egg images (6494 C. sinensis and 2961 Metagonimus spp.), randomly divided at the slide/patient level into training (6862), validation (1301), and test (1292) sets. Diagnostic performance was evaluated using mean average precision, confusion matrix analysis, and correlation with conventional microscopy. The model achieved a classification accuracy of up to 97.8%. C. sinensis showed higher recall and F1 scores, whereas Metagonimus spp. showed higher precision and specificity. Species identification showed complete concordance with conventional microscopy, and egg quantification was strongly correlated. These results indicate that the proposed AI system may serve as a supportive diagnostic tool comparable to conventional microscopy. Full article

Background/Objectives: Low image quality reduces diagnostic accuracy. We wanted to develop a framework for assessing transthoracic echocardiography (TTE) image quality in apical 2-, 3-, and 4-chamber views, and to use this framework to characterise segment-level visualisation patterns in patients with heart failure (HF). Methods: In this cross-sectional study, 268 TTE examinations from 230 patients qualified for ICD/CRT implantation in primary prevention of sudden cardiac death were analysed. Patient demographic, electrocardiographic, echocardiographic, and clinical characteristics were collected, and apical 2-, 3-, and 4-chamber views were extracted for image quality evaluation. Mean scores for each segment were calculated. The proportion of well-visualised segments per view was also evaluated. Risk factors for poor image quality were assessed. Results: We internally assessed the reliability of the framework (intra-class correlation coefficient > 0.9). The anterior and anterolateral walls consistently demonstrated the poorest quality, and the inferior segments the best. Clear inner-edge-to-outer-edge delineation of ≥5 segmental borders was achieved in only 30% of studies, while ≥5 endocardial border segments were visualised in 65% of cases. Reduced quality was frequently observed in patients with higher BMI and BSA, presence of HF risk factors (diabetes, prior myocardial infarction, and atrial fibrillation), and heart abnormalities (increased left ventricular end-diastolic value and hypokinesis). Conclusions: The prevalence of imaging challenges in TTE examinations performed in patients qualified for CRT-D/ICD implantation is high. These findings underscore the need for thorough training of echocardiographers and for sustained attention to technical details affecting image quality to achieve consistently high-quality images in routine practice. Full article

Accurate cloud identification is crucial for understanding the rapid evolution of weather systems, improving the accuracy of short-term forecasts, and ensuring aviation safety. Compared with traditional cloud image recognition methods, deep learning technology has advantages such as automatic learning of complex features, high-precision recognition, and strong robustness in changing environments, providing more reliable and detailed cloud information. This study utilized 256 cloud image observation data points collected by an all-sky imager from 3 to 30 November 2023, at the Tunchang County Meteorological Bureau in Hainan Province (19°21′N, 110°06′ E). A Convolutional Neural Network (CNN) model was employed for cloud image recognition. The results show that in terms of cloud recognition, the constructed CNN model achieved an accuracy rate, recall rate, and ${\mathrm{F}}_1$ score of 100%, 91%, and 95%, respectively, for clear skies and stratus clouds, cumulus clouds, and cirrus clouds, with an average recognition accuracy rate of 95%. In terms of cloud cover detection, when comparing the Normalized Red Blue Ratio (NRBR) and K-Means clustering algorithm with the system’s built-in monitoring results, the NRBR method performed optimally in cloud region segmentation, with cloud cover estimates closer to the actual distribution. In summary, deep learning technology demonstrates higher accuracy and strong robustness in all-sky cloud image recognition. Full article

This paper presents a method for modeling and predicting ISC in gel-electrolyte lithium-ion batteries, addressing critical safety concerns in electric vehicles. While gel-electrolytes are highlighted for their superior stability and performance advantages over liquid-electrolytes, they remain susceptible to IISC due to factors such as dendrite formation or mechanical stress. This study provides a detailed analysis of the unique ISCs mechanism in gel-electrolytes, emphasizing the differences between gel-electrolyte and liquid-electrolyte batteries in terms of ion transport dynamics and thermal performance. Based on these characteristics, an electrochemical–thermal–ISC coupling model was developed, and an external short-circuit resistance test was conducted to validate the model’s accuracy. By simulating various ISC states using the coupling model, a comprehensive dataset of battery ISC parameters was obtained, encompassing voltage, current, temperature, SOC, capacity loss, and internal resistance. ISC prediction models were subsequently developed using BP, CNN, and LSTM networks, with a comparative analysis of their prediction accuracy. This research advances the ISC prediction framework for gel-electrolyte batteries and demonstrates the potential of CNN-based models to achieve higher accuracy in fault prediction. Accurate ISC prediction is crucial for ensuring safe battery operation in electric vehicles. Full article

