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3D graphene porous materials (3GPM), which have low density, large porosity, excellent compressibility, high conductivity, hold huge promise for a wide range of applications. Nevertheless, most 3GPM have brittle and weak network structures, which limits their widespread use. Therefore, the preparation of a robust and elastic graphene porous network is critical for the functionalization of 3GPM. Herein, the recent research of 3GPM with excellent mechanical properties are summarized and the focus is on the effect factors that affect the mechanical properties of 3GPM. Moreover, the applications of elastic 3GPM in various fields, such as adsorption, energy storage, solar steam generation, sensors, flexible electronics, and electromagnetic wave shielding are comprehensively reviewed. At last, the new challenges and perspective for fabrication and functionalization of robust and elastic 3GPM are outlined. It is expected that the perspective will inspire more new ideas in preparation and functionalization of 3GPM. This perspective provides a comprehensive overview of the recent research on 3D graphene porous materials (3GPM) with excellent mechanical properties. It analyzes the factors that affect the mechanical behaviour of 3GPM and examines their diverse applications in various fields, such as adsorption, energy storage, solar steam generation, sensors, flexible electronics, and electromagnetic wave shielding. Finally, it outlines the new challenges and opportunities for the fabrication and functionalization of robust and elastic 3GPM.

Solar desalination is a promising strategy to utilize solar energy to purify saline water. However, the accumulation of salt on the solar evaporator surface severely reduces light absorption and evaporation performance. Herein, a simple and eco‐friendly method to fabricate a 3D gradient graphene spiral sponge (GGS sponge) is presented that enables high‐rate solar evaporation and zero liquid discharge (ZLD) desalination of high‐salinity brine. The spiral structure of the GGS sponge enhances energy recovery, while the gradient network structures facilitate radial brine transport and directional salt crystallization, which cooperate to endow the sponge with superior solar evaporation (6.5 kg m−2 h−1 for 20 wt.% brine), efficient salt collection (1.5 kg m−2 h−1 for 20 wt.% brine), ZLD desalination, and long‐term durability (continuous 144 h in 20 wt.% brine). Moreover, the GGS sponge shows an ultrahigh freshwater production rate of 3.1 kg m−2 h−1 during the outdoor desalination tests. A continuous desalination–irrigation system based on the GGS sponge for crop growth, which has the potential for self‐sustainable agriculture in remote areas is demonstrated. This work introduces a novel evaporator design and also provides insight into the structural principles for designing next‐generation solar desalination devices that are salt‐tolerant and highly efficient. A gradient graphene spiral sponge is demonstrated for solar desalination, which has a unique structure for energy recovery, radial brine transport, and directional salt crystallization. It exhibits excellent performance in solar evaporation (6.5 kg m−2 h−1), salt collection (1.5 kg m−2 h−1), freshwater production (3.1 kg m−2 h−1), durability (144 h), and zero liquid discharge desalination. This sponge can potentially enable continuous desalination–irrigation in remote areas for crop growth.

Purpose The purpose of this paper is to reduce the drag loss and study the effects of operating conditions and groove parameters such as flow rate and temperature of automatic transmission fluid, clearance between plates, groove depth and groove ratio on the drag torque of a wet clutch for vehicles, parametric analysis of the drag torque model of wet multi-plate friction clutch with groove consideration. Design/methodology/approach Both experimental and numerical research was carried out in this work. Parametric groove models, full film lubrication flow model and pressure distribution model are established to investigate the effects of the grooves on drag torque of a wet clutch. Multigrid method is used to simplify the solution. Findings In this paper, a drag torque model of a wet multi-plate friction clutch based on the basic theory of viscous fluid dynamics is examined through experimental and numerical methods that take grooves into account, and the change trend of drag torque with operating conditions and groove parameters is analyzed. Originality/value Multigrid method is used to solve the governing equations, which simplifies the solution process because of the restrictions and interpolation operations between the adjacent layers of coarser and fine grids. These works provide insight into the effect regularity of operating conditions and groove parameters on drag torque of a wet multi-plate friction clutch. Furthermore, variable test conditions and sufficient experimental data are the main functions in the experimental research.

