Search Results (890)

Background: Accurate mortality prediction remains a major challenge in public health due to the complex interactions among demographic, socioeconomic, behavioral, and medical factors. This problem is particularly relevant for identifying high-risk groups and improving preventive healthcare strategies. While existing studies demonstrate strong predictive performance, they mainly rely on clinically structured data and focus on model performance. Challenges such as misclassification and atypical cases remain less explored. Methods: Using the Integrated Public Use Microdata Series National Health Interview Survey (IPUMS-NHIS) 2010 and 2015 datasets (193,765 records, 104 features), this study investigates mortality prediction through comparative Machine Learning. Data preprocessing included feature engineering, categorical encoding, and removal of missing entries. Class imbalance was addressed using SMOTE and SMOTE-ENN resampling, followed by hyperparameter tuning. Three models—Logistic Regression, Random Forest, and XGBoost—were trained to classify mortality, with recall prioritized to ensure accurate identification of deceased cases. Results: Results showed that XGBoost achieved the best performance (Recall = 69%, F1 = 0.39, AUC = 0.92), outperforming other models in balancing sensitivity and specificity. Feature importance and permutation analyses highlighted age, employment status, self-reported health, and lifestyle indicators as key predictors. Misclassification analysis combined with Isolation Forest revealed atypical profiles not captured by standard models. Conclusions: The findings underscore XGBoost’s effectiveness and demonstrate the value of integrating anomaly detection with classification to improve mortality prediction and inform public health planning. Full article

Zinner syndrome is a rare congenital anomaly of the male genitourinary tract, characterized by the triad of unilateral renal agenesis, ipsilateral seminal vesicle cyst, and ejaculatory duct obstruction. Owing to its low prevalence and nonspecific clinical presentation, diagnosis is often delayed or incidental, with imaging playing a central role in detection and characterization. This study presents a narrative review with an illustrative case report, aiming to summarize the imaging features of Zinner syndrome, outline the main radiologic differential diagnoses of seminal vesicle cysts, and highlight common diagnostic pitfalls, with emphasis on cross-sectional imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI). The narrative review of the literature highlights that CT and MRI are essential for accurate anatomical localization, characterization of cystic content, and identification of associated genitourinary anomalies. MRI, in particular, provides superior soft-tissue contrast and is considered the reference modality for diagnosis and differential evaluation of male pelvic cystic lesions. Key differential diagnoses include Müllerian duct cysts, prostatic utricle cysts, and ejaculatory duct cysts. As an illustrative example, we report the case of a young adult male presenting with pelvic discomfort, infertility, and mild lower urinary tract symptoms. Imaging findings, including ultrasound and cross-sectional studies, demonstrated a seminal vesicle cyst associated with ipsilateral renal agenesis, consistent with Zinner syndrome. Zinner syndrome should be considered in the evaluation of male pelvic cystic lesions, particularly in the presence of unilateral renal agenesis. Awareness of its characteristic imaging features is essential for accurate diagnosis and appropriate management, with MRI playing a pivotal role in confirming the diagnosis and distinguishing it from other pelvic cystic entities. Full article

Objectives: Primary hyperoxaluria type III (PH3) causes kidney stones in children and adults. Gas chromatography/mass spectrometry (GC/MS)-based metabolomics has been applied to study patients with primary hyperoxaluria types I and II, 2,8-dihydroxyadenine lithiasis, and xanthinuria types I to III. This study was performed to verify the usefulness of this technique for the diagnosis of PH3. Specifically, we evaluated an 8-month-old infant with recurrent kidney stones. Methods: GC/MS-based metabolomics was performed on spot urine samples using initial urease pretreatment without fractionation. Results: Metabolomics revealed increased levels of 2,4-dihydroxyglutarate and 4-hydroxyglutamate. No simultaneous elevations of these two critical biomarkers were observed in other patients, except for one case of PH3 confirmed by the identification of HOGA1 mutations. A moderate increase in 4-hydroxyglutamate has been observed only in cases of primary hyperammonemia, in which analytes such as orotate, uridine, glutamine, or proline, but not 2,4-dihydroxyglutarate, are biomarkers, thus distinguishing PH3 from primary hyperammonemia. Conclusions: GC/MS-based urine metabolomics enables the rapid screening and chemical diagnosis of PH3 and other congenital anomalies that cause urolithiasis. This technique can also be used to monitor disease progression, as patients with PH3 benefit from long-term follow-up, particularly when transitioning from childhood to adulthood. The timely identification of patients with hereditary urolithiasis is crucial. To address this, a discussion was had about the current diagnostic criteria. Full article

