Search Results (48,197)

Rubber material with high elasticity and viscoelasticity has become the most widely used universal material, and the study of the aging failure mechanism of rubber has been meaningful research in the polymer materials field. Cis-polyisoprene was employed to analyze the mechanism of oxidative degradation under artificial UV irradiation, and the GQDs/g-C₃N₄ photocatalysis with a 2D layered structure prepared by the method of microwave-assisted polymerization enabled to accelerate the degradation procedure. The results showed that the oxidation of cis-polyisoprene occurred during the irradiation for 3 days and the structure of cis-polyisoprene changed. The α-H of the double bond was attacked by oxygen to form hydroperoxide. Then, aldehydes and ketones generated as the addition reaction of double bonds occurred. The content of the hydrogen of C=C reduced, and the oxidative degradation was dominant at the initial aging stage. The crosslinking reaction was dominant at the final aging stage and the average molecular weight decreased from 15.49 × 10⁴ to 8.78 × 10⁴. The GQDs could promote the charge transfer and the photodegradation efficiency and inhibit the electron–hole recombination. The light capture ability of GQDs was improved after compositing with g-C₃N₄. The free radicals ·O₂²⁻ generated after adding GQDs/g-C₃N₄ nanoparticles, and the molecular weight of cis-polyisoprene decreased to 5.79 × 10⁴, with the photocatalytic efficiency increasing by 20%. This work provided academic bases and reference values for the application of photocatalysts in the field of natural rubber degradation and rubber wastewater treatment. Full article

Novel coronavirus (SARS-CoV-2) is highly infectious and pathogenic. Novel coronavirus infection can not only cause respiratory diseases but also lead to multiple organ damage through direct or indirect mechanisms, in which the liver is one of the most frequently affected organs. It has been reported that 15–65% of coronavirus disease 2019 (COVID-19) patients experience liver dysfunction, mainly manifested as mild to moderate elevation of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Severe patients may progress to liver failure, develop hepatic encephalopathy, or have poor coagulation function. The mechanisms underlying this type of liver injury are complex. Pathways—including direct viral infection (via ACE2 receptors), immune-mediated responses (e.g., cytokine storm), ischemic/hypoxic liver damage, thrombosis, oxidative stress, neutrophil extracellular trap formation (NETosis), and the gut–liver axis—remain largely speculative and lack robust clinical causal evidence. In contrast, drug-induced liver injury (DILI) has been established as a well-defined causative factor using the Roussel Uclaf Causality Assessment Method (RUCAM). Treatment should simultaneously consider antiviral therapy and liver protection therapy. This article systematically reviewed the mechanism, clinical diagnosis, treatment, and management strategies of COVID-19-related liver injury and discussed the limitations of current research and the future directions, hoping to provide help for the diagnosis and treatment of such patients. Full article

In underground mine roadways, enlarged cross-sections have led to escalating surrounding rock stress, resulting in frequent support failures, elevated accident risk, and increased maintenance costs. However, the potential of fiber reinforcement to improve shotcrete under these high-stress conditions remains under-investigated. To address these issues, this study developed a novel fiber-reinforced cement-based composite using field construction-grade washed sand. The effects of binder-to-material ratios, fiber types (polyvinyl alcohol (PVA), polypropylene (PP), and basalt (BF)), and fiber dosages (1%, 2%, and 3%) were systematically investigated under uniaxial tension, uniaxial compression, and variable-angle shear. Based on the experimental results, an optimal mix formulation was determined via orthogonal experimental design to meet mining operational requirements. The findings demonstrate that fiber incorporation significantly enhances mechanical performance. Notably, PP fiber reinforcement increased the tensile strength by up to 675%, while BF fibers improved compressive strength by up to 198.5%, relative to unreinforced shotcrete. This study provides a theoretical foundation for optimizing fiber-reinforced shotcrete mix designs for mining and offers technical insights for field applications. Full article