The complex environment of electrical work sites presents hazards that are diverse in form, easily concealed, and difficult to distinguish from their surroundings. Due to poor model generalization, most traditional visual recognition methods are prone to errors and cannot meet the current safety management needs in electrical work. This paper presents a novel framework for hazard identification that integrates chain-of-thought reasoning and self-verification mechanisms within a visual-language large model (VLLM) to enhance accuracy. First, typical hazard scenario data for crane operation and escalator work areas were collected. The Janus-Pro VLLM model was selected as the base model for hazard identification. Then, designing a chain-of-thought enhanced the model’s capacity to identify critical information, including the status of crane stabilizers and the zones where personnel are located. Simultaneously, a self-verification module was designed. It leveraged the multimodal comprehension capabilities of the VLLM to self-check the identification results, outputting confidence scores and justifications to mitigate model hallucination. The experimental results show that integrating the self-verification method significantly improves hazard identification accuracy, with average increases of 2.55% in crane operations and 4.35% in escalator scenarios. Compared with YOLOv8s and D-FINE, the proposed framework achieves higher accuracy, reaching up to 96.3% in crane personnel intrusion detection, and a recall of 95.6%. It outperforms small models by 8.1–13.8% in key metrics without relying on massive labeled data, providing crucial technical support for power operation hazard identification. Full article

Amid the growing global environmental challenges, precise and efficient vehicle emission management plays a critical role in achieving energy-saving and emission reduction goals. At the same time, the rapid development of connected vehicles and autonomous driving technologies has generated a large amount of high-dimensional vehicle operation data. This not only provides a rich data foundation for refined emission accounting but also raises higher demands for the construction of accounting models. Therefore, this study aims to develop an accurate and efficient emission accounting model to contribute to the precise nitrogen oxide (NO_x) emission accounting for hybrid electric vehicles (HEVs). A systematic approach is proposed that combines incremental dimensionality reduction with advanced regression algorithms to achieve refined and efficient emission accounting based on multiple variables. Specifically, the dimensionality of the real driving emission (RDE) data is first reduced using the feature selection and t-distributed stochastic neighbor embedding (t-SNE) feature extraction method to capture key parameter information and reduce subsequent computational complexity. Next, an incremental dimensionality reduction method based on dictionary learning is employed to efficiently embed new data into a low-dimensional space through straightforward matrix operations. Given the computational cost of the dictionary learning training process, this study introduces the FISTA (Fast Iterative Shrinkage-Thresholding Algorithm) for accelerated iterative optimization and enhances the computational efficiency through parameter optimization, while maintaining the accuracy of dictionary learning. Subsequently, an NO_x emission factor correction factor prediction model is trained using the low-dimensional data obtained from t-SNE embeddings, enabling direct computation of the corresponding correction factor when presented with new incremental low-dimensional embeddings. Finally, validation on independent HEV datasets shows that parameter K improves to 1 ± 0.05 and R² increases up to 0.990, laying a foundation for constructing an emission accounting model with broad applicability based on multiple variables. Full article

