Search Results (473)

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

This paper addresses a well-known research topic in the design of complex systems, specifically within the class of reliability optimization problems (ROPs). It focuses on optimal reliability–redundancy allocation problems (RRAPs) for large binary systems with hybrid structures. Two main objectives are considered: (1) to maximize system reliability under cost and volume constraints, and (2) to achieve the required reliability at minimal cost under a volume constraint. The system reliability model includes components with only two states: normal operating or failed. High reliability can result from directly improving component reliability, allocating redundancy, or using both approaches together. Several redundancy strategies are covered: active, passive, hybrid standby with hot, warm, or cold spares, static redundancy such as TMR and 5MR, TMR structures with control logic and spares, and reconfigurable TMR/Simplex structures. The proposed method uses a zero–one integer programming formulation that applies log-transformed reliability functions and binary decision variables to represent subsystem configurations. The experimental results validate the approach and confirm its efficiency. Full article

Background and Objectives: Metabolic tumor volume (MTV) and inflammation-based indices have recently gained attention as potential prognostic markers of diffuse large B-cell lymphoma (DLBCL). We aimed to evaluate the prognostic significance of metabolic and systemic inflammatory parameters in predicting treatment response, relapse, and overall survival (OS) in patients with DLBCL. Materials and Methods: This retrospective cohort study included 70 patients with DLBCL. Clinical characteristics, laboratory values, and metabolic parameters, including maximum standardized uptake value (SUVmax^liver and SUVmax), heterogeneity indices HI1 and HI2, and MTV were analyzed. Survival outcomes were assessed using Kaplan–Meier and log-rank tests. Receiver operating characteristic analyses helped evaluate the diagnostic performance of the selected biomarkers in predicting relapse and mortality. Univariate and multivariate logistic regression analyses were conducted to identify the independent predictors. Results: The mean OS and mean relapse-free survival (RFS) were 71.6 ± 7.4 and 38.7 ± 2.9 months, respectively. SUVmax^liver ≤ 22 and HI2 > 62.3 were associated with a significantly shorter OS. High lactate dehydrogenase (LDH) levels and HI2 > 87.9 were significantly associated with a reduced RFS. LDH, SUVmax^liver, and HI2 had a significant predictive value for relapse. SUVmax^liver and HI2 levels were also predictive of mortality; SUVmax^liver ≤ 22 and HI2 > 62.3 independently predicted mortality, while HI2 > 87.9 independently predicted relapse. MTV was not significantly associated with survival. Conclusions: Metabolic tumor burden and inflammation-based markers, particularly SUVmax^liver and HI2, are significant prognostic indicators of DLBCL and may enhance risk stratification and aid in identifying patients with an increased risk of relapse or mortality, potentially guiding personalized therapy. Full article

This study introduces a time-efficient rotating bending fatigue testing system featuring 11 dual-end spindles, enabling simultaneous testing of 22 specimens. Designed for high-throughput fatigue life (S–N curve) assessment, the system theoretically allows over 93% reduction in total test duration, with 87.5% savings demonstrated in validation experiments using AISI 304 stainless steel. The PLC-based architecture provides autonomous operation, real-time failure detection, and automatic cycle logging. ER16 collet holders are easily replaceable within one minute, and all the components are selected from widely available industrial-grade parts to ensure ease of maintenance. The modular design facilitates straightforward adaptation to different station counts. The validation results confirmed an endurance limit of 421 MPa, which is consistent with the established literature and within ±5% deviation. Fractographic analysis revealed distinct crack initiation and propagation zones, supporting the observed fatigue behavior. This high-throughput methodology significantly improves testing efficiency and statistical reliability, offering a practical solution for accelerated fatigue life evaluation in structural, automotive, and aerospace applications. Full article

With the accelerated digital transformation of the power industry, critical infrastructures such as power grids are increasingly migrating to cloud-native architectures, leading to unprecedented growth in service scale and complexity. Traditional operation and maintenance (O&M) methods struggle to meet the demands for real-time monitoring, accuracy, and scalability in such environments. This paper proposes a novel service performance anomaly detection system based on large language models (LLMs) and multi-agent systems (MAS). By integrating the semantic understanding capabilities of LLMs with the distributed collaboration advantages of MAS, we construct a high-precision and robust anomaly detection framework. The system adopts a hierarchical architecture, where lower-layer agents are responsible for tasks such as log parsing and metric monitoring, while an upper-layer coordinating agent performs multimodal feature fusion and global anomaly decision-making. Additionally, the LLM enhances the semantic analysis and causal reasoning capabilities for logs. Experiments conducted on real-world data from the State Grid Corporation of China, covering 1289 service combinations, demonstrate that our proposed system significantly outperforms traditional methods in terms of the F1-score across four platforms, including customer services and grid resources (achieving up to a 10.3% improvement). Notably, the system excels in composite anomaly detection and root cause analysis. This study provides an industrial-grade, scalable, and interpretable solution for intelligent power grid O&M, offering a valuable reference for the practical implementation of AIOps in critical infrastructures. Evaluated on real-world data from the State Grid Corporation of China (SGCC), our system achieves a maximum F1-score of 88.78%, with a precision of 92.16% and recall of 85.63%, outperforming five baseline methods. Full article

