Search Results (11,514)

The article presents a study of the computational complexity and efficiency of various parallel algorithms that implement the numerical solution of the equation in the hereditary $\alpha \left(t\right)$-model of radon volumetric activity (RVA) in a storage chamber. As a test example, a problem based on such a model is solved, which is a Cauchy problem for a nonlinear fractional differential equation with a Gerasimov–Caputo derivative of a variable order and variable coefficients. Such equations arise in problems of modeling anomalous RVA variations. Anomalous RVA can be considered one of the short-term precursors to earthquakes as an indicator of geological processes. However, the mechanisms of such anomalies are still poorly understood, and direct observations are impossible. This determines the importance of such mathematical modeling tasks and, therefore, of effective algorithms for their solution. This subsequently allows us to move on to inverse problems based on RVA data, where it is important to choose the most suitable algorithm for solving the direct problem in terms of computational resource costs. An analysis and an evaluation of various algorithms are based on data on the average time taken to solve a test problem in a series of computational experiments. To analyze effectiveness, the acceleration, efficiency, and cost of algorithms are determined, and the efficiency of CPU thread loading is evaluated. The results show that parallel algorithms demonstrate a significant increase in calculation speed compared to sequential analogs; hybrid parallel CPU–GPU algorithms provide a significant performance advantage when solving computationally complex problems, and it is possible to determine the optimal number of CPU threads for calculations. For sequential and parallel algorithms implementing numerical solutions, asymptotic complexity estimates are given, showing that, for most of the proposed algorithm implementations, the complexity tends to be $n^2$ in terms of both computation time and memory consumption. Full article

Deep-sea submersibles, often featuring a symmetrical design for hydrodynamic stability, operate as safety-critical systems in extreme environments, where the tight dynamic coupling between subsystems like hydraulics and propulsion creates complex failure modes that are challenging to diagnose. A localized fault in one system can propagate, inducing anomalous behavior in another and confounding conventional single-system monitoring approaches. This paper introduces a novel unsupervised anomaly detection framework, the Dual-Stream Coupled Autoencoder (DSC-AE), designed specifically to address this cross-system fault challenge. Our approach leverages a dual-encoder, single-decoder architecture that explicitly models the normal coupling relationship between the hydraulic and propulsion systems by forcing them into a shared latent representation. This architectural design establishes a holistic and accurate baseline of healthy, system-wide operation. Any deviation from this learned coupling manifold is robustly identified as an anomaly. We validate our model using real-world operational data from the deep-sea submersible, including curated test cases of intra-system and inter-system faults. Furthermore, we demonstrate that the proposed framework offers crucial diagnostic interpretability; by analyzing the model’s reconstruction error heatmaps, it is possible to trace fault origins and their subsequent propagation pathways, providing intuitive and actionable decision support for submersible operation and maintenance. This powerful diagnostic capability is substantiated by superior quantitative performance, where the DSC-AE significantly outperforms baseline methods in detecting propagated faults, achieving higher accuracy and recall, among other performance metrics. Full article

