Attention-Shared Multi-Agent Actor–Critic-Based Deep Reinforcement Learning Approach for Mobile Charging Dynamic Scheduling in Wireless Rechargeable Sensor Networks

The breakthrough of wireless energy transmission (WET) technology has greatly promoted the wireless rechargeable sensor networks (WRSNs). A promising method to overcome the energy constraint problem in WRSNs is mobile charging by employing a mobile charger to charge sensors via WET. Recently, more and more studies have been conducted for mobile charging scheduling under dynamic charging environments, ignoring the consideration of the joint charging sequence scheduling and charging ratio control (JSSRC) optimal design. This paper will propose a novel attention-shared multi-agent actor–critic-based deep reinforcement learning approach for JSSRC (AMADRL-JSSRC). In AMADRL-JSSRC, we employ two heterogeneous agents named charging sequence scheduler and charging ratio controller with an independent actor network and critic network. Meanwhile, we design the reward function for them, respectively, by considering the tour length and the number of dead sensors. The AMADRL-JSSRC trains decentralized policies in multi-agent environments, using a centralized computing critic network to share an attention mechanism, and it selects relevant policy information for each agent at every charging decision. Simulation results demonstrate that the proposed AMADRL-JSSRC can efficiently prolong the lifetime of the network and reduce the number of death sensors compared with the baseline algorithms.     

能量输运对柱状等离子体开关磁场穿透过程的影响

 对于高密度、导通时间为μs级的柱状等离子体开关,利用磁流体动力学理论(MHD),对其导通阶段的磁场穿透过程进行了模拟,得到了磁场分布随时间的变化;研究了开关导通过程中能量输运导致的温度不均匀分布对磁场穿透过程的影响。模拟结果表明：对于高密度等离子体开关,磁场以远大于磁扩散速率的速度穿透到等离子体中;在磁压对等离子体产生的压缩效应和欧姆加热效应共同作用下,激波区域的等离子体温度显著升高,这进一步加速了磁场穿透;当考虑能量输运方程时,开关导通时间为0.87 μs,比等温模型的结果0.92 μs短,与实验结果0.87 μs相一致。     

Development of Green and Efficient Extraction of Bioactive Ginsenosides from Panax ginseng with Deep Eutectic Solvents

The extraction of active constituents from natural sources in a green and efficient manner is considered an important field in the pharmaceutical industry. In recent years, deep eutectic solvents (DESs), a new type of green solvent, have attracted increasing attention. Therefore, we aimed to establish a green and high-efficiency extraction method for ginsenosides based on DESs. This study takes Panax ginseng as a model sample. Eighteen different DESs were produced to extract polar ginsenosides. Ultrasound-assisted extraction (UAE) was applied for simplicity and efficiency. A binary DES synthesized using choline chloride and urea at a proportion of 1:2 prepared by a heating stirring method is proven to be more effective than other solvents, such as the widely used 70% ethanol for the extraction of ginsenosides. Three variables that might affect the extraction, including the DES content in the extraction solvent, liquid/solid ratio, and ultrasound extraction time, were evaluated for optimization. The optimum extraction conditions for ginsenosides were determined as follows: DES water content of 20 wt%, liquid/solid ratio of 15 mL g⁻¹, and an ultrasonic extraction time of 15 min. The extraction yield for the optimized method is found to be 31% higher than that for 70% ethanol, which achieves efficient extraction. This study shows that DESs are available to extract ginsenosides for use in traditional Chinese medicine. The discovery also contributes to further research into the green extraction of ginsenosides.     

n型硅微尖场发射电子能谱的模拟计算

结合金属的场发射电子能谱,模拟计算了场渗透对n型半导体硅微尖的场发射能谱的影响,并与n型硅微尖的场发射能谱实验结果进行了比较,讨论了模拟计算误差的来源。计算结果表明电场渗透现象导致硅的场发射能谱向低能方向偏移,表面电场越高,能谱的偏移量越大,其偏移程度可超过1eV。导致硅微尖的场发射能谱偏移的主要因素是半导体的场渗透现象。     

