Search Results (441)

The time-domain electromagnetic method has been widely applied in mineral exploration, oil, and gas fields in recent years. However, its response characteristics remain unclear, and there is an urgent need to study the response characteristics of the borehole-surface transient electromagnetic(BSTEM) field. This study starts from the time-domain electric field diffusion equation and discretizes the calculation area in space using tetrahedral meshes. The Galerkin method is used to derive the finite element equation of the electric field, and the vector interpolation basis function is used to approximate the electric field in any arbitrary tetrahedral mesh in the free space, thus achieving the three-dimensional forward simulation of the BSTEM field based on the finite element method. Following validation of the numerical simulation method, we further analyze the electromagnetic field response excited by vertical line sources.. Through comparison, it is concluded that measuring the radial electric field is the most intuitive and effective layout method for BSTEM, with a focus on the propagation characteristics of the electromagnetic field in both low-resistance and high-resistance anomalies at different positions. Numerical simulations reveal that BSTEM demonstrates superior resolution capability for low-resistivity anomalies, while showing limited detectability for high-resistivity anomalies Numerical simulation results of BSTEM with realistic orebody models, the correctness of this rule is further verified. This has important implications for our understanding of the propagation laws of BSTEM as well as for subsequent data processing and interpretation. Full article

Metabolic dysfunction-associated steatohepatitis (MASH) represents a critical hepatic manifestation within the broader spectrum of metabolic syndrome. The pathogenesis of MASH is characterized by disruptions in lipid metabolism, inflammation, and fibrosis. Bile acids and their receptors are integral to the progression of MASH, primarily through their regulatory influence on the metabolic networks of the gut–liver axis. This review offers a comprehensive and systematic examination of the molecular mechanisms underlying bile acid biosynthesis, metabolic dysregulation, and receptor signaling anomalies in MASH. Furthermore, it explores the translational potential of these insights into clinical therapies. Bile acids and their receptors emerge as pivotal therapeutic targets for MASH. Future research should focus on an in-depth analysis of dynamic regulatory mechanisms and the optimization of multi-target combination therapies, thereby paving the way for significant clinical advancements. Full article

Solar power generation is rapidly emerging within renewable energy due to its cost-effectiveness and ease of deployment. However, improper inspection and maintenance lead to significant damage from unnoticed solar hotspots. Even with inspections, factors like shadows, dust, and shading cause localized heat, mimicking hotspot behavior. This study emphasizes interpretability and efficiency, identifying key predictive features through feature-level and What-if Analysis. It evaluates model training and inference times to assess effectiveness in resource-limited environments, aiming to balance accuracy, generalization, and efficiency. Using Unmanned Aerial Vehicle (UAV)-acquired thermal images from five datasets, the study compares five Machine Learning (ML) models and five Deep Learning (DL) models. Explainable AI (XAI) techniques guide the analysis, with a particular focus on MPEG (Moving Picture Experts Group)-7 features for hotspot discrimination, supported by statistical validation. Medium Gaussian SVM achieved the best trade-off, with 99.3% accuracy and 18 s inference time. Feature analysis revealed blue chrominance as a strong early indicator of hotspot detection. Statistical validation across datasets confirmed the discriminative strength of MPEG-7 features. This study revisits the assumption that DL models are inherently superior, presenting an interpretable alternative for hotspot detection; highlighting the potential impact of domain mismatch. Model-level insight shows that both absolute and relative temperature variations are important in solar panel inspections. The relative decrease in “blueness” provides a crucial early indication of faults, especially in low-contrast thermal images where distinguishing normal warm areas from actual hotspot is difficult. Feature-level insight highlights how subtle changes in color composition, particularly reductions in blue components, serve as early indicators of developing anomalies. Full article

Obstructive sleep apnea syndrome (OSAS) in children and adolescents is a prevalent and multifactorial disorder associated with significant short- and long-term health consequences. While adenotonsillectomy (AT) remains the first-line treatment, a substantial proportion of patients—especially those with obesity, craniofacial anomalies, or comorbid conditions—exhibit persistent or recurrent symptoms, underscoring the need for individualized and multimodal approaches. This review provides an updated and comprehensive overview of current and emerging treatments for pediatric OSAS, with a focus on both surgical and non-surgical options, including pharmacological, orthodontic, and myofunctional therapies. A narrative synthesis of recent literature was conducted, including systematic reviews, randomized controlled trials, and large cohort studies published in the last 10 years. The review emphasizes evidence-based indications, mechanisms of action, efficacy outcomes, safety profiles, and limitations of each therapeutic modality. Adjunctive and alternative treatments such as rapid maxillary expansion, mandibular advancement devices, myofunctional therapy, intranasal corticosteroids, leukotriene receptor antagonists, and hypoglossal nerve stimulation show promising results in selected patient populations. Personalized treatment plans based on anatomical, functional, and developmental characteristics are essential to optimize outcomes. Combination therapies appear particularly effective in children with residual disease after AT or with specific phenotypes such as Down syndrome or maxillary constriction. Pediatric OSAS requires a tailored, multidisciplinary approach that evolves with the child’s growth and clinical profile. Understanding the full spectrum of available therapies allows clinicians to move beyond a one-size-fits-all model, offering more precise and durable treatment pathways. Emerging strategies may further redefine the therapeutic landscape in the coming years. Full article

