Search Results (2,623)

This paper studies the reliability and availability of a k-out-of-(M+S) retrial machine repair system with two-way communication, consisting of M primary components and S warm standby components. The system incorporates the retrial behavior of failed components. When the repairman becomes idle, he initiates outgoing calls after a random period either to failed components in the orbit for repair or to components outside the orbit for preventive maintenance. The main contribution of this study is the incorporation of proactive repairman behavior, which more realistically captures operational practices in certain engineering systems. By employing the matrix analytic method together with a recursive approach, the steady-state probabilities of the system are obtained, and several important performance measures are derived. Furthermore, the Runge–Kutta method is used to evaluate the system reliability and the mean time to failure. A sensitivity analysis is conducted to investigate the effects of key system parameters, supported by numerical experiments and graphical illustrations. Finally, a cost–benefit model is formulated, and a genetic algorithm is implemented to determine the optimal values of the decision variables that minimize the cost–benefit ratio. Full article

Modern standards and requirements for ensuring the reliability and safety of transport infrastructure are aimed at shifting from routine maintenance to preventive maintenance, focused on predicting technical conditions and lifecycle management. Modern engineering approaches are based on the logic of state assessment and ensuring structural strength and dimensional stability. Therefore, they focus on recording defects or deviations from acceptable values without revealing the failure mechanism, which limits the ability to identify degradation processes and predict failures. The purpose of this article is to develop a formal conceptual framework for operationalizing process-oriented reliability analysis. Within this methodological framework, state is viewed as a snapshot of a dynamic process, while process stability is defined as the ability of a system to maintain its key behavioral characteristics under changing operating conditions and the geometric and physical–mechanical properties of system elements. The proposed framework expands on classical state-based diagnostics by introducing process invariants as prognostic indicators. The transition to trajectory-based behavior analysis allows monitoring systems to evolve into lifecycle management tools. Full article

The preventive maintenance (PM) of asphalt pavements reduces life-cycle costs and minimizes resource consumption compared with reactive rehabilitation, yet its cost-effectiveness is highly sensitive to application timing. This study develops a data-driven framework for determining optimal PM timing on Korean expressways by integrating network-level pavement management system (PMS) deterioration modeling with life-cycle cost analysis (LCCA). Using 10-year PMS time-series data from approximately 2200 asphalt pavement sections (2012–2021), a nonlinear regression of the Highway Pavement Condition Index (HPCI) yielded an exponential deterioration model with exponent β = 1.87 (R² = 0.996), confirming accelerating deterioration beyond a critical service age. The HPCI inflection coincides with the Grade-2 boundary (3.5–4.0), where surface distress growth—dominated by linear cracking (91.3% of total SD)—also peaks. A LCCA across 44 scenarios demonstrated that PM applied immediately before this acceleration onset minimizes the 40-year net present value (NPV; discount rate 4.5%). The optimal first PM application time was estimated at 10.8 years (≈56% of the 19.3-year average service life), reducing the 40-year NPV by up to 7 million KRW per section relative to the milling and overlay baseline (up to 16 million KRW in absolute NPV terms for concrete overlay sections). These findings provide a quantitative, reproducible basis for PM timing decisions applicable to the approximately 4000 km of expressway pavement managed by Korea Expressway Corporation. Full article

Background and aims: Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the progressive dilation of the aorta, culminating in rupture. At present, there are no pharmacological treatments to prevent AAA development or reduce rupture rate. A recent study showed that patients prescribed Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have significantly lower risks of mortality, AAA repair, and acute abdominal aortic syndrome. Semaglutide is a GLP-1RA with increased agonist capacity and longer half-life, compared to earlier generations of GLP-1RAs. In this study, we aimed to investigate the role and mechanisms of semaglutide in the prevention of AAA development and rupture in a murine model. Methods: AAA was induced in apolipoprotein-E-deficient mice, by continuous subcutaneous infusion of angiotensin II. Treatment with semaglutide (12 µg/kg) began seven days after disease induction (rescue trial) or simultaneously with disease induction (prophylactic trial). At experimental endpoint, aortic diameter was measured by high-frequency ultrasound and the aortic tissue was collected for histological analysis. Results: Prophylactic treatment with semaglutide drastically reduced mortality by dissection and rupture during the first seven days of disease development, but did not affect AAA formation at 28 days. Histological evaluation of the aorta at day seven showed a normal vessel wall thickness with a trend for a higher content of collagen in the aortic wall in mice treated with semaglutide, compared to controls. Conclusions: Semaglutide prevents aortic rupture and dissection in the early phases of AAA development in the angiotensin II mouse model, likely by promoting the maintenance of an adequate proportion of collagen in the vessel wall. Full article