Micro-energy network realizes the complementary of distributed energy production according to the characteristics of users' energy demand and local resource conditions. In order to realize the efficient operation of micro-energy network group, this paper proposed an optimal power transfer model based on Mutual Energy Supporting Pattern in micro-energy network group. The concept of micro-energy network is given and the basic structure of micro-energy network group based on the interconnection of external energy hubs is first established. Then, the micro-energy network model of internal energy hub and optimal power transfer model is established. The optimal power transfer model of micro-energy network group is subdivided into two kinds of operation modes, the internal cooperation mode and the synergy cooperation mode. The internal cooperation mode is to address the problem of energy shortage at the micro-energy network, while the synergy cooperation mode is to solve the problem through the coordination of multiple energy hubs in the micro-energy network group. Finally, the model is validated by a system including electricity, gas, heat/cooling load and external energy hub.

Object detection has been used in a wide range of industries. For example, in autonomous driving, the task of object detection is to accurately and efficiently identify and locate a large number of predefined classes of object instances (vehicles, pedestrians, traffic signs, etc.) from videos of roads. In robotics, the industry robot needs to recognize specific machine elements. In the security field, the camera should accurately recognize each face of people. With the wide application of deep learning, the accuracy and efficiency of object detection have been greatly improved, but object detection based on deep learning still faces challenges. Different applications of object detection have different requirements, including highly accurate detection, multi-category object detection, real-time detection, robustness to occlusions, etc. To address the above challenges, based on extensive literature research, this paper analyzes methods for improving and optimizing mainstream object detection algorithms from the perspective of evolution of one-stage and two-stage object detection algorithms. Furthermore, this article proposes methods for improving object detection accuracy from the perspective of changing receptive fields. The new model is based on the original YOLOv5 (You Look Only Once) with some modifications. The structure of the head part of YOLOv5 is modified by adding asymmetrical pooling layers. As a result, the accuracy of the algorithm is improved while ensuring the speed. The performances of the new model in this article are compared with original YOLOv5 model and analyzed from several parameters. And the evaluation of the new model is presented in four situations. Moreover, the summary and outlooks are made on the problems to be solved and the research directions in the future.

Obesity is a serious social and public health problem in the world, especially in children and adolescents. For school-age children with obesity, this stage is in the transition from childhood to adolescence, and both physical, psychological, and external environments will be full of challenges. Studies have showed that school-age children are the largest proportion of people who continue to be obese in adulthood. Physical exercise is considered as an effective way to control weight. Therefore, we focus on this point to study which factors will be improved to reduce childhood obesity. To assess the effects of aerobic and resistance exercise on physical indexes, such as body mass index (BMI) and body fat percentage, and cardiovascular risk factors such as VO.sub.2 peak, triglycerides (TG) and low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol (TC), insulin and insulin resistance in school-age children who are overweight or obese. PubMed, SPORTDiscus, Medline, Cochrane-Library, Scopus, Ovid and Web of Science were searched to locate studies published between 2000 and 2021 in obese and overweight school-age children between 6-12 years old. The articles are all randomized controlled trials (RCTs) and in English. Data were synthesized using a random-effect or a fixed-effect model to analyze the effects of aerobic and resistance exercise on six elements in in school-age children with overweight or obese. The primary outcome measures were set for BMI. A total of 13 RCTs (504 participants) were identified. Analysis of the between-group showed that aerobic and resistance exercise were effective in improving BMI (MD = -0.66; p < 0.00001), body fat percentage (MD = -1.29; p = 0.02), TG (std.MD = -1.14; p = 0.005), LDL (std.MD = -1.38; p = 0.003), TC (std.MD = -0.77; p = 0.002), VO.sub.2 peak (std.MD = 1.25; p = 0.001). However, aerobic and resistance exercise were not significant in improving HDL (std.MD = 0.13; p = 0.27). Aerobic exercise and resistance exercise are associated with improvement in BMI, body fat percentage, VO.sub.2 peak, TG, LDL, TC, while not in HDL in school-age children with obesity or overweight. Insulin and insulin resistance were not able to be analyzed in our review. However, there are only two articles related to resistance exercise in children with obesity and overweight at school age, which is far less than the number of 12 articles about aerobic exercise, so we cannot compare the effects of the two types of exercises.

