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Abstract Limb motion capture is essential in human motion-recognition, motor-function assessment and dexterous human-robot interaction for assistive robots. Due to highly dynamic nature of limb activities, conventional inertial methods of limb motion capture suffer from serious drift and instability problems. Here, a motion capture method with integral-free velocity detection is proposed and a wearable device is developed by incorporating micro tri-axis flow sensors with micro tri-axis inertial sensors. The device allows accurate measurement of three-dimensional motion velocity, acceleration, and attitude angle of human limbs in daily activities, strenuous, and prolonged exercises. Additionally, we verify an intra-limb coordination relationship exists between thigh and shank in human walking and running, and establish a neural network model for it. Using the intra-limb coordination model, dynamic motion capture of human lower limbs including thigh and shank is tactfully implemented by a single shank-worn device, which simplifies the capture device and reduces cost. Experiments in strenuous activities and long-time running validate excellent performance and robustness of the wearable device in dynamic motion recognition and reconstruction of human limbs.

Fine particulate matter (PM2.5) is a severe air pollution problem in China. Observations of PM2.5 have been available since 2013 from a large network operated by the China National Environmental Monitoring Center (CNEMC). The data show a general 30 %–50 % decrease in annual mean PM2.5 across China over the 2013–2018 period, averaging at −5.2 µg m−3 a−1. Trends in the five megacity cluster regions targeted by the government for air quality control are -9.3±1.8 µg m−3 a−1 (±95 % confidence interval) for Beijing–Tianjin–Hebei, -6.1±1.1 µg m−3 a−1 for the Yangtze River Delta, -2.7±0.8 µg m−3 a−1 for the Pearl River Delta, -6.7±1.3 µg m−3 a−1 for the Sichuan Basin, and -6.5±2.5 µg m−3 a−1 for the Fenwei Plain (Xi'an). Concurrent 2013–2018 observations of sulfur dioxide (SO2) and carbon monoxide (CO) show that the declines in PM2.5 are qualitatively consistent with drastic controls of emissions from coal combustion. However, there is also a large meteorologically driven interannual variability in PM2.5 that complicates trend attribution. We used a stepwise multiple linear regression (MLR) model to quantify this meteorological contribution to the PM2.5 trends across China. The MLR model correlates the 10 d PM2.5 anomalies to wind speed, precipitation, relative humidity, temperature, and 850 hPa meridional wind velocity (V850). The meteorology-corrected PM2.5 trends after removal of the MLR meteorological contribution can be viewed as being driven by trends in anthropogenic emissions. The mean PM2.5 decrease across China is −4.6 µg m−3 a−1 in the meteorology-corrected data, 12 % weaker than in the original data, meaning that 12 % of the PM2.5 decrease in the original data is attributable to meteorology. The trends in the meteorology-corrected data for the five megacity clusters are -8.0±1.1 µg m−3 a−1 for Beijing–Tianjin–Hebei (14 % weaker than in the original data), -6.3±0.9 µg m−3 a−1 for the Yangtze River Delta (3 % stronger), -2.2±0.5 µg m−3 a−1 for the Pearl River Delta (19 % weaker), -4.9±0.9 µg m−3 a−1 for the Sichuan Basin (27 % weaker), and -5.0±1.9 µg m−3 a−1 for the Fenwei Plain (Xi'an; 23 % weaker); 2015–2017 observations of flattening PM2.5 in the Pearl River Delta and increases in the Fenwei Plain can be attributed to meteorology rather than to relaxation of emission controls.

Since its first confirmed case at the end of 2019, COVID-19 has become a global pandemic in three months with more than 1.4 million confirmed cases worldwide, as of early April 2020. Quantifying the changes of pollutant emissions due to COVID-19 and associated governmental control measures is crucial to understand its impacts on economy, air pollution, and society. We used the WRF-GC model and the tropospheric NO2 column observations retrieved by the TROPOMI instrument to derive the top-down NOx emission change estimation between the three periods: P1 (January 1st to January 22nd, 2020), P2 (January 23rd, Wuhan lockdown, to February 9th, 2020), and P3 (February 10th, back-to-work day, to March 12th, 2020). We found that NOx emissions in East China averaged during P2 decreased by 50% compared to those averaged during P1. The NOx emissions averaged during P3 increased by 26% compared to those during P2. Most provinces in East China gradually regained some of their NOx emissions after February 10, the official back-to-work day, but NOx emissions in most provinces have not yet to return to their previous levels in early January. NOx emissions in Wuhan, the first epicenter of COVID-19, had no sign of emission recovering by March 12. A few provinces, such as Zhejiang and Shanxi, have recovered fast, with their averaged NOx emissions during P3 almost back to pre-lockdown levels.

