Search Results (5,888)

Magnetic anomaly detection (MAD) is a passive technique for detecting ferromagnetic targets, but weak magnetic anomaly signals are often submerged in background noise. Existing stochastic resonance (SR)-based MAD methods mainly focus on target detection and generally provide limited capability for waveform and amplitude reconstruction. To address this problem, this paper proposes a weak magnetic anomaly signal enhancement method based on an adaptive variable-structure stochastic resonance (AVSSR) system. A potential function capable of switching among monostable, bistable, and multistable structures is designed to improve the adaptability of SR processing under different noise conditions. The noisy vector magnetic signals are processed by the AVSSR system, and the normalized sliding-window standard deviation is combined with a scaling factor to reconstruct the magnetic anomaly signal’s waveform and amplitude. The system parameters are optimized using the differential evolution algorithm. Simulation results show that the proposed method can effectively reconstruct magnetic anomaly signals under Gaussian white noise and colored $1/f^{\alpha }$ noise, even at an input SNR of −15 dB. Comparisons with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method and an adaptive multistable SR method demonstrate better waveform preservation and more stable amplitude reconstruction. Experimental results using measured $B_t$ signals further verify its practical applicability. Full article

Background: PPP1CB-related Noonan syndrome-like disorder with loose anagen hair type 2 (NSLH2; OMIM #617506) is a rare RASopathy caused by pathogenic variants in PPP1CB, encoding the catalytic beta subunit of protein phosphatase 1 (PP1C). Since its first description in 2016, only a limited number of patients have been reported, leaving the full phenotypic spectrum and genotype–phenotype correlations largely undefined. Objectives: To systematically review the clinical, molecular, and functional characteristics of NSLH2, we define its phenotypic spectrum, explore genotype–phenotype correlations, and summarize current evidence on therapeutic management. Methods: A systematic literature search was conducted across PubMed/MEDLINE, Embase, Web of Science, and Google Scholar, supplemented by searches of Orphanet, OMIM, and ClinVar, from 2016 to 2026. Studies reporting patients with pathogenic or likely pathogenic variants in PPP1CB were included. Individual patient-level data were extracted and analyzed descriptively. Additionally, we report a novel patient identified at our institution. Results: Thirty patients from 14 publications were included, harboring nine distinct PPP1CB variants. The most frequently identified variant was p.Pro49Arg (n = 17, 56.7%), followed by p.Met182Lys (n = 4, 13.3%) and p.Glu183Ala (n = 3, 10.0%). The majority of variants arose de novo (n = 26, 86.7%). Ectodermal anomalies, predominantly slow-growing and structurally abnormal hair consistent with loose anagen hair, were present in 79.3% of patients. Congenital heart defects were identified in 75.9%, with pulmonary stenosis and atrial septal defect representing the most common lesions. Short stature was documented in 69.2% of cases, and neurodevelopmental delay—encompassing motor and language delay—affected the majority of patients (72.4–84.6%). Brain structural anomalies were detected in 35.7%. Facial dysmorphic features were universal. Macrocephaly was present in 58.6% of cases, intellectual disability was reported in 26.9%, and epilepsy in 6.7%. Three familial cases with inherited p.Met182Lys transmission from an affected mother to three children are described, representing the largest reported familial cluster. Conclusions: NSLH2 is a clinically recognizable RASopathy with a consistent core phenotype comprising loose anagen hair, congenital heart defects, short stature, macrocephaly, and neurodevelopmental delay. The p.Pro49Arg variant accounts for the majority of reported cases and appears associated with a broad phenotypic expression. Larger cohorts and functional studies are needed to fully delineate genotype–phenotype correlations and guide therapeutic strategies. Full article

