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Static corrosion tests in LBE under oxygen-saturated and –depleted conditions at 500 °C for 1000 h were conducted to investigate the corrosion-induced diffusion layers on T91 and SS316L steels subject to dissolution and oxidation corrosion. Following nano-indentation experiments examined the variations of local mechanical properties within the corrosion layers. Under the present conditions, T91 steel showed better resistance to dissolution corrosion while higher susceptibility to oxidation corrosion against SS316L. For both steels, nano-indentation results showed a gradual decrease in mechanical properties from steel matrix to the corrosion side after dissolution corrosion. When subject to oxidation corrosion, the oxide scale formed on T91 steel showed lower Er-modulus and enhanced hardness in comparison with those of the steel matrix. An obvious decrease in both modulus and hardness was found at the interface between steel matrix and oxide scale.

Abstract Background Cognitive frailty (CF) is currently a significant issue, and most of the associated factors discovered in current studies are not modifiable. Therefore, it is crucial to identify modifiable risk factors that can be targeted for interventions in patients with chronic heart failure (CHF). This study aimed to investigate the prevalence and modifiable risk factors of CF in CHF patients in China. Methods In this cross-sectional study, we sequentially enrolled patients diagnosed with CHF. CF served as the dependent variable, assessed through the Montreal Cognitive Assessment (MoCA) Scale and the FRAIL Scale. The independent variable questionnaire encompassed various components, including general demographic information, the Social Support Rating Scale (SSRS), the Simplified Nutrition Appetite Questionnaire (SNAQ), the Hamilton Depression Scale (HAMD), the Hamilton Anxiety Scale (HAMA), and the Minnesota Living with Heart Failure Questionnaire (MLHFQ). Logistic regression analysis was employed to identify independent factors contributing to CF. Results A total of 271 patients with CHF were included in the study. The overall prevalence of CF was found to be 49.4%, with 28.8% of patients exhibiting potentially reversible cognitive frailty and 20.7% showing reversible cognitive frailty. Among middle-young CHF patients, 10.7% had reversible cognitive frailty and 6.4% had potentially reversible cognitive frailty, with a prevalence of CF at 17.1%. Logistic regression analysis revealed that body mass index (OR = 0.826, 95%CI = 0.726–0.938), blood pressure level (OR = 2.323, 95%CI = 1.105–4.882), nutrition status (OR = 0.820, 95%CI = 0.671–0.979), and social support (OR = 0.745, 95%CI = 0.659–0.842) were independent factors associated with CF (p

A coupled thermomechanical non-ordinary state-based peridynamics (NOSB-PD) model is developed to simulate the dynamic response arising from temperature and to predict the crack propagation with thermal shocks in brittle and ductile solids. A unified multiaxial constitutive model with damage growth is proposed to simultaneously describe the ductile and brittle fracture mechanisms. The main idea is the use of Lemaitre’s model to describe ductile damage behavior and the use of tensile strength instead of yield stress in Lemaitre’s model to describe brittle damage behavior. A damage-related fracture criterion is presented in the PD framework to predict crack propagation, which avoids numerical oscillations when using the traditional bond stretch criterion. To capture the dynamic plastic response induced by thermal shocks, the time and stress integration are achieved by an alternating solving strategy and implicit return-mapping algorithm. Several numerical examples are presented to show the performance of the proposed model. Firstly, a thermomechanical problem simulation based on both the proposed model and the FEM illustrate the accuracy of the proposed model in studying the thermal deformation. Moreover, a benchmark brittle fracture example of the Kalthoff–Winkler impact test is simulated, and the crack path and angle are similar to the experimental observations. In addition, the simulation of ductile fracture under different loads illustrates the effect of temperature on crack propagation. Finally, the simulation of the 2D quenching test shows the ability of the proposed model in predicting crack propagation under thermal shocks.

This article proposes a direction-of-arrival (DOA) estimation algorithm for a random sparse linear array based on a novel graph neural network (GNN). Unlike convolutional layers and fully connected layers, which do not interact well with information between different antennas, the GNN model can adapt to the goniometry problem of non-uniform random sparse linear arrays without any prior information by applying neighbor nodes’ aggregation and update operations. This helps the model in learning signal features under complex environmental conditions. We train the model in an end-to-end way to reduce the complexity of the network. Experiments are conducted on the uniform and sparse linear arrays for various signal-to-noise ratio (SNR) and numbers of snapshots for comparison. We prove that the GNN model has superior angle estimation performance on arrays with large sparsity that cannot be used by traditional algorithms and surpasses existing deep learning models based on convolutional or fully connected structures. The proposed algorithm shows excellent DOA estimation performance under the complex conditions of limited snapshots, low signal-to-noise ratio, and large array sparsity as well. In addition, the algorithm has a low time calculation cost and is suitable for scenarios that require low latency.

