Research on Noise Reduction Approach of Raman-Based Distributed Temperature Sensor Based on Nonlinear Filter

The accuracy of temperature measurement is often reduced due to random noise in Raman-based distributed temperature sensor (RDTS). A noise reduction method based on a nonlinear filter is thus proposed in this paper. Compared with the temperature demodulation results of raw signals, the proposed method in this paper can reduce the average maximum deviation of temperature measurement results from 4.1°C to 1.2°C at 40.0°C, 50.0°C and 60.0°C. And the proposed method in this paper can improve the accuracy of temperature measurement of Raman-based distributed temperature sensor better than the commonly used wavelet transform-based method. The advantages of the proposed method in improving the accuracy of temperature measurement for Raman-based distributed temperature sensor are quantitatively reflected in the maximum deviation and root mean square error of temperature measurement results. Therefore, this paper proposes an effective and feasible method to improve the accuracy of temperature measurement results for Raman-based distributed temperature sensor.

Learning Specialized Activation Functions for Physics-Informed Neural Networks

Physics-informed neural networks(PINNs)are known to suffer from optimization difficulty.In this work,we reveal the connection between the optimization difficulty of PINNs and activation functions.Specifically,we show that PINNs exhibit high sensitivity to activation functions when solving PDEs with distinct properties.Existing works usually choose activation functions by inefficient trial-and-error.To avoid the inefficient manual selection and to alleviate the optimization difficulty of PINNs,we introduce adaptive activation functions to search for the optimal function when solving different problems.We compare different adaptive activation functions and discuss their limitations in the context of PINNs.Furthermore,we propose to tailor the idea of learning combinations of candidate activation functions to the PINNs optimization,which has a higher requirement for the smoothness and diversity on learned functions.This is achieved by removing activation functions which cannot provide higher-order derivatives from the candidate set and incorporating elementary functions with different properties according to our prior knowledge about the PDE at hand.We further enhance the search space with adaptive slopes.The proposed adaptive activation function can be used to solve different PDE systems in an interpretable way.Its effectiveness is demonstrated on a series of benchmarks.Code is available at https://github.com/LeapLabTHU/AdaAFforPINNs.

Smart manufacturing systems for Industry 4.0： Conceptual framework, scenarios, and future perspectives

Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems （CPS）, which mark the advent of the fourth stage of industrial production （i.e., Industry 4.0）. The widespread application of CPS in manufacturing environments renders manufacturing sys- tems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.

藏猪小肠形态、消化酶及微生物多样性研究

【目的】肠道是动物的主要消化器官,同时也是机体抵抗外源病原菌的重要屏障,已有研究表明,动物的品种、饲养方式、生长阶段均会影响动物的肠道菌群结构,但对舍饲和放牧饲养条件下藏猪的肠道菌群结构,以及藏猪和长白、约克与杜洛克三元杂交猪(DLY猪)的肠道菌群结构是否有差异,尚未见报道。【方法】本研究选取6–7月龄的放牧藏猪、舍饲藏猪和DLY猪的小肠组织,分别采用组织切片法测定各试验猪的肠道形态、酶活性测定试剂盒测定肠道内容物的消化酶活性,高通量测序法测定肠道微生物。【结果】DLY猪小肠的肌层厚度和绒毛高度高于藏猪,而隐窝深度低于藏猪;舍饲藏猪和放牧藏猪的小肠形态没有显著变化。DLY猪小肠的胰蛋白酶活性高于藏猪,而淀粉酶活性低于藏猪。三组猪小肠微生物的优势菌门均为Proteobacteria、Firmicutes和Bacteroidetes;藏猪的优势菌属为Ralstonia和Escherichia,而DLY猪的优势菌属为Ralstonia和Bradyrhizobium,但含量却存在显著性差异。舍饲藏猪与放牧藏猪肠道菌群结构相似度较高,而藏猪与DLY猪肠道菌群结构相似度较低。【结论】放牧藏猪、舍饲藏猪和DLY猪的小肠形态、消化酶活性和肠道菌群结构均存在显著性差异。

Electrical transport properties of Fe Se single crystal under high magnetic field

Understanding the normal electronic state is crucial for unveiling the mechanism of unconventional superconductivity(SC). In this paper, by applying a magnetic field of up to 37T on FeSe single crystals, we could reveal the normal-state transport properties after SC was completely suppressed. The normal-state resistivity exhibited a Fermi liquid behavior at low temperatures. Large orbital magnetoresistance(MR) was observed in the nematic state with H//c, whereas MR was negligible with H//ab. The magnitude of the orbital MR showed an unusual reduction, and Kohler’s rule was severely violated below 10-25 K;these were attributable to spin fluctuations. The results indicated that spin fluctuations played a paramount role in the normalstate transport properties of FeSe albeit the Fermi liquid nature was at low temperature.

Magnetic-field enhanced high-thermoelectric performance in topological Dirac semimetal Cd3As2 crystal

Thermoelectric materials can be used to convert heat to electric power through the Seebeck effect. We study magneto-thermoelectric figure of merit （ZT） in three-dimensional Dirac semimetal Cd3A 5 2 crystal. It is found that enhancement of power factor and reduction of thermal conductivity can be realized at the same time through magnetic field although magnetoresistivity is greatly increased. ZT can be highly enhanced from 0.17 to 1.1 by more than six times around 350 K under a perpendicular magnetic field of 7 T. The huge enhancement of ZT by magnetic field arises from the linear Dirac band with large Fermi velocity and the large electric thermal conductivity in CdsA 5 2. Our work paves a new way to greatly enhance the thermoelectric performance in the quantum topological materials.