Search Results (15,513)

Photocatalytic anti-corrosion materials are an emerging intelligent protective material that has been widely used in marine and offshore engineering in recent years. However, its failure mechanism under multi-factor coupling is complex, and it is difficult for traditional methods to achieve accurate life prediction and mechanism analysis. This article takes submarine pipelines as the research object and designs an innovative multi-source environmental data-driven method combined with deep learning (DL), aiming to establish an intelligent prediction model for the failure of the material. This article first systematically collects the multi-source heterogeneous data of materials during service; on this basis, this article constructs a hybrid DL model. Firstly, a multi-scale multimodal image feature fusion network (MMFCT) based on the combination of convolutional neural network (CNN) and Transformer is adopted to automatically extract corrosion features from microscopic images and capture the dynamic correlation between environmental temporal data and performance degradation; then, the Sparrow Search Algorithm (SSA) was constructed to optimize the BP neural network (BPNN) model for predicting the ultimate bearing capacity of submarine corroded pipelines. Simulation experiments show that the proposed method achieves accurate prediction of material remaining life and key performance degradation paths. The corrosion recognition precision reaches 94.7%, and the bearing capacity prediction error remains below 3.1%. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Heat pipe cooled reactors rely on heat pipes for passive heat transfer and exhibit high reliability and compactness. Therefore, they are considered candidate nuclear reactor systems for future deep space exploration missions. To enable a deeper investigation of heat pipe reactor systems, particularly the transient response characteristics of the core, a transient coupled analysis framework is developed based on the multi-physics coupling code MOOSE. This framework includes the core heat transfer module, point kinetics module, heat pipe module, and Stirling engine module. A novel strategy that allows two distinct heat pipe models to be simultaneously invoked within a single simulation in MOOSE is developed. All modules are developed within the MOOSE framework and do not rely on any external programs. The heat pipe module is validated using experimental data from heat pipe startup and operation tests within the maximum relative error of only 0.45%. The entire coupled framework is validated against the KRUSTY operational experiments and is compared with other multi-physics models, demonstrating higher accuracy within the maximum relative error of only 13.7% in core load variation conditions. Meanwhile, transient coupled analyses of the KRUSTY reactor are performed to evaluate its safety performance under accident conditions. In the hypothetical positive reactivity step insertion accident and heat pipe failure accidents, the KRUSTY core exhibits excellent safety performance. And the mechanism of heat pipe power redistribution following heat pipe failure is examined in detail. Full article

Background: Weightlifters commonly use upper-extremity high-load training, which encompasses techniques ranging from momentary failure to non-failure. However, little is known about how this training affects posture and core endurance, despite knowing that these factors are risk factors for weightlifting injuries. Therefore, this study aimed to determine the immediate effects of upper-extremity high-load training to momentary failure versus non-failure, using the dumbbell overhead press, on posture and core endurance in recreational collegiate weightlifters. Methods: Fifty recreational weightlifters aged 18–24 with two years of upper extremity resistance training experience were recruited for this study. The participants performed dumbbell overhead press exercises under high-load failure (HL-F) and high-load non-failure (HL-NF) conditions two days after 1RM testing and calculation of the 80% 1RM load. The study analyzed postural changes using photographic data processed in Kinovea, while core endurance was assessed during a prone plank test. Standardized warm-ups, controlled exercise execution, and pre- and post-exercise assessments were conducted to measure core endurance and postural alterations. Results: The thoracic kyphosis angle, together with scapular balance angle and lateral scapular slide distance, increased significantly after HL-F compared to the unloading state, while the craniovertebral angle and prone plank time decreased significantly (p < 0.05). The HL-NF condition showed no statistically significant differences relative to the unloading measurements (p > 0.05). The unloading measurements across testing days were consistent, indicating no carryover effect (p > 0.05). Conclusions: The findings indicate that high-load training to failure adversely affects posture and core endurance, increasing fatigue and potentially increasing the risk of acute injuries. Non-failure training maintains stability, underscoring the importance of strategic program design for achieving optimal performance while minimizing adverse effects. Full article

This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R² reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article

