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The aim of this study is to explore the association between the aspartate amino transferase (AST)/alanine aminotransferase (ALT) ratio and all-cause mortality (ACM) in stable coronary artery disease (CAD) patients treated by percutaneous coronary intervention (PCI). The study is a secondary analysis of a retrospective cohort study involving 203 stable CAD patients. Patients were divided into two groups, based on the optimal AST/ALT ratio threshold calculated by the ROC curve (low group: AST/ALT ratio < 1.40; high group: AST/ALT ratio ≥ 1.40). Results were compared using hazard ratio (HR) and a 95% confidence interval (CI). ACM occurred in 18 patients after an average follow-up time of 749 (435–1122) days. Among them, ACM occurred in 6 patients in the low group and 12 patients in the high group, with significant differences between the groups (4.65% versus 16.22%, P value = 0.005). In the Kaplan–Meier analysis, an elevated AST/ALT ratio was associated with increased ACM in stable ACD patients (HR 3.78, 95% CI 1.44–9.93, P value < 0.001). An elevated AST/ALT ratio was still found to be an independent prognostic factor for ACM (HR 2.93, 95% CI 1.08–7.91, P value = 0.034) after adjusting for potential confounders. Therefore, an elevated AST/ALT ratio is an independent prognostic factor for ACM in stable ACD patients.

The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and further reduce the operation and maintenance cost at wind farms. However, in reality, wind turbines are not allowed to operate with faults, so few fault samples could be obtained. With a small amount of training data, traditional fault diagnosis models that need huge samples under a deep learning framework are difficult to maintain with high accuracy and effectiveness. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples in model training. In view of model-agnostic meta-learning (MAML), this paper proposes a model for few-shot fault diagnosis of the wind turbines drivetrain, which is named model-agnostic meta-baseline (MAMB). The training data is input to the base classification model for pre-training, then, some data is randomly selected from the training set to form multiple meta-learning tasks that are utilized to train the MAML to finally fine-tune the later layers of the model at a smaller learning rate. The proposed model was analyzed by the small samples of the bearing data from Case Western Reserve University (CWRU) data, the generator bearings, and gearboxes vibration data in wind turbines under randomly changing operating conditions. The results verified that the proposed method was superior in one-shot, five-shot, and ten-shot tasks of wind turbines.

The deep ocean, Earth’s untouched expanse, presents immense challenges for exploration due to its extreme pressure, temperature, and darkness. Unlike traditional marine robots that require specialized metallic vessels for protection, deep-sea species thrive without such cumbersome pressure-resistant designs. Their pressure-adaptive forms, unique propulsion methods, and advanced senses have inspired innovation in designing lightweight, compact soft machines. This perspective addresses challenges, recent strides, and design strategies for bioinspired deep-sea soft robots. Drawing from abyssal life, it explores the actuation, sensing, power, and pressure resilience of multifunctional deep-sea soft robots, offering game-changing solutions for profound exploration and operation in harsh conditions. High pressure and low temperature are the greatest challenges faced by scientists to explore deep oceans, which remain largely unknow to us today. Li et al. review these challenges and give insight into designing soft robots, inspired by deep-sea creatures, that enable resilient operations in harsh conditions.

Gas saturation is a critical parameter for the selection and development of coalbed methane, as well as a key indicator reflecting the challenges in coalbed methane development and productivity evaluation of coalbed methane wells. As one of the significant factors influencing gas saturation, gas content plays a vital role in comprehensively investigating coal pore properties to fully comprehend the process and conditions of methane adsorption and desorption. In this study, 3 # and 15 # coals from Qinshui Basin, China was selected as research subjects. The experimental evaluation encompassed an examination of composition, pore characteristics, permeability characteristics of coal, rock mechanical parameters while discussing the impact of temperature and pressure on coal's adsorption and desorption capacity. The mineral characteristics analysis revealed that vitrinite is the main component with varying percentages and reflectance values in both 3 # and 15 # coal seams. The gas content and methane concentration in the 15 # coal seam are higher than those in the 3 # coal seam. The relationship between gas content within a coal seam and burial depth depends on achieving a balance between positive pressure effects caused by overburden stress exertion on gases trapped within pores under high pressures during burial history versus negative temperature effects due to cooling during geological processes over time. Predictions were made regarding deep-coal gas content which holds significant implications for accurately understanding variations in desorption behavior along with optimizing fracturing engineering.

Remote sensing images present formidable classification challenges due to their complex spatial organization, high inter-class similarity, and significant intra-class variability. To address the balance between computational efficiency and feature extraction capability in existing methods, this paper innovatively proposes a lightweight convolutional network, STConvNeXt. In its architectural design, the model incorporates a split-based mobile convolution module with a hierarchical tree structure. It employs parameterized depthwise separable convolutions to reduce computational complexity and constructs a multi-level feature tree to facilitate cross-scale feature fusion. For feature enhancement, a fast pyramid pooling module replaces the traditional spatial pyramid structure, effectively reducing the number of parameters while preserving large-scale contextual awareness. In terms of training strategy, a dynamic threshold loss function is introduced, utilizing a learnable inter-class margin to improve the model’s ability to distinguish difficult-to-classify samples. Systematic experiments on the UCMerced, AID, and NWPU-RESISC45 benchmark datasets validate the effectiveness of the proposed approach: compared with the ConvNeXt baseline, STConvNeXt reduces both parameter count (by 56.49%) and FLOPs (by 49.89%), while improving classification accuracy by 1.2–2.7%. Furthermore, compared with the current state-of-the-art remote sensing scene classification models, our method still exhibits significant advantages. Ablation studies further confirm the effectiveness of each module design, particularly demonstrating that the model maintains excellent classification accuracy despite a substantial reduction in parameters.