Against the backdrop of global warming and intensified anthropogenic activities, groundwater reserves are rapidly depleting and facing unprecedented threats to their long-term sustainability. Consequently, investigating groundwater reserves is of critical importance for ensuring water security and promoting sustainable development. This study takes the Heilongjiang (Amur) River Basin as the research area. Groundwater storage was estimated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite and the Global Land Data Assimilation System (GLDAS) covering the period from 2002 to 2024. A combination of Random Forest (RF), SHapley Additive exPlanation (SHAP) models, and Pearson partial correlation coefficients was employed to analyze the spatiotemporal evolution characteristics, driving mechanisms, and spatial linear correlations of the primary influencing factors. The results indicate that the basin’s groundwater storage anomaly (GWSA) exhibits an overall declining trend. GWSA is influenced by multiple factors, including climatic and anthropogenic drivers, with temperature (TEM) and precipitation (PRE) identified as the primary controlling variables. Spatiotemporal analysis reveals significant spatial heterogeneity in the relationship between GWSA evolution and its primary drivers. This study adopts a “retrieval–attribution–spatial analysis” framework to provide a scientific basis for enhancing regional groundwater security and supporting sustainable development goals. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Forests are pivotal to hydrologic regulation, yet province-wide dynamics across complex terrain remain insufficiently quantified. We quantified Sichuan’s forest water conservation dynamics (1990–2023), coupling the InVEST water yield model with a topographic–hydraulic correction (topographic index, saturated hydraulic conductivity, land-cover-specific flow velocity). The model used precipitation and potential evapotranspiration, land-use/cover, soil texture, and rooting depth, and was calibrated to provincial water resources statistics. Outputs were stratified by elevation and slope and monetized via a replacement cost (reservoir capacity) method. Sichuan exhibited a persistent high-capacity belt along basin–mountain transitions and the southeastern ranges, contrasting with low values on the western plateau; period maxima intensified in 2020–2023. Interannual variability closely tracked precipitation anomalies against largely stable atmospheric demand; per-unit capacity declined monotonically with slope, and total capacity generally increased with elevation, with >3500 m both highest and most variable. Economic value rose overall but fluctuated and showed marked inter-city heterogeneity. We conclude that climate pacing operating on a terrain-anchored template governs Sichuan’s forest water conservation service, supporting precision, slope-aware forest management, and differentiated ecological compensation to stabilize hydrologic regulation under climate variability. Full article

Distributed measurement networks, from semiconductor testing arrays to environmental sensor grids, medical diagnostic systems, and agricultural monitoring stations, face a fundamental challenge: undetected site-to-site variations that silently corrupt data integrity. These variations create systematic biases between supposedly identical measurement units, which undermine scientific reproducibility and yield. The current site-to-site variation detection methods require extensive sampling or make rigid distributional assumptions, making them impractical for many applications. We introduce a novel framework that transforms measurement data into density-based feature vectors using Kernel Density Estimation, followed by anomaly detection with Isolation Forest. To automate the final classification, we then apply a novel probabilistic thresholding method using Gaussian Mixture Models, which removes the need for user-defined anomaly proportions. This approach identifies problematic measurement sites without predefined anomaly proportions or distributional constraints. Unlike traditional methods, our method works efficiently with limited samples and adapts to diverse measurement contexts. We demonstrate its effectiveness using semiconductor multisite testing as a case study, where our approach consistently outperforms state-of-the-art methods in detection accuracy and sample efficiency when validated against industrial testing environments. Full article

Patterned fabrics are characterized by strong periodic and symmetric structures, and defect detection in such materials is essentially the task of identifying local disruptions of global texture symmetry. Conventional low-rank decomposition methods separate defect-free regions as low-rank and defects as sparse components, yet singular value decomposition (SVD)-based formulations inevitably lose structural details, hindering faithful recovery of symmetric background patterns. Autoencoder (AE)-based reconstruction provides nonlinear modeling capacity but tends to over-reconstruct defective areas, thereby reducing the separability between anomalies and symmetric textures. To address these challenges, this study proposes WOT-AE (Weighted Optimal Transport Autoencoder), a unified framework that exploits the inherent symmetry of patterned fabrics for robust defect detection. The framework integrates three key components: (1) AE-based low-rank modeling, which replaces SVD to preserve fine-grained repetitive patterns; (2) weighted sparse isolation guided by pixel-level priors, which suppresses false positives in symmetric but defect-free regions; and (3) optimal transport alignment in the encoder feature space, which enforces distributional consistency of symmetric textures while allowing deviations caused by asymmetric defects. Through extensive experiments on benchmark patterned fabric datasets, WOT-AE demonstrates superior performance over six state-of-the-art methods, achieving more accurate detection of symmetry-breaking defects with improved robustness. Full article

Recent advances in bionic intelligence are reshaping humanoid-robot design, demonstrating unprecedented agility, dexterity and task versatility. These breakthroughs drive an increasing need for large scale and high-quality data. Current data generation methods, however, are often expensive and time-consuming. To address this, we introduce Digital Twin Loong (DT-Loong), a digital twin system that combines a high-fidelity simulation environment with a full-scale virtual replica of the humanoid robot Loong, a bionic robot encompassing biomimetic joint design and movement mechanism. By integrating optical motion capture and human-to-humanoid motion re-targeting technologies, DT-Loong generates data for training and refining embodied AI models. We showcase the data collected from the system is of high quality. DT-Loong also proposes a Priority-Guided Quadratic Optimization algorithm for action retargeting, which achieves lower time delay and enhanced mapping accuracy. This approach enables real-time environmental feedback and anomaly detection, making it well-suited for monitoring and patrol applications. Our comprehensive framework establishes a foundation for humanoid robot training and further digital twin applications in humanoid robots to enhance their human-like behaviors through the emulation of biological systems and learning processes. Full article