Predictive maintenance plays a key role in digitalization initiatives; however, in real settings, issues related to failure prediction occur when failure instances are rare compared to normal instances, leading to class imbalance. In this study, we systematically compare five machine learning (ML) models—random forest, XGBoost, support vector machine, k-nearest neighbors, and multinomial logistic regression (MLR)—to detect multiclass rare failures using four imbalance-handling approaches (i.e., no handling, manual oversampling, selective manual oversampling, and class weighting), forming 20 configurations. Using the AI4I 2020 predictive maintenance dataset, which contains five failure types, we determined that XGBoost with no handling achieved the highest macro-averaged F1 (macro-F1) score (0.842) but obtained 0% recall for tool wear failure (TWF). MLR with selective manual oversampling achieved approximately 50% TWF recall with lower overall performance (0.636 macro-F1) than top-performing models such as XGBoost. We also found that very rare classes remain difficult to detect. Even high-performing models fail to consistently detect all five failure types. Overall, no single strategy can achieve a high detection rate across all performance measures. Full article

The stabilization of soft, organic-rich soils with cement is often hindered by retarded hydration and poor long-term performance under cyclic loads. While nano-silica or sand are known modifiers, their individual efficacy in high-organic environments remains limited, and a systematic comparison of their composite effect across different soil types is lacking. This study investigates the synergistic enhancement of cement-stabilized soils using a combined nano-SiO₂ and sand composite, comparing its effectiveness in high-organic soft soil and low-organic clay. Laboratory tests, including unconfined compressive strength (UCS), cyclic loading, scanning electron microscopy (SEM), and X-ray diffraction (XRD), were conducted. Results showed a stark contrast in 28-day UCS between unmodified soft soil cement (0.13 MPa) and clay cement (1.04 MPa). The optimal composite of 3.5% nano-SiO₂ and 40% sand increased the 28-day UCS to 1.39 MPa for soft soil (a 969% improvement) and 5.51 MPa for clay (a 430% improvement), respectively. Notably, under a cyclic stress ratio (CSR) of 0.7~0.8, unmodified specimens failed after fewer than 120 load cycles, whereas the composite-modified soils withstood 20,000 cycles without failure, demonstrating exceptional fatigue resistance independent of static strength gain. Microstructural analysis revealed that the composite effectively promoted the formation of cementitious hydration products, counteracting the inhibitory effect of organic matter. This research demonstrates that the nano-silica sand composite provides a superior and more broadly applicable improvement for cement-stabilized soils across the tested organic content range (3.3–7.7% LOI) compared to single-additive approaches, significantly enhancing both mechanical strength and long-term durability. Full article

In Time Division Duplex (TDD) Direct-Sequence Code Division Multiple Access (DS/CDMA) architectures, Pre-Rake filtering serves as a powerful transmitter-side strategy to alleviate receiver hardware constraints by leveraging channel reciprocity. Nevertheless, rapid channel fluctuations induced by high Doppler spreads critically undermine this reciprocity assumption. This failure is primarily driven by the unavoidable latency between uplink reception and downlink transmission, leading to severe performance deterioration. To address these challenges and enhance system robustness in modern high-speed scenarios, we propose an improved hybrid transceiver architecture. This scheme integrates multiplexed Pre-Rake processing with a Matched Filter-based Rake receiver and employs a Minimum Mean Square Error (MMSE) equalizer to suppress the severe Inter-Symbol Interference (ISI) and Multi-User Interference (MUI). Furthermore, we conduct a comparative analysis of channel estimation methods tailored for a 10 Mbps high-speed transmission environment.Our investigation reveals that while complex quadratic interpolation is often prioritized in low-data-rate studies, simple averaging is sufficient and even superior in high-speed communications. This is because the shortened slot duration allows simple averaging to effectively track channel variations while avoiding the noise overfitting associated with higher-order interpolation. The simulation results demonstrate that the proposed MMSE-optimized architecture achieves superior Bit Error Rate (BER) performance, providing a practical and computationally efficient solution for next-generation mobile networks. Full article

Cloud computing is one of the essential computing platforms for modern enterprises. A total of 84 percent of large businesses use cloud computing services in 2025 to enable remote working and higher flexibility of operation with reduction in the cost of operation. Cloud environments are dynamic and multitenant, often demanding high computational resources for real-time processing. However, the cloud system’s behavior is subjected to various kinds of anomalies in which patterns of data deviate from the normal traffic. The varieties of anomalies that exist are performance anomalies, security anomalies, resource anomalies, and network anomalies. These anomalies disrupt the normal operation of cloud systems by increasing the latency, reducing throughput, frequently violating service level agreements (SLAs), and experiencing the failure of virtual machines. Among all anomalies, virtual machine failures are one of the potential anomalies in which the normal operation of the virtual machine is interrupted, resulting in the degradation of services. Virtual machine failure happens because of resource exhaustion, malware access, packet loss, Distributed Denial of Service attacks, etc. Hence, there is a need to detect the chances of virtual machine failures and prevent it through proactive measures. Traditional machine learning techniques often struggle with high-dimensional data and nonlinear correlations, ending up with poor real-time adaptation. Hence, quantum machine learning is found to be a promising solution which effectively deals with combinatorially complex and high-dimensional data. In this paper, a novel quantum-enhanced support vector machine (QSVM) is designed as an optimized binary classifier which combines the principles of both quantum computing and support vector machine. It encodes the classical data into quantum states. Feature mapping is performed to transform the data into the high-dimensional form of Hilbert space. Quantum kernel evaluation is performed to evaluate similarities. Through effective optimization, optimal hyperplanes are designed to detect the anomalous behavior of virtual machines. This results in the exponential speed-up of operation and prevents the local minima through entanglement and superposition operation. The performance of the proposed QSVM is analyzed using the QuCloudSim 1.0 simulator and further validated using expected value analysis methodology. Full article

