Search Results (3,550)

During the high-temperature deposition of tungsten thin films on alumina ceramic substrates, the inherent mismatch in thermal expansion coefficients frequently triggers interfacial delamination, where uncontrollable factors in stochastic surface topographies can exacerbate localized stress concentrations. To resolve these interfacial failures, the enhancement of interfacial adhesion through a deterministic surface microgroove design is identified as the general objective of the present research. Within this framework, the establishment of a robust quantitative mapping between the transverse scratching offset distances and the resultant periodic microgeometry is first pursued as a specific experimental objective. This methodological approach effectively transforms the stochastic nature of the substrate into deterministic geometric configurations. Second, a specific numerical objective is fulfilled by evaluating the interfacial stress redistribution and damage evolution utilizing refined thermomechanical coupled simulations based on the cohesive zone model. The integrated findings demonstrate that optimizing the microgroove spacing effectively governs the morphological transition and broadens stress diffusion pathways to mitigate thermal mismatch effects. Specifically, the structural optimization at a spacing of 28.8 μm facilitates an approximately 31.8% reduction in the maximum interfacial stress and a 10% decrease in the average film stress compared to the 13.6 μm spacing. Finally, this research clarifies the underlying mechanisms of stress buffering and provides a rigorous engineering methodology for the structural design of reliable high-performance ceramic–metal interfaces in extreme environments. Full article

Dynamic response measurements support bridge monitoring and structural assessment because they are obtainable under operational loading and are sensitive to changes in stiffness, boundary conditions, and mass distribution. This article presents a structured scoping review of dynamic-response-based bridge monitoring and assessment. It covers damage-sensitive indicators, stiffness/capacity proxy inference, interpretation under operational and extreme loading, sensing with acquisition (contact, and indirect/drive-by), and data processing, machine learning and digital-twin integration for decision support. Evidence was identified through targeted searches in Scopus and The Lens with duplicate resolution in Zotero. The cited studies are compiled into a traceable evidence inventory linked to method families and decision objectives. The synthesis shows that global modal properties enable change screening but are highly confounded by environmental/operational variability. Localization and state characterization typically require denser or higher-fidelity sensing and signal conditioning. Finally, capacity-related inference using calibrated conversion models or machine learning (ML) surrogates remains context-bounded and validation-dependent. This review provides an end-to-end pipeline, evidence-maturity rubric, and conservative failure-mode checks with escalation logic that tie SHM outputs to inspection and analysis rather than direct condition declarations for bridge owners. This review is intentionally scoped and does not claim PRISMA-style comprehensiveness. Full article

This study examines how coordination, power, and interdependence shape regulatory governance in the decentralized edible bird’s nest (EBN) sector in Pulang Pisau, Indonesia. While decentralization is often associated with improved responsiveness and local adaptability, it frequently produces fragmented regulatory systems in which authority is distributed without effective coordination. Using an actor-centered qualitative design combined with the MACTOR method, this study analyzes influence–dependence relations, objective alignment, and coordination bottlenecks across key actors. The findings show that regulatory performance is shaped less by formal mandates than by relational positioning within the governance system. Actors controlling technical verification and documentary gateways occupy high-influence positions, while licensing authorities remain operationally dependent. Although most actors share common objectives—such as hygiene, quality assurance, and traceability—these are pursued through fragmented procedures, resulting in coordination failures and regulatory inequality. Producers bear the greatest compliance burdens despite having limited influence over regulatory processes. The study introduces the concept of relational administrative capacity to explain how decentralized governance outcomes depend on the alignment of authority, expertise, and procedural sequencing across interdependent actors. The findings suggest that improving regulatory performance requires strengthening coordination architectures rather than adding new rules. Full article

In underground coal mining, the gear of a shearer’s rocker arm endures extreme stress and environmental fluctuations. Failures in this vital component can pose serious safety hazards, cause prolonged operational downtime, and result in significant financial losses. Therefore, accurate gear fault diagnosis is crucial. However, conventional diagnostic methods often struggle with limited feature extraction and poor performance when dealing with non-stationary, noisy signals typical of this environment. To address these challenges, a hybrid model consisting of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Markov Transition Model (MTM) is proposed. In this framework, the CNN is used to extract both global and local features related to gear fault. A time-distributed feature extractor is then integrated with the LSTM to capture the temporal progression of these features, aiding in effective modeling of fault evolution over time. Finally, the MTM further refines classification by incorporating probabilistic state transition between fault conditions, thereby improving diagnostic stability and robustness under noise. Experimental validation was done using vibration data from the Taizhong Coal Machinery rocker arm test platform and gear data from Southeast University and achieved up to 99.79% accuracy. These results show this proposed method outperformed other advanced diagnostic methods, offering dependable fault diagnosis and strong noise resistance even under extreme noise conditions of −5 dB SNR. Full article