The deployment of deep learning models in Internet of Things (IoT) systems is increasingly threatened by adversarial attacks. To address the challenge of effectively detecting adversarial examples generated by Generative Adversarial Networks (AdvGANs), this paper proposes an unsupervised detection method that integrates spatial statistical features and multidimensional distribution characteristics. First, a collection of adversarial examples under four different attack intensities was constructed on the CIFAR-10 dataset. Then, based on the VGG16 and ResNet50 classification models, a dual-module collaborative architecture was designed: Module A extracted spatial statistics from convolutional layers and constructed category prototypes to calculate similarity, while Module B extracted multidimensional statistical features and characterized distribution anomalies using the Mahalanobis distance. Experimental results showed that the proposed method achieved a maximum AUROC of 0.9937 for detecting AdvGAN attacks on ResNet50 and 0.9753 on VGG16. Furthermore, it achieved AUROC scores exceeding 0.95 against traditional attacks such as FGSM and PGD, demonstrating its cross-attack generalization capability. Cross-dataset evaluation on Fashion-MNIST confirms its robust generalization across data domains. This study presents an effective solution for unsupervised adversarial example detection, without requiring adversarial samples for training, making it suitable for a wide range of attack scenarios. These findings highlight the potential of the proposed method for enhancing the robustness of IoT systems in security-critical applications. Full article

A butterfly vertebra is an uncommon but clinically and radiologically significant pathology. The etiological factor of this pathology is a congenital defect in the formation of the vertebral body during embryogenesis, resulting in a cleft within the vertebral body that, in an X-ray, resembles the shape of a butterfly. Butterfly vertebrae are most often found in the thoracic and lumbar spine and more rarely in the sacral region. The clinical manifestations of this condition do not differ from the symptoms of other diseases, and it may also be asymptomatic. Only the recognition of its characteristic radiologic signs allows for accurate and timely diagnosis, as well as differentiation from other pathological processes such as fractures, metastases, and inflammation. In these cases, magnetic resonance imaging is the first-choice method. An important aspect in recognizing this pathology is its correlation with other congenital syndromes, even in cases of a single vertebral defect. We present 2 cases with an isolated S1 butterfly vertebra. The first is a 47-year-old male who presented to the hospital with complaints of chronic pain in the lower back and sacral region, more pronounced on the right side. The second is of a 39-year-old male who also presented to the hospital with chronic pain. All diagnostic modalities for this pathology have been used to demonstrate high-quality pictures, including X-ray, computed tomography (CT), and magnetic resonance imaging (MRI). Full article

This study investigates spatiotemporal b value variations and seismic interaction networks preceding the M_wg 7.1 Dingri earthquake that struck southern Tibet on 7 January 2025. Using relocated earthquake catalogs (2021–2025) and dual-method analysis combining b value mapping with Granger causality network modeling, we reveal systematic precursory patterns. Spatial analysis shows that the most significant b value reduction (Δb > 0.5) occurred north of the mainshock epicenter at seismogenic depths (5–15 km), closely aligning with subsequent aftershock concentration zones. Granger causality analysis reveals a progressive network simplification: from 73 causal links among 28 nodes during the background period (2021–2023) to 49 links among 34 nodes pre-mainshock (2023–2025) and finally to 6 localized links post-rupture. This transition from distributed system-wide interactions to localized “locked-in” dynamics reflects the stress concentration onto the primary asperity approaching critical failure. The convergence of b value anomalies and network evolution provides a comprehensive framework linking quasi-static stress states with dynamic system behavior. These findings offer valuable insights for understanding earthquake nucleation processes and improving seismic hazard assessment in the Tibetan Plateau and similar complex tectonic environments. Full article