Seismic hazards in underground coal mines are frequently triggered by geological anomalies and residual coal pillars, posing severe threats to safe mining. A protected seam longwall in a Chinese coal mine was used as a case to analyse the seismic clustering characteristics under the combined influence of the B4 anticline and a 30-m-wide overlying residual chain pillar. A simulation-testing-based source locating accuracy (STSLA) method was used to study the vector characteristics of seismic location errors in the longwall. A new seismic clustering index, the Number of Possible Clustered Events (NPCE), was proposed to quantify fracture connectivity in the coal-rock mass while reducing the influence of locating errors. The spatial–temporal evolution of NPCE, seismic event frequency, and energy magnitude was compared. Results show that NPCE exhibits a strong positive correlation with imminent high-energy seismic events and outperforms conventional indicators in recall rate and overall early-warning performance. The confusion matrix method demonstrates that NPCE achieves a better balance between precision and recall, especially at high pre-warning thresholds. NPCE = 0.7 is determined as the optimal threshold for seismic hazard risk identification. The proposed method provides a reliable approach for seismic-data-based seismic hazard early warning. Full article

Reliable operation of public facilities depends on continuous power-quality (PQ) monitoring and timely triage of anomalies that can compromise voltage stability, equipment life, and safety. We developed an analytical method for a legislative complex that fuses harmonic-based health indices with event persistence and a transparent priority score to rank PQ alarms in near real time. Feeder-level interval and harmonic measurements were collected from the Legislative Building (LB) of a Philippine municipal complex. A rolling, outlier-robust PQ index was constructed, and disturbances were detected using an unsupervised Isolation Forest algorithm. Point anomalies were subsequently consolidated into multi-sample events. Each event was scored according to a severity × duration × criticality rule to support dispatch decisions. On a one-week dataset comprising 2009 five-minute samples across six channels, the method flagged 41 anomalous points (2.0% of samples), which were consolidated into seven events when a 30 min guard band was applied. One dominant disturbance, lasting 2.25 h and reaching a maximum index of 8.91, had the highest composite priority score (20.05), while shorter or less severe excursions received lower scores. The method yields operator-ready artifacts, including ranked tables and summary plots, while remaining simple, explainable, and consistent with established PQ guidance. This makes it suitable for incremental deployment in public-sector buildings where resources for advanced analytics are limited. Full article

Automatic video anomaly detection remains challenging because abnormal events are infrequent, visually heterogeneous, and weakly bounded in time. This study proposes an unsupervised framework trained only with normal video segments. The framework integrates sliding-window segment construction, dual-view perturbation, a two-branch spatio-temporal encoder, exponential moving-average prototype updating, prototype-guided contrastive optimization, and a robust anomaly score composed of prototype deviation, second-order temporal residual, and local-neighborhood sparsity. Experiments were conducted on UCSD Ped2, CUHK Avenue, and ShanghaiTech under the same input size, segment length, optimizer, and threshold protocol. The proposed model achieved AUC values of 97.4%, 91.8%, and 83.7% on the three datasets, respectively, with an average AUC of 91.0% and an average F1 score of 88.1%. Relative to the baseline contrastive model, the average AUC increased by 2.4 percentage points, and the average F1 score increased by 2.8 percentage points. Across three independent runs, the improvement over the contrastive baseline was statistically significant (paired two-sided t-test, p = 0.018). Ablation and sensitivity analyses indicate that the performance gain is mainly attributable to spatio-temporal joint encoding, prototype traction, temporal residual scoring, and local-neighborhood support. These results show that contrastive representation learning, explicit prototype updating, and temporal-aware scoring can jointly produce a stable representation of normal behavior without using abnormal samples during training. Full article