Magnetic rings are widely used in automotive, home appliances, and consumer electronics. Due to the materials used, processing techniques, and other factors, there will be top cracks, internal cracks, adhesion, and other defects on individual magnetic rings during the manufacturing process. To find such defects, the most sophisticated YOLOv5 target identification algorithm is frequently utilized. However, it has problems such as high computation, sluggish detection, and a large model size. This work suggests an enhanced lightweight YOLOv5 (MR-YOLO) approach for the identification of magnetic ring surface defects to address these issues. To decrease the floating-point operation (FLOP) in the feature channel fusion process and enhance the performance of feature expression, the YOLOv5 neck network was added to the Mobilenetv3 module. To improve the robustness of the algorithm, a Mosaic data enhancement technique was applied. Moreover, in order to increase the network's interest in minor defects, the SE attention module is inserted into the backbone network to replace the SPPF module with substantially more calculations. Finally, to further increase the new network's accuracy and training speed, we substituted the original CIoU-Ioss for SIoU-Loss. According to the test, the FLOP and Params of the modified network model decreased by 59.4% and 47.9%, respectively; the reasoning speed increased by 16.6%, the model's size decreased by 48.1%, and the mAP only lost by 0.3%. The effectiveness and superiority of this method are proved by an analysis and comparison of examples.

Carbonaceous aerosols significantly affect global radiative forcing and climate through absorption and the scattering of sunlight. Black carbon (BC) and brown carbon (BrC) are light-absorbing carbonaceous aerosols. The direct radiative effect (DRE) of BrC is uncertain. A recent study suggests that BrC absorption is comparable to BC in the upper troposphere over biomass burning regions and that the resulting radiative heating tends to stabilize the atmosphere. Yet current climate models do not include proper physical and chemical treatments of BrC. In this study, we derived a BrC global biomass burning emission inventory on the basis of the Global Fire Emissions Database version 4 (GFED4), developed a module to simulate the light absorption of BrC in the Community Atmosphere Model version 5 (CAM5) of the Community Earth System Model (CESM), and investigated the photobleaching effect and convective transport of BrC on the basis of Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry Project (DC3) measurements. The model simulations of BC were also evaluated using HIAPER (High-Performance Instrumented Airborne Platform for Environmental Research) Pole-to-Pole Observations (HIPPO) measurements. We found that globally BrC is a significant absorber, the DRE of which is 0.10 W m−2, more than 25 % of BC DRE (+0.39 W m−2). Most significantly, model results indicated that BrC atmospheric heating in the tropical mid and upper troposphere is larger than that of BC. The source of tropical BrC is mainly from wildfires, which are more prevalent in the tropical regions than higher latitudes and release much more BrC relative to BC than industrial sources. While BC atmospheric heating is skewed towards the northern mid-latitude lower atmosphere, BrC heating is more centered in the tropical free troposphere. A possible mechanism for the enhanced convective transport of BrC is that hydrophobic high molecular weight BrC becomes a larger fraction of the BrC and less easily activated in a cloud as the aerosol ages. The contribution of BrC heating to the Hadley circulation and latitudinal expansion of the tropics is likely comparable to BC heating.

To promote the comprehensive utilization of corn stover and the development of field water-saving irrigation technology, a method of returning corn stover to the field was prosed; in this method, the crop stalks were crushed, mixed with soil in different proportions of adulteration, and then extruded to form hollow round tubes. To compare the influence of the winch blade with or without a diameter change on the composite pipe molding performance, two composite pipe molding devices were theoretically designed, simulated, and analyzed using discrete element simulation software, and a composite pipe molding bench test was performed. The simulation test revealed that the composite pipe molding rate of the winch blade without the reducer molding device was 3.45 kg/s, the output power of the winch shaft was 20.7 kW, the composite pipe molding rate of the winch blade with the reducer molding device was 1.20 kg/s, and the output power of the winch shaft was 18.75 kW. By calculating the weighted average of two indices, the composite pipe forming rate and the winch shaft output power, the comprehensive performance index of the composite pipe forming device without a reducer was greater than that of the device with a reducer. The composite pipe forming bench test revealed two kinds of molding devices with an extrusion molding with an outer diameter of 100 mm and an inner diameter of 30 mm. The composite pipe density test average was greater than 1.30 g/cm3 and met the requirements of composite pipe molding; the winch blade without a reducer molding device had an average composite pipe molding rate of 3.23 kg/s, and the winch blade with an average reducer molding rate of 2.07 kg/s. The forming rate of the composite pipe without a reducer was faster. Therefore, a winch blade without a reducer composite pipe molding device is more conducive to improving the composite pipe molding performance.