In predictive maintenance frameworks, risk curves are used as interpretable, real-time indicators of equipment degradation. However, existing approaches generally assume a monotonically increasing trend and neglect the corrective effect of maintenance, resulting in unrealistic or overly conservative risk estimations. This paper addresses this limitation by introducing a novel method that dynamically corrects risk curves through a quantitative measure of maintenance effectiveness. The method adjusts the evolution of risk to reflect the actual impact of preventive and corrective interventions, providing a more realistic and traceable representation of asset condition. The approach is validated with case studies on critical feedwater pumps in a combined-cycle power plant. First, individual maintenance actions are analyzed for a single failure mode to assess their direct effectiveness. Second, the cross-mode impact of a corrective intervention is evaluated, revealing both direct and indirect effects. Third, corrected risk curves are compared across two redundant pumps to benchmark maintenance performance, showing similar behavior until 2023, after which one unit accumulated uncontrolled risk while the other remained stable near zero, reflected in their overall performance indicators (0.67 vs. 0.88). These findings demonstrate that maintenance-corrected risk curves enhance diagnostic accuracy, enable benchmarking between comparable assets, and provide a missing piece for the development of realistic, risk-informed predictive maintenance strategies. Full article

While methods like cyclic triaxial testing and p-y model updating theory exist in geotechnical and offshore wind engineering, they have not been systematically applied to solve the specific deformation problems of offshore PV piles. This study investigates a specific offshore photovoltaic (PV) project in Qinhuangdao City, Hebei Province. Initially, field tests of horizontal static load on steel pipe pile foundations were conducted. A finite element model (FEM) of single piles was subsequently developed and validated. Further analysis examined the failure modes, initial stiffness, and ultimate resistance of offshore PV single piles in sandy soil foundations under varying pile diameters and embedment depths. The hyperbolic p-y curve model was modified by incorporating pile diameter size effects and embedment depth considerations. Key findings reveal the following: (1) The predominant failure mechanism of fixed offshore PV monopiles manifests as wedge-shaped failure in shallow soil layers. (2) Conventional API specifications and standard hyperbolic models demonstrate significant deviations in predicting p-y (horizontal soil resistance-pile displacement) curves, whereas the modified hyperbolic model shows good agreement with field measurements and numerical simulations. This research provides critical data support and methodological references for calculating the horizontal bearing capacity of offshore PV steel pipe pile foundations. Full article

Background: Homocysteine (Hcy) plays a pivotal role in human reproduction, influencing gamete quality, embryo development, implantation, and pregnancy outcomes. It is central to folate and methionine metabolism and supports methylation-dependent epigenetic processes. Hyperhomocysteinemia (HHcy) exerts diverse biological effects and is associated with reproductive impairments in both sexes, affecting both spontaneous fertility and the outcome of assisted reproduction, including In Vitro Fertilization (IVF). Although the mechanisms of HHcy toxicity in reproduction are not fully understood, significant progress has been made in elucidating its effects. The emerging picture is complex, as Hcy and its metabolites impact biomolecules and cellular processes in a tissue- and sex-specific manner. Results: In men, HHcy compromises sperm deoxyribonucleic acid (DNA) integrity, methylation, and testicular microcirculation, reducing fertility potential. In women, HHcy disrupts follicular growth, oocyte competence, embryo quality, and endometrial receptivity, increasing the risk of implantation failure, miscarriage, and pregnancy complications. In assisted reproduction, HHcy and 5,10-methylenetetrahydrofolate reductase (MTHFR) variants may lower oocyte yield and embryo quality, although folate and B-vitamin supplementation can mitigate these effects. Conclusions: These effects largely reflect oxidative, inflammatory, vascular and epigenetic mechanisms, highlighting Hcy as a modifiable factor for improving natural fertility, optimizing IVF outcomes, and supporting healthy offspring development. Full article

Predictive maintenance (PdM) of biomedical equipment is increasingly recognized as a strategic lever to enhance reliability and ensure continuity of care. Yet, in resource-limited hospitals, implementation is hindered by fragmented data sources, non-standardized codification, and weak interoperability. Few studies have demonstrated the feasibility of structuring PdM data from real hospital interventions in middle-income countries. This work presents a prototype data structuring pipeline applied to six public hospitals in the Casablanca–Settat region of Morocco. The pipeline consolidates 6816 validated maintenance interventions from 780 devices across 30 departments and integrates normalized reliability indicators (Failure Rate, MTBF, MTTR corrected with IQR, and Downtime Hours). It ensures semantic harmonization, auditability, and reproducibility, resulting in a structured and interoperable dataset that constitutes a regional first in the Moroccan hospital context. To illustrate predictive potential, a proof-of-concept Random Forest model was evaluated. It achieved AUROC = 0.65 on the full imbalanced dataset and AUROC = 0.82 on a balanced 2000-intervention subset, confirming the dataset’s discriminative value while reflecting real-world challenges. This work bridges the gap between conceptual PdM frameworks and operational hospital realities, and establishes a replicable foundation for AI-driven predictive maintenance in low-resource healthcare environments. Full article