Background/Objectives: The gut microbiota (GM) is pivotal for immune regulation, metabolism, and neurodevelopment. Infants undergoing surgery for congenital gastrointestinal anomalies are especially prone to microbial imbalances, with a paucity of beneficial bacteria (e.g., Bifidobacteria and Bacteroides) and diminished short-chain fatty acid production. Dysbiosis has been associated with severe complications, including necrotizing enterocolitis, sepsis, and feeding intolerance. This narrative review aims to critically examine strategies for microbiota modulation in this high-risk cohort. Methods: An extensive literature analysis was performed to compare the evolution of GM in healthy neonates versus those requiring gastrointestinal surgery, synthetizing strategies to maintain eubiosis, such as early nutritional interventions—particularly the use of human milk—along with antibiotic management and supplementary treatments including probiotics, prebiotics, postbiotics, and lactoferrin. Emerging techniques in metagenomic and metabolomic analysis were also evaluated for their potential to elucidate microbial dynamics in these patients. Results: Neonates undergoing gastrointestinal surgery exhibit significant alterations in microbial communities, characterized by reduced levels of eubiotic bacteria and an overrepresentation of opportunistic pathogens. Early initiation of enteral feeding with human milk and careful antibiotic stewardship are linked to improved microbial balance. Adjunctive therapies, such as the administration of probiotics and lactoferrin, show potential in enhancing gut barrier function and immune modulation, although confirmation through larger-scale studies remains necessary. Conclusions: Modulating the GM emerges as a promising strategy to ameliorate outcome in neonates with congenital gastrointestinal surgical conditions. Future research should focus on the development of standardized therapeutic protocols and the execution of rigorous multicenter trials to validate the efficacy and safety of these interventions. Full article

Accurate identification of electricity price anomalies is essential for enhancing transparency, stability, and efficiency in modern electricity markets. While prior methods primarily focus on temporal patterns, this study introduces a novel approach to detecting spatial anomalies by leveraging latent symmetry structures in nodal price data. The method consists of two key stages: (1) applying dimensionality reduction and density-based clustering (t-SNE + DBSCAN) to uncover symmetrical price zones, and (2) deploying the Isolation Forest algorithm to identify anomalous nodes and zones based on intra-zone and inter-zone data density deviations. Empirical tests on a full-year dataset from the PJM market (over 2000 nodes, 15 min intervals) show that the proposed method (M1) achieves a spatial anomaly detection accuracy above 95%, with false alarm rates consistently below 13%. Compared to benchmark models—including unzoned Isolation Forest (M2) and K-means-based methods (M3)—the proposed framework demonstrates superior stability and interpretability, especially in identifying clustered and zone-level anomalies linked to congestion or structural disturbances. By integrating spatial symmetry awareness into the detection framework, this approach enhances both sensitivity and traceability, enabling early-stage identification of systemic anomalies. The method is data-efficient and adaptable to diverse electricity market architectures. Overall, the proposed framework contributes a scalable and interpretable tool for anomaly surveillance in electricity markets, supporting more resilient and transparent market operations. Full article

Environmental monitoring systems often operate continuously, measuring various parameters, including carbon dioxide levels (CO₂), relative humidity (RH), temperature (T), and other factors that affect environmental conditions. Such systems are often referred to as smart systems because they can autonomously monitor and respond to environmental conditions and can be integrated both indoors and outdoors to detect, for example, structural anomalies. However, these systems typically have high energy consumption, data overload, and large equipment sizes, which makes them difficult to install in constrained spaces. Therefore, three challenges remain unresolved: efficient energy use, accurate data measurement, and compact installation. To address these limitations, this study proposes a two-to-one threshold sampling approach, where the CO₂ measurement is activated when the specified T and $RH$ change thresholds are exceeded. This event-driven method avoids redundant data collection, minimizes power consumption, and is suitable for resource-constrained embedded systems. The proposed approach was implemented on a low-power, small-form and self-made multivariate sensor based on the PIC16LF19156 microcontroller. In contrast, a commercial monitoring system and sensor modules based on the Arduino Uno were used for comparison. As a result, by activating only key points in the T and $RH$ signals, the number of CO₂ measurements was significantly reduced without loss of essential signal characteristics. Signal reconstruction from the reduced points demonstrated high accuracy, with a mean absolute error (MAE) of 0.0089 and root mean squared error (RMSE) of 0.0117. Despite reducing the number of CO₂ measurements by approximately 41.9%, the essential characteristics of the signal were saved, highlighting the efficiency of the proposed approach. Despite its effectiveness in controlled conditions (in buildings, indoors), environmental factors such as the presence of people, ventilation systems, and room layout can significantly alter the dynamics of CO₂ concentrations, which may limit the implementation of this approach. Future studies will focus on the study of adaptive threshold mechanisms and context-dependent models that can adjust to changing conditions. This approach will expand the scope of application of the proposed two-to-one sampling technique in various practical situations. Full article

