A collision-free path planning method is proposed based on learning from demonstration (LfD) to address the challenges of cumbersome manual teaching operations caused by complex action of yarn storage, variable mechanism positions, and limited workspace in preform weaving. First, by utilizing extreme learning machines (ELM) to autonomously learn the teaching data of yarn storage, the mapping relationship between the starting and ending points and the teaching path points is constructed to obtain the imitation path with similar storage actions under the starting and ending points of the new task. Second, an improved rapidly expanding random trees (IRRT) method with adaptive direction and step size is proposed to expand path points with high quality. Finally, taking the spatical guidance point of imitation path as the target direction of IRRT, the expansion direction is biased toward the imitation path to obtain a collision-free path that meets the action yarn storage. The results of different yarn storage examples show that the ELM-IRRT method can plan the yarn storage path within 2s–5s when the position of the mechanism changes in narrow spaces, avoiding tedious manual operations that program the robot movements, which is feasible and effective.

The present study focuses on two-dimensional direct numerical simulations of shallow-water breaking waves, specifically those generated by a wave plate at constant water depths. The primary objective is to quantitatively analyse the dynamics, kinematics and energy dissipation associated with wave breaking. The numerical results exhibit good agreement with experimental data in terms of free-surface profiles during wave breaking. A parametric study was conducted to examine the influence of various wave properties and initial conditions on breaking characteristics. According to research on the Bond number ($Bo$, the ratio of gravitational to surface tension forces), an increased surface tension leads to the formation of more prominent parasitic capillaries at the forwards face of the wave profile and a thicker plunging jet, which causes a delayed breaking time and is tightly correlated with the main cavity size. A close relationship between wave statistics and the initial conditions of the wave plate is discovered, allowing for the classification of breaker types based on the ratio of wave height to water depth, $H/d$. Moreover, an analysis based on inertial scaling arguments reveals that the energy dissipation rate due to breaking can be linked to the local geometry of the breaking crest $H_b/d$, and exhibits a threshold behaviour, where the energy dissipation approaches zero at a critical value of $H_b/d$. An empirical scaling of the breaking parameter is proposed as $b = a(H_b/d - \chi _0)^n$, where $\chi _0 = 0.65$ represents the breaking threshold and $n = 1.5$ is a power law determined through the best fit to the numerical results.

The association between early reproductive events and health status in later life has always been of interest across disciplines. The purpose of this study was to investigate whether there was an association between the number of children born in the early years of elderly women and their depression in later life based on a sample of older women aged 65 years and above with at least one child in rural China. Data from the Chinese Longitudinal Healthy Longevity Survey in 2018, this study used the ordinary least square method to conduct empirical research. This study has found a significant correlation between an increase in the number of children and depression in older rural women. When considering the sex of the child, the number of daughters had a greater and more significant impact on depression. Number of children may exacerbate depression of older women through declining self-rated health and reduced social activity, while increased inter-generational support alleviated depression. The association between number of children born and depression also existed in urban older women, though not significant. Therefore, it is suggested to accelerate the improvement of supporting policies related to childbirth, developing a healthy and scientific fertility culture, and improving rural maternal and child health services. Women should be assisted in balancing their roles in the family and in society, and in particular in sharing the burden of caring for children. Targeted efforts to increase old-age protection for older people.

The International Design Engineering Annual (IDEA) Challenge is a virtually hosted hackathon for Engineering Design researchers with aims of: i) generating open access datasets; ii) fostering community between researchers; and, iii) applying great design minds to develop solutions to real design problems. This paper presents the 2022 IDEA challenge and elements of the captured dataset with the aim of providing insights into prototyping behaviours at virtually hosted hackathons, comparing it with the 2021 challenge dataset and providing reflections and learnings from two years of running the challenge. The dataset is shown to provide valuable insights into how designers spend their time at hackathon events and how, why and when prototypes are used during their design processes. The dataset also corroborates the findings from the 2021 dataset, demonstrating the complementarity of physical and sketch prototypes. With this paper, we also invite the wider community to contribute to the IDEA Challenge in future years, either as participants or in using the platform to run their own design studies.

