Impact of Extracellular Polymeric Substance in the Inactivation of Harmful Algae by Ag2O/g-C3N4 under Visible Light

Photocatalysis has attracted much attention as an emerging algae removal technology, but the inactivation performance is inevitably affected by the extracellular polymeric substance (EPS) produced by algae. In this study, a photocatalyst (Ag₂O/g-C₃N₄) with efficient algae inactivation is adopted to investigate the interactions with EPS, and the impact of EPS on photocatalytic algae removal is studied. The results show that EPS can adhere to the surface of Ag₂O/g-C₃N₄ by electrostatic force. The interaction with EPS decreases the surface zeta potential of the Ag₂O/g-C₃N₄ from 7.71 to −22.3 mV with the increase in EPS concentration, and the maximum ratio of particle size increases from 825 to 1281 nm. In addition, the interaction with EPS inhibits the release of Ag⁺ in Ag₂O/g-C₃N₄ by half, thus, the toxicity of metal ions will be alleviated. Meanwhile, EPS can also be degraded by Ag₂O/g-C₃N₄, indicating that EPS can work as a radical scavenger to protect the algae cells. Without the protection of EPS, 97.8% of algae cells are inactivated after 5 h photocatalysis. Therefore, more attention should be given to the interaction between EPS and photocatalyst to promote the design and application of the photocatalytic.     

An Interpretation Architecture for Deep Learning Models with the Application of COVID-19 Diagnosis

The Coronavirus disease 2019 (COVID-19) has become one of the threats to the world. Computed tomography (CT) is an informative tool for the diagnosis of COVID-19 patients. Many deep learning approaches on CT images have been proposed and brought promising performance. However, due to the high complexity and non-transparency of deep models, the explanation of the diagnosis process is challenging, making it hard to evaluate whether such approaches are reliable. In this paper, we propose a visual interpretation architecture for the explanation of the deep learning models and apply the architecture in COVID-19 diagnosis. Our architecture designs a comprehensive interpretation about the deep model from different perspectives, including the training trends, diagnostic performance, learned features, feature extractors, the hidden layers, the support regions for diagnostic decision, and etc. With the interpretation architecture, researchers can make a comparison and explanation about the classification performance, gain insight into what the deep model learned from images, and obtain the supports for diagnostic decisions. Our deep model achieves the diagnostic result of 94.75%, 93.22%, 96.69%, 97.27%, and 91.88% in the criteria of accuracy, sensitivity, specificity, positive predictive value, and negative predictive value, which are 8.30%, 4.32%, 13.33%, 10.25%, and 6.19% higher than that of the compared traditional methods. The visualized features in 2-D and 3-D spaces provide the reasons for the superiority of our deep model. Our interpretation architecture would allow researchers to understand more about how and why deep models work, and can be used as interpretation solutions for any deep learning models based on convolutional neural network. It can also help deep learning methods to take a step forward in the clinical COVID-19 diagnosis field.     

A Structural Entropy Measurement Principle of Propositional Formulas in Conjunctive Normal Form

The satisfiability (SAT) problem is a core problem in computer science. Existing studies have shown that most industrial SAT instances can be effectively solved by modern SAT solvers while random SAT instances cannot. It is believed that the structural characteristics of different SAT formula classes are the reasons behind this difference. In this paper, we study the structural properties of propositional formulas in conjunctive normal form (CNF) by the principle of structural entropy of formulas. First, we used structural entropy to measure the complex structure of a formula and found that the difficulty solving the formula is related to the structural entropy of the formula. The smaller the compressing information of a formula, the more difficult it is to solve the formula. Secondly, we proposed a λ-approximation strategy to approximate the structural entropy of large formulas. The experimental results showed that the proposed strategy can effectively approximate the structural entropy of the original formula and that the approximation ratio is more than 92%. Finally, we analyzed the structural properties of a formula in the solution process and found that a local search solver tends to select variables in different communities to perform the next round of searches during a search and that the structural entropy of a variable affects the probability of the variable being flipped. By using these conclusions, we also proposed an initial candidate solution generation strategy for a local search for SAT, and the experimental results showed that this strategy effectively improves the performance of the solvers CCAsat and Sparrow2011 when incorporated into these two solvers.     

Impurity effects of the Λhyperon in the hypernuclear systems ${}_{{\rm{\Lambda }}}^{25}\mathrm{Mg}$ and ${}_{{\rm{\Lambda }}}^{29}\mathrm{Si}$