The replacement of synchronous generators (SGs) with inertia-less renewable energy sources (RESs) poses a significant challenge to grid stability due to the reduction of system inertia. To prevent grid instability, energy storage systems (ESSs) with frequency-derivative controls are used to emulate inertia. However, the limited lifetime of ESSs, along with their maintenance requirements, large footprint, and high cost, imposes an additional economic burden on microgrids. This paper proposes an enhanced grid-frequency support approach by coordinating two inertia-emulation mechanisms in parallel: (i) inertia support derived from DC-link capacitor dynamics and (ii) inertia support enabled by operating the PV plant with a power reserve. The proposed method enhances the grid support capacity of the PV energy system and energy sustainability through the efficient utilization of available support resources. Moreover, the DC-link voltage is restored smoothly and naturally to its rated value without the need for a complex control algorithm. The dynamic performance of the proposed system is evaluated under different disturbance conditions and different parameter settings. Simulation results using MATLAB/Simulink R2023a show that, under a 7% load increase, the proposed controller improves the frequency nadir by 0.04 Hz and decreases RoCoF by 10% compared with the baseline controller. Full article

Industrial maintenance and predictive diagnostics constitute fundamental pillars of modern manufacturing that prevent equipment failures, minimize operational downtime, and optimize maintenance costs across diverse industrial environments. Vibration-based fault classification plays an important role in industrial operations, necessitating highly sophisticated diagnostic methodologies. This research addresses these industrial imperatives through a comprehensive investigation of novel hybrid deep learning architectures for vibration-based fault classification. This study introduces a strategic integration of Quadratic Neural Networks (QNNs), which demonstrate superior non-linear feature extraction capabilities on a vibration signal compared to traditional convolutional approaches. A systematic evaluation of seven sophisticated architectures establishes a clear performance hierarchy, with QuCNN-LSTM-Transformer emerging as the optimal model achieving 99.26% average accuracy. All proposed models demonstrate excellence, with test accuracies consistently surpassing 95% across all evaluated scenarios. The data analyzed is emprical utilizing sensor data collected from an experimental rig and shows exceptional performance consistency on CWRU and HUST datasets. This investigation establishes a new paradigm in intelligent diagnostics, offering functional guidance and definitive analysis of hybrid architectures that advance industrial fault classification applications. Full article

Biofilms rapidly form on medical devices such as urinary catheters and surgical materials. These biofilms compromise patient safety and undermine infection prevention and control (IPC). Biofilms also reduce the effectiveness of antibiotics and disinfectants. As a result, they increase healthcare-associated infections and increase costs through device failure and the need for maintenance or replacement. Researchers are increasingly exploring biosurfactants (BSs) as surface coatings and cleaning additives to prevent microbial attachment and disrupt early biofilm formation on medical devices and healthcare-related surfaces. This review examines the translational potential of biosurfactants as preventive, disruptive, and adjunctive antibiofilm agents for medical devices and healthcare-related surfaces. Literature evidence on glycolipids (rhamnolipids, sophorolipids) and lipopeptides (surfactin) from static, flow-based, and microfluidic in vitro models that used clinically relevant materials, such as silicone and polydimethylsiloxane (PDMS), were examined. In our literature search, we focused on pathogens central to IPC, such as Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus spp., and Candida spp., and it was generally noted that BSs reduced microbial adhesion and delayed early biofilm formation on medical devices and healthcare-related surfaces. Significant evidence also suggests that they partially disrupt biofilms and improve antimicrobial penetration when co-applied, mainly through membrane disruption, destabilization of extracellular substances, interfering with quorum sensing, and synergistic and/or antagonistic interactions with other molecules. Their performance varied with class, formulation, hydrodynamic conditions, and microbial composition. BSs function better as preventive and adjunctive IPC tools than stand-alone antimicrobial agents and can help to reduce biofilm formation on devices and improve surface disinfection. However, translating this promise into practice demands more robust data on long-term safety, stability, and product quality. Full article