The synergistic preparation of geopolymer from lithium slag, fly ash, and slag for underground construction can facilitate the extensive recycling of lithium slag. The effects of different activator moduli and water–binder ratios on the mechanical properties and damage mechanisms of the lithium-slag-based geopolymer were investigated by uniaxial compression tests and acoustic emission (AE) monitoring. The results show that, based on a comprehensive evaluation of peak stress, crack closure stress, plastic deformation stress, and elastic modulus, the optimal activator modulus is determined to be 1.0, and the optimal water-to-binder ratio is 0.42. At low modulus values (0.8 and 1.0) and low water–binder ratio (0.42), the AE events exhibit a steady pattern, indicating slow crack initiation and propagation within the geopolymer; with the increasing activator modulus and water-to-binder ratios, the frequency of AE events increases significantly, indicating more-frequent crack propagation and stress mutation within the geopolymer. Similarly, when the modulus is 0.8 or 1.0 and the water–binder ratio is 0.42, the sample presents a macroscopic tensile failure mode; as the modulus and water–binder ratio increase, the sample presents a tensile–shear composite failure mode. The energy evolution laws of geopolymer specimens with different activator moduli and water-to-binder ratios were analyzed, and a damage constitutive model was established. The results indicate that, with optimized mix proportions, the material can be used as a supporting material for underground spaces. Full article

Predictive maintenance of semi-autogenous grinding (SAG) mills reduces unplanned downtime and improves throughput. This study develops a data-driven prognostic model for production SAG mill using four years of operational data (temperature, voltage, current, motor speed, etc.). We follow a MATLAB (R2025a)-based prognostics and health management (PHM) workflow: data cleaning and synchronization; feature engineering in time and frequency domains (statistical moments, spectral power, bandwidth); normalization and clustering to separate operating regimes; and labeling of run-to-failure sequences for a recurring electrical failure mode. A health indicator is derived by scoring candidate features for monotonicity, trendability, and prognosability and fusing them into a condition index. Using MATLAB Predictive Maintenance Toolbox, we train and validate multiple Remaining Useful Life (RUL) learners including similarity-based, regression, and survival models on run-to-failure histories, selecting the best via cross-validated error and prediction stability. On held-out sets, the selected model forecasts RUL consistent with observed failure dates, providing actionable lead time for maintenance planning. The results highlight the practicality of deploying a PHM pipeline for SAG mills using existing plant data and commercial toolchains. Full article

Food insecurity, financial loss, and a decline in agricultural output are among the significant challenges to the global food chain caused by extreme climatic events, high variability and change, rapid urbanization, and land degradation. Therefore, it is essential to explore alternative, sustainable agricultural practices to meet the growing population’s food needs. Sustainable agriculture is foundational to farm management, rural development, and water conservation. This includes sustainable practices such as crop rotation, intercropping, and planting crops with varying rooting depths to maximize soil moisture absorption, as well as mulching to improve nutrient recycling and enhance productivity in smallholder cropping systems. The adoption of sustainable agricultural practices has become a priority for smallholder farmers, policymakers, extension agents, and agricultural experts to improve agricultural productivity, contribute to food security, and generate income. However, adoption rates have been slow, especially in Southern Africa, due to a lack of access to technology, financial constraints, limited information, and limited knowledge. This review was conducted using a comprehensive literature search on the adoption of sustainable agricultural practices by legume smallholders, examining various factors that contribute to the failure of legume smallholder farmers to adopt new agricultural practices. The timeframe of the reviewed literature was from 2010 to 2024. The results showed that smallholder farmers face numerous challenges, including limited access to technology, inadequate knowledge, and insufficient financial resources. Research conducted by the Water Research Commission (WRC) indicates that commercial farmers have access to technology, and this group of farmers possesses more substantial financial resources compared to smallholder farmers. In the adoption of sustainable agricultural practices. It is essential to strengthen the linkage between researchers, agricultural extension, and legume smallholder farmers to promote sustainable agricultural practices (SAPs). Smallholder farmers must be informed about such interventions and sustainable agricultural practices to improve rural livelihoods and enhance resilience, adaptation, and responsiveness. Full article