Differential operators often admit multiple algebraically equivalent symbolic formulations, yet those formulations can differ in the organization of their internal structure prior to solution analysis. A reproducible symbolic framework is introduced to compare such formulations at the level of operator expressions. Within a declared symbolic specification consisting of a fixed grammar, an admissible weight class, canonical compression rules, and an admissible family of reformulations, we define four encoding-relative structural descriptors: structural strain τ, structural curvature κ, compressibility σ, and the balance ratio Γ = κ/τ. Structural strain compares an encoding to a designated reference representation, while compressibility measures reduction under canonical symbolic compression. These quantities are deterministic descriptors within the declared encoding class rather than coordinate-free invariants of the underlying operator. The structural length functional underlying these descriptors is developed, canonical compression is formalized, and finite symbolic comparison is distinguished from pathwise symbolic deformation. A robustness theorem shows that, away from the threshold surface Γ = σ, sufficiently small admissible perturbations preserve the induced diagnostic label. A supporting weight-robustness result further shows that qualitative labels persist across a local admissible family of weight choices under corresponding nondegeneracy conditions. The framework serves as a reproducible diagnostic for operator representations alongside Lyapunov, spectral, pseudospectral, and energy-based stability theories. Examples of representative ordinary and partial differential operators illustrate how the descriptors are computed and how they behave under admissible re-expression, while the appendices provide the technical backbone of the paper: formal definitions, reproducibility protocol, extended perturbation arguments, and explicit failure-mode analysis. Additional sensitivity checks regarding encoding, weights, and threshold variation clarify the method’s scope, and explicit failure modes delineate the boundary cases in which the descriptors cease to apply. The main contribution of this study is a formally delimited and reproducible symbolic framework for comparing differential operators under a fixed, declared specification, together with robustness results and worked examples that clarify the method’s scope. Full article

Mesoscale modeling provides an efficient and cost-effective approach for investigating the damage mechanisms of fiber-reinforced concrete. To address the physical distortion in conventional models that arises from neglecting the volumetric effect of steel fibers and to construct a more realistic random mesoscale model of steel fiber-reinforced concrete (SFRC), this study proposes an efficient modeling method based on geometric entity expansion and point–line topology. First, polygonal aggregates with diverse morphologies are generated using a polar-coordinate perturbation scheme combined with a convex-hull correction algorithm. Next, abandoning the traditional zero-thickness line-segment assumption, steel fibers are expanded into rectangular entities via rigid-body kinematics to explicitly represent their excluded volume. Furthermore, a vector-cross-product-based Point–Line Method is developed to replace conventional circumscribed-circle screening, enabling accurate discrimination of interference interactions between fiber–aggregate and fiber–fiber pairs. An automated framework—consisting of skeleton placement, entity generation, topological discrimination, and mesh mapping—is implemented through a Python 3.13.9 scripting interface, allowing efficient batch generation of high-content mesoscale models with aggregate area fractions up to 70%. The proposed model is then used to simulate the failure process of SFRC specimens under uniaxial compression and benchmarked against experimental results. The results show that the developed mesoscale model accurately reproduces the nonlinear mechanical response and the strengthening–toughening effects of SFRC, achieving a relative error of only 0.31% in peak stress and a root mean square error (RMSE) as low as 1.70 MPa over the full stress–strain curve. The simulations not only confirm the pronounced strength gain due to steel fiber incorporation (~19.7%), but also reveal, at the mesoscale, the mechanism by which fiber bridging suppresses damage localization, thereby demonstrating the reliability and practical effectiveness of the proposed modeling approach. Full article

The Taoping paleo-landslide poses a significant risk to local residents and critical infrastructure. However, traditional field surveys and deformation monitoring methods are often inadequate for capturing subtle, localized deformation characteristics—particularly at the head scarp and lateral margins—thereby limiting comprehensive assessments of slope instability. Surface strain data offer direct insights into internal stress redistribution during slope evolution and are essential for interpreting landslide mechanisms and forecasting failure. Given the current limitations in dense and wide-area strain monitoring technologies, this study proposes a novel method for modeling landslide strain fields based on Interferometric Synthetic Aperture Radar (InSAR) phase gradients. Using the phase gradient stacking approach, InSAR-derived phase gradients are transformed into strain-related parameters, enabling estimation of shear strain rates, principal strain rates, and their directional distributions. The application to the Taoping paleo-landslide reveals clear spatial patterns of compressive and tensile strain across the landslide body. Field investigations corroborate the InSAR-derived strain features through corresponding geomorphological evidence observed in both compressional and extensional zones. The proposed method enhances the understanding of landslide deformation behavior, supports evaluation of shear surface continuity and evolution, and offers a robust framework for early warning and risk mitigation in complex landslide-prone areas. Full article