This work presents the development of NOVA, an educational virtual assistant designed for the Parallel Computing course, built using a Retrieval-Augmented Generation (RAG) architecture combined with Large Language Models (LLMs). The assistant operates entirely in Spanish, supporting native-language learning and increasing accessibility for students in Latin American academic settings. It integrates vector and relational databases to provide an interactive, personalized learning experience that supports the understanding of complex technical concepts. Its core functionalities include the automatic generation of questions and answers, quizzes, and practical guides, all tailored to promote autonomous learning. NOVA was deployed in an academic setting at Universidad Politécnica Salesiana. Its modular architecture includes five components: a relational database for logging, a vector database for semantic retrieval, a FastAPI backend for managing logic, a Next.js frontend for user interaction, and an integration server for workflow automation. The system uses the GPT-4o mini model to generate context-aware, pedagogically aligned responses. To evaluate its effectiveness, a test suite of 100 academic tasks was executed—55 question-and-answer prompts, 25 practical guides, and 20 quizzes. NOVA achieved a 92% excellence rating, a 21-second average response time, and 72% retrieval coverage, confirming its potential as a reliable AI-driven tool for enhancing technical education. Full article

This study presents an algorithm developed by the Clinical Engineering department to automatically match surgical events recorded by robotic systems with corresponding entries in the hospital’s OR management software. At ASST Grande Ospedale Metropolitano Niguarda, robotic procedures were previously identified manually by surgical staff within the operating room management system, often leading to frequent inconsistencies and data quality issues. Two heterogeneous datasets—robot logs and hospital procedure records—were aligned using common features such as date, duration, and operating room, despite the absence of a unique identifier. The matching algorithm enables accurate identification of robotic procedures within the hospital system and facilitates integration of clinical and technical data into a unified framework. This integrated approach supports more effective data utilization for clinical engineering activities, operational monitoring, and Health Technology Assessment (HTA) analyses. The work provides a practical solution to a real-world data integration challenge and lays the foundation for future developments, including the application of machine learning to enhance matching precision. Full article

The intensive use of various sensors in industrial machines has the potential to indicate the real-time health status of critical equipment. This is achieved through the connectivity of their automation systems (PIMS and MES), enabling the optimization of the preventive maintenance interval, a reduction in corrective maintenance and safety-related failures, an increase in productivity and reliability and a reduction in maintenance costs. Through the use of the CRISP-DM data analysis methodology, the fault logs of ceramic plates applied in an iron ore filtration process are coupled with sensor readings of the process variables over the time of operation to create exponential survival models via two techniques: a multiple linear regression model with averaged data and a random forest regression machine learning model with individual instant data. The instantaneous reliability of ceramic plates is then used in the online prediction of the remaining useful life of the components. The model obtained from the instantaneous reading of 12 sensors led to the estimation of the remaining useful life for ceramic plates with up to 5600 h of use, allowing the adoption of a strategy of replacing these components by condition instead of replacing them by a fixed time, leading to increased process reliability and improved stock planning. The linear regression model for reliability prediction had an R² of 78.32%, whereas the random forest regression model had an R² of 63.7%. The final model for predicting the remaining useful life had an R² of 99.6%. Full article

Process mining (PM) is a growing field that looks at how to find, analyze, and improve process models using data from information systems. It automates much of the detailed work that usually requires a lot of manual effort. But there are concerns about privacy when dealing with this kind of data. This research introduces a novel, goal-oriented model evaluation methodology leveraging the privacy-preserving process mining (PPPM) cycle for diverse domains. The methodology encompasses the following: establishing goals and questions, targeted data acquisition, data refinement, log inspection, PPPM analysis, question resolution and interpretation, performance assessment, and possible improvement recommendations. The proposed methodology was applied in a case study analyzing four real-life event logs from different domains, yielding quantitative insights into the operational efficiency of the privacy-preserving approaches. To improve how well PPPM approaches work, we identified key issues and errors that affect their results and time utility performance. Our preliminary application of the proposed methodology indicates its potential to facilitate improvements by guiding the implementation of PPPM techniques across various domains. Full article