To investigate the influence of bedrock fragmentation by intensive tillage on the hydrological characteristics and soil erosion processes on slopes, two experimental treatments (soil–bedrock mixtures, WB, and pure soil, CK) in steel tanks were subjected to simulated rainfall under five rainfall patterns (constant, increasing, decreasing, decreasing–increasing, and increasing–decreasing) with the same total rainfall of 90 mm. For each rainfall event, runoff and sediment concentration were sampled at regular intervals. The flow velocity (v), effective/critical shear stress (τ/τc), Darcy–Weisbach resistance coefficient (f), unit stream power (p), and soil erodibility factor (Kr) were calculated to analyze the differences in hydrodynamic characteristics between the WB and CK. Our experimental findings show that significant differences in runoff volume and sediment yield were observed among different rainfall patterns and stages. Bedrock fragmenting significantly promoted runoff and sediment production under different rainfall patterns, with runoff volume and sediment yield increasing by averages of 59.29% and 71.62%, respectively. An increasing trend in average contribution rate of bedrock to runoff volume and sediment yield was observed across three distinct intensities: 6.37% and 4.61% for 30 mm h−1, 12.53% and 7.53% for 90 mm h−1, as well as 14.79% and 36.98% for 150 mm h−1, respectively. The v and p values under various rainfall patterns exhibited an increasing trend from the upper to the bottom slope positions, whereas the f and τ values showed an opposite trend, regardless of the WB and CK. Compared with the CK, the v, f, and p values for the WB increased by 23.34% to 48.94%, 1.59% to 53.16%, and 3.86% to 27.86%, respectively, whereas the τ value decreased by 1.52% to 22.19% for varying-intensity rainfall patterns. Among the variable rainfall patterns, the WB significantly increased sediment yield and also had a promoting effect on runoff generation. However, the WB displayed better erosion resistance compared to the CK under constant rainfall patterns. Therefore, varied-intensity patterns had a profound impact on bedrock-induced runoff and sediment transport processes.

This investigation mainly focused on revealing the importance and function mechanism of intensive mixer on sintering performance with 100% fine-grained iron ore materials (the proportion of −0.074 mm exceeding 80%) through both lab-scale tests and statistic analyses. Results indicated that compared with conventional drum mixing, the intensive mixing reduced the proper moisture consumption considerably from 9.25% to 8.75%, while both the granulation effect and sinter product quality achieved a remarkable improvement. Intensive mixer realized the uniform dispersion of moisture, Ca(OH)2 and coarse nuclear particles (>0.5 mm), contributing to improving the utilization of moisture, and benefiting the granulation of fine iron ores through capillary force; the uniform dispersion of Ca(OH)2 benefited the growth of granules, and also improved the mechanical strength due to the binding effect of Ca(OH)2; the uniform dispersion of nuclear particles benefited forming granules mainly within the desirable size range of 3–8 mm. All these improvements optimized the bed permeability and mineralization process of raw materials during sintering process, which were the key factors to the improved sinter performance. The result findings are useful for guiding the application of intensive mixers in practical plants using high-proportion fine-grained iron ore materials.

Magnetorheological elastomer (MRE) is a kind of smart material, whose mechanical property can be controlled by the external magnetic field quickly and reversibly. The damping property of MRE is one of the most concerned properties when designing MRE based devices. In this work, the influence of gamma radiation on the damping property of MRE was investigated. Six different exposures of gamma radiation were applied to the MRE samples. The highest gamma radiation dose was up to 1 × 105 Gy(Si), which can cover most of the engineering application scenarios. The influence of gamma radiation on the damping-strain relation and the damping-magnetic-field relation were studied. The probable mechanisms were discussed in detail. It is found that the gamma radiation does not affect the variation trend of loss factor of MRE with increasing strain amplitude or magnetic flux density. But it affects the variation trend of the maximum change of strain-induced or magnetic-field-induced loss factor of MRE. Besides, with constant strain and constant magnetic flux density, the loss factor of MRE shows w-shape variation trend with increasing gamma radiation dose. It is considered to be resulted from the combined action of the intrinsic damping and the interfacial friction damping of MRE.