To investigate the effects of incorporating nanomaterials—carbon nanotubes (CNTs) and graphene oxide (GO)—on the axial compressive mechanical properties of alkali-activated recycled aggregate concrete (AARAC) after high-temperature exposure, this study designed 51 sets of specimens with recycled coarse aggregate replacement rate, nanomaterial content, and temperature as the main parameters. Compression tests were conducted to analyze the failure mode and strength variation in AARAC specimens after heating. In addition, microscopic tests, including X-ray diffraction, scanning electron microscopy, and computed tomography (CT scanning), were performed to analyze the microstructural characteristics of the post-heated AARAC specimens. The results indicate that as the replacement rate of recycled coarse aggregate increased from 0% to 100%, the residual compressive strength after exposure to 600 °C decreased from 33.6 MPa to 19 MPa. When 0.1 wt% of CNTs is added, the compressive strength of AARAC after exposure to a high temperature of 600 °C increases by approximately 30.4% compared to that of AARAC without nanomaterial addition. When 0.1 wt% of CNTs and 0.05 wt% of GO are added, the compressive strength after exposure to a high temperature of 600 °C increases by approximately 44.3%, while the size of scattered fragments upon failure increased, and the failure mode appeared more complete. Microscopic test results indicate that the high-temperature treatment did not cause significant changes in the main phase composition of AARAC. The synergistic effect of the nanomaterials CNTs and GO can fully utilize their functions as nucleation sites, pore fillers, and crack bridging agents. By strengthening the Interfacial Transition Zone between the recycled coarse aggregate and the cement paste, refining the Matrix Pore Structure, dispersing local thermal stress, and suppressing the propagation of high-temperature cracks, the mechanical properties of AARAC after high-temperature exposure can be effectively maintained. Full article

This study presents the application of an in-house developed safety assessment method on the scaled flight demonstrator e-Genius-Mod, which is equipped with distributed electric propulsion. Thereby, simplified aerodynamic and propulsive models are derived from existing flight test data. The safety assessment method is extended by modeling approaches for spanwise lift distribution and propeller slipstream effects on lift generation to incorporate an approximation of aero-propulsive effects. Selected failure case scenarios, namely single propulsor failures, are used to define suitable flight test scenarios as preparation for future validation of model predictions against flight test data. The application of the safety assessment method is shown to yield valuable predictions of failure effects on top-level aircraft performance and indicates that yaw moment-related failure effects are still dominant. Therefore, the effect of reducing vertical tail size on aircraft controllability and performance is examined. Model predictions indicate that propulsor failures at high thrust and low speed may exceed the yaw control authority of the aircraft, especially for the configurations with reduced vertical tail size. Furthermore, a simplified non-dimensionalised failure case depiction is presented to ease the transfer of insights to larger-scale aircraft designs and different powertrain architectures. Full article

Background: Heart failure (HF) is a major public health challenge. Management at or transfer to advanced therapy centers (ATCs) is linked to greater procedural use and better outcomes for HF, however there is little data on the impact of patient sex on access to ATCs and transfer patterns. We evaluated sex-based differences in HF management and outcomes during admissions across center types and transfer status. Method: Adult HF admissions were identified in the 2016–19 Nationwide Readmissions Database. Centers performing ≥1 heart transplant or LVAD were classified as ATCs. Patients were stratified by sex and center type: (A) non-ATC admission, (B) ATC admission, (C) transfer to ATC. Multivariable regression adjusted for comorbidities and HF decompensations. Results: Among 2,872,268 weighted HF admissions (51.3% male), females were older, while males had more HF decompensations (cardiogenic shock, ventricular arrhythmias, mechanical ventilation, AKI). Females comprised only 39.6% of all transfers to ATCs (0.4% vs. 0.6%, OR 0.69, p < 0.001) and had a lower unadjusted mortality (2.6% vs. 2.8%, p < 0.001); however, rates of transfer and mortality were similar between sexes when adjusted for comorbidities and HF decompensations. Female patients were significantly less likely to receive invasive procedures (CRT/ICD, PCI, right heart catheterization, CABG, temporary mechanical support, ECMO, LVAD or heart transplant) across all hospital types and transfers. This disparity in procedural utilization persisted after multivariable adjustment and in sensitivity analysis of patients with severe HF. Conclusions: Females had lower frequency of transfer to ATCs. In-hospital mortality and transfer rates to ATCs were similar across patient sex when adjusted for comorbidities and HF decompensations. Females consistently underwent fewer diagnostic and therapeutic interventions across all center types and transfers. Full article