The microstructure of a high-manganese low-activation austenitic steel after aging for 500 and 1000 h at 700 °C was investigated using transmission and scanning electron microscopy. Two structural states were examined: cold rolling (CR) and high-temperature thermomechanical treatment (HTMT). After CR, aging leads to the precipitation of dispersed M₂₃C₆ carbides (M = Cr, W), primarily along grain and deformation twin boundaries. After HTMT, these particles are mainly localized at grain and low-angle boundaries. With increasing aging time, both the size and volume fraction of the particles increase. In both states, the microtwin and substructure are partially retained after aging. Local regions corresponding to the early stages of recrystallization were identified after both treatments. These regions were associated with intense decomposition of the supersaturated solid solution and the coarsening of carbide particles. The mechanical properties were evaluated by tensile testing at 20, 650, and 700 °C. Aging reduced average ductility after both treatments and at all test temperatures, with this trend persisting with increasing aging time. After CR and aging, a significant scatter in elongation to failure was observed, with minimum values of ≈2–3%. This behavior is attributed to the high density of plate-like M₂₃C₆ carbides at grain and microtwin boundaries. Microcrack formation and intercrystalline fracture features were observed, directly linked to the high density of boundary carbides. These effects were less pronounced in the HTMT condition after aging. In this paper, strategies for suppressing carbide precipitation in high-manganese low-activation austenitic steels via chemical composition and thermomechanical processing optimization are discussed. 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

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

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

Background: Contemporary coronary artery bypass grafting (CABG) is often performed in patients with diffuse atherosclerosis, severe calcification, prior percutaneous coronary intervention (PCI), and fragile myocardium, creating intraoperative scenarios that can compromise target selection, anastomotic quality, and completeness of revascularization. We synthesize operative strategies and outcomes across five predefined “complex CABG” scenarios. Methods: A focused literature review was performed targeting intraoperative CABG challenges in adult patients. Two reviewers independently screened titles/abstracts and selected studies describing operative details, technical considerations, or outcomes relevant to (1) intramyocardial/embedded coronaries, (2) severely calcified or diffuse disease requiring reconstruction, (3) small-caliber targets/flow-limited grafting, (4) iatrogenic right ventricular (RV) injury, and (5) failed PCI/stent-related surgical management. Disagreements were resolved through discussion and consensus. Results: Thirty core publications were synthesized across five complex intraoperative CABG scenarios (intramural/embedded coronaries n = 7; calcified/diffuse disease n = 7; small-caliber/flow-limited targets n = 7; iatrogenic RV injury n = 5; failed PCI/stent-related management n = 5). Intramural/embedded targets: reported intramyocardial LAD prevalence ranged from 2.2–13%, and studies emphasized structured localization strategies with a small but real risk of ventricular injury depending on technique. Severely calcified/diffuse disease: reconstructive approaches (endarterectomy, patch angioplasty, long-segment LAD reconstruction) were used to create graftable beds when standard anastomosis was not feasible, with series reporting acceptable early mortality and generally high early-to-midterm patency when paired with planned antithrombotic and imaging follow-up strategies. Small-caliber targets: vessel size alone did not preclude durable grafting when flow was optimized, with evidence supporting flow-augmenting designs (e.g., sequential grafting) and intraoperative flow verification to reduce low-flow failure in limited runoff beds. Iatrogenic RV injury: bailout techniques prioritized rapid hemostasis while preserving LAD/graft patency using buttressed closure concepts designed for constrained exposure and ongoing bleeding risk. Failed PCI/stent-related pathology: long stented segments shifted operative planning from distal target selection to target reconstruction (stentectomy/endarterectomy with long-segment LAD reconstruction), with angiographic follow-up cohorts demonstrating feasible revascularization but variable patency by territory and lesion extent. Conclusions: Complex CABG is best approached as structured, anatomy-driven problem-solving: deliberate target localization, creation of a graftable bed when needed, flow-augmenting graft design, and predefined bailout options. Standardized comparative studies are needed to define optimal strategies across these common clinically important scenarios. Full article

Aileron fault diagnosis in fixed-wing unmanned aerial vehicles (UAVs) faces significant challenges due to strong noise, multi-modal coupling, and limited fault samples. This paper presents a hybrid fault diagnosis framework that integrates variational mode decomposition (VMD) with a cross-attention-based feature fusion mechanism. First, residual signals are generated from UAV kinematic models and decomposed into multi-scale intrinsic mode functions (IMFs) using VMD to extract multiscale frequency-localized features. An integrated framework is then constructed, where Transformer encoders capture the global features and bidirectional long short-term memory (BiLSTM) networks extract local temporal dynamics. To effectively combine these complementary features, a cross-attention fusion module is designed to focus on the discriminative time-frequency features. Furthermore, a hybrid pooling strategy integrating max pooling and attention pooling is introduced to enhance classification robustness. Experiments on the AirLab failure and anomaly (ALFA) dataset demonstrate that the proposed method achieves 95.12% accuracy with improved fault separability, outperforming VMD + BiLSTM (87.66%), VMD + Transformer (86.89%), Transformer + BiLSTM (84.83%), Transformer (72.24%), CNN + LSTM (94.05%), and HDMTL (94.86%). Full article