Background: Patients with kidney failure requiring dialysis experience a high burden of vaccine-preventable diseases, and vaccine hypo-responsiveness is a key contributor. Uraemic toxins and gut dysbiosis are potential causes of hypo-responsiveness. Aim: This study aimed to determine whether uraemic toxin concentrations or gut dysbiosis are associated with vaccine response in haemodialysis patients. Methods: This was a single centre, observational cohort study of maintenance dialysis patients receiving a conventional 2-dose primary COVID-19 vaccination course. Demographic, clinical and vaccination data were collected from the eMR. Vaccine response (Elecsys Anti-SARS-CoV-2 immunoassay), serum uraemic toxin concentrations (indoxyl sulphate, p-cresyl sulphate, and trimethylamine N-oxide by liquid chromatography), and stool microbiome (16S rRNA gene sequencing) were measured 8 weeks after the second dose of vaccine. Results: Forty participants (43% female, mean age 66 years; 59% Caucasian) were included, 70% of whom were classified as a vaccine responder. Antibiotic exposure, prednisolone use and lymphopenia were significantly associated with hypo-responsiveness. Microbiome profiling identified differences in beta diversity between responders and non-responders, positively correlated with short-chain fatty acid producers (Parabacteriodes) and negatively with pathobionts (Escherichia/Shigella). Differential abundance analysis identified lower levels of Tyzzerella, Gemmiger, and Hungatella and higher levels of Turicibacter in vaccine responders. Total uraemic toxin burden and individual toxin concentrations did not differ between responders and hypo-responders (all p > 0.05). Stratification by low versus high/very high toxin burden groupings was not associated with response (p > 0.99). Conclusions: Differences in gut microbial composition were observed between vaccine responder groups, while uraemic toxin concentrations were not associated with vaccine responsiveness. These findings suggest gut microbiota composition may contribute to vaccine hypo-responsiveness in individuals receiving dialysis and warrant further investigation in larger mechanistic studies. Full article

Monitoring oil pipelines is crucial for effective infrastructure management and maintenance, as it helps prevent threats such as vandalism and leaks that can lead to catastrophic events. Pipeline leaks pose significant environmental and economic risks; however, existing detection methods are often expensive, slow, or unreliable, limiting their effectiveness for real-time applications. This study proposes a lightweight thermal-imaging-based intelligent leak detection system that integrates Convolutional Neural Networks (CNN), Autoencoder (AE), and Knowledge Distillation (KD), suitable for deployment on edge devices. The proposed system addresses challenges associated with existing pipeline detection techniques, including large model sizes, high transmission latency, and excessive energy consumption. Thermal images of pipelines are captured and compressed using an autoencoder before being processed by a CNN model optimized through knowledge distillation. The model was trained and tested on a locally collected thermal image dataset and designed for deployment on edge devices such as Raspberry Pi to simulate edge computing scenarios. Experimental results demonstrate that the proposed CNN + KD + AE model achieved 98% accuracy, 98% precision, 98% recall, and an F1-score of 98%, outperforming baseline models such as MobileNetV2 (91%), InceptionV3 (84%), EfficientNet-Lite (81%), and ResNet (74%). Furthermore, the number of trainable parameters was significantly reduced to 1.18 million, with a compact model size of 4.51 MB. These findings confirm the system’s suitability for real-time leak detection in remote and resource-constrained environments, contributing to the development of cost-effective, scalable, and energy-efficient solutions for intelligent pipeline monitoring. Full article