This paper investigates the feasibility of using Large Language Models (LLMs) for the structural analysis of flow-based network data. This analysis is carried out in the presence of a structural difference between the multidimensional numerical space of IoT features and the symbolic space in which LLMs operate. The primary objective was the development of a formal framework that enables the controlled transformation of numerical data into linguistically analyzable semantic representations, without resorting to classification or machine learning mechanisms. We propose the Semantic Flow Encoding (SFE) mechanism, a deterministic method for robust discretization and behavioral abstraction that converts the numerical characteristics of Internet of Things (IoT) flows into structural semantic descriptions using the Canadian Institute for Cybersecurity Internet of Things Device Identification and Anomaly Detection (CIC IoT-DIAD) 2024 dataset. Through formal informational measures, it is demonstrated that the existence of an intrinsic structural difference between benign and DDoS traffic in the analyzed dataset. In the validation stage, we evaluated whether these informational differences are reflected at the level of linguistic abstraction through controlled inference experiments in IBM WatsonX. The present paper suggests that LLMs may support semantic auditing of distributional structure when guided by a formal encoding layer. In this manner, a reproducible framework for integrating numerical security data into language-model-based analysis is suggested. Full article

Firewall policy misconfigurations remain a major source of security vulnerabilities in modern networks, particularly as firewall rule sets grow in size and complexity. Such misconfigurations, commonly referred to as firewall anomalies, can lead to unintended access control behavior and undermine network security. In this paper, we propose a formal logic rule-based framework for the systematic detection and investigation of firewall anomalies, supported by knowledge graph-based visualization. First-order logic (FOL) is employed to precisely model firewall rules and to define major anomaly types, including shadowing, redundancy, correlation, generalization, and irrelevance, in both single and distributed firewall environments. The proposed framework introduces explicit and comprehensive logical definitions for each anomaly type, enabling deterministic, interpretable, and complete detection of rule conflicts and overlaps. Complex anomalies, particularly correlation and generalization, are systematically decomposed into well-defined logical cases to facilitate the accurate identification of subtle, order-dependent interactions among firewall rules. To enhance usability and analysis, firewall rules and detected anomalies are represented using Neo4j knowledge graphs, providing intuitive visual insights into rule relationships and anomaly causes. The effectiveness of the proposed approach is validated using a real operational backbone network dataset collected from Stanford University’s campus network. Experimental results demonstrate the framework’s ability to accurately detect both simple and complex firewall anomalies under realistic network conditions. To further validate the proposed logic rules, a machine learning-based evaluation was conducted. The findings confirm their effectiveness in accurately characterizing firewall anomalies. Unlike machine learning or heuristic-based methods, the proposed approach does not require training data and guarantees formal correctness and explainability. These features make it a robust and practical solution for firewall policy verification and network security management. Full article

Accurately monitoring the wheelset–rail friction condition is crucial for ensuring the safety and operational efficiency of the traction drive system. However, the friction characteristics of wheelsets are easily influenced by factors such as ramp transitions and variable railway conditions in the complex environment. These factors significantly increase the difficulty of detecting friction anomalies and accurately locating faulty wheelsets in a timely manner. To address this issue, this paper proposes a sliding mode coordinate positioning–based friction anomaly monitoring scheme for multiple wheelsets in traction drive systems. First, a multi-sliding mode fusion-based friction characteristic observer is developed. Then, an friction coordinate analysis-based anomaly identification method is proposed. Finally, the proposed method is validated on a hardware-in-the-loop (HIL)-based experimental platform. Experimental results demonstrate that the proposed scheme can effectively detect friction anomalies and accurately locate abnormal wheelsets in multi-wheelset traction systems. Compared with traditional methods, the proposed scheme exhibits stronger robustness to varying railway conditions and does not require complex optimization mechanisms, making it suitable for practical on-board applications. Full article

The Wulonggou gold district is located on the northern margin of the Qinghai–Tibet Plateau and represents the most promising area for mineral exploration within the East Kunlun mineralized belt in Qinghai Province. Previous studies on this gold district have lacked a comprehensive assessment of its metal mineralization potential. This paper conducts a comprehensive investigation of the distribution patterns of geochemical data in the Wulonggou gold district, employing multivariate statistical analysis to explore the distribution characteristics of different geochemical elements. Based on the analysis of geochemical anomaly patterns, the median + 2MAD method and fractal method were further introduced to delineate geochemical anomalies. For comparison, machine learning methods—including the radial basis function link network (RBFLN) model and the Bayesian-optimized random forest (BO-RF) model—were also applied to generate different geochemical anomaly maps. By comparing the results obtained from each method, we found that the BO-RF model performed best in predicting geochemical anomalies. Based on the above information, the BO-RF model was integrated with geological background information to delineate prospective areas. These findings provide important clues for mineral exploration and development in the Wulonggou area and can serve as a reference for other regions with similar geological backgrounds. Full article

