Search Results (2,167)

As cyber threats continue to grow in complexity and persistence, resilience has become a critical requirement for cyber–physical systems (CPSs). Resilience quantitative assessment is essential for supporting secure system design and ensuring reliable operation. Although various methods have been proposed for evaluating CPS resilience, major challenges remain in accurately modeling the interaction between cyber and physical domains and in providing structured guidance for resilience-oriented design. This study proposes an integrated CPS resilience assessment framework that combines cyber-layer anomaly modeling based on Markov chains with mathematical modeling of performance degradation and recovery in the physical domain. The framework establishes a structured evaluation process through parameter normalization and cyber–physical coupling, enabling the generation of resilience curves that clearly represent system performance changes under adverse conditions. A case study involving an industrial controller equipped with a diversity-redundancy architecture is conducted to demonstrate the applicability of the proposed method. Modeling and simulation results indicate that the framework effectively reveals key resilience characteristics and supports performance-informed design optimization. Full article

Using neural networks has become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformer architectures, including Swin Transformer and MaxViT, for several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset included different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer achieved the highest accuracy, with an average of 99.0% on single datasets and reaching 99.43% on the combined dataset. This research highlights the adaptability of Transformer-based models to various human organs and image modalities. The main contribution lies in evaluating multiple ViT architectures across multi-organ tumor datasets, demonstrating their generalization to multi-organ classification. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient healthcare solutions. Full article

The rapid expansion of 5G-enabled Internet of Things (IoT) devices in smart homes has heightened the need for robust, privacy-preserving, and real-time cybersecurity mechanisms. Traditional cloud-based security systems often face latency and privacy bottlenecks, making them unsuitable for edge-constrained environments. In this work, we propose Edge-FLGuard+, a federated and lightweight anomaly detection framework specifically designed for 5G-enabled smart home ecosystems. The framework integrates edge AI with federated learning to detect network and device anomalies while preserving user privacy and reducing cloud dependency. A lightweight autoencoder-based model is trained across distributed edge nodes using privacy-preserving federated averaging. We evaluate our framework using the TON_IoT and CIC-IDS2018 datasets under realistic smart home attack scenarios. Experimental results show that Edge-FLGuard+ achieves high detection accuracy (≥95%) with minimal communication and computational overhead, outperforming traditional centralized and local-only baselines. Our results demonstrate the viability of federated AI models for real-time security in next-generation smart home networks. Full article

Total Knee Arthroplasty (TKA) is a well-established surgical intervention for the management of end-stage knee osteoarthritis. While the procedure is generally successful, postoperative rehabilitation remains a key determinant of long-term functional outcomes. Traditional rehabilitation protocols, particularly those requiring in-person clinical visits, often encounter limitations in accessibility, patient adherence, and personalization. In response, emerging sensor technologies have introduced innovative solutions to support and enhance recovery following TKA. This review provides a thematically organized synthesis of the current landscape and future directions of sensor-assisted rehabilitation in TKA. It examines four main categories of technologies: wearable sensors (e.g., IMUs, accelerometers, gyroscopes), smart implants, pressure-sensing systems, and mobile health (mHealth) platforms such as ReHub^® and BPMpathway. Evidence from recent randomized controlled trials and systematic reviews demonstrates their effectiveness in tracking mobility, monitoring range of motion (ROM), detecting gait anomalies, and delivering real-time feedback to both patients and clinicians. Despite these advances, several challenges persist, including measurement accuracy in unsupervised environments, the complexity of clinical data integration, and digital literacy gaps among older adults. Nevertheless, the integration of artificial intelligence (AI), predictive analytics, and remote rehabilitation tools is driving a shift toward more adaptive and individualized care models. This paper concludes that sensor-enhanced rehabilitation is no longer a future aspiration but an active transition toward a smarter, more accessible, and patient-centered paradigm in recovery after TKA. Full article

The Moxi area in the Sichuan Basin hosts abundant deep geothermal resources, but their thermal regime and accumulation mechanisms remain poorly understood. Using 2D/3D seismic data, drilling records, and temperature measurements (DST), we analyze deep thermal fields, reservoir–caprock systems, and structural features. The following are our key findings: (1) Heat transfer is conduction-dominated, with thermal anomalies in Late Permian–Early Cambrian strata. Four mudstone/shale caprocks and three carbonate reservoirs occur, with the Longtan Formation as the key seal. Reservoir geothermal gradients (25.05–32.55 °C/km) exceed basin averages. (2) Transtensional strike-slip faults form E-W/NE/NW networks; most terminate at the Permian Longtan Formation, with few extending into the Lower Triassic while penetrating the Archean–Lower Proterozoic basement. (3) Structural highs positively correlate with higher geothermal gradients. (4) The deep geothermal reservoirs and thermal accumulation mechanisms in the Moxi area are jointly controlled by crustal thinning, basement uplift, and structural architecture. Mantle-derived heat converges at basement uplift cores, generating localized thermal anomalies. Fault networks connect these deep heat sources, facilitating upward fluid migration. Thick Longtan Formation shale seals these rising thermal fluids, causing anomalous heating in underlying strata and concentrated thermal accumulation in reservoirs—enhanced by thermal focusing effects from uplift structures. This study establishes a theoretical framework for target selection and industrial-scale geothermal exploitation in sedimentary basins, highlighting the potential for repurposing oil/gas infrastructure. Full article