Although the Power Internet of Things (PIoT) significantly improves operational efficiency by enabling real-time monitoring, intelligent control, and predictive maintenance across the grid, its inherently open and deeply interconnected cyber-physical architecture concurrently introduces increasingly complex and severe security threats. Existing IoT security solutions are not fully adapted to the specific requirements of power systems, such as safety-critical reliability, protocol heterogeneity, physical/electrical context awareness, and the incorporation of domain-specific operational knowledge unique to the power sector. These limitations often lead to high false positives (flagging normal operations as malicious) and false negatives (failing to detect actual intrusions), ultimately compromising system stability and security response. To address these challenges, we propose a domain knowledge-driven threat source detection and localization method for the PIoT. The proposed method combines multi-source features—including electrical-layer measurements, network-layer metrics, and behavioral-layer logs—into a unified representation through a multi-level PIoT feature engineering framework. Building on advances in multimodal data integration and feature fusion, our framework employs a hybrid neural architecture combining the TabTransformer to model structured physical and network-layer features with BiLSTM to capture temporal dependencies in behavioral log sequences. This design enables comprehensive threat detection while supporting interpretable and fine-grained source localization. Experiments on a real-world Power Internet of Things (PIoT) dataset demonstrate that the proposed method achieves high detection accuracy and enables the actionable attribution of attack stages aligned with the MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) framework. The proposed approach offers a scalable and domain-adaptable foundation for security analytics in cyber-physical power systems. Full article

Process anomaly detection in financial services systems is crucial for operational compliance and risk management. However, traditional process mining techniques frequently neglect the detection of significant low-frequency abnormalities due to their dependence on frequency and the inadequate incorporation of domain-specific knowledge. Therefore, we develop an enhanced process mining algorithm by incorporating a domain-specific follow-relationship matrix derived from standard operating procedures (SOPs). We empirically evaluated the effectiveness of the proposed algorithm based on real-world event logs from a corporate account-opening process conducted from January to December 2022 in a Chinese commercial bank. Additionally, we employed large language models (LLMs) for root cause analysis and process optimization recommendations. The empirical results demonstrate that the E-Heuristic Miner significantly outperforms traditional machine learning methods and process mining algorithms in process anomaly detection. Furthermore, the integration of LLMs provides promising capabilities in semantic reasoning and offers explainable optimization suggestions, enhancing decision-making support in complex financial scenarios. Our study significantly improves the precision of process anomaly detection in financial contexts by incorporating banking-specific domain knowledge into process mining algorithms. Meanwhile, it extends theoretical boundaries and the practical applicability of process mining in intelligent, semantic-aware financial service management. Full article

The increasing deployment of wind energy systems, particularly offshore wind farms, necessitates advanced monitoring and maintenance strategies to ensure optimal performance and minimize downtime. Supervisory Control And Data Acquisition (SCADA) systems have become indispensable tools for monitoring the operational health of wind turbines, generating vast quantities of time series data from various sensors. Anomaly detection techniques applied to this data offer the potential to proactively identify deviations from normal behavior, providing early warning signals of potential component failures. Traditional model-based approaches for fault detection often struggle to capture the complexity and non-linear dynamics of wind turbine systems. This has led to a growing interest in data-driven methods, particularly those leveraging machine learning and deep learning, to address anomaly detection in wind energy applications. This study focuses on the development and application of a semi-supervised, multivariate anomaly detection model for horizontal axis wind turbines. The core of this study lies in Bidirectional Long Short-Term Memory (BI-LSTM) networks, specifically a BI-LSTM autoencoder architecture, to analyze time series data from a SCADA system and automatically detect anomalous behavior that could indicate potential component failures. Moreover, the approach is reinforced by the integration of the Isolation Forest algorithm, which operates in an unsupervised manner to further refine normal behavior by identifying and excluding additional anomalous points in the training set, beyond those already labeled by the data provider. The research utilizes a real-world dataset provided by EDP Renewables, encompassing two years of comprehensive SCADA records collected from a single offshore wind turbine operating in the Gulf of Guinea. Furthermore, the dataset contains the logs of failure events and recorded alarms triggered by the SCADA system across a wide range of subsystems. The paper proposes a multi-modal anomaly detection framework orchestrating an unsupervised module (i.e., decision tree method) with a supervised one (i.e., BI-LSTM AE). The results highlight the efficacy of the BI-LSTM autoencoder in accurately identifying anomalies within the SCADA data that exhibit strong temporal correlation with logged warnings and the actual failure events. The model’s performance is rigorously evaluated using standard machine learning metrics, including precision, recall, F1 Score, and accuracy, all of which demonstrate favorable results. Further analysis is conducted using Cumulative Sum (CUSUM) control charts to gain a deeper understanding of the identified anomalies’ behavior, particularly their persistence and timing leading up to the failures. Full article