There were two similar series of burst waiting time experiments carried out on Godiva-II and Caliban respectively and analyzed for neutron initiation study, unfortunately both the strength of spontaneous fission neutron source given in the published papers are not consistent with the spontaneous fission data of U-235 and U-238. In this paper, the discrepancies of spontaneous fission data of U-235 and U-238 from different references are reviewed and clarified and the correct strength of spontaneous fission neutron source for Godiva-II and Caliban are calculated respectively, then the multiplication equivalence method of arbitrary source is introduced and the equivalent fundamental-mode(EFM) co-efficiencies of spontaneous fission neutron source for Godiva-II and Caliban are calculated with MC software, lastly the strength of neutron source for these two HEU metal burst reactor in EFM are deduced. The EFM neutron strength of this two burst reactors are much less than proposed values used for burst waiting investigation in published papers respectively, and with the new data, the inconsistency of burst waiting time to the published results is presented and discussed.

Benthic oxygen flux with complex spatiotemporal variations is essential for the global budget of carbon dioxide and the regional security of water quality and ecology, but its dominant driver under different circumstances has yet to be identified. In this study, a parametric scheme of oxygen flux was proposed and validated with aquatic eddy correlation measurements and then coupled with a diagenesis model and a water environment model. The coupled model was applied to a river‐reservoir with significant environmental gradients in hydrodynamics, diagenesis, and hypoxia, which are three factors that competitively drive the variation in benthic oxygen flux. The results indicate that hydrodynamics dominate the flux in the riverine and thalweg areas, diagenesis is the dominant driver of the lacustrine and bank areas, and hypoxia shows dominance only in the hypolimnetic anoxic area. In general, diagenesis is the dominant driver of oxygen flux in river‐reservoirs, followed by hydrodynamics, both of which are more prominent than hypoxia. If the operated reservoir experiences a wet year, the dominance of hydrodynamics tends to increase, while that of diagenesis and hypoxia decreases. The three divers exhibit similar but more stable dominance in riverine systems than in reservoirs, while diagenesis becomes the exclusive driver of oxygen fluxes in lacustrine systems. Plain Language Summary The consumption of dissolved oxygen in sediments regulates the types of substances released from them. When the oxygen supply to the sediments is insufficient, they release substances with a stronger greenhouse effect (such as methane) and a more significant threat to water quality (such as heavy metals). Flow conditions, sediment features, and water properties collectively drive the variation in sediment oxygen consumption, but their relative importance within complex aquatic environments remains to be investigated. This study quantifies the dominance of these three drivers on sediment oxygen consumption in a river‐reservoir system and then establishes their relationship with spatiotemporal variations in the aquatic environment. Sediment features are the primary drivers of sediment oxygen consumption in the studied river‐reservoir, followed by flow conditions and water properties represented by oxygen concentrations. The results also suggest that the relative dominance of these three drivers in river‐type systems is similar to that in reservoirs, but the dominance of sediment features could further increase in lake‐type systems. This study highlights the variable role of factors driving sediment oxygen consumption, which is critical for quantifying the release of deoxygenation products in complex aquatic ecosystems. Key Points A practical parametric scheme for determining benthic oxygen fluxes was proposed and validated River‐reservoirs have large environmental gradients in hydrodynamics, sedimentation, and stratification Diagenesis is the primary driver of benthic oxygen fluxes in river‐reservoirs, followed by hydrodynamics and hypoxia

The role of Long noncoding RNA OIP5-AS1 in myocardial ischemia/reperfusion (I/R) injury-induced apoptosis remains to be fully elucidated. The present study was conducted with the objective of investigating the function of OIP5-AS1 in myocardial I/R injury and exploring its potential mechanisms. In order to simulate the conditions of I/R, H9c2 cells were cultured in hypoxic/reoxygenated environments. Induction of I/R in Sprague-Dawley rats was achieved by ligating the left anterior descending coronary artery for 30 minutes followed by 180 minutes of reperfusion. OIP5-AS1 expression levels were assessed, and the degree of apoptosis was evaluated by TUNEL staining. Bioinformatic analysis was conducted to predict the interaction between microRNA-145-5p (miR-145-5p) and OIP5-AS1, and the expression levels of miR-145-5p and ROCK1 were determined. Elevated levels of OIP5-AS1 were observed in H/R-treated H9c2 cells and in rat I/R models. Elevated OIP5-AS1 expression was associated with an increased incidence of apoptosis. The silencing of OIP5-AS1 in I/R conditions resulted in a significant suppression of cell apoptosis, reduced cleavage of caspase-3, decreased Bax levels, and increased Bcl-2 levels. Bioinformatic analysis predicted binding sites between miR-145-5p and OIP5-AS1. Furthermore, depletion of OIP5-AS1 in I/R conditions resulted in a substantial increase in miR-145-5p expression and a decrease in ROCK1 expression. The suppression of miR-145-5p reversed the effects of OIP5-AS1 depletion in I/R conditions. Downregulation of OIP5-AS1 may prevent apoptosis in myocardial I/R injury by modulating the miR-145-5p/ROCK1 axis.