Tailings storage facilities (TSFs) have experienced numerous global failures, many linked to active deposition on tailings beaches. Understanding these processes is vital for effective management. As deposition alters surface elevation, developing an explainable model to predict the changes can enhance insight into deposition dynamics and support proactive TSF management. This study applies deep learning (DL) to predict surface elevation changes in tailings storage facilities (TSFs) from high-resolution digital elevation models (DEMs) generated from UAV photogrammetry. Three DL architectures, including multilayer perceptron (MLP), fully convolutional network (FCN), and residual network (ResNet), were evaluated across spatial patch sizes of 64 × 64, 128 × 128, and 256 × 256 pixels. The results show that incorporating broader spatial contexts improves predictive accuracy, with ResNet achieving an $R2$ of 0.886 at the 256 × 256 scale, explaining nearly 89% of the variance in observed deposition patterns. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing that spatial coordinates and curvature exert the strongest influence, linking deposition patterns to discharge distance and microtopographic variability. By prioritizing predictive performance while providing mechanistic insight, this framework offers a practical and quantitative tool for reliable TSF monitoring and management. Full article

In the operational management of e-commerce platforms, online reviews and user feedback render the issue of anticipated product failure more transparent. The anticipated product failures are often negatively correlated with product quality, while related service guarantees can help customers avoid utility losses caused by such failures. Additionally, the network effect characteristics of products significantly influence customer purchasing behavior and firms’ pricing strategies. This paper employs the multinomial logit (MNL) model to establish an optimization framework for product line and value-added services pricing that accounts for the anticipated failure and associated services. It analyses three scenarios: developing a single product, homogeneous products, and heterogeneous products, deriving optimal price, market share, and maximum profit. Theoretical analysis focuses on how the optimal solutions for single and homogeneous products vary with changes in anticipated failure-induced utility losses, negative network effects, product quality, and service quality. In the numerical experiment, the study explores the effects of variations in utility losses from anticipated failure, network effects, and product and service quality on optimal solutions for heterogeneous products. Finally, the importance of incorporating anticipated failure-induced utility losses into product line and service pricing decisions is emphasized. Full article

The two-decade-long occupation of East Timor by Indonesia has long been the focus of debate within genocide studies, with scholars on one side arguing for its recognition as “genocide” and, on the other, insisting on its exclusion from acknowledgment as such due to its inability to satisfy certain legal criteria. Our article revisits this conflict and the surrounding debate in order to stake out a larger claim about the logic of the legal form in contemporary global order. Following a growing critical scholarship in genocide studies, we argue that the concept of genocide itself entrenches harmful understandings of global order and contributes to structures which encourage the mass violence it nominally aims to identify and prevent. Far from being singular, it further represents fundamental limitations regarding the legal form as a mechanism of justice and resistance. To support this claim, we use the failure of various justice and reconciliation mechanisms to prosecute genocide in East Timor to illustrate the ways in which a legal system predicated on imperialism shapes both the behavior of a newly minted domestic elite and the larger project of state sovereignty itself. Full article

Electrical cable insulation, mainly composed of polymeric materials, progressively deteriorates under thermal, electrical, mechanical, and environmental stress factors. This degradation reduces dielectric strength, thermal stability, and mechanical integrity, thereby increasing susceptibility to failure modes such as partial discharges, arcing, and surface tracking—recognized precursors of fire ignition. This review consolidates current knowledge on the degradation pathways of cable insulation and their direct link to fire hazards. Emphasis is placed on mechanisms including thermal-oxidative aging, electrical treeing, surface tracking, and thermal conductivity decline, as well as the complex interactions introduced by flame-retardant additives. A bibliometric analysis of 217 publications reveals strong clustering around material degradation phenomena, while underlining underexplored areas such as ignition mechanisms, diagnostic monitoring, and system-level fire modeling. Comparative experimental findings further demonstrate how insulation aging modifies ignition thresholds, heat release rates, and smoke toxicity. By integrating perspectives from materials science, electrical engineering, and fire dynamics, this review establishes the nexus between aging mechanisms and fire hazards. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article