This article explores the effects of social media on government accountability under authoritarian regimes. It examines whether online discussions have a disciplining effect on officials' scandals. We use a unique dataset containing records of scandals discussed on microblogs in China to systematically study their effects on the government response process and officials' disciplining. We find that the government employs clear strategies: higher levels of online discussion lead to quicker government responses and more severe punishment of the officials involved. Scandals involving sexual and economic factors, which initially capture more attention, involve quicker responses and more severe punishments. Even when we exploit rainfall as the instrumental variable to mitigate the endogeneity, the results are still robust. Our findings highlight the accountability mechanism facilitated by social media and the power of social media empowerment.

Shoulder exoskeletons (SEs) can assist the shoulder joint of workers during overhead work and are usually passive for good portability. However, current passive SEs face the challenge that their torque generators are often attached to the human arm, which adds a significant amount of weight to the user’s arms, resulting in additional energy consumption of the user. In this paper, we present a novel passive SE whose torque generator is attached to the user’s back and assists the shoulder joint through Bowden cables. Our approach greatly reduces the weight on the user’s arms and can accommodate complex shoulder joint movements with simple and lightweight mechanical structure based on Bowden cables. In addition, to match the nonlinear torque requirements of the shoulder joint, a unique spring-cam mechanism is proposed as the torque generator. To verify the effectiveness of the device, we conducted a usability test based on muscle activations of 10 healthy subjects. When assisting overhead work, the SE significantly reduced the mean and maximum electromyography signals of the shoulder-related muscles by up to 25%. The proposed SE contributes to further research on passive SE design to improve usability, especially in terms of reducing weight on human arms.

Conditional value-at-risk (CVaR) and conditional expected shortfall (CES) are widely adopted risk measures which help monitor potential tail risk while adapting to evolving market information. In this paper, we propose an approach to constructing simultaneous confidence bands (SCBs) for tail risk as measured by CVaR and CES, with the confidence bands uniformly valid for a set of tail levels. We consider one-sided tail risk (downside or upside tail risk) as well as relative tail risk (the ratio of upside to downside tail risk). A general class of location-scale models with heavy-tailed innovations is employed to filter out the return dynamics. Then, CVaR and CES are estimated with the aid of extreme value theory. In the asymptotic theory, we consider two scenarios: (i) the extreme scenario that allows for extrapolation beyond the range of the available data and (ii) the intermediate scenario that works exclusively in the case where the available data are adequate relative to the tail level. For finite-sample implementation, we propose a novel bootstrap procedure to circumvent the slow convergence rates of the SCBs as well as infeasibility of approximating the limiting distributions. A series of Monte Carlo simulations confirm that our approach works well in finite samples.

In this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

As an important part of the manufacturing industry, redundant robots can undertake heavy and tough tasks, which human operators are difficult to sustain. Such onerous and repetitive industrial manipulations, that is, positioning and carrying, impose heavy burdens on the load bearing for redundancy robots’ joints. Under the circumstances of long-term and intense industrial operations, joints of redundant robots are conceivably to fall into functional failure, which may possibly cause abrupt joint lock or freeze at unknown time instants. Therefore, task accuracy by end-effectors tends to diminish considerably and gradually because of broken-down joints. In this paper, a sparsity-based method for fault-tolerant motion planning of redundant robots is provided for the first time. The developed fault-tolerant redundancy resolution approach is defined as L1-norm based optimization with immediate variables involved to avoid discontinuity in the dynamic solution process. Meanwhile, those potential faulty joint(s) can be located by the designed fault observer with the proposed fault-diagnosis algorithm. The proposed fault-tolerant motion planning method with fault diagnosis is dynamically optimized by resultant primal dual neural networks with provable convergence. Moreover, the sparsity of joint actuation by the proposed method can be enhanced by around 43.87% and 36.51%, respectively, for tracking circle and square paths. Simulation and experimental findings on a redundant robot (KUKA iiwa) prove the efficacy of the developed defect tolerant approach based on sparsity.

