Search Results (725)

Phenyl-silicone rubber is the elastomer of choice for cryogenic and high-temperature static seals, yet quantitative links between thermo-oxidative aging and sealing reliability are still lacking. Here, sub-ambient (−70 °C to 25 °C) and room-temperature mechanical tests, compression set aging, SEM, FT-IR, and finite-element simulations are integrated to trace how aging translates into contact-pressure decay of an Omega-profile gasket. Compression set rises monotonically with time and temperature; an Arrhenius model derived from 80 to 140 °C data predicts 34 d (10% set) and 286 d (45% set) of storage life at 25 °C. SEM reveals a progressive shift from ductile dimple fracture to brittle, honeycomb porosity, while FT-IR confirms limited surface oxidation without bulk chain scission. Finite element analyses show that contact pressure always peaks at the two lateral necks; short-term aging increases in the shear modulus C₁₀ from 1.87 to 2.27 MPa, raising CPRESS by 8~21%, yet this benefit is ultimately offset by displacement loss from compression set (8.0 mm to 6.1 mm), yielding a net pressure reduction of 0.006 MPa. Critically, even under the most severe coupled condition (56 days aging with compression set), the predicted CPRESS remains above the 0.1 MPa leak-tightness criterion across the entire cryogenic service envelope. This framework provides deterministic boundaries for temperature, aging duration, and allowable preload relaxation, enabling risk-informed maintenance and replacement scheduling for safety-critical phenyl-silicone seals. Full article

Efficient waste management is a critical challenge in urban areas. This paper explores the optimization of power consumption in a smart bin management system using LoRa (long-range) communication technology. LoRa’s low-power, wide-area capabilities make it an ideal choice for IoT-based waste management systems. However, energy efficiency remains a crucial factor for ensuring the long-term sustainability of such systems, to avoid frequent intervention and reduce operating costs. This study employs advanced optimization techniques to minimize the energy usage of LoRa nodes while maintaining a reliable data transmission and system performance. By integrating a dynamic scheduling algorithm based on the usage of bins, and a custom adaptive data rate and power algorithm, the proposed solution significantly reduces the system’s energy impact. The performance of the system is evaluated through simulations and real-world deployment, where the results demonstrate a significant reduction in energy usage, over 84%, a longer battery life, and fewer maintenance interventions. The findings provide a scalable and energy-efficient framework for deploying smart waste management systems in resource-constrained environments. Full article

Defrost cycles in shipboard refrigeration plants are typically initiated on fixed schedules or by operator judgement, which can lead to unnecessary energy use and temperature variability during the transport of frozen products. This study proposes a data-driven predictive maintenance approach to trigger defrost on demand in a merchant reefer vessel carrying frozen tuna. A total of 76,692 operational records from cargo-hold air coolers and holds were analysed, including delivery and return air temperatures, hold air temperature and relative humidity. The records were modelled to show their behaviour under real voyage conditions. The modelled strategy indicates that 66.55% of defrost cycles performed during the study period were unnecessary, suggesting substantial scope to reduce defrost frequency and associated disturbances. These findings demonstrate the feasibility of implementing machine learning (ML)-based decision support for maritime refrigeration. This enables defrost-on-demand scheduling, which has the potential to enhance operational efficiency while supporting product quality, sustainability and traceability in the frozen tuna supply chain. Full article

This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by a UAV. A DJI Matrice 300 RTK (Real-Time Kinematic) equipped with a Zenmuse P1 camera (DJI, China) is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment. From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning. Full article

Background/Objectives: Relapsed or refractory follicular lymphoma (rrFL) remains difficult to treat in elderly or frail patients who cannot tolerate standard-dose immuno-chemotherapy as well as novel therapies. Metronomic chemotherapy (mCHEMO) may offer sustained antitumor activity with reduced toxicity. This study assessed the clinical activity and safety of R-DEVEC or R-DEVEC-light in rrFL patients following lenalidomide discontinuation or ineligibility. Methods: Data from the ReLLi Lymphoma Registry (2013–2025) were retrospectively analyzed. Eligible patients had rrFL after ≥1 prior therapy and initiated mCHEMO at least six months before data cutoff. Thirteen patients received DEVEC or the etoposide-free DEVEC-light regimen; all but one also received rituximab. Responders received maintenance vinorelbine, low-dose prednisone, and rituximab, followed by vinorelbine-only maintenance until progression or intolerance. Responses were assessed by CT after cycle two and PET/CT at completion of six induction cycles. Results: median age was 77 years (range 58–92); most patients were frail and had advanced disease. At the end of induction, 84% achieved remission (46% CR, 38% PR), with three PR converting to CR during maintenance. After a median follow-up of 27 months, the PFS was 42% (95CI 15–69%) and the OS 73% (95CI 47–100%). A transformation occurred in one patient; the main toxicity was grade 3 neutropenia (31%). DEVEC-light showed improved tolerability versus full DEVEC, with manageable infections and rare discontinuations. Conclusions: Metronomic R-DEVEC-light is a feasible and effective disease-controlling strategy for frail, heavily pretreated rrFL patients who do not tolerate lenalidomide and are excluded from modern therapies. This schedule warrants further prospective evaluation and exploration in combination with targeted agents. Full article

