Metaheuristic algorithms for groundwater model parameter inversion:Advances and prospects

Groundwater inverse modeling is a vital technique for estimating unmeasurable model parameters and enhancing numerical simulation accuracy.This paper comprehensively reviews the current advances and future prospects of metaheuristic algorithm-based groundwater model parameter inversion.Initially,the simulation-optimization parameter estimation framework is introduced,which involves the integration of simulation models with metaheuristic algorithms.The subsequent sections explore the fundamental principles of four widely employed metaheuristic algorithms-genetic algorithm(GA),particle swarm optimization(PSO),simulated annealing(SA),and differential evolution(DE)-highlighting their recent applications in water resources research and related areas.Then,a solute transport model is designed to illustrate how to apply and evaluate these four optimization algorithms in addressing challenges related to model parameter inversion.Finally,three noteworthy directions are presented to address the common challenges among current studies,including balancing the diverse exploration and centralized exploitation within metaheuristic algorithms,local approxi-mate error of the surrogate model,and the curse of dimensionality in spatial variational heterogeneous pa-rameters.In summary,this review paper provides theoretical insights and practical guidance for further advancements in groundwater inverse modeling studies.

Controlling the Diameter of Single-Walled Carbon Nanotubes by Improving the Dispersion of the Uniform Catalyst Nanoparticles on Substrate

To have uniform nanoparticles individually dispersed on substrate before single-walled carbon nanotubes(SWNTs)growth at high temperature is the key for controlling the diameter of the SWNTs.In this letter,a facile approach to control the diameter and distribution of the SWNTs by improving the dispersion of the uniform Fe/Mo nanoparticles on silicon wafers with silica layer chemically modified by 1,1,1,3,3,3-hexamethyldisilazane under different conditions is reported.It is found that the dispersion of the catalyst nanoparticles on Si wafer surface can be improved greatly from hydrophilic to hydrophobic,and the diameter and distribution of the SWNTs depend strongly on the dispersion of the catalyst on the substrate surface.Well dispersion of the catalyst results in relatively smaller diameter and narrower distribution of the SWNTs due to the decrease of aggregation and enhancement of dispersion of the catalyst nanoparticles before growth.It is also found that the diameter of the superlong aligned SWNTs is smaller with more narrow distribution than that of random nanotubes.

具有双环左翼的二芳基嘧啶类HIV-1抑制剂的设计与合成

鉴于3,4-亚甲二氧苯基片段在许多药物中均为重要的药效团,本研究以经典的二芳基嘧啶类HIV-1抑制剂代表性化合物TMC125为先导化合物,将其左翼苯基替换为3,4-亚甲二氧苯基设计得到具有双环左翼的目标分子,以期得到活性更佳的新化合物。分子对接验证了设计的合理性。目标化合物的合成方法如下:以2-硫脲嘧啶为起始原料,通过三步反应得到关键中间体2-(对氰基苯胺基)-4-氯嘧啶;该中间体与芝麻酚发生亲核取代反应得到目标化合物,收率为89%。采用核磁共振氢谱(1H NMR)、核磁共振碳谱(13C NMR)和质谱(MS)表征了其结构。

Fooling intrusion detection systems using adversarially autoencoder

Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

基于校园网环境的网络性能测试系统研究与实现

北京化工大学校园网络性能测试系统基于开源项目进行二次开发,采用容器技术进行部署,为全校广大师生提供校园网测试服务。系统为B/S架构,基于HTTP请求进行网络上传、下载带宽、延迟、抖动等性能测试,并将每条测试记录与具体的接入方式、接入IP地址、接入物理地点相对应,以实现数据分类。系统依托数据可视化平台,将测试记录以折线图、帕累托图等形式进行展示,以帮助网络管理人员掌握网络情况。同时,用户依托该系统可了解自己接入端的网络状况,以便排查网路故障。

Path guided motion synthesis for Drosophila larvae

The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions.Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity,and have difficulty in generating realistic and multi-pattern mollusk motions.In this work,we present a large-scale dynamic pose dataset of Drosophila larvae and propose a motion synthesis model named Path2Pose to generate a pose sequence given the initial poses and the subsequent guiding path.The Path2Pose model is further used to synthesize long pose sequences of various motion patterns through a recursive generation method.Evaluation analysis results demonstrate that our novel model synthesizes highly realistic mollusk motions and achieves state-of-the-art performance.Our work proves high performance of deep neural networks for mollusk motion synthesis and the feasibility of long pose sequence synthesis based on the customized body shape and guiding path.