Fine-grained energy management in smart-campus buildings requires accurate load forecasting together with reliable and interpretable anomaly diagnosis. This study presents an integrated forecasting–diagnosis framework for building energy systems. Hourly energy demand is modeled using a Transformer-based sequence-to-sequence architecture, in which a domain-aware attention mechanism is introduced to separately represent historical consumption dynamics, environmental influences, and temporal regularities commonly observed in building energy use. Anomaly diagnosis is conducted through a dual-scale strategy that supports both the timely detection of abrupt abnormal events and the identification of gradual performance degradation. Short-term anomalies are detected from forecasting residuals using adaptive thresholds, while long-term anomalies are identified by comparing current residual patterns with same-season historical baselines and validating multi-window trends over a 48 h horizon. The two detection streams are jointly used to distinguish point, pattern, and composite anomalies. To support practical operation and maintenance, SHAP-based explanations are provided to interpret both energy predictions and detected anomalies. Case studies on two educational buildings from the Building Data Genome Project 2 demonstrate that the proposed framework achieves the best overall forecasting performance against both conventional baselines and stronger recent Transformer-based models, with mean absolute percentage errors of approximately 3%. The results indicate that the proposed framework provides a practical solution for data-driven energy monitoring and decision support in smart buildings. Full article

Generally, land seismic data acquisition in arid areas is a labor-intensive, costly, and challenging process, often hindered by challenging terrain and safety risks. To overcome these limitations, we propose the integration of autonomous Unmanned Aerial Vehicles (UAVs) into land seismic data acquisition, enabling efficient data collection in difficult, inaccessible terrain. This is a cooperative mission workflow combining a Scouting UAV for high-resolution aerial scouting, followed by the swarm deployment of an Autonomous Seismic Acquisition Device (ASAD) for seismic data recording. The cooperative system allows for precise landing and subsequent deployment of seismic sensors in optimal locations. Previously, we demonstrated the applicability of passive seismic recorded with ASAD drones to near-surface characterization. This study covers the results of a field trial, where both the ASAD and Scouting UAV systems successfully acquired high-resolution seismic data with an active source, comparable to that of a conventional seismic data acquisition system. The results show that the ASAD seismic data exhibit a slightly higher noise level due to coupling variances and the fact that geophones were hardwired into 9-sensor arrays. However, due to its single-point sensing nature, it yields a superior frequency bandwidth, making it suitable for imaging shallow anomalies. The system underwent P-wave refraction tomography modeling and accurately detected a shallow subsurface cavity, showcasing its potential for near-surface characterization and shallow geohazard identification. This heterogeneous robotic system can support seismic data acquisition by enhancing safety, improving efficiency, and streamlining equipment mobilization, while minimizing environmental footprint. Full article

Fetal therapy has evolved into a rapidly advancing field with the potential to alter the natural history of many severe congenital and genetic disorders before irreversible injury occurs. Progress in prenatal imaging, molecular diagnostics, and fetal intervention techniques now enables the earlier identification of disease and, in select settings, targeted prenatal treatment. This review synthesizes the current landscape of fetal therapies, spanning established surgical interventions for structural anomalies and emerging biologic and molecular approaches, including enzyme replacement therapy, stem cell-based strategies, gene therapy, and gene editing. The intrauterine environment provides a distinct therapeutic context, with developmental plasticity, immune immaturity, enhanced tissue accessibility, and relatively permissive central nervous system exposure that together define a time-sensitive window for intervention. Preclinical studies and early clinical experience across both structural anomalies and genetic disorders, including lysosomal storage disorders, osteogenesis imperfecta, and spinal muscular atrophy, support the premise that prenatal treatment can preserve organ development and improve pediatric outcomes. However, translation remains constrained by procedural risks, uncertainty regarding long-term safety and durability, ethical and regulatory complexities, and challenges with equitable access, alongside the need for robust comparative evidence versus early postnatal therapy. As the field advances, multidisciplinary collaboration, rigorous trial design with meaningful developmental endpoints, and ethically grounded implementation frameworks will be essential to guide responsible clinical adoption and maximize benefit for children and families. Full article