Ensuring the structural integrity of tower cranes is paramount for construction safety, yet jib lower chords—serving as trolley tracks—often undergo coupled wear–fatigue degradation that is rarely quantified in conventional service-life assessments. This study proposes a quantitative, maintenance-focused framework for integrity evaluation and life prediction of in-service tower cranes, validated through a decommissioned unit with 26 years of service in high-rise building construction. Through the integration of on-site construction operational statistics, ANSYS (Version 2022 R1, ANSYS, Inc., Canonsburg, PA, USA)—driven stress simulations, and rainflow counting, a multi-condition load spectrum was developed to quantify cumulative damage. Field measurements pinpointed Segment b03 as the critical damage zone, showcasing a maximum wear depth of 2.3 mm and roughly 30% thickness loss in the 20–30 m range, driven by stress concentration and high-frequency trolley movements during material handling. Theoretical fatigue life estimates of 42.1 years were revised to 24.1 years by incorporating wear geometry evolution and other degradation factors, resulting in a prediction error of approximately 7–8% relative to the actual service life. The proposed approach effectively bridges the gap between mechanical-based calculations and construction engineering practice, providing robust support for inspection scheduling, maintenance prioritization, and lifecycle management of aging tower cranes. Full article

Road safety and maintenance strategy optimization depend on accurate pavement surface friction prediction. In order to predict the Friction Number for Continuously Reinforced Concrete Pavement (CRCP) sections using data taken from the Long-Term Pavement Performance (LTPP) database, this study presents a hybrid machine learning framework that combines Gradient Boosting Machines (GBMs) with Genetic Algorithm (GA) optimization. Twenty input variables from the structural, climatic, traffic, and performance categories were used in the analysis of 395 data points from 33 CRCP sections. With a mean Root Mean Squared Error (RMSE) of 3.644 and a mean R-squared (R²) value of 0.830, the GA-optimized GBM model outperformed baseline models such as non-optimized GBM, Linear Regression, Random Forest, Support Vector Regression (SVR), and Artificial Neural Networks (ANN). The most significant predictors, according to sensitivity analysis, were AADT, Total Thickness, Freeze Index, and Pavement Age. The marginal effects of these variables on the expected friction levels were illustrated using partial dependence plots (PDPs). The results show that the suggested GA-GBM model offers a strong and comprehensible instrument for forecasting pavement friction, with substantial potential for improving safety evaluations and maintenance scheduling in networks of rigid pavement. Full article

With the advancement of industrial systems toward greater complexity and higher asset value, unexpected equipment failures now risk severe production interruptions, substantial economic costs, and critical safety hazards. Conventional maintenance strategies, which are primarily reactive or schedule-based, have proven inadequate in preventing unplanned downtime, underscoring a pressing demand for more intelligent monitoring solutions. In this context, intelligent spectral detection has arisen as a transformative methodology to bridge this gap. This review explores the integration of spectroscopic techniques with machine learning for equipment defect diagnosis and prognosis, with a particular focus on applications such as hydrogen leak detection and transformer oil aging assessment. Key aging indicators derived from spectral data are systematically evaluated to establish a robust basis for condition monitoring. The paper also identifies prevailing challenges in the field, including spectral data scarcity, limited model interpretability, and poor generalization across different operational scenarios. Future research directions emphasize the construction of large-scale, annotated spectral databases, the development of multimodal data fusion frameworks, and the optimization of lightweight algorithms for practical, real-time deployment. Ultimately, this work aims to provide a clear roadmap for implementing predictive maintenance paradigms, thereby contributing to safer, more reliable, and more efficient industrial operations. Full article