This study presents a hybrid approach integrating metaheuristic optimization and machine learning methods to quickly and reliably estimate the optimum design parameters of dome-shaped axially symmetric cylindrical reinforced concrete (RC) walls. A comprehensive dataset was created using the Jaya algorithm to minimize total material cost for hinged and fixed support conditions. For each optimized design case, total wall height (H), dome height (Hd), dome thickness (hd), and fluid unit weight (γ) were considered as input parameters; optimum wall thickness (hw) and total cost were determined as output parameters. Using the obtained dataset, a total of thirteen different regression-based machine learning algorithms, including linear regression-based models, tree-based ensemble methods, and neural network models, were trained and tested. Hyperparameter adjustments for all models were performed using the Optuna framework, and model performances were evaluated using a ten-fold cross-validation method and holdout dataset results. The results showed that machine learning models can learn the optimum design space obtained from metaheuristic optimization outputs with high accuracy. In optimum wall thickness estimation, Gradient Boosting-based models provided the highest accuracy under both hinged and fixed support conditions. In total cost estimation, the Gradient Boosting model stood out under hinged support conditions, while the XGBoost model yielded the most successful results for fixed support conditions. The findings clearly show that no single machine learning model exhibits the best performance for all output parameters and support conditions. The proposed approach offers significantly higher computational efficiency compared to traditional iterative optimization processes and allows for rapid estimation of optimum design parameters without the need for any iterations. In this respect, this study provides an effective decision support tool that can be used especially in the preliminary design phases and contributes to sustainable, cost-effective reinforced concrete structure design. Full article

Tunnel point cloud semantic segmentation is a critical step in achieving refined perception and intelligent management of tunnel structures. Addressing common challenges including indistinct boundaries and fine-grained category discrimination, this paper proposes MFPNet, a multi-scale feature perception network specifically designed for tunnel scenarios. This approach employs kernel convolution to effectively model local point cloud geometries within continuous spaces. Building upon this foundation, an error-feedback-based local-global feature fusion mechanism is designed. Through bidirectional information exchange, higher-level semantic information compensates for and constrains lower-level geometric features, thereby mitigating information fragmentation across semantic hierarchies. Furthermore, an adaptive feature re-calibration and cross-scale contextual correlation mechanism is introduced to dynamically modulate multi-scale feature responses. This explicitly models contextual dependencies across scales, enabling collaborative aggregation and discriminative enhancement of multi-scale semantic information. Experimental results on tunnel point cloud datasets demonstrate that the proposed MFPNet has achieved significant improvements in both overall segmentation accuracy and category balance, with mIoU reaching 87.5%, which is 5.1% to 33.0% higher than mainstream methods such as PointNet++ and RandLA-Net, and the overall classification accuracy reaching 96.3%. These results validate the method’s efficacy in achieving high-precision three-dimensional semantic understanding within complex tunnel environments, providing robust technical support for tunnel digital twin and intelligent detection applications. Full article

The growing availability of spatial data from remote sensing, laser scanning (LiDAR), and photogrammetric techniques stimulates the dynamic development of methods for the automatic detection and classification of topographic objects. In recent years, both classical machine learning (ML) algorithms and deep learning (DL) methods have found wide application in the analysis of large and complex data sets. Despite significant achievements, literature on the subject remains scattered, and a comprehensive review that systematically compares algorithm classes with respect to data modality, performance, and application context is still needed. The aim of this article is to provide a critical analysis of the current state of research on the use of ML and DL algorithms in the detection and classification of topographic objects. The theoretical foundations of selected methods, their applications to various data sources, and the accuracy and computational requirements reported in the literature are presented. Attention is paid to comparing classical ML algorithms (including SVM, RF, KNN) with modern deep architectures (CNN, U-Net, ResNet), with respect to different data types such as satellite imagery, aerial orthophotos, and LiDAR point clouds, indicating their effectiveness in the context of cartographic and elevation data. The article also discusses the main challenges related to data availability, model interpretability, and computational costs, and points to promising directions for further research. The summary of the results shows that DL methods are frequently reported to achieve several to over ten percentage points higher segmentation and classification accuracy than classical ML approaches, depending on data type and object complexity, particularly in the analysis of raster data and LiDAR point clouds. The conclusions emphasize the practical significance of these methods for spatial planning, infrastructure monitoring, and environmental management, as well as their potential in the automation of topographic analysis. Full article