Artificial intelligence (AI) plays an increasingly important role in maritime systems, enabling advanced monitoring, anomaly detection, and decision support. However, the reliability of such systems is challenged by distributional drift, which may significantly degrade model performance over time. While anomaly detection has been extensively studied in the context of data irregularities, considerably less attention has been devoted to detecting anomalies in AI model behaviour itself. In this study, we propose MARLIN-AD (Maritime AI Reliability and Learning Intelligence Network—Anomaly Detection), a dual-layer anomaly detection framework designed to jointly monitor anomalies in data streams and anomalies in model behaviour. The framework integrates data-centric detection methods with model-centric monitoring techniques, including distributional shift detection and prediction stability analysis, within a unified anomaly scoring mechanism. The evaluation is conducted using a fully controlled synthetic data generation process, enabling precise injection of anomalies and systematic simulation of distributional drift across multiple scenarios. Experimental results demonstrate a strong and consistent degradation of model performance under drift conditions. Statistical validation using non-parametric tests, permutation-based inference, and Bayesian bootstrap analysis confirms that the observed degradation is both statistically significant and practically meaningful. In particular, posterior distributions of performance differences indicate a near-zero probability that drifted configurations outperform the baseline model. The results highlight that model degradation under drift exhibits a consistent and structured pattern, reproducible across multiple independent random seeds. Furthermore, the study shows that model-centric monitoring provides the primary signal for detecting degradation—a finding corroborated by ablation analysis—while data-centric monitoring enhances interpretability and root-cause attribution. A pilot validation on publicly available Automatic Identification System (AIS) data from the Danish Maritime Authority confirms the applicability of the data-level component to real operational trajectories. The proposed framework contributes to the development of trustworthy AI systems by enabling comprehensive monitoring of both data integrity and model behaviour in dynamic environments. Full article

The possibility of zero-day attacks on Internet of Things (IoT) networks is high, particularly in dynamic and heterogeneous IoT environments, including emerging battlefield scenarios (IoBT). Detecting these attacks requires adaptive and generalizable security mechanisms. Due to the unique and unknown signatures of these attacks, they go undetected using signature-based Intrusion Detection Systems (IDSs) on the one side. On the other side, current anomaly-based IDSs that employ traditional machine learning on statistical features struggle to adapt and generalize to unknown networks, which is the case in IoBT. Transformer-based deep learning models have shown the capability of learning complex sequential patterns. This ability can be leveraged to analyze packet payloads that encompass opcodes capable of executing malicious patterns within an IoT network. In this work, we propose a dual-stage Transformer IDS that operates on the raw payload of network packets to detect zero-day attacks. Due to the lack of IoBT datasets, we evaluate the algorithm on three comprehensive IoT traffic benchmarks—MQTT-IoT, IoT-23, and CIC-IoT-2022—which have a high number of IoT devices and various attacks. Importantly, model evaluation is performed in two cross-validation settings to address the key operational challenges associated with unseen scenarios and networks. The evaluation settings are split-at-scenario to evaluate the detection ability of zero-day attacks and split-at-dataset to evaluate the model’s generalizability to new environments. In the former, the average increase in the F1-score of the proposed algorithm over the baseline model is 44% in detecting four zero-day attacks presented in the MQTT-IoT dataset. In the latter, the average increase in the F1-score is 16% in detecting malicious attacks across the three datasets. These results show the benefit of advanced AI in securing the next generation of IoT systems in future Internet applications. Full article

This study proposes a time-domain feature-based anomaly detection method for vibration data of bridge piers collected by underwater seismometers operating under alternating submerged and exposed conditions. The method aims to accurately identify anomalies under both normal and extreme events. Taking the Fuzhou Pushang Bridge as a case study, the acceleration root mean square (a_RMS) is adopted as the representative vibration feature to investigate the effects of vehicular loads, water level variations, and tidal fluctuations. The results show that pier vibrations are primarily dominated by vehicular loads, exhibiting pronounced daily periodicity, intraday non-stationarity, and non-normality, while the influences of water level and tidal variations are relatively minor. Based on these characteristics, an anomaly detection framework integrating STL decomposition (Seasonal-trend decomposition using Loess), Yeo–Johnson transformation, and control charts is developed. Historical data are used to establish control limits and conduct self-validation, yielding an anomaly rate of 0.14%, which is consistent with the theoretical probability of ±3σ control limits. When applied to the subsequent monitoring period, the anomaly rate under normal conditions is 0.18%, demonstrating the stability of the proposed method. Further analysis reveals that anomalies are primarily caused by direct hydrodynamic impacts on the instrument. Under flood conditions, continuous anomalies occur during nighttime, with the anomaly rate increasing to 4.44%. Under seismic conditions, the control chart statistic reaches 5.03, significantly exceeding the control limits. Comparative analysis shows that the percentile-based method yields a higher anomaly rate (0.65%), indicating a higher false alarm rate. Overall, the proposed method demonstrates strong generalization capability and reliability, providing effective support for long-term structural health monitoring of bridge substructures in complex environments. Full article

