A step-wise numerical thermal control method for advanced composite curing process using digital image based programming

Accurate temperature control is required in advanced heat treatment processes such as composite curing. The crux is how to determine the appropriate heat sources which would result in the required temperature distribution during part heating process. This paper presents a new thermal control method using digital image based programming, which efficiently and accurately estimates required heat sources and controls temperature distribution in a step-wise numerical manner. The method was validated in both simulation and real heating tests for microwave curing of composites, showing excellent temperature uniformity and consistency with given target temperature profiles, compared with existing technologies

Electrically empowered microcomb laser

Optical frequency comb underpins a wide range of applications from communication, metrology, to sensing. Its development on a chip-scale platform -- so called soliton microcomb -- provides a promising path towards system miniaturization and functionality integration via photonic integrated circuit (PIC) technology. Although extensively explored in recent years, challenges remain in key aspects of microcomb such as complex soliton initialization, high threshold, low power efficiency, and limited comb reconfigurability. Here we present an on-chip laser that directly outputs microcomb and resolves all these challenges, with a distinctive mechanism created from synergetic interaction among resonant electro-optic effect, optical Kerr effect, and optical gain inside the laser cavity. Realized with integration between a III-V gain chip and a thin-film lithium niobate (TFLN) PIC, the laser is able to directly emit mode-locked microcomb on demand with robust turnkey operation inherently built in, with individual comb linewidth down to 600 Hz, whole-comb frequency tuning rate exceeding $\rm 2.4\times10^{17}$ Hz/s, and 100% utilization of optical power fully contributing to comb generation. The demonstrated approach unifies architecture and operation simplicity, high-speed reconfigurability, and multifunctional capability enabled by TFLN PIC, opening up a great avenue towards on-demand generation of mode-locked microcomb that is expected to have profound impact on broad applications

Generation of obese rat model by transcription activator-like effector nucleases targeting the leptin receptor gene

Abstract The laboratory rat is a valuable mammalian model organism for basic research and drug discovery. Here we demonstrate an efficient methodology by applying transcription activator-like effector nucleases (TALENs) technology to generate Leptin receptor (Lepr) knockout rats on the Sprague Dawley (SD) genetic background. Through direct injection of in vitro transcribed mRNA of TALEN pairs into SD rat zygotes, somatic mutations were induced in two of three resulting pups. One of the founders carrying bi-allelic mutation exhibited early onset of obesity and infertility. The other founder carried a chimeric mutation which was efficiently transmitted to the progenies. Through phenotyping of the resulting three lines of rats bearing distinct mutations in the Lepr locus, we found that the strains with a frame-shifted or premature stop codon mutation led to obesity and metabolic disorders. However, no obvious defect was observed in a strain with an in-frame 57 bp deletion in the extracellular domain of Lepr. This suggests the deleted amino acids do not significantly affect Lepr structure and function. This is the first report of generating the Lepr mutant obese rat model in SD strain through a reverse genetic approach. This suggests that TALEN is an efficient and powerful gene editing technology for the generation of disease models.</jats:p

Spatiotemporal Evolution, Spatial Agglomeration and Convergence of Environmental Governance in China—A Comparative Analysis Based on a Basin Perspective

Scientifically measuring the level of environmental governance (EGL) and understanding its spatial convergence has important reference value for ecological governance. In this paper, the global entropy method is applied to measure the EGL of 284 prefecture-level cities in China from 2007 to 2019, which are divided into three major river basins, including the Yellow River, Yangtze River, and Pearl River, to observe the spatial–temporal evolutionary characterization through a standard deviation ellipse model. The coefficient of variation and the spatial econometric model are the tools used to conduct the spatial convergence test. The results are as follows: (1) China’s EGL is low overall, though it is fluctuating upward at low magnitude, and the three major river basins follow the ranking: The Pearl River Basin > The Yangtze River Basin > The Yellow River Basin. (2) Spatially, the distribution pattern of China’s EGL changes from “scattered and sporadic” to “multipolar core”. (3) The center of China’s environmental governance was concentrated in the east from 2007 to 2019, and the EGL in the midstream and downstream regions of the three major river basins increased rapidly. (4) Environmental governance in China has significant absolute and conditional β-convergence characteristics, as do the three major basins, while the ranking of convergence speed remains “Yangtze River Basin > Yellow River Basin > Pearl River Basin”. Of these, economic development accelerated the convergence rate of environmental governance in China and its three major river basins; financial pressure significantly inhibited the convergence of the EGL of the Yellow River Basin. The improvement of the EGL in the Pearl River Basin was also negatively influenced by the industrial structure

Prediction of Cable Junction Temperature in Power Transmission System based on BP Neural Network optimized by Genetic Algorithm

Two forward neural networks were established in this study. Training and learning of reflection factor data and prediction results were conducted respectively then the weights and thresholds of the two networks are optimized by genetic algorithm, finally the set of target values can still be predicted without reflection factor data. In order to predict the temperature of the conductor in the cable joint of a power transmission system, the genetic algorithm is used to optimize the BP neural network to establish an effective prediction model based on the analysis of the related reflection factors. This model not only has the strong learning ability of BP neural network, but also combines the excellent global searching ability of genetic algorithm. The innovation of this research is that the network 1 is used to train the reflective factor data to get the corresponding time point temperature value, and then the reflective factor data of three consecutive time points are trained by the network 2 to get the fourth time point temperature value. The whole process of solving the temperature value of the fourth time point does not need the reflective factor data of the time point

Prediction of Cable Junction Temperature in Power Transmission System based on BP Neural Network optimized by Genetic Algorithm

Two forward neural networks were established in this study. Training and learning of reflection factor data and prediction results were conducted respectively then the weights and thresholds of the two networks are optimized by genetic algorithm, finally the set of target values can still be predicted without reflection factor data. In order to predict the temperature of the conductor in the cable joint of a power transmission system, the genetic algorithm is used to optimize the BP neural network to establish an effective prediction model based on the analysis of the related reflection factors. This model not only has the strong learning ability of BP neural network, but also combines the excellent global searching ability of genetic algorithm. The innovation of this research is that the network 1 is used to train the reflective factor data to get the corresponding time point temperature value, and then the reflective factor data of three consecutive time points are trained by the network 2 to get the fourth time point temperature value. The whole process of solving the temperature value of the fourth time point does not need the reflective factor data of the time point

Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method