This article delves into the innovative application of Docker containers as High-Performance-Computing (HPC) environments, presenting the construction and operational efficiency of virtual container clusters. The study primarily focused on the integration of Docker technology in HPC, evaluating its feasibility and performance implications. A portion of the research was devoted to developing a virtual container cluster using Docker. Although the first Docker-enabled HPC studies date back several years, the approach remains highly relevant today, as modern AI-driven science demands portable, reproducible software stacks that can be deployed across heterogeneous, accelerator-rich clusters. Furthermore, the article explores the development of advanced distributed applications, with a special emphasis on Machine Learning (ML) algorithms. Key findings of the study include the successful implementation and operation of a Docker-based cluster. Additionally, the study successfully showcases a Python application using ML for anomaly detection in system logs, highlighting its effective execution in a virtual cluster. This research not only contributes to the understanding of Docker’s potential in distributed environments but also opens avenues for future explorations in the field of containerized HPC solutions and their applications in different areas. Full article

Background/Objectives: Patients with peripheral artery disease (PAD) have a heightened risk of major adverse cardiovascular events (MACE), including myocardial infarction, stroke, and death. Despite this, limited progress has been made in identifying reliable biomarkers to prognosticate such outcomes. Circulating growth factors, known to influence endothelial function and the progression of atherosclerosis, may hold prognostic value in this context. The objective of this research was to evaluate a broad range of blood-based growth factors to investigate their potential as predictors of MACE in patients diagnosed with PAD. Methods: A total of 465 patients with PAD were enrolled in a prospective cohort study. Baseline plasma levels of five different growth factors were measured, and participants were monitored over a two-year period. The primary outcome was the occurrence of MACE within those two years. Comparative analysis of protein levels between patients who did and did not experience MACE was performed using the Mann–Whitney U test. To assess the individual prognostic significance of each protein for predicting MACE within two years, Cox proportional hazards regression was performed, adjusting for clinical and demographic factors including a history of coronary and cerebrovascular disease. Subgroup analysis was performed to assess the prognostic value of these proteins in females, who may be at higher risk of PAD-related adverse events. Net reclassification improvement (NRI), integrated discrimination improvement (IDI), and area under the receiver operating characteristic curve (AUROC) were calculated to assess the added value of significant biomarkers to model performance for predicting 2-year MACE when compared to using demographic/clinical features alone. Kaplan–Meier curves stratified by IGFBP-1 tertiles compared using log-rank tests and Cox proportional hazards analysis were used to assess 2-year MACE risk trajectory based on plasma protein levels. Results: The average participant age was 71 years (SD 10); 31.1% were female and 47.2% had diabetes. By the end of the two-year follow-up, 18.1% (n = 84) had experienced MACE. Of all proteins studied, only insulin-like growth factor-binding protein 1 (IGFBP-1) showed a significant elevation among patients who suffered MACE versus those who remained event-free (20.66 [SD 3.91] vs. 13.94 [SD 3.80] pg/mL; p = 0.012). IGFBP-1 remained a significant independent predictor of 2-year MACE occurrence in the multivariable Cox analysis (adjusted hazard ratio [HR] 1.57, 95% CI 1.21–1.97; p = 0.012). Subgroup analyses revealed that IGFBP-1 was significantly associated with 2-year MACE occurrence in both females (adjusted HR 1.52, 95% CI 1.16–1.97; p = 0.015) and males (adjusted HR 1.04, 95% CI 1.02–1.22; p = 0.045). Incorporating IGFBP-1 into the clinical risk prediction model significantly enhanced its predictive performance, with an increase in the AUROC from 0.73 (95% CI 0.71–0.75) to 0.79 (95% CI 0.77–0.81; p = 0.01), an NRI of 0.21 (95% CI 0.07–0.36; p = 0.014), and an IDI of 0.041 (95% CI 0.015–0.066; p = 0.008), highlighting the prognostic value of IGFBP-1. Kaplan–Meier analysis showed an increase in the cumulative incidence of 2-year MACE across IGFBP-1 tertiles. Patients in the highest IGFBP-1 tertile experienced a significantly higher event rate compared to those in the lowest tertile (log-rank p = 0.008). In the Cox proportional hazards analysis, the highest tertile of IGFBP-1 was associated with increased 2-year MACE risk compared to the lowest tertile (adjusted HR 1.81; 95% CI: 1.31–2.65; p = 0.001). Conclusions: Among the growth factors analyzed, IGFBP-1 emerged as the sole biomarker independently linked to the development of MACE over a two-year span in both female and male PAD patients. The addition of IGFBP-1 to clinical features significantly improved model predictive performance for 2-year MACE. Measuring IGFBP-1 levels may enhance risk stratification and guide the intensity of therapeutic interventions and referrals to cardiovascular specialists, ultimately supporting more personalized and effective management strategies for patients with PAD to reduce systemic vascular risk. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article