Juvenile idiopathic arthritis (JIA) represents the most common cause of chronic arthritis in childhood; however, not all early-onset arthropathies are inflammatory in origin. We report the case of a 4-year-old girl initially diagnosed with oligoarticular JIA and treated with methotrexate followed by a tumor necrosis factor inhibitor, without significant clinical improvement and despite persistently normal inflammatory markers. Clinical reassessment raised suspicion of a non-inflammatory arthropathy, supported by characteristic radiographic findings including metaphyseal flaring of the distal femora and proximal tibiae. Genetic analysis identified compound heterozygous pathogenic variants in the PRG4 gene, confirming the diagnosis of camptodactyly–arthropathy–coxa vara–pericarditis (CACP) syndrome (OMIM #208250). PRG4 encodes lubricin, a mucin-like glycoprotein essential for boundary lubrication of articular cartilage and maintenance of synovial joint homeostasis. Loss-of-function variants disrupt joint lubrication, leading to mechanical synovial hyperplasia and chronic non-inflammatory joint effusion. This case highlights common diagnostic pitfalls in pediatric rheumatology and underscores the importance of considering genetic causes of chronic arthropathy when clinical and laboratory features are atypical for inflammatory disease. Early molecular diagnosis prevents unnecessary immunosuppressive therapy and enables appropriate multidisciplinary management. Full article

Background and Objectives: represents a major public health challenge in rapidly aging societies. While lifestyle behaviors are established modifiable risk factors for frailty, the longitudinal impact of composite lifestyle trajectories—particularly by sex—remains poorly understood. This study examined sex-stratified associations between Healthy Lifestyle Score Trajectories (HLSTs) and frailty among community-dwelling middle-aged and older adults in South Korea. Using 19 years of nationally representative panel data from the Korean Longitudinal Study of Aging (2006–2024), we analyzed 6603 participants (2684 males; 3919 females). Materials and Methods: Group-Based Trajectory Modeling was applied to Waves 1–6 to derive sex-specific HLSTs based on smoking, alcohol consumption, physical activity, and body mass index. Generalized Estimating Equations were used to assess longitudinal associations between HLSTs and Frailty Index (FI) scores across Waves 6–10, adjusting for sociodemographic covariates. Results: Five distinct HLSTs were identified in both sexes. In both males and females, persistently poor or deteriorating trajectories were independently associated with higher FI scores relative to the Favorable HLST reference group. The effect size for Poor HLST was more than twice as large in females (B = 0.039) than in males (B = 0.018), consistent with the sex-frailty paradox. Among females, the Improving HLST group did not demonstrate a statistically significant frailty benefit (B = 0.014, p = 0.091). Stratified analyses revealed that the lifestyle–frailty association among males was significant only in rural-dwelling participants, whereas in females the association was consistent across both urban and rural settings. Conclusions: Persistently unfavorable composite lifestyle trajectories were independently associated with higher frailty burden, with disproportionately greater impact in women. Late-life lifestyle improvement was not significantly associated with reduced frailty in women, reinforcing the importance of early and sustained behavioral maintenance. The rural-specific association in men highlights the role of structural disadvantage in amplifying lifestyle-related frailty risk. However, given the observational design of this study, the possibility of reverse causality cannot be excluded, and these findings should be interpreted as associative rather than causal. These findings support sex-sensitive, trajectory-based, and geographically tailored frailty prevention strategies. Full article

Hymenoptera venom allergy is a cause of anaphylaxis, which significantly affects patients’ daily lives due to the constant fear of accidental stings. Venom immunotherapy (VIT) is the only treatment capable of preventing severe systemic reactions (SSRs). Limited long-term real-life data are available, integrating both clinical and immunological outcomes. A five-year prospective observational study was conducted on 35 patients with a history of SSR who underwent VIT at a tertiary allergy center in Southern Italy; two of them had a diagnosis of systemic mastocytosis. Most patients were sensitized to Vespula, but others to Apis, Polistes dominula and Vespa crabro, reflecting the exposure pattern characteristic of Mediterranean regions. Clinical outcomes following accidental re-stings and serological trends, including total IgE, venom-specific IgE, and baseline serum tryptase, were assessed at treatment initiation and after five years of maintenance therapy. During the entire follow-up, all patients tolerated VIT. No SSRs occurred after accidental stings in 17/35 patients, confirming clinical protection achieved with VIT. Vespula serum-specific IgE presented a highly significant decrease; total IgE, tryptase and specific IgE for Apis, Polistes dominula and Vespa crabro showed a statistically significant decrease. Our findings reinforce the role of VIT as a well-tolerated, effective and disease-modifying treatment in a real-world setting. Full article