Automated thermal inspection supports scalable photovoltaic asset management by reducing the subjectivity and limited temporal coverage of manual surveys. This study benchmarks a lightweight machine vision framework for low-resolution infrared inspection of photovoltaic modules using native $24\times 40$ pixel thermal images. Morphological and textural descriptors, namely HOG, LBP, and GLCM, were evaluated with optimized SVM, Random Forest, and XGBoost classifiers under a unified experimental protocol. The HOG + SVM_Opt configuration achieved the best performance, with a Macro F1-score of $0.80\pm 0.02$ and an average accuracy of $0.80\pm 0.02$. The same pipeline maintained an end-to-end CPU latency of $12.45\pm 0.85$ ms per image, including preprocessing, descriptor extraction, and prediction. The results indicate that gradient-based structural descriptors provide the most favorable balance between predictive performance and computational cost among the evaluated configurations. The proposed pipeline is therefore presented as an interpretable reference for first-stage thermal screening in low-cost photovoltaic inspection workflows. Full article

Honeybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions. In particular, early detection of queen absence is critical for maintaining colony viability. This study investigates the effectiveness of machine learning and deep learning models for acoustic-based queen-presence detection using short-duration hive audio recordings. Audio data collected from multiple sources were processed to extract spectrogram, Mel-spectrogram, and Mel-frequency cepstral coefficient features, which were evaluated using classical ML classifiers and convolutional neural networks. Experimental results indicate that MFCC-based representations consistently outperform spectrogram-based features across segment lengths, achieving higher accuracy and greater stability. The best performance was obtained with Mel features using convolutional neural networks for short segments and gradient-boosted models for longer windows. These findings demonstrate that brief acoustic segments are sufficient for reliable classification, supporting real-time monitoring under realistic urban recording conditions with moderate environmental noise. The proposed approach offers a scalable and low-cost framework for precision beekeeping and contributes to sustainable beekeeping through early, automated anomaly detection. The proposed framework supports real-time, low-cost deployment scenarios, enabling scalable precision apiculture solutions. Full article

Smart microgrids depend on continuous communication between controllers, sensors, and actuators over industrial protocols like Modbus TCP, message queuing telemetry transport (MQTT), and distributed network protocol 3 (DNP3), which were designed without built-in security mechanisms. The gateway that aggregates this traffic represents a single point of failure and is vulnerable to distributed denial-of-service (DDoS) attacks. Most existing detection methods require labeled attack data for training, a condition rarely met in operational technology (OT) environments. This paper presents an unsupervised convolutional neural network–long short-term memory (CNN-LSTM) model trained exclusively on normal microgrid gateway traffic to predict the next traffic window; anomalies are flagged when the prediction error exceeds a threshold derived from the training distribution. A dual-branch architecture processes metric time-series through LSTM layers and flow aggregate features through CNN layers, fusing both representations for prediction. The model is evaluated against three protocol-specific DDoS attack scenarios—Modbus supervisory control and data acquisition (SCADA) flooding, MQTT publish storm, and DNP3 response flooding—none of which are seen during training. Compared against an isolation forest baseline and an autoencoder baseline under identical unsupervised conditions, the CNN-LSTM achieves higher precision and recall on all attack types. The framework is deployed within a web-based monitoring platform that supports real-time detection and anomaly logging. Full article

