Too hot to hold: the effects of high temperatures during pregnancy on birth weight and adult welfare outcomes

Exposure to high temperatures during pregnancy is generally associated with low birth weight---a proxy for endowment. But whether such early life shock is further related to welfare losses in adulthood is still unknown. Utilizing random temperature fluctuations across 123 counties in China, we examine the relationships between high temperatures during pregnancy and birth weight and later outcomes. One standard deviation of high temperature days during pregnancy triggers about 0.17 kilograms loss of birth weight, and further in adulthood 1.63 cm decrease in height and 0.86 years less of schooling. Health and intelligence outcomes are adversely affected as well. The impacts are concentrated in the first and third trimesters. Such effects should become part of the calculations of the costs of global warming. Back-of-the-envelope predictions suggest that at the end of the 21st century newborns on average weigh 54.36-210.44 grams less. And the losses in height and education years are 0.52-2.02 centimeters and 0.26-1.01 years, respectively. We also argue these patterns are more likely consistent with physiological effects than with income effects, because total precipitation and high temperatures in the growing season of one year before birth have no significant effects

Too hot to hold: the effects of high temperatures during pregnancy on endowment and adult welfare outcomes

We examine the relationships between high temperatures during pregnancy and birth weight and later outcomes using random temperature fluctuations across 131 counties in China. One standard deviation increase of high-temperature days during pregnancy triggers about 0.07 kg lower birth weight, and, in adulthood, a 0.80 cm decrease in height, 0.27 fewer years of schooling, 13.30% less annual earnings, and 8.77%, 10.96%, and 7.31% of one standard deviation lower for evaluated health, word-, and math-test score, respectively. The impacts seem to be concentrated in the second trimester. Such effects should be included in calculations of the costs of global warming. Back-of-the-envelope predictions suggest that at the end of the 21st century, newborns on average will weigh 0.02-0.09 kg less; losses in height and education years will be 0.27-1.05 cm and 0.09-0.35 years, respectively. We also conclude that adverse effects of high temperatures are more likely to be consistent with physiological effects than income effects, because: (i) places with the high proportion of heat-tolerant crop area do not mitigate any estimated temperature sensitivity during pregnancy and (ii) total precipitation and high temperatures in the last year growing season before birth have no significant effects on all outcomes

LightMixer: A novel lightweight convolutional neural network for tomato disease detection

Tomatoes are among the very important crops grown worldwide. However, tomato diseases can harm the health of tomato plants during growth and reduce tomato yields over large areas. The development of computer vision technology offers the prospect of solving this problem. However, traditional deep learning algorithms require a high computational cost and several parameters. Therefore, a lightweight tomato leaf disease identification model called LightMixer was designed in this study. The LightMixer model comprises a depth convolution with a Phish module and a light residual module. Depth convolution with the Phish module represents a lightweight convolution module designed to splice nonlinear activation functions with depth convolution as the backbone; it also focuses on lightweight convolutional feature extraction to facilitate deep feature fusion. The light residual module was built based on lightweight residual blocks to accelerate the computational efficiency of the entire network architecture and reduce the information loss of disease features. Experimental results show that the proposed LightMixer model achieved 99.3% accuracy on public datasets while requiring only 1.5 M parameters, an improvement over other classical convolutional neural network and lightweight models, and can be used for automatic tomato leaf disease identification on mobile devices

Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method

International audienceA measurement method of bridge vibration by unmanned aerial vehicles (UAVs) combined with convolutional neural networks (CNNs) and Kanade-Lucas-Tomasi (KLT) optical-flow method is proposed. In this method, the stationary reference points in the structural background are required, a UAV is used to shoot the structure video, and the KLT optical-flow method is used to track the target points on the structure and the background reference points in the video to obtain the coordinates of these points on each frame. Then, the characteristic relationship between the reference points and the target points can be learned by a CNN according to the coordinates of the reference points and the target points, so as to correct the displacement time-history curves of target points containing the false displacement caused by the UAV's egomotion. Finally, operational modal analysis (OMA) is used to extract the natural frequency of the structure from the displacement signal. In addition, the reliability of UAV measurement combined with CNN is proved by comparing the measurement results of the fixed camera and those of UAV combined with CNN, and the reliability of the KLT optical-flow method is proved by comparing the tracking results of the digital image correlation (DIC) and KLT optical-flow method in the experiment of this paper