These notes are an informal overview of techniques related to deformation theory in the context of physics. Beginning from motivation for the concept of a sheaf, they build up through derived functors, resolutions, and the functor of points to the notion of a moduli problem, emphasizing physical motivation and the principles of locality and general covariance at each step. They are primarily aimed at those who have some prior exposure to quantum field theory and are interested in acquiring some intuition or orientation regarding modern mathematical methods. A couple of small things are new, including a discussion of the twist of $\mathcal{N}=1$ conformal supergravity in generic backgrounds at the level of the component fields and a computation relating the two-dimensional local cocycle for the Weyl anomaly to the one for the Virasoro anomaly. I hope they will serve as a useful appetizer for the more careful and complete treatments of this material that are already available. Full article

This paper provides a comparative study of unsupervised Deep Learning (DL) methods for anomaly detection in Flight Data Monitoring (FDM). The paper applies Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), Convolutional Neural Network (CNN), classic Transformer architecture, and LSTM combined with a self-attention mechanism to real-world flight data and compares the results to the current state-of-the-art flight data analysis techniques applied in the industry. The paper finds that LSTM, when integrated with a self-attention mechanism, offers notable benefits over other deep learning methods as it effectively handles lengthy time series like those present in flight data, establishes a generalized model applicable across various airports and facilitates the detection of trends across the entire fleet. The results were validated by industrial experts. The paper additionally investigates a range of methods for feeding flight data (lengthy time series) to a neural network. The innovation of this paper involves utilizing Transformer architecture and LSTM with self-attention mechanism for the first time in the realm of aviation data, exploring the optimal method for inputting flight data into a model and evaluating all deep learning techniques for anomaly detection against the ground truth determined by human experts. The paper puts forth a compelling case for shifting from the existing method, which relies on examining events through threshold exceedances, to a deep learning-based approach that offers a more proactive style of data analysis. This not only enhances the generalization of the FDM process but also has the potential to improve air transport safety and optimize aviation operations. Full article

The evolution of electrical power systems into smart grids has brought about significant advancements in electricity generation, transmission, and utilization. These cutting-edge grids have shown potential as an effective way to maximize energy efficiency, manage resources effectively, and enhance overall reliability and sustainability. However, with the integration of complex technologies and interconnected systems inherent to smart grids comes a new set of safety and security challenges that must be addressed. First, this paper provides an in-depth review of the key considerations surrounding safety and security in smart grid environments, identifying potential risks, vulnerabilities, and challenges associated with deploying smart grid infrastructure within the context of the Internet of Things (IoT). In response, we explore both cryptographic and non-cryptographic countermeasures, emphasizing the need for adaptive, lightweight, and proactive security mechanisms. As a key contribution, we introduce a layered classification framework that maps smart grid attacks to affected components and defense types, providing a clearer structure for analyzing the impact of threats and responses. In addition, we identify current gaps in the literature, particularly in real-time anomaly detection, interoperability, and post-quantum cryptographic protocols, thus offering forward-looking recommendations to guide future research. Finally, we present the Multi-Layer Threat-Defense Alignment Framework, a unique addition that provides a methodical and strategic approach to cybersecurity planning by aligning smart grid threats and defenses across architectural layers. Full article

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

Amid the global shift toward clean energy, wind power has emerged as a critical pillar of the modern energy matrix. To improve the reliability and maintainability of wind farms, this work proposes a novel hybrid fault detection approach that combines expert-driven diagnostic knowledge with data-driven modeling. The framework integrates autoencoder-based neural networks with Failure Mode and Symptoms Analysis, leveraging the strengths of both methodologies to enhance anomaly detection, feature selection, and fault localization. The methodology comprises five main stages: (i) the identification of failure modes and their observable symptoms using FMSA, (ii) the acquisition and preprocessing of SCADA monitoring data, (iii) the development of dedicated autoencoder models trained exclusively on healthy operational data, (iv) the implementation of an anomaly detection strategy based on the reconstruction error and a persistence-based rule to reduce false positives, and (v) evaluation using performance metrics. The approach adopts a fault-specific modeling strategy, in which each turbine and failure mode is associated with a customized autoencoder. The methodology was first validated using OpenFAST 3.5 simulated data with induced faults comprising normal conditions and a 1% mass imbalance fault on a blade, enabling the verification of its effectiveness under controlled conditions. Subsequently, the methodology was applied to a real-world SCADA data case study from wind turbines operated by EDP, employing historical operational data from turbines, including thermal measurements and operational variables such as wind speed and generated power. The proposed system achieved 99% classification accuracy on simulated data detect anomalies up to 60 days before reported failures in real operational conditions, successfully identifying degradations in components such as the transformer, gearbox, generator, and hydraulic group. The integration of FMSA improves feature selection and fault localization, enhancing both the interpretability and precision of the detection system. This hybrid approach demonstrates the potential to support predictive maintenance in complex industrial environments. Full article