A structurally complex Paleozoic succession in southwestern Sinai hosts uranium and associated metals, and brittle deformation controls fluid flow and ore localization. The study integrates structural mapping with mineralogical, geochemical, and radiometric data to evaluate how fault architecture controls uranium and polymetallic mineral occurrences in the east Abu Zeneima area. Eleven representative samples were collected from major fault zones and host lithofacies, and 652 ground gamma-ray spectrometric measurements were acquired across mineralized localities and Paleozoic stratigraphic units. Heavy mineral separation, SEM–BSE/EDX, X-ray diffraction, and whole-rock geochemistry were used to identify ore and accessory phases and quantify their elemental composition. The middle carbonate member of the Um Bogma Formation is the primary host lithology and contains primary U dispersed within carbonaceous sandy dolostone and locally abundant secondary U phases coexisting with Cu–Fe–Mn phases and REE-bearing silicates and phosphates. Uranium enrichment (locally > 2900 ppm eU) in the targeted anomalous samples shows a positive association with P₂O₅ and a weaker positive association with ΣREEs. Together with SEM–BSE/EDX and XRD identification of uranyl phosphates and REE-bearing accessory minerals, these observations suggest that phosphate-bearing secondary phases and REE-rich accessories locally contributed to uranium hosting. Seventy-four radioactive anomalies are predominantly associated with normal faults and are concentrated along fault cores and highly fractured downthrown blocks, especially along a NW–SE trend that forms the main mineralized corridor. The study findings emphasize the importance of fault zone architecture for targeting new uranium resources in Paleozoic basins. Full article

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization. Full article

Automatic Identification System (AIS) data provide valuable insights into ship behavior, supporting maritime safety, situational awareness, and operational efficiency capabilities that are increasingly required for autonomous ship functions and harbor maneuvering assistance. This review synthesizes recent research on AIS-based ship behavior analysis and modeling published between 2022 and 2024 using a structured literature search and screening process informed by PRISMA principles. The review presents a five-stage workflow, spanning data processing, data analysis, knowledge extraction, modeling, and runtime applications with emphasis on how these stages contribute to perception, prediction, and decision support in automated navigation. Four dimensions are considered in data analysis, including statistical analysis, safety indicators, situational awareness, and anomaly detection. The modeling approaches are categorized into classification, regression, and optimization, highlighting current limitations such as data quality, algorithmic transparency, and real-time performance, while also assessing runtime feasibility for onboard or edge deployment. Three runtime application directions are identified: autonomous vessel functions, remote monitoring and control operations, and onboard decision-support tools, with numerous studies focusing on constrained waterways and port-approach scenarios. Future directions suggest integrating multi-source data and advancing machine learning models to improve robustness in complex traffic and harbor environments. By linking theoretical insights with practical onboard needs, this study provides guidance for developing intelligent, adaptive, and safety-enhancing maritime systems. Full article

The adoption of instrumented wheelsets on diagnostic trains offers the possibility of continuous monitoring of wheel–rail contact forces. The collection of large datasets can be exploited for diagnostic purposes, aiming to localize specific track defects, allowing significant improvements in terms of safety and maintenance costs. Machine learning (ML) techniques can be used to automate anomaly detection. In this work, the authors compare the application of various ML algorithms based on the identification of different frequency or time-based features of analyzed signals. To perform the activity, a significant number and variety of local defects have been included in the recorded data. From a practical point of view, the insertion of real known defects into an existing line is extremely time-consuming, expensive, and not immune to safety issues. On the other hand, the design of anomaly detection algorithms involves the usage of relatively extended datasets with different faulty conditions. The authors propose deliberately adding real contact force profiles of healthy lines to a mix of synthetic signals, which substantially reproduce the behavior and the variability of foreseen faulty conditions. The results of this work, although preliminary and still to be completed, offer a contribution to the scientific community both in terms of obtained results and adopted methodologies. Full article

Real-world evidence for wearable noninvasive glucose monitoring (NIGM) remains limited. To evaluate the functional equivalence of a wearable NIGM device and explore its utility for T2DM and prediabetes screening. In this multicenter study, 12-h daytime glucose profiles obtained by a flexible reverse iontophoresis-based electrochemical sensor were compared with capillary glucose using functional equivalence. Subgroup analyses were conducted. Screening models of T2DM and prediabetes were developed using elastic net and Logistic regression. A total of 135 participants (mean age 35.3 years; 60.0% female) were included, and no serious device-related adverse events were reported. Compared to the capillary measurements, functional equivalence was confirmed (T = −6.537 < threshold = −2.081) in the general population but not in older adults or T2DM patients. The T2DM noninvasive screening model demonstrated discrimination and reclassification performance comparable to those of the capillary-based model (AUC: 0.906 vs. 0.850, NRI: 0.044, IDI: −0.078, p > 0.05). Functional principal component scores facilitated the identification of prediabetes (AUC = 0.760). The device demonstrated acceptable accuracy and functional equivalence with reference methods. Its capability to detect T2DM and early glycemic anomalies supports its feasibility as a wearable, interpretative adjunct tool for large-scale screening in free-living populations. Full article