DNA methylation is one of the most important epigenetic modifications in breast cancer (BC) development, and long-term dietary habits can alter DNA methylation. Cadherin-4 (CDH4, a member of the cadherin family) encodes Ca2+-dependent cell–cell adhesion glycoproteins. We conducted a case–control study (380 newly diagnosed BC and 439 cancer-free controls) to explore the relationship of CDH4 methylation in peripheral blood leukocyte DNA (PBL DNA), as well as its combined and interactive effects with dietary factors on BC risk. A case-only study (335 newly diagnosed BC) was conducted to analyse the association between CDH4 methylation in breast tissue DNA and dietary factors. CDH4 methylation was detected using quantitative methylation-specific PCR. Unconditional logistic regressions were used to analyse the association of CDH4 methylation in PBL DNA and BC risk. Cross-over analysis and unconditional logistic regression were used to calculate the combined and interactive effects between CDH4 methylation in PBL DNA and dietary factors in BC. CDH4 hypermethylation was significantly associated with increased BC risk in PBL DNA (ORadjusted (ORadj) = 2·70, (95 % CI 1·90, 3·83), P < 0·001). CDH4 hypermethylation also showed significant combined effects with the consumption of vegetables (ORadj = 4·33, (95 % CI 2·63, 7·10)), allium vegetables (ORadj = 7·00, (95 % CI 4·17, 11·77)), fish (ORadj = 7·92, (95 % CI 3·79, 16·53)), milk (ORadj = 6·30, (95 % CI 3·41, 11·66)), overnight food (ORadj = 4·63, (95 % CI 2·69, 7·99)), pork (ORadj = 5·59, (95 % CI 2·94, 10·62)) and physical activity (ORadj = 4·72, (95 % CI 2·87, 7·76)). Moreover, consuming milk was significantly related with decreased risk of CDH4 methylation (OR = 0·61, (95 % CI 0·38, 0·99)) in breast tissue. Our findings may provide direct guidance on the dietary intake for specific methylated carriers to decrease their risk for developing BC.

Generating designs via machine learning has been an on-going challenge in computer-aided design. Recently, deep learning methods have been applied to randomly generate images in fashion, furniture and product design. However, such deep generative methods usually require a large number of training images and human aspects are not taken into account in the design process. In this work, we seek a way to involve human cognitive factors through brain activity indicated by electroencephalographic measurements (EEG) in the generative process. We propose a neuroscience-inspired design with a machine learning method where EEG is used to capture preferred design features. Such signals are used as a condition in generative adversarial networks (GAN). First, we employ a recurrent neural network Long Short-Term Memory as an encoder to extract EEG features from raw EEG signals; this data are recorded from subjects viewing several categories of images from ImageNet. Second, we train a GAN model conditioned on the encoded EEG features to generate design images. Third, we use the model to generate design images from a subject’s EEG measured brain activity. To verify our proposed generative design method, we present a case study, in which the subjects imagine the products they prefer, and the corresponding EEG signals are recorded and reconstructed by our model for evaluation. The results indicate that a generated product image with preference EEG signals gains more preference than those generated without EEG signals. Overall, we propose a neuroscience-inspired artificial intelligence design method for generating a design taking into account human preference. The method could help improve communication between designers and clients where clients might not be able to express design requests clearly.