In this study, diurnal characteristics and long-term changes in extreme precipitation (PR) in the Republic of Korea (KR) are investigated. Hourly PR data from 59 ASOS stations across the country over a 50-year period (1973–2022) are used. The focus is on the summer season (June to September), during which extreme PR frequently occurs. During the period 1973–1997 (FP), both the amount and frequency of extreme PR events peak between 01 and 09 LST. In contrast, during the period 1998–2022 (LP), a notable increase in extreme PR and its frequency is observed between 04 and 12 LST, with the peak occurrence hours shifting to this time frame. An analysis of atmospheric variables related to extreme PR is conducted for the 04–12 LST time frame. Compared to all PR events during the summer season, a low-level low-pressure anomaly is found west of the KR, leading to southerly winds and positive specific humidity anomalies over the south of the KR. Relative to the FP period, both the amplitude and frequency of high water vapor content have increased during the LP period. This intensified moisture may be associated with the observed increase in extreme PR during 04–12 LST. However, no significant changes are found in the strength and frequency of the southerly wind. Full article

The early detection of brain tumors is critical for improving clinical outcomes and patient survival. However, medical imaging datasets frequently exhibit class imbalance, posing significant challenges for traditional classification algorithms that rely on balanced data distributions. To address this issue, this study employs a One-Class Support Vector Machine (OCSVM) trained exclusively on features extracted from healthy brain MRI images, using both deep learning architectures—such as DenseNet121, VGG16, MobileNetV2, InceptionV3, and ResNet50—and classical feature extraction techniques. Experimental results demonstrate that combining Convolutional Neural Network (CNN)-based feature extraction with OCSVM significantly improves anomaly detection performance compared with simpler handcrafted approaches. DenseNet121 achieved an accuracy of 94.83%, a precision of 99.23%, and a sensitivity of 89.97%, while VGG16 reached an accuracy of 95.33%, a precision of 98.87%, and a sensitivity of 91.32%. MobileNetV2 showed a competitive trade-off between accuracy (92.83%) and computational efficiency, making it suitable for resource-constrained environments. Additionally, the pure CNN model—trained directly for classification without OCSVM—outperformed hybrid methods with an accuracy of 97.83%, highlighting the effectiveness of deep convolutional networks in directly learning discriminative features from MRI data. This approach enables reliable detection of brain tumor anomalies without requiring labeled pathological data, offering a promising solution for clinical contexts where abnormal samples are scarce. Future research will focus on reducing inference time, expanding and diversifying training datasets, and incorporating explainability tools to support clinical integration and trust in AI-based diagnostics. Full article

Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global–local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset. Full article

We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks. We exploit SOP data under three different threat scenarios: (i) malicious or critical vibration events, (ii) overlapping mechanical disturbances, and (iii) malicious fiber tapping (eavesdropping). We used various supervised machine learning techniques like k-Nearest Neighbor (k-NN), random forest, extreme gradient boosting (XGBoost), and decision trees to classify different vibration events. We also assessed the framework’s resilience to background interference by superimposing sinusoidal noise at different frequencies and examining its effects on the polarization signatures. This analysis provides insight into how subsurface installations, subject to ambient vibrations, affect detection fidelity. This highlights the sensitivity to which external interference affects polarization fingerprints. Crucially, it demonstrates the system’s capacity to discern and alert on malicious vibration events even in the presence of environmental noise. However, we focus on the necessity of noise-mitigation techniques in real-world implementations while providing a potent, real-time mechanism for multi-threat recognition in the fiber networks. Full article