Accurate and energy-efficient localization of autonomous underwater vehicles (AUVs) remains a fundamental challenge due to the complex, bandwidth-limited, and highly dynamic nature of underwater acoustic environments. This paper proposes a fully adaptive deep reinforcement learning (DRL)-driven localization framework for AUVs operating in Underwater Acoustic Sensor Networks (UAWSNs). The localization problem is formulated as a Markov Decision Process (MDP) in which an intelligent agent jointly optimizes beacon selection and transmit power allocation to minimize long-term localization error and energy consumption. A hierarchical learning architecture is developed by integrating four actor–critic algorithms, which are (i) Twin Delayed Deep Deterministic Policy Gradient (TD3), (ii) Soft Actor–Critic (SAC), (iii) Multi-Agent Deep Deterministic Policy Gradient (MADDPG), and (iv) Distributed DDPG (D2DPG), enabling robust learning under non-stationary channels, cooperative multi-AUV scenarios, and large-scale deployments. A round-trip time (RTT)-based geometric localization model incorporating a depth-dependent sound speed gradient is employed to accurately capture realistic underwater acoustic propagation effects. A multi-objective reward function jointly balances localization accuracy, energy efficiency, and ranging reliability through a risk-aware metric. Furthermore, the Cramér–Rao Lower Bound (CRLB) is derived to characterize the theoretical performance limits, and a comprehensive complexity analysis is performed to demonstrate the scalability of the proposed framework. Extensive Monte Carlo simulations show that the proposed DRL-based methods achieve significantly lower localization error, lower energy consumption, faster convergence, and higher overall system utility than classical TD3. These results confirm the effectiveness and robustness of DRL for next-generation adaptive underwater localization systems. Full article

The Social Internet of Things (SIoT) enables collaborative service provisioning among interconnected devices by leveraging socially inspired trust relationships. This paper proposes a socially driven SIoT protocol for trust-aware service selection, enabling dynamic friendship formation and ranking among distributed service-providing devices based on observed execution behavior. The protocol integrates detection accuracy, round-trip time (RTT), processing time, and device characteristics within a graph-based friendship model and employs PageRank-based scoring to guide service selection. Industrial computer vision workloads are used as a representative testbed to evaluate the proposed SIoT trust-evaluation framework under realistic execution and network constraints. In homogeneous environments with comparable service-provider capabilities, friendship scores consistently favor higher-accuracy detection pipelines, with F1-scores in the range of approximately 0.25–0.28, while latency and processing-time variations remain limited. In heterogeneous environments comprising resource-diverse devices, trust differentiation reflects the combined influence of algorithm accuracy and execution feasibility, resulting in clear service-provider ranking under high-resolution and high-frame-rate workloads. Experimental results further show that reducing available network bandwidth from 100 Mbps to 10 Mbps increases round-trip communication latency by approximately one order of magnitude, while detection accuracy remains largely invariant. The evaluation is conducted on a physical SIoT testbed with three interconnected devices, forming an 11-node, 22-edge logical trust graph, and on synthetic trust graphs with up to 50 service-providing nodes. Across all settings, service-selection decisions remain stable, and PageRank-based friendship scoring is completed in approximately 20 ms, incurring negligible overhead relative to inference and communication latency. Full article

The pressure-gradient Micro-Electro-Mechanical Systems (MEMS) vector hydrophone is a novel type of sensor capable of simultaneously acquiring both scalar and vectorial information within an acoustic field. Conventional azimuth estimation methods struggle to achieve high-resolution localization using a single pressure-gradient MEMS vector hydrophone. In practical marine environments, the multiple signal classification (MUSIC) algorithm is hampered by significant resolution performance loss. Similarly, the complex acoustic intensity (CAI) method is constrained by a high-resolution threshold for multiple targets, often resulting in inaccurate azimuth estimates. Therefore, a cross-spectral model between the acoustic pressure and the particle velocity for the pressure-gradient MEMS vector hydrophone was established. Integrated with an improved particle swarm optimization (IPSO) algorithm, a high-resolution azimuth estimation method utilizing this hydrophone is proposed. Furthermore, the corresponding Cramér-Rao Bound is derived. Simulation results demonstrate that the proposed algorithm accurately resolves two targets separated by only 5° at a low signal-to-noise ratio (SNR) of 5 dB, boasting a root mean square error of approximately 0.35° and a 100% success rate. Compared with the CAI method and the MUSIC algorithm, the proposed method achieves a lower resolution threshold and higher estimation accuracy, alongside low computational complexity that enables efficient real-time processing. Field tests in an actual seawater environment validate the algorithm’s high-resolution performance as predicted by simulations, thus confirming its practical efficacy. The proposed algorithm addresses key limitations in underwater detection by enhancing system robustness and offering high-resolution azimuth estimation. This capability holds promise for extending to multi-target scenarios in complex marine settings. Full article