Pavement Surface Defect Detection Using Mask Region-Based Convolutional Neural Networks and Transfer Learning

Pavement defect detection is critical for pavement maintenance and management. Meanwhile, the accurate and timely detection of pavement defects in complex backgrounds is a huge challenge for maintenance work. Therefore, this paper used a mask region-based convolutional neural network (Mask R-CNN) and transfer learning to detect pavement defects in complex backgrounds. Twelve hundred pavement images were collected, and a dataset containing corresponding instance labels of the defects was established. Based on this dataset, the performance of the Mask R-CNN was compared with faster region-based convolutional neural networks (Faster R-CNNs) under the transfer of six well-known backbone networks. The results confirmed that the classification accuracy of the two algorithms (Mask R-CNN and Faster R-CNN) was consistent and reached 100%; however, the average precision (AP) of the Mask R-CNN was higher than that of Faster R-CNNs. Meanwhile, the testing time of the models using a feature pyramid network (FPN) was lower than that of other models, which reached 0.21 s per frame (SPF). On this basis, the segmentation performance of the Mask R-CNN was further analyzed at three learning rates (LRs). The Mask R-CNN performed best with ResNet101 plus FPN as its backbone structure, and its AP reached 92.1%. The error rate of defect quantification was between 4% and 16%. It has an ideal detection effect on multi-object and multi-class defects on pavement surfaces, and the quantitative results of the defects can provide a reference for pavement maintenance personnel

Recent progress and perspective of cathode recycling technology for spent LiFePO4 batteries

Due to the shortage of fossil fuels and increasing environmental pressures, the Li-ion battery industry is developing rapidly. Currently, most electric vehicles are powered by lithium iron phosphate batteries, and a lot of lithium–iron phosphate batteries will be retired after the cycle life termination. If not addressed promptly, they will pollute the environment and waste metal resources. The recycling spent batteries holds immense significance for environmental protection and resource recycling. This article provides a comprehensive review on the current state of waste LiFePO 4 cathode materials from four perspectives: pretreatment, wet recovery, direct regeneration, and production of high-value-added products. Furthermore, it compares their respective advantages and disadvantages.</p

Dispersion characteristics of switching transients from large‐scale experiments in a 1100 kV gas insulated switchgear circuit

Abstract The switching operation of gas insulated switchgear (GIS) disconnector will produce multiple random spark discharges between the gas gaps of the disconnector. Each spark discharge can be affected by various random factors, such as trapped charge on the load side, the initial operating phase, gap distance and operating speed of the disconnector, leading to dispersion characteristics. At present, some studies have mentioned this dispersion, but its impact is still unclear due to the lack of experimental data, resulting in a deviation between the simulation results and the measured results. In this study, thousands of switching operations were carried out based on the 1100 kV full‐scale GIS circuit in Wuhan ultra‐high voltage alternating current Test Base. The maximal information coefficient analysis, linear regression fitting and other statistical methods were used to discuss the dispersion characteristics of very fast transient overvoltage (VFTO) and transient enclosure voltage (TEV) from large‐scale experimental data. The results show that the dispersion of VFTO and TEV in amplitude aspects both increase gradually as the breakdown voltage increases, with the maximum difference of five times of TEV amplitude at normalised breakdown voltage. In terms of frequency characteristics, the dispersion of different frequency components in TEV is always greater than that of VFTO, especially for the high‐frequency components. Moreover, by comparing the frequency characteristics of TEV at different positions, we notice that the dispersion is almost independent of the spatial position but only determined by the randomness of spark discharges. These discoveries reveal the importance of the dispersion in switching transients and make up for the lack of theoretical understanding of the correlation among them