In late December 2019, patients of atypical pneumonia due to an unidentified microbial agent were reported in Wuhan, Hubei Province, China. Subsequently, a novel coronavirus was identified as the causative pathogen which was named SARS-CoV-2. As of 12 February 2020, more than 44 000 cases of SARS-CoV-2 infection have been confirmed in China and continue to expand. Provinces, municipalities and autonomous regions of China have launched first-level response to major public health emergencies one after another from 23 January 2020, which means restricting movement of people among provinces, municipalities and autonomous regions. The aim of this study was to explore the correlation between the migration scale index and the number of confirmed coronavirus disease 2019 (COVID-19) cases and to depict the effect of restricting population movement. In this study, Excel 2010 was used to demonstrate the temporal distribution at the day level and SPSS 23.0 was used to analyse the correlation between the migration scale index and the number of confirmed COVID-19 cases. We found that since 23 January 2020, Wuhan migration scale index has dropped significantly and since 26 January 2020, Hubei province migration scale index has dropped significantly. New confirmed COVID-19 cases per day in China except for Wuhan gradually increased since 24 January 2020, and showed a downward trend from 6 February 2020. New confirmed COVID-19 cases per day in China except for Hubei province gradually increased since 24 January 2020, and maintained at a high level from 24 January 2020 to 4 February 2020, then showed a downward trend. Wuhan migration scale index from 9 January to 22 January, 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Wuhan from 22 January to 4 February. Hubei province migration scale index from 10 January to 23 January and 11 January to 24 January was correlated with the number of new confirmed COVID-19 cases per day in China except for Hubei province from 22 January to 4 February. Our findings suggested that people who left Wuhan from 9 January to 22 January, and those who left Hubei province from 10 January to 24 January, led to the outbreak in the rest of China. The ‘Wuhan lockdown’ and the launching of the first-level response to this major public health emergency may have had a good effect on controlling the COVID-19 epidemic. Although new COVID-19 cases continued to be confirmed in China outside Wuhan and Hubei provinces, in our opinion, these are second-generation cases.

The present study explores whether embodied meaning is activated in comprehension of action-related Mandarin counterfactual sentences. Participants listened to action-related Mandarin factual or counterfactual sentences describing transfer events (actions towards or away from the participant), and then performed verb-compatible or -incompatible motor action after a transfer verb (action towards or away from the participant) onset. The results demonstrated that motor simulation, specifically the interfering action-sentence compatibility effect (ACE), was obtained in both factual and counterfactual sentences. Additionally, the temporal course of motor resonance was slightly different between factual and counterfactual sentences. We concluded that embodied meaning was activated in action-related Chinese counterfactual sentences. The results supported a neural network model of Chersi, Thill, Ziemke, and Borghi (2010), proposed within the embodiment approach, which explains the interaction between processing action-related sentences and motor performance. Moreover, we speculated that the neural network model of Chersi et al. was also applicable to action-related Mandarin counterfactual comprehension.

Underground Nuclear Astrophysics in China (JUNA) will take the advantage of the ultra-low background in Jinping underground lab. High current accelerator with an ECR source and detectors were commissioned. JUNA plans to study directly a number of nuclear reactions important to hydrostatic stellar evolution at their relevant stellar energies. At the first period, JUNA aims at the direct measurements of 25Mg(p,γ)26 Al, 19F(p,α) 16 O, 13C(α, n) 16O and 12C(α,γ) 16O near the Gamow window. The current progress of JUNA will be given.

Magnesium is a biodegradable material that has potential application in cardiovascular stent development: its excellent mechanical properties and blood compatibility make it highly useful in interventional therapy. Nevertheless, the following shortcomings restrict its further application: antihyperplasia function and promoting surface endothelialization. In this study, we immobilized a specific link peptide of endothelial cells, Arg-Glu-Asp-Val (REDV), onto polydopamine (PDA)-deposited Mg–Zn–Y–Nd alloy surface via covalent reaction to improve the growth of the endothelial cells. The PDA/REDV coating with optimized parameters maintained the good blood compatibility of the Mg–Zn–Y–Nd alloy at the biomimetic speed of the blood flow and significantly inhibited the growth of the vascular smooth muscle cells and macrophage attachment/activation, which indicated its better functions in antihyperplasia and anti-inflammation. In particular, the PDA/REDV coating not only showed consistent results in promoting the attachment of endothelial cells as reported elsewhere, but also displayed the ability of enhancing the viability of endothelial cells (or inhibiting apoptosis), suggesting its pro-endothelialized function through different pathways. In summary, this PDA/REDV coating addressed the above-mentioned shortcomings of the magnesium alloy, which may promise its wider application.