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina. Full article

To address the challenges of large-scale production data, complex temporal dynamics, and the difficulty in extracting key reservoir performance indicators, this study proposes an intelligent time-series analytics approach, validated using an offshore oilfield case. The methodology integrates a cascaded outlier detection framework combining the 3-Sigma rule and the One-Class Support Vector Machine (OC-SVM). The 3-Sigma rule is first used for rapid statistical screening of extreme outliers, followed by OC-SVM for nonlinear anomaly detection, enhancing the accuracy of dynamic production data preprocessing. Key indicators—including initial production capacity, decline rate, water-cut trend, and recoverable reserves—are automatically extracted through hybrid modeling combining production decline analysis and waterflood characteristic curves. Algorithm reliability is rigorously evaluated using error metrics (SSE: Sum of Squared Errors, MSE: Mean Squared Error, MAE: Mean Absolute Error, RMSE :Root Mean Squared Error) and goodness−of−fit (R²). Experimental results demonstrate that the proposed method outperforms manual extraction, achieving <10% error in daily oil production and waterflood performance curve fitting, while significantly enhancing accuracy and automation. This framework provides a robust data−driven foundation for intelligent reservoir management. Full article

Epidemiological and geochemical evidence suggests that coal-fired fluorosis in Southwest China is mechanistically linked to the presence of fluoride-rich geochemical anomalies. However, the severity of dental fluorosis does not consistently align with the distribution pattern of fluoride geochemistry, suggesting that other factors may interfere with the dose–effect relationship of fluorosis. To investigate the potential biotoxicity impacts of fluoride, this study conducted an analysis of soil fluoride–selenium spatial correlation in the central areas of coal-fired fluorosis in China. The results revealed that 59.1% of soil fluoride contents were more than the average soil fluoride content of China (800 mg·kg⁻¹) and 77.9% of soil selenium contents were above 0.45 mg·kg⁻¹. Soil fluoride (1.11 × 10³ mg·kg⁻¹) and selenium contents (0.78 mg·kg⁻¹) were significantly high states, but agricultural products and drinking water sources showed relatively low levels, not significantly influenced by soil conditions. The severity of fluorosis was evaluated using Dean’s dental fluorosis index (DFI). The spatial association of soil selenium or fluoride with DFI suggested that there was a reverse relationship between soil selenium or selenium/fluoride and the DFI. The generalized additive model (GAM) showed the onset of DFI correlated with soil fluoride content, showcasing a distinctive “W” pattern, while DFI decreased steeply or gradually as soil selenium content or selenium/fluoride ratio increased. In conclusion, our findings suggest that the geochemical anomaly of soil fluoride likely contributes to the occurrence of fluorosis. However, the significantly elevated levels of soil selenium might alleviate the severity of dental fluorosis to some extent. Full article

The Central Asian Orogenic Belt (CAOB) represents one of the largest Phanerozoic accretionary orogenic systems globally, with its easternmost segment located in Northeast China. This study integrated broadband magnetotelluric (MT) surveys, geochemical analyses, and high-pressure, high-temperature electrical conductivity experiments to elucidate the deep structural characteristics and tectonic evolution of the Heihe-Hegenshan Suture (HHS) within the CAOB. A dense MT profile survey comprising 15 stations was deployed across the HHS, revealing distinct high-conductivity anomalies interpreted as the suture zone and associated tectonic features. Geochemical and petrophysical analyses of representative andesite and granite samples under simulated crustal conditions (573–973 K, 1.0 GPa) provided critical constraints for MT data interpretation. The integration of MT inversion results with aeromagnetic and Bouguer gravity anomaly data delineates the strike and spatial extent of the HHS, confirming its continuity and northward extension beyond previously recognized limits. Numerical modeling of geothermal gradients and electrical conductivity–depth relationships highlights the dominant role of hydrothermal fluids and alteration minerals in controlling shallow high-conductivity anomalies (<5 km), while deeper structures (>5 km) reflect temperature-controlled rock conductivity. These findings offer novel insights into the lithospheric-scale architecture and geodynamic processes governing the HHS, advancing our understanding of complex accretionary orogenesis in the CAOB. Full article