Deep neural networks (DNNs) are highly effective for intrusion detection systems (IDS) due to their ability to learn complex patterns and detect potential anomalies within the systems. However, their high resource consumption requirements including memory and computation make them difficult to deploy on low-powered platforms. This study explores the possibility of using knowledge distillation (KD) to reduce constraints such as power and hardware consumption and improve real-time inference speed but maintain high detection accuracy in IDS across all attack types. The technique utilizes the transfer of knowledge from DNNs (teacher) models to more lightweight shallow neural network (student) models. KD has been proven to achieve significant parameter reduction (92–95%) and faster inference speed (7–11%) while improving overall detection performance (up to 6.12%). Experimental results on datasets such as NSL-KDD, UNSW-NB15, CIC-IDS2017, IoTID20, and UAV IDS demonstrate DNN with KD’s effectiveness in achieving high accuracy, precision, F1 score, and area under the curve (AUC) metrics. These findings confirm KD’s ability as a potential edge computing strategy for IoT and UAV devices, which are suitable for resource-constrained environments and lead to real-time anomaly detection for next-generation distributed systems. Full article

Biallelic pathogenic variants in DHCR7 result in decreased activity of 7-dehydrocholesterol (7-DHC) reductase, which converts 7-DHC to cholesterol, and causes Smith–Lemli–Opitz syndrome (SLOS). Elevated serum 7-DHC levels are indicative of SLOS as are intellectual disability (ID), growth retardation, microcephaly, craniofacial anomalies, and 2–3 toe syndactyly. Additional congenital malformations may be present in SLOS, and broad clinical variability has been recognized in SLOS. Rarely, biallelic pathogenic DHCR7 variants were reported with low-normal and normal intelligence quotient (IQ) and development. We report here a pair of siblings with mild global developmental delay, infrequent epileptic seizures, and elevated serum 7-DHC levels, associated with the homozygous DHCR7 variant c.988G>A (p.Val330Met). Remarkably, neither sibling displayed congenital anomalies nor dysmorphisms. Quattro-exome sequencing performed for global delay and mild ID in both siblings did not identify other ID causes. c.988G>A affects a highly conserved amino acid and displays a relatively high global population allele frequency of 0.04%, with absence of homozygotes from the population database gnomADv4.1.0. Our observation leads us to suggest that DHCR7 variant c.988G>A and other DHCR7 variants might be generally considered as underlying non-syndromic ID. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

Marine heatwaves (MHWs) and Marine cold spells (MCSs) are oceanic events characterized by prolonged periods of anomalously warm or cold sea surface temperatures, which pose significant ecological and socio-economic threats on a global scale. These extreme temperature events exhibit an asymmetric trend under ongoing climate change in recent decades: MHWs have increased markedly in both frequency and intensity, whereas MCSs have shown an overall decline. Among the potential drivers, mesoscale eddies play a critical role in modulating sea surface temperature anomalies (SSTAs). Anticyclonic eddies (AEs) promote downwelling, generating positive SSTAs that potentially favor MHWs, while cyclonic eddies (CEs) enhance upwelling and negative anomalies that are potentially related to MCSs. In this paper, we investigate the relationship between mesoscale eddies and MHWs/MCSs using global satellite-derived datasets from 2010 to 2019. By analyzing the spatial overlap and intensity correlation between eddies and MHWs/MCSs, it is found that 12.2% of MHWs are accompanied by AEs, and 13.4% of MCSs by CEs, with a high degree of spatial containment where approximately 90.2% of MHW events are found within the mean eddy contour of AEs, and about 93.1% of MCS events fall inside the mean eddy contour of CEs. Stronger eddies tend to be associated with more intense MHWs/MCSs. This study provides new insights into the role of mesoscale eddies in regulating extreme oceanic temperature events, offering valuable information for future predictions in the context of climate change. Full article

Utilizing deep learning models to detect malicious anomalies within the traffic of application layer J1939 protocol networks, found on heavy-duty commercial vehicles, is becoming a critical area of research in platform protection. At the physical layer, the controller area network (CAN) bus is the backbone network for most vehicles. The CAN bus is highly efficient and dependable, which makes it a suitable networking solution for automobiles where reaction time and speed are of the essence due to safety considerations. Much recent research has been conducted on securing the CAN bus explicitly; however, the importance of protecting the J1939 protocol is becoming apparent. Our research utilizes long short-term memory models to predict the next binary data sequence of a J1939 packet. Our primary objective is to compare the performance of our J1939 detection system trained on data sub-fields against a published CAN system trained on the full data payload. We conducted a series of experiments to evaluate both detection systems by utilizing a simulated attack representation to generate anomalies. We show that both detection systems outperform one another on a case-by-case basis and determine that there is a clear requirement for a multifaceted security approach for vehicular networks. Full article