Myocarditis, an inflammatory disease of the myocardium, is increasingly recognized as a potential contributor to sudden infant death syndrome (SIDS), though often underdiagnosed. This study reviews the current literature on the association between myocarditis and sudden death in infants, with a focus on autopsy and histopathological findings. A comprehensive search of the PubMed database yielded 64 studies published between 1960 and 2024; after applying specific inclusion criteria—such as patient age (0–6 years), presence of autopsy data, and forensic investigation—40 studies were analyzed in detail. The review identified myocarditis—especially lymphocytic—as an underrecognized but critical cause of sudden death in infants and children. Histological, molecular, and immunohistochemical findings highlighted viral infections, immune dysregulation, and structural anomalies as frequent etiological factors. Several SIDS cases were reclassified as myocarditis upon in-depth examination. These findings underscore the value of standardized autopsy protocols and integrated diagnostic approaches. Advanced postmortem diagnostic techniques, including polymerase chain reaction (PCR) and immunohistochemistry, have enhanced the detection of viral myocarditis. In addition, structural cardiac anomalies, such as cardiomyopathies and coronary abnormalities, may co-exist and contribute to sudden cardiac death. These findings emphasize the need for standardized autopsy protocols and the integration of molecular diagnostics in forensic investigations of SIDS. Further research is essential to improve early detection, refine diagnostic criteria, and develop preventive strategies to reduce the incidence of sudden infant death related to myocarditis. Full article

The Industrial Internet of Things (IIoT) has revolutionized smart manufacturing by enhancing automation, operational efficiency, and data-driven decision making. However, the interconnected nature of IIoT devices raises significant concerns about security and system integrity. This paper examines the application of blockchain technology to address these challenges, with a focus on data integrity, access control, and traceability. This paper proposes a blockchain-based framework that leverages decentralized security, smart contracts, and edge computing to mitigate vulnerabilities, including unauthorized access and data manipulation. The framework is evaluated for practicality, scalability, and constraints within IIoT environments. Additionally, this paper discusses the integration of complementary security mechanisms, such as Zero Trust architecture and AI-driven anomaly detection, to provide a comprehensive cybersecurity solution for the Industrial Internet of Things (IIoT). Full article

Condition Monitoring (CM) is a key component of Self-Adaptive and Self-Organizing (SASO) systems. By analyzing sensor data, CM enables systems to react to dynamic conditions, supporting the core principles of Organic Computing (OC): robustness, adaptability, and autonomy. This survey presents a structured overview of CM techniques, application areas, and input data. It also assesses the extent to which current approaches support self-* properties, real-time operation, and predictive functionality. Out of 284 retrieved publications, 110 were selected for detailed analysis. About 38.71% focus on manufacturing, 65.45% on system-level monitoring, and 6.36% on static structures. Most approaches (69.09%) use Machine Learning (ML), while only 18.42% apply Deep Learning (DL). Predictive techniques are used in 16.63% of the studies, with 38.89% combining prediction and anomaly detection. Although 58.18% implement some self-* features, only 42.19% present explicitly self-adaptive or self-organizing methods. A mere 6.25% incorporate feedback mechanisms. No study fully combines self-adaptation and self-organization. Only 5.45% report processing times; however, 1000 Hz can be considered a reasonable threshold for high-frequency, real-time CM. These results highlight a significant research gap and the need for integrated SASO capabilities in future CM systems—especially in real-time, autonomous contexts. Full article

Background/Objectives: Brain arteriovenous malformations (bAVMs) are rare vascular anomalies characterized by direct connections between arteries and veins, bypassing the capillary network. This study aimed to identify potential genetic factors contributing to the development of sporadic bAVMs. Methods: Three patients (AVM1–3) from Kazakhstan who underwent microsurgical resection at the National Centre for Neurosurgery (NCN) in Astana, Kazakhstan, were analyzed. Brain AVMs were diagnosed using magnetic resonance imaging (MRI). Genomic DNA was isolated from whole venous blood samples, and whole-exome sequencing was performed on the NovaSeq 6000 platform (Illumina). Variants were filtered according to standard bioinformatics protocols, and candidate gene prioritization was conducted using the ToppGene tool. Results: In silico analysis further revealed candidate genes likely associated with lesion development, including COL3A1, CTNNB1, LAMA1, NPHP3, SLIT2, SLIT3, SMO, MAPK3, LRRK2, TTN, ERBB2, PARD3, and OBSL1. It is essential to focus on the genetic variants affecting the following prioritized genes: ERBB2, SLIT3, SMO, MAPK3, and TTN. Mutations in these genes were predicted to be “damaging”. Most of these genes are involved in signaling pathways that control vasculogenesis and angiogenesis. Conclusions: Defects in genes associated with ciliary structure and function may be critical to the pathogenesis of brain AVMs. These findings provide valuable insights into the molecular underpinnings of bAVM development, emphasizing key biological pathways and potential candidate genes. Further research is needed to establish robust correlations between specific genetic mutations and clinical phenotypes, which could ultimately inform the development of improved diagnostic, therapeutic, and prognostic approaches. Full article