Carbonaceous aerosols affect the global radiative balance by absorbing and scattering radiation, which leads to warming or cooling of the atmosphere, respectively. Black carbon is the main light-absorbing component. A portion of the organic aerosol known as brown carbon also absorbs light. The climate sensitivity to absorbing aerosols rapidly increases with altitude, but brown carbon measurements are limited in the upper troposphere. Here we present aircraft observations of vertical aerosol distributions over the continental United States in May and June 2012 to show that light-absorbing brown carbon is prevalent in the troposphere, and absorbs more short-wavelength radiation than black carbon at altitudes between 5 and 12 km. We find that brown carbon is transported to these altitudes by deep convection, and that in-cloud heterogeneous processing may produce brown carbon. Radiative transfer calculations suggest that brown carbon accounts for about 24% of combined black and brown carbon warming effect at the tropopause. Roughly two-thirds of the estimated brown carbon forcing occurs above 5 km, although most brown carbon is found below 5 km. The highest radiative absorption occurred during an event that ingested a wildfire plume. We conclude that high-altitude brown carbon from biomass burning is an unappreciated component of climate forcing.

Although theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this relationship. The present study aimed to fill this gap in the literature. Using a between-subject design, 17 programming novices and 18 advanced students performed an n-back working-memory task. During the experiment, their prefrontal hemodynamic responses were measured using a 48-channel functional near-infrared spectroscopy (fNIRS) device. The results indicated that the advanced students had a higher working-memory capacity than the novice students, validating the relationship between programming ability and working memory. The analysis results also showed that the hemodynamic responses in the prefrontal cortex can be used to discriminate between novices and advanced students. Additionally, we utilized an attention-based convolutional neural network to analyze the spatial domains of the fNIRS signals and demonstrated that the left prefrontal cortex was more important than other brain regions for programming ability prediction. This result was consistent with the results of statistical analysis, which in turn improved the interpretability of neural networks.

We use 2010–2015 observations of atmospheric methane columns from the GOSAT satellite instrument in a global inverse analysis to improve estimates of methane emissions and their trends over the period, as well as the global concentration of tropospheric OH (the hydroxyl radical, methane's main sink) and its trend. Our inversion solves the Bayesian optimization problem analytically including closed-form characterization of errors. This allows us to (1) quantify the information content from the inversion towards optimizing methane emissions and its trends, (2) diagnose error correlations between constraints on emissions and OH concentrations, and (3) generate a large ensemble of solutions testing different assumptions in the inversion. We show how the analytical approach can be used, even when prior error standard deviation distributions are lognormal. Inversion results show large overestimates of Chinese coal emissions and Middle East oil and gas emissions in the EDGAR v4.3.2 inventory but little error in the United States where we use a new gridded version of the EPA national greenhouse gas inventory as prior estimate. Oil and gas emissions in the EDGAR v4.3.2 inventory show large differences with national totals reported to the United Nations Framework Convention on Climate Change (UNFCCC), and our inversion is generally more consistent with the UNFCCC data. The observed 2010–2015 growth in atmospheric methane is attributed mostly to an increase in emissions from India, China, and areas with large tropical wetlands. The contribution from OH trends is small in comparison. We find that the inversion provides strong independent constraints on global methane emissions (546 Tg a−1) and global mean OH concentrations (atmospheric methane lifetime against oxidation by tropospheric OH of 10.8±0.4 years), indicating that satellite observations of atmospheric methane could provide a proxy for OH concentrations in the future.

Energy‐autonomous wearable human activity monitoring is imperative for daily healthcare, benefiting from long‐term sustainable uses. Herein, a fully self‐powered wearable system, enabling real‐time monitoring and assessments of human multimodal health parameters including knee joint movement, metabolic energy, locomotion speed, and skin temperature, which are fully self‐powered by highly‐efficient flexible thermoelectric generators (f‐TEGs) is proposed and developed. The wearable system is composed of f‐TEGs, fabric strain sensors, ultra‐low‐power edge computing, and Bluetooth. The f‐TEGs worn on the leg not only harvest energy from body heat and supply power sustainably for the whole monitoring system, but also serve as zero‐power motion sensors to detect limb movement and skin temperature. The fabric strain sensor made by printing PEDOT: PSS on pre‐stretched nylon fiber‐wrapped rubber band enables high‐fidelity and ultralow‐power measurements on highly‐dynamic knee movements. Edge computing is elaborately designed to estimate multimodal health parameters including time‐varying metabolic energy in real‐time, which are wirelessly transmitted via Bluetooth. The whole monitoring system is operated automatically and intelligently, works sustainably in both static and dynamic states, and is fully self‐powered by the f‐TEGs. A fully self‐powered wearable system is developed for monitoring knee joint movement, skin temperature, locomotion speed, and metabolic energy, which is entirely powered by highly‐efficient flexible thermoelectric generators (f‐TEGs). The wearable system integrates f‐TEGs, fabric strain sensor, edge computing, and Bluetooth, which works sustainably in both static and dynamic states, enabling real‐time monitoring and assessment of human motion and health.