Traditional methods of asset management in electric power systems rely upon fixed schedules and reactive measurements, leading to challenges in the transparent prioritization of maintenance under evolving operating conditions and incomplete data. In this paper, we introduce a new, fully integrated artificial intelligence (AI)-driven approach for enhancing the resilience and reliability of open-source asset management tools to support improved performance and decisions in electric power system operations. This methodology addresses and overcomes several significant challenges, including data heterogeneity, algorithmic limitations, and inflexible decision-making, through a three-module workflow. The data fidelity module provides a domain-aware pipeline for identifying structural (missing) values from explicit missingness using sophisticated imputation methods, including Multiple Imputation Chain Equations (MICE) and Generative Adversarial Network (GAN)-based hybrids. The characterization module employs seven complementary weighting strategies, including PCA, Autoencoder, GA-based optimization, SHAP, Decision-Tree Importance, and Entropy Weighting, to achieve objective feature weight assignment, thereby eliminating the need for subjective manual rules. The optimization module enhanced the action space through multi-objective optimization, balancing reliability maximization and cost minimization. A synthetic dataset of 100 power transformers was used to validate that the MICE achieved better imputation than other methods. The optimized weighting framework successfully categorizes Health Index values into five condition levels, while the multi-objective maintenance policy optimization generates decisions that align with real-world asset management practices. The proposed framework provides the Transmission and Distribution System Operators (TSOs/DSOs) with an adaptable, industry-oriented decision-support workflow system for enhancing reliability, optimizing maintenance expenses, and improving asset management policies for critical power infrastructure. Full article

This paper explores simulation application functions for the computer monitoring system of a hydro–wind–solar integrated control center, focusing on five core areas: platform management, operational training, performance optimization, exception handling, and emergency drills. Against the “dual carbon” backdrop, multi-energy complementary system simulation faces key challenges including multi-energy coupling, real-time response, and cybersecurity protection. Research shows that integrating digital twin, heterogeneous computing, and artificial intelligence technologies markedly improve simulation accuracy and intelligent decision-making. Dispatch strategies have shifted from single-energy optimization to system-level coordination, while cybersecurity frameworks now provide comprehensive safeguards covering algorithms, data, systems, user behavior, and architecture. Intelligent operation and maintenance with fault diagnosis—powered by big data and deep learning—enables equipment condition prediction, and emergency drill platforms boost response capacity via 3D visualization and scriptless modeling. Current hurdles include absent multi-energy modeling standards, poor extreme-condition adaptability, and inadequate knowledge transfer mechanisms. Future research should prioritize hybrid physical–data-driven approaches, multi-dimensional robust scheduling, federated learning-based diagnostics, and integrated digital twin, edge computing, and decentralized ledger technologies. These advances will drive simulation platforms toward greater intelligence, interoperability, and reliability, laying the technical foundation for unified hydro–wind–solar control centers. Full article

Reliability and safety of high-voltage transmission lines are essential for stable and continuous operation of a power system. Environmental factors such as pressure, temperature, surface contamination, humidity, etc., significantly affect the dielectric strength of air, often causing unpredictable voltage breakdowns. This research presents a novel machine learning-based predictive framework that integrates Paschen’s Law with simulated and empirical data to estimate the breakdown voltage $\left(V_{bk}\right)$ of transmission lines in various environmental conditions. The main contribution is to demonstrate that data-driven prediction of breakdown voltage $\left(V_{bk}\right)$ using a hybrid machine learning model is in agreement with physical discharge theory. The model achieved root mean square error (RMSE) of 5.2% and mean absolute error (MAE) of 3.5% when validated against field data. Despite the randomness of avalanche breakdown, model predictions strongly match experimental measurements. This approach enables early detection of insulation stress, real-time monitoring, and optimises maintenance scheduling to reduce outages, costs, and safety risks. Its robustness is confirmed experimentally. Overall, this work advances the prediction of avalanche breakdown behaviour using machine learning. Full article

Runway friction is a critical factor in aircraft safety, affecting braking performance during landing and take-off. This study evaluates friction measurement variability and runway life-cycle dynamics at four typical Australian airports, using GripTester data from calibration strips and operational runways. The results show that friction measurements are influenced by seasonal effects, random errors, and testing equipment tire wear, with greater variability at lower speed (65 km/h) than at higher speed (95 km/h). Analysis of runway friction decay indicates that friction reduction rates are higher in touchdown zones and decelerating rate gradually decrease as friction declines, while regular rubber removal significantly restores friction, sometimes exceeding post-construction levels. Current internationally recommended friction testing intervals may not adequately ensure safety, with a sufficient probability of friction dropping below maintenance planning levels between tests. Based on observed reduction rates, updated intervals of approximately 3000 to 4000 landings are proposed to achieve 90% confidence in maintaining safe friction levels. The findings provide practical guidance for friction management and maintenance scheduling as part of an optimized airport pavement management system. Full article