This review paper provides insights the into current developments in additive manufacturing (AM) techniques. The comprehensive presentations about AM methods, material properties (i.e., irradiation damage, as-built defects, residual stresses and fatigue fracture), experiments, numerical simulations and standards are discussed as well as their advantages and shortages for the application in the field of nuclear reactor. Meanwhile, some recommendations that need to be focused on are presented to advance the development and application of AM techniques in nuclear reactors. The knowledge included in this paper can serve as a baseline to tailor the limitations, utilize the superiorities and promote the wide feasibilities of the AM techniques for wide application in the field of nuclear reactors.

The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

The environmental fatigue correction factor (Fen) is mainly used to analyze the influence of the coolant environment on the fatigue life of primary metal materials. Because the calculation of the transformed strain rate is related to the stress history of the component structure, how to determine the strain rate is the most critical step in calculating the Fen. The approaches of the detailed method were given by the Electric Power Research Institute (EPRI) guidelines and RCC-M-2017 Edition Section VI- RPP No. 3 separately, so a gap analysis was performed between the two methods. Furthermore, another average method was also proposed to determine the average strain rate and strain range. Based on the analysis benchmark provided in the EPRI guideline, a simple case study was performed to account for the effect on the fatigue life in applications with different strain rate approaches and different Fen expressions. Finally, two industry case studies were also completed, including on materials of low alloy steel, austenitic stainless steel, and nickel-base alloy. We suggest adopting a more accurate detailed method, and its methodology is recommended to provide more reasonable solutions.

In order to simulate the fracture process of particle-reinforced materials on the micro-scale, an image-based double-smoothing cohesive finite element framework is proposed in the present paper. Two separate smoothing processes are performed to reduce the noise in the digital image and eliminate the jagged elements in the finite element mesh. The main contribution of the present study is the proposed novel image-based cohesive finite element framework, and this method improved the quality of the meshes effectively. Meanwhile, the artificial resistance due to the jagged element is reduced with the double-smoothing cohesive finite element framework during the crack propagation. Therefore, the image-based double-smoothing cohesive finite element framework is significant for the simulation of fracture mechanics.

To establish a description of realistic structural evolution of a growth film, we propose a local definite continuous-random-network (CRN) structure combined with a kinetic Monte Carlo (KMC) method based on an atomic-scale mechanism from first-principles density-functional-theory computations and molecular-dynamics computations. The proposed CRN-KMC method elucidates the evolution of elaborate topological structure and the transformation from amorphous phase to nanocrystalline phase of Si films, which is essentially attributed to the atomic interactive behavior of film growth. The method further predicts the realistic structural networks of a growing film at various temperatures based on various atomic-scale mechanisms competing with each other, mechanisms that not only essentially drive the radical from physisorption to chemisorption with the film surface, but also decidedly influence the film-surface chemical composition. In particular, we find the evolution of topological structure’s critical dependence on the compositions of the film surface and H-induced crystallization mechanism, which provide the important information for the strategy for determining optimized deposition conditions for local crystal formation. The results of the evolution of the structural network indicate that the structure of film is similar the CRN model’s representation at relative lower temperature, and is in full agreement with the inhomogeneous crystalline model at relative higher temperature without an abrupt phase change from polycrystalline to amorphous. Our CRN-KMC realistic structure model has significance for exploring the relation of various atomic-scale mechanisms to the phase transformation of growing films.

To improve the spatial resolution of reconstructed images/videos, this paper proposes a Superresolution (SR) reconstruction algorithm based on iterative back projection. In the proposed algorithm, image matching using critical-point filters (CPF) is employed to improve the accuracy of image registration. First, a sliding window is used to segment the video sequence. CPF based image matching is then performed between frames in the window to obtain pixel-level motion fields. Finally, high-resolution (HR) frames are reconstructed based on the motion fields using iterative back projection (IBP) algorithm. The CPF based registration algorithm can adapt to various types of motions in real video scenes. Experimental results demonstrate that, compared to optical flow based image matching with IBP algorithm, subjective quality improvement and an average PSNR score of 0.53 dB improvement are obtained by the proposed algorithm, when applied to video sequence.

Hand gesture-based interaction provides a natural and powerful means for human-computer interaction. It is also a good interface for human-robot interaction. However, most of the existing proposals are likely to fail when they meet some skin-coloured objects, especially the face region. In this paper, we present a novel hand tracking method which can track the features of the hand based on the obstacle avoidance flocking behaviour model to overcome skin-coloured distractions. It allows features to be split into two groups under severe distractions and merge later. The experiment results show that our method can track the hand in a cluttered background or when passing the face, while the Flocking of Features (FoF) and the Mean Shift Embedded Particle Filter (MSEPF) methods may fail. These results suggest that our method has better performance in comparison with the previous methods. It may therefore be helpful to promote the use of the hand gesture-based human-robot interaction method.