Diamond nitrogen-vacancy (NV) centers are regarded as promising microwave sensors owing to their excellent magnetic sensitivity, stability, and environmental compatibility. However, traditional confocal test platforms based on diamond NV centers are bulky, which limits their practical applications. In this paper, a fiber-coupled compact NV microwave magnetometer is designed that employs the continuous heterodyne measurement method and a fast Fourier transform to measure multiple microwave fields. We integrated the laser excitation module, microwave antenna module, and fluorescence collection module into a single unit, reducing the volume of the magnetometer to 13 cubic centimeters. By adjusting the frequency and power of the measured microwave signals, the applicability of the device under different frequency and power conditions was verified. Experimental tests show that the microwave magnetometer can simultaneously detect multiple microwave fields with different frequencies and power levels, achieving a frequency resolution on the order of millihertz (mHz) and a microwave detection sensitivity of 0.385 nT/Hz^1/2. These results demonstrate the magnetometer’s multi-microwave-field measurement capability, making it highly promising for applications such as microwave anomaly localization and medical diagnosis. Full article

Cybersecurity has become a critical concern for individuals, organizations, and governments, especially with the rise of sophisticated cyberattacks and remote work environments. Traditional security approaches are no longer sufficient, leading to the adoption of advanced frameworks such as the zero-trust model, which operates on the principle “never trust, always verify.” This model enforces strict access controls and continuous monitoring across all network activities. Designing an intelligent zero-trust system is challenging due to the complexity of network environments and the evolving nature of malicious threats. This project proposes an advanced zero-trust architecture that integrates machine learning and multi-factor authentication (MFA) to strengthen security. Specifically, it employs Multilayer Perceptron models and k-Nearest Neighbors algorithms to analyze system logs and user behavior, enabling real-time anomaly detection and adaptive authentication mechanisms. The proposed framework is experimentally evaluated using the H-MOG behavioral–contextual authentication dataset, which captures multimodal user interaction patterns and supports continuous authentication analysis within Zero Trust environments. The integration of machine learning enhances the system’s ability to identify suspicious activities quickly and accurately, while MFA provides an additional layer of protection against unauthorized access. Moreover, the proposed framework emphasizes usability, ensuring that enhanced security does not impose excessive burden on users or IT teams. This allows the framework to respond more effectively to potential threats while maintaining usability. Overall, the proposed approach offers a practical and scalable solution that improves detection performance and strengthens continuous authentication and adaptive access control within Zero Trust environments. Full article

For the challenging scenario in which only normal node labels are available in semi-supervised graph anomaly detection, existing generative methods usually synthesize abnormal nodes through random perturbation or feature interpolation. However, these methods fail to consider node abnormality comprehensively from both structural and attribute perspectives, resulting in generated pseudo-anomalies of limited quality and insufficient reliability. In order to address this problem, we propose DGAM (dual-guided anomaly mining), a framework for selecting pseudo-anomaly nodes based on the dual-index measurement of topological anomaly and feature consistency. The core of the framework is the joint anomaly evaluation module, which quantifies node anomaly through two computable metrics. The topological boundary score (TBS) measures the boundary of a node’s topological position based on the proportion of connections between a node and labeled normal nodes in its K-hop neighborhood. The feature deviation score (FDS) evaluates the consistency of a node’s local features by calculating the average cosine similarity between its features and those of its K-hop neighbors. The module selects a fixed set of nodes with higher comprehensive anomaly scores from the labeled normal nodes as pseudo-anomalies, so as to construct a training set containing explicit supervision signals. The model adopts a shared encoder architecture and jointly optimizes the classification loss based on pseudo-labels and the embedding regularization loss of the graph nodes to learn a more discriminative node representation. Experimental results on multiple real-world graph datasets show that DGAM can stably improve anomaly detection performance, effectively verifying the effectiveness of the proposed screening mechanism and joint training strategy. Full article

The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection. Full article