This paper considers the two parallel-machine scheduling problem with intree-precedence constraints where machines are subject to non-availability constraints. In the literature, this problem is considered to be an open problem of unknown complexity. The proposed solution proves that the problem under consideration has polynomial complexity. Periods of machine unavailability are predetermined, and both task execution and inter-task communication are modeled as requiring one unit of time. The optimization criterion central to this study is the minimization of the makespan. Such a scheduling challenge is directly applicable to manufacturing environments, where production equipment can be intermittently offline for reasons such as unscheduled repairs or planned preventative maintenance. Adopting a unit-time task model offers a valuable framework for subsequently scheduling larger, preemptable jobs.This work presents a new method, called Scheduling Intrees with Unavailability Constraints (SIwUC), which operates by aggregating tasks into distinct groups. The analysis establishes that the SIwUC algorithm produces optimal schedules and reveals how the underlying problem architecture and its solutions demonstrate a symmetrical property in the distribution of tasks across the two parallel machines. This paper demonstrates that the proposed SIwUC algorithm builds optimal schedules and highlight how the problem structure and its solutions exhibit a form of symmetry in balancing task allocation between the two parallel machines. Full article

Existing inspection systems for railway point machines often suffer from high latency and poor interpretability, which impedes the real-time detection of critical mechanical anomalies, thereby increasing the risks of derailment and leading to cascading schedule delays. Addressing these challenges, this study proposes a lightweight computer vision-based detection framework deployed on the RK3588S edge platform. First, to overcome the accuracy degradation of segmentation networks on constrained edge NPUs, a Sensitivity-Aware Mixed-Precision Quantization and Heterogeneous Scheduling (SMPQ-HS) strategy is proposed. Second, a Multimodal Semantic Diagnostic Framework is constructed. By integrating geometric engagement depths—calculated via perspective rectification—with visual features, a Hard-Constrained Knowledge Embedding Paradigm is designed for the Qwen2.5-VL model. This approach constrains the stochastic reasoning of the Qwen2.5-VL model into standardized diagnostic conclusions. Experimental results demonstrate that the optimized model achieves an inference speed of 38.5 FPS and an mIoU of 0.849 on the RK3588S, significantly outperforming standard segmentation models in inference speed while maintaining high precision. Furthermore, the average depth-estimation error remains approximately 3%, and the VLM-based fault identification accuracy reaches 88%. Overall, this work provides a low-cost, deployable, and interpretable solution for intelligent point machine maintenance under edge-computing constraints. Full article

In this paper, we take the longitudinal two-stage and two-yard EMU (Electric Multiple Unit) depot as an example and discusses the optimization challenges of the first-level maintenance shunting operation plan under the background of limited maintenance capacity. A multi-objective programming is constructed, which adopts the lexicographic ordering method and aims to minimize the occupancy time of key line areas and the number of train storage times. In order to enhance the flexibility and solution efficiency of the shunting operation plan, we design an efficient three-stage strategy algorithm. Specifically, in the first stage, the genetic and mutation rules are integrated, and the fast iterative advantage of the genetic algorithm is utilized to solve the time decision variables in the optimization problem. In the second stage, the allocation of track occupancy variables is further solved. The third stage focuses on the optimized allocation of maintenance team variables to ensure the scientific scheduling of maintenance resources. Finally, a validation experiment was conducted using the maintenance tasks of 19 EMU sets as the test scenario. The results indicate that when the number of maintenance teams is set to 4, an optimal balance between maintenance efficiency and operational cost is achieved, the occupancy duration of key line zones reaches 3034 min (the theoretical optimum), the number of maintenance teams is reduced by 33.33% compared to the initial 6 teams, and the number of storage operations is optimized to 27 times. Additionally, the algorithm’s solution time remains under 50 s, demonstrating significantly improved computational efficiency. Comparative experiments with baseline algorithms show that the proposed method reduces the occupancy duration of key line zones by up to 0.49%, decreases the number of storage operations by 14 times, and advances the maximum completion time by 20 min. In summary, the proposed method provides solid theoretical support for the formulation of maintenance plans and shunting schedules in EMU depots. Particularly in complex scenarios with limited maintenance capacity, it offers innovative and robust decision-making foundations, demonstrating significant practical guidance value. Full article