Application of Deep Learning Workflow for Autonomous Grain Size Analysis

Traditional grain size determination in materials characterization involves microscopy images and a laborious process requiring significant manual input and human expertise. In recent years, the development of computer vision (CV) has provided an alternative approach to microstructural characterization with preliminary implementations greatly simplifying the grain size determination process. Here, an end-to-end workflow to measure grain size in microscopy images without any manual input is presented. Following the ASTM standards for grain size determination, results from the line intercept (Heyn’s method) and planimetric (Saltykov’s method) approaches are used as the baseline. A pre-trained holistically nested edge detection (HED) model is used for CV-based edge detection, and the results are further compared to the classic Canny edge detection method. Post-processing was performed using open-source image processing packages to extract the grain size. In optical microscope images, the pre-trained HED model achieves much higher accuracy than the Canny edge detection method while reducing the image processing time by one to two orders of magnitude compared to traditional methods. The effects of morphological operations on the predicted grain size accuracy are also explored. Overall, the proposed end-to-end convolutional neural network (CNN)-based workflow can significantly reduce the processing time while maintaining the same accuracy as the traditional manual method.     

Learnability of the Boolean Innerproduct in Deep Neural Networks

In this paper, we study the learnability of the Boolean inner product by a systematic simulation study. The family of the Boolean inner product function is known to be representable by neural networks of threshold neurons of depth 3 with only 2n+1 units (n the input dimension)—whereas an exact representation by a depth 2 network cannot possibly be of polynomial size. This result can be seen as a strong argument for deep neural network architectures. In our study, we found that this depth 3 architecture of the Boolean inner product is difficult to train, much harder than the depth 2 network, at least for the small input size scenarios n≤16. Nonetheless, the accuracy of the deep architecture increased with the dimension of the input space to 94% on average, which means that multiple restarts are needed to find the compact depth 3 architecture. Replacing the fully connected first layer by a partially connected layer (a kind of convolutional layer sparsely connected with weight sharing) can significantly improve the learning performance up to 99% accuracy in simulations. Another way to improve the learnability of the compact depth 3 representation of the inner product could be achieved by adding just a few additional units into the first hidden layer.     

Physics-Informed Neural Networks for Solving Coupled Stokes–Darcy Equation

In this paper, a grid-free deep learning method based on a physics-informed neural network is proposed for solving coupled Stokes–Darcy equations with Bever–Joseph–Saffman interface conditions. This method has the advantage of avoiding grid generation and can greatly reduce the amount of computation when solving complex problems. Although original physical neural network algorithms have been used to solve many differential equations, we find that the direct use of physical neural networks to solve coupled Stokes–Darcy equations does not provide accurate solutions in some cases, such as rigid terms due to small parameters and interface discontinuity problems. In order to improve the approximation ability of a physics-informed neural network, we propose a loss-function-weighted function strategy, a parallel network structure strategy, and a local adaptive activation function strategy. In addition, the physical information neural network with an added strategy provides inspiration for solving other more complicated problems of multi-physical field coupling. Finally, the effectiveness of the proposed strategy is verified by numerical experiments.     

Utilization of Deep Eutectic Solvents to Reduce the Release of Hazardous Gases to the Atmosphere: A Critical Review

The release of certain gases to the atmosphere is controlled in many countries owing to their negative impact on the environment and human health. These gases include carbon dioxide (CO₂), sulfur oxides (SOx), nitrogen oxides (NOx), hydrogen sulfide (H₂S) and ammonia (NH₃). Considering the major contribution of greenhouse gases to global warming and climate change, mitigation of these gases is one of the world’s primary challenges. Nevertheless, the commercial processes used to capture these gases suffer from several drawbacks, including the use of volatile solvents, generation of hazardous byproducts, and high-energy demand. Research in green chemistry has resulted in the synthesis of potentially green solvents that are non-toxic, efficient, and environmentally friendly. Deep eutectic solvents (DESs) are novel solvents that upon wise choice of their constituents can be green and tunable with high biocompatibility, high degradability, and low cost. Consequently, the capture of toxic gases by DESs is promising and environmentally friendly and has attracted much attention during the last decade. Here, we review recent results on capture of these gases using different types of DESs. The effect of different parameters, such as chemical structure, molar ratio, temperature, and pressure, on capture efficiency is discussed.