Effectively monitoring the operational status of reactor coolant pumps (RCPs) is crucial for enhancing the safety and stability of nuclear power operations. To address the challenges of limited interpretability and suboptimal detection performance in existing methods for detecting abnormal operating states of RCPs, this paper proposes an interpretable, unsupervised anomaly detection approach. This innovative method designs a framework that combines Kernel Self-Organizing Map (Kernel SOM) clustering with Bayesian Posterior Inference. Specifically, the proposed method uses Kernel SOM to extract typical patterns from normal operation data. Subsequently, a distance probability distribution model reflecting the data distribution structure within each cluster is constructed, providing a robust tool for data distribution analysis for anomaly detection. Finally, based on prior knowledge, such as distance probability distribution, the Bayesian Posterior Inference is employed to infer the probability of the equipment being in a normal state. By constructing distribution models that reflect data distribution structures and combining them with posterior inference, this approach realizes the traceability and interpretability of the anomaly detection process, improving the transparency of anomaly detection and enabling operators to understand the decision logic and the analysis of the causes of anomalous occurrences. Verification via real-world operational data demonstrates the method’s superior effectiveness. This work offers a highly interpretable solution for RCP anomaly detection, with significant implications for safety-critical applications in the nuclear energy sector.

Bow-tie antennas are utilized extensively in ground-penetrating radar (GPR) systems. In order to achieve sufficient penetration depth and resolution, the bow-tie antennas for GPR applications require low operating frequency, high gain, and excellent broadband. A novel ultra-wideband (UWB) bow-tie antenna with gain enhancement for GPR applications is proposed in this paper. First, a UWB bow-tie antenna with resistive loading is designed. The metal reflector and metamaterial loading make the bow-tie antenna directional, and loading the same metamaterial on the front side of the antenna further improves directional gain. After testing, the lowest frequency of the fabricated antenna is 317 MHz, the relative bandwidth is 98.6%, the peak gain in the frequency range is 9.3 dBi, and the size is only 0.38 λ at the lowest frequency. The proposed compact antenna takes both gain and bandwidth into consideration. Finally, in order to further verify the effectiveness of the proposed antenna in the GPR system, a stepped frequency continuous wave ground-penetrating radar (SFCW-GPR) system was built. The experimental results show that the designed antenna is suitable for the GPR system of deep penetration and high-resolution detection, which is beneficial to the imaging of underground structures.

The viral polymerase complex, comprising the large protein (L) and phosphoprotein (P), is crucial for both genome replication and transcription in non-segmented negative-strand RNA viruses (nsNSVs), while structures corresponding to these activities remain obscure. Here, we resolved two L–P complex conformations from the mumps virus (MuV), a typical member of nsNSVs, via cryogenic-electron microscopy. One conformation presents all five domains of L forming a continuous RNA tunnel to the methyltransferase domain (MTase), preferably as a transcription state. The other conformation has the appendage averaged out, which is inaccessible to MTase. In both conformations, parallel P tetramers are revealed around MuV L, which, together with structures of other nsNSVs, demonstrates the diverse origins of the L-binding X domain of P. Our study links varying structures of nsNSV polymerase complexes with genome replication and transcription and points to a sliding model for polymerase complexes to advance along the RNA templates. The viral polymerase complex is crucial for both genome replication and transcription in non-segmented negative-strand RNA viruses. Here, the authors link varying structures of polymerase complexes with their dual functions and propose a sliding model for them to advance along the RNA templates.

Deep learning has become a popular tool for computer-aided diagnosis using medical images, sometimes matching or exceeding the performance of clinicians. However, these models can also reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Despite this concern, the problem of improving model fairness in medical image classification by deep learning has yet to be fully studied. To address this issue, we propose an algorithm that leverages the marginal pairwise equal opportunity to reduce bias in medical image classification. Our evaluations across four tasks using four independent large-scale cohorts demonstrate that our proposed algorithm not only improves fairness in individual and intersectional subgroups but also maintains overall performance. Specifically, the relative change in pairwise fairness difference between our proposed model and the baseline model was reduced by over 35%, while the relative change in AUC value was typically within 1%. By reducing the bias generated by deep learning models, our proposed approach can potentially alleviate concerns about the fairness and reliability of image-based computer-aided diagnosis. Deep learning models can reflect and amplify human bias, potentially resulting inaccurate missed diagnoses. Here, the authors show that by leveraging the marginal pairwise equal opportunity, their model reduces bias in medical image classification by over 35% compared to baseline models, with minimal impact on AUC values.