Based on the beyond-mean-field Skyrme–Hartree–Fock model, impurity effects of the Λhyperon in the hypernuclear systems ${}_{\,{\rm{\Lambda }}}^{25}$ Mg and ${}_{\,{\rm{\Lambda }}}^{29}$ Si are investigated, respectively. Four cases, in which the Λhyperon occupies the single-particle orbitals ${\rm{\Lambda }}$[000]${\tfrac{1}{2}}^{+}$, ${\rm{\Lambda }}$[110]${\tfrac{1}{2}}^{-}$, ${\rm{\Lambda }}$[101]${\tfrac{3}{2}}^{-}$ and ${\rm{\Lambda }}$[101]${\tfrac{1}{2}}^{-}$, are focused. In each case, the potential energy surface and the energy curves projected on certain angular momenta are employed to show the influence of the Λhyperon on the nuclear core. Beside the shrinkage effect that is induced by the Λhyperon occupying the s_Λ orbital, it is found that the Λhyperon on the p_Λ orbital, ${\rm{\Lambda }}$[110]${\tfrac{1}{2}}^{-}$, drives the nuclear core toward a prolate shape, while the ones on the other two p_Λ orbitals, ${\rm{\Lambda }}$[101]${\tfrac{3}{2}}^{-}$ and ${\rm{\Lambda }}$[101]${\tfrac{1}{2}}^{-}$, drive the nuclear core toward an oblate shape. The energy spectra and the corresponding intra-band E2 transition rates for the rotational bands are given as a prediction for future experiments.     

BiVO4/TiO2 heterojunction with rich oxygen vacancies for enhanced electrocatalytic nitrogen reduction reaction

The large-scale production of ammonia mainly depends on the Haber–Bosch process, which will lead to the problems of high energy consumption and carbon dioxide emission. Electrochemical nitrogen fixation is considered to be an environmental friendly and sustainable process, but its efficiency largely depends on the activity and stability of the catalyst. Therefore, it is imperative to develop highefficient electrocatalysts in the field of nitrogen reduction reaction (NRR). In this paper, we developed a BiVO₄/TiO₂ nanotube (BiVO₄/TNT) heterojunction composite with rich oxygen vacancies as an electrocatalytic NRR catalyst. The heterojunction interface and oxygen vacancy of BiVO₄/TNT can be the active site of N₂ dynamic activation and proton transition. The synergistic effect of TiO₂ and BiVO₄ shortens the proton transport path and reduces the over potential of chemical reaction. BiVO₄/TNT has high ammonia yield of 8.54 μg·h⁻¹·cm⁻² and high Faraday efficiency of 7.70% in −0.8 V vs. RHE in 0.1 M Na₂SO₄ solution.     

Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns

The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.     

晚期HEMP作用下铁路牵引供电系统GIC算法研究

高空核爆电磁脉冲晚期效应（E₃）会引起地磁场剧烈变化并形成地面感应电场。感应电场等效为激励源与地面长距离导体和大地构成回路,产生地磁感应电流 （GIC）。GIC可引起牵引供电系统中变压器直流偏磁,从而严重威胁牵引供电系统的安全运行。本文基于平面波理论、分层大地电导率模型并结合牵引供电系统的电路模型,提出E₃作用下的牵引供电系统GIC算法,并以带回流线的直接供电方式的铁路牵引供电系统为例,首次计算了系统GIC情况。结果表明,该供电方式下牵引供电系统中的GIC远大于系统中变压器等设备的耐受值,为进一步研究E₃作用下牵引供电系统效应及我国铁路设备选型、灾害防治等提供支撑。     

基于FDTD的时域混合方法及其在天线前门耦合数值模拟中的应用

强电磁脉冲环境下的平台-机载天线一体化耦合计算属于典型多尺度时域电磁计算问题,采用传统的FDTD方法数值模拟时,由于精细结构的存在导致网格量巨大,计算效率低下。介绍了一种将非均匀FDTD方法与细导线FDTD方法以及多网格集总元件FDTD方法相结合的时域混合方法,能够有效降低计算开销,结合并行计算技术,快速计算得到天线端口上耦合产生的瞬态电压和电流响应,并将该方法成功应用于无人机平台-天线一体化前门耦合数值模拟中。     

新体制引信干扰机理分析

为应对新体制无线电引信带来的威胁,从引信对抗角度以超宽带无线电引信为研究对象,研究干扰作用下其失效机理。揭示了超宽带无线电引信敏感干扰波形响应机理,理论推导了周期调制干扰信号作用下引信响应特性,仿真计算了在射频噪声、正弦波调幅、正弦波调频以及扫频正弦波调幅干扰信号作用下超宽带引信接收机相关器输出响应特性。以干信比增益为表征参量,通过理论计算、仿真及试验验证得出结论：周期类调制干扰中调幅类干扰对超宽带引信干扰效果最好。     

电气线路互联系统线缆抗高空核电磁脉冲耦合效应

高空核电磁脉冲（HEMP）对电子设备的耦合途径主要有两方面：一方面是通过装备（产品）上的天线耦合通道进入到电子系统内的“前门耦合”方式;另一方面则是“后门耦合”,即通过装备（产品）上的壳体、电源线、电缆、机箱的缝隙、孔洞等途径进行耦合。主要研究电气线路互联系统(EWIS)线缆抗高空核电磁脉冲耦合效应,通过研究HEMP干扰的特征、能量分布,搭建HEMP数学模型,采用控制变量法,改变EWIS线缆类型、离地高度等要素,通过在CST上建立仿真模型以及开展试验,分析HEMP对电子设备造成的影响程度,得到HEMP耦合效应的一般性结论与规律。