Different strategies have been proposed to optimize injection mold cooling to reduce cycle time and improve efficiency. While recent research has focused on the design of additively manufactured conformal cooling inserts, the impact of mold maintenance conditions on cooling performance has received limited attention, particularly regarding the formation of limescale. This work presents a numerical modeling approach to quantify the combined effects of thermal resistance and hydraulic restriction caused by limescale accumulation in high-aspect-ratio cooling channels used in slender mold cores. An integrated thermal-fluid analysis is employed to evaluate coolant flow behavior and heat-transfer performance and to assess their coupled influence on cooling efficiency and part dimensional stability. The results show that, in slender cooling channels, even thin limescale deposits can significantly reduce cooling performance, with hydraulic restriction emerging as the dominant mechanism under the investigated conditions due to the reduced effective flow area. Design strategies that reduce effective frictional length and mitigate limescale deposition reduced part temperature by approximately 10 °C and shortened cooling time by about 17%. Further optimization of coolant flow conditions yielded an additional 65% reduction in cooling time. These findings highlight the importance of integrating cooling design with preventive maintenance to achieve robust injection molding performance. Full article

Operating multi-unit combined heat and power (MUCHP) plants involves determining unit commitment (UC) and coupled heat and power dispatch under demand uncertainty and progressive equipment degradation. This paper proposes a reinforcement learning approach to jointly optimize UC, dispatch, and preventive maintenance (PM). Specifically, we develop a Proximal Policy Optimization (PPO)-based policy that shifts the computational burden to offline training, enabling near-real-time decisions during operation. The trained agent is evaluated on an hourly five-unit CHP system model based on operational data from a district heating plant in the Republic of Korea, using a full-year simulation. The robustness of the proposed method is assessed against demand forecast noise and structural system shifts covering reduced, expanded, homogeneous, and heterogeneous unit configurations. The experiments indicate that the proposed approach reduced the total operating cost by 4.69 to 8.35 percent compared to three heuristic baselines across the evaluated scenarios. Moreover, it mitigates supply shortages during high-volatility seasons through proactive pre-commitment and preserves asset health by distributing production loads evenly. These results indicate that integrating PM into operational planning improves both the economic efficiency and operational stability of MUCHP systems. Full article

Research on landslides around reservoirs is necessitated to strengthen risk prevention and mitigation, as their occurrence has catastrophic consequences. For reservoir safety assessments, landslide susceptibility analysis is commonly concentrated on single reservoir bank slopes or individual landslides. However, focusing solely on bank slopes and individual landslides gives an incomplete picture of how safe the reservoir is from possible landslide related risks, since landslides from distant slopes can also adversely affect the reservoir in different ways. In this paper, landslide susceptibility assessment was conducted using machine learning models (Gradient Boosting Machine, XGBoost, Random Forest and Ensemble Stacking) in the area around the San Pietro Dam, an earth dam located in Southern Italy, in a region highly prone to landslide hazards. The landslide inventory for the area was used to generate landslide and non-landslide points for model training and testing. The area under curve (AUC) of a receiver operating characteristic (ROC) curve approach was used to evaluate, validate, and compare the performance of the four models. Results indicated that the ROC AUC values of the models ranged from 0.76 to 0.77, with the Random Forest, Gradient Boosting and Ensemble stacking models having AUC values of 0.77. All the models classified about 15–20% of the reservoir basin as highly susceptible to landslides. The generated basin-scale landslide susceptibility maps can be used to prioritize monitoring and maintenance in areas around the dam that have been identified as highly susceptible. Full article