Rain-induced shallow landslides persistently disrupt Nepal’s mountain roads, frequently leading to fatalities, transport disruptions, and economic losses. This study develops physically validated, site-specific rainfall thresholds for the landslide-prone Kanti National Roadway (H37) by integrating empirical intensity–duration (I-D) analysis, antecedent rainfall metrics, and satellite-derived soil moisture data. Using 35 years of rainfall records (1990–2024) and 59 field-verified landslides (2017–2024), we derived a localized I-D threshold: I = 19.37 × D^−0.6215 (I: rainfall intensity in mm/h; D: duration in hours), effective for durations of 48–308 h, encompassing short intense storms and prolonged moderate rainfall. The Cumulative Antecedent Rainfall (CAR) method associated most failures with 3-day totals, while the Antecedent Precipitation Index (API) showed superior performance, with a 10-day threshold of 77 mm capturing all events. For physical validation, NASA’s SMAP Level-4 root-zone (0–100 cm) soil moisture data revealed a 1-day lag in response to rainfall; after adjustment, trends matched API saturation predictions and identified an inverse rainfall–moisture pattern before the 11 August 2019 landslide, indicating a potential instability precursor. This integration enhances predictive accuracy, bolsters mechanistic understanding of landslide hazards, and offers a scalable, cost-effective early-warning framework for data-scarce mountain regions, aiding climate-resilient infrastructure in regions with intensifying rainfall extremes. Full article

With the continuous expansion of deepwater oil and gas development, annular pressure buildup in gas wells has become an increasingly critical safety concern. Rupture discs, as passive pressure relief devices, have attracted attention for potential application in annular pressure management in deepwater wells. However, their performance under complex downhole environments characterized by high temperature, dynamic loading, gas flow, and corrosion remains insufficiently understood. In this study, a laboratory-scale rupture disc burst-pressure experimental system with independently controllable temperature, pressure, and gas flow rate was developed. By simulating the coupled loading process caused by thermal expansion and controlled gas pressurization in a sealed annulus, a series of systematic experiments considering multiple operating factors were conducted to investigate rupture disc activation behaviour under representative deepwater well conditions. The experimental programme examined the effects of temperature, annular pressure ramp rate, gas flow rate, and acidic corrosion degradation. The results show that increasing temperature, higher annular pressure ramp rates, and elevated gas flow rates significantly reduce the rupture disc burst pressure and increase its statistical dispersion, indicating a transition of the loading state from quasi-static to dynamically coupled conditions. Under high flow rates and rapid pressurization, transient stress redistribution and amplification of local defects become dominant, shifting the failure mechanism from strength-controlled to defect-controlled behaviour. In contrast, corrosion degradation exhibits a stage-dependent influence: although burst pressure decreases with increasing corrosion time, the reduction rate gradually stabilizes, and the variability of burst pressure decreases as corrosion severity increases. These findings provide experimental insights into rupture disc behaviour under coupled environmental and operational factors and offer useful guidance for rupture disc selection and safety margin design in annular pressure control systems for deepwater gas wells. Full article

This study presents a comprehensive numerical and experimental investigation into the influence of punch shaft geometry on punching force and tool durability in the cold forming of S500MC steel sheets using an AISI D2 punch. Finite element analyses were conducted to evaluate the effects of varying punch shaft diameters on stress distribution, deformation behavior, and resultant punching forces. Experimental validation was performed through controlled punching tests, measuring force responses and assessing tool wear. The results demonstrate that optimizing the punch shaft diameter reduces the maximum punching force and minimizes stress concentrations, thereby enhancing tool life. Specifically, larger punch shaft diameters contribute to more uniform stress distribution and decreased risk of premature tool failure. These findings provide valuable insights for tooling design in high-strength steel sheet forming processes, enabling improved efficiency and cost-effectiveness in manufacturing operations. Full article