Phononic Weyl Nodal Lines and Weyl Pairs in van der Waals Heavy Fermion Material CeSiI

Topological phonon is a new frontier in the field of topological materials.Different from electronic structures,phonons are bosons and most topological phonons are metallic form.Previous studies about topological phonon states were focused on three-dimensional(3D)materials.

MRm-DLDet:a memory-resident malware detection framework based on memory forensics and deep neural network

Cyber attackers have constantly updated their attack techniques to evade antivirus software detection in recent years.One popular evasion method is to execute malicious code and perform malicious actions only in memory.Mali-cious programs that use this attack method are called memory-resident malware,with excellent evasion capability,and have posed huge threats to cyber security.Traditional static and dynamic methods are not effective in detect-ing memory-resident malware.In addition,existing memory forensics detection solutions perform unsatisfactorily in detection rate and depend on massive expert knowledge in memory analysis.This paper proposes MRm-DLDet,a state-of-the-art memory-resident malware detection framework,to overcome these drawbacks.MRm-DLDet first builds a virtual machine environment and captures memory dumps,then creatively processes the memory dumps into RGB images using a pre-processing technique that combines deduplication and ultra-high resolution image cropping,followed by our neural network MRmNet in MRm-DLDet to fully extract high-dimensional features from memory dump files and detect them.MRmNet receives the labeled sub-images of the cropped high-resolution RGB images as input of ResNet-18,which extracts the features of the sub-images.Then trains a network of gated recurrent units with an attention mechanism.Finally,it determines whether a program is memory-resident malware based on the detection results of each sub-image through a specially designed voting layer.We created a high-quality dataset consisting of 2,060 benign and memory-resident programs.In other words,the dataset contains 1,287,500 labeled sub-images cut from the MRm-DLDet transformed ultra-high resolution RGB images.We implement MRm-DLDet for Windows 10,and it performs better than the latest methods,with a detection accuracy of up to 98.34%.Moreover,we measured the effects of mimicry and adversarial attacks on MRm-DLDet,and the experimental results demonstrated the robustness of MRm-DLDet.

Brain Systems Underlying Fundamental Motivations of Human Social Conformity

From birth to adulthood,we often align our behaviors,attitudes,and opinions with a majority,a phenomenon known as social conformity.A seminal framework has proposed that conformity behaviors are mainly driven by three fundamental motives:a desire to gain more information to be accurate,to obtain social approval from others,and to maintain a favorable self-concept.Despite extensive interest in neuroimaging investigation of social conformity,the relationship between brain systems and these fundamental motivations has yet to be established.Here,we reviewed brain imaging findings of social conformity with a componential framework,aiming to reveal the neuropsychological substrates underlying different conformity motivations.First,information-seeking engages the evaluation of social information,information integration,and modification of task-related activity,corresponding to brain networks implicated in reward,cognitive control,and tasks at hand.Second,social acceptance involves the anticipation of social acceptance or rejection and mental state attribution,mediated by networks of reward,punishment,and mentalizing.Third,self-enhancement entails the excessive representation of positive self-related information and suppression of negative self-related information,ingroup favoritism and/or outgroup derogation,and elaborated mentalizing processes to the ingroup,supported by brain systems of reward,punishment,and mentalizing.Therefore,recent brain imaging studies have provided important insights into the fundamental motivations of social conformity in terms of component processes and brain mechanisms.

基于材料基因工程的储氢材料数据库构建及其应用

高密度储氢材料的加速研发对于我国能源经济转型、早日实现双碳目标至关重要.集成高通量计算、数据库及机器学习预测的数据驱动材料研发新范式有望缩短研发周期并降低研发成本.由于组分、结构、工艺及形貌等多重复杂性,目前储氢材料相关的数据驱动性能预测研究较少,尚缺乏一个较为系统的性质性能数据库.因此,本文中我们开发了智能化的数据挖掘引擎,通过已发表的学术论文中发掘储氢材料热力学、动力学储氢性能数据,以及现有的材料基因工程数据库数据中获取含氢材料物理化学性质,并结合高通量第一性原理计算数据,构建了储氢材料性质性能数据集.基于所构建的数据集进一步建立了储氢材料数据库,并应用晶体图形神经网络等机器学习方法对储氢材料的吸放氢质量、吸放氢温度进行预测.相关工作将数据驱动的材料研发新模式与储氢材料相结合,为发展实用高效的新型储氢材料提供有效的平台支持、数据支撑、方法指引.

铋系含氧酸盐改性镁水解制氢的动/热力学研究

Mg基制氢材料具有来源广泛、反应温和、工艺简单、安全可控、理论产氢量高等优势,是当今的研究热点.本文提出采用高能球磨方法制备Mg-Bi系含氧酸盐Bi_(x)M_(y)O_(z)(M=Ti,V,Cr,Mo,W)复合材料以改善Mg水解制氢性能.本工作研究发现,掺杂Bi_(2)Mo O_(6)的Mg基复合制氢材料具有较好的性能,Mg-7 wt%Bi_(2)Mo O_(6)在298.15 K的最大产氢速率为756.1 m L g^(-1)min(-1).通过引入多壁碳纳米管(CNTs)可以进一步改善Mg-Bi_(2)Mo O_(6)的产氢性能,Mg-7 wt%Bi_(2)Mo O_(6)/CNTs的最大产氢速率达2172.4 m L g^(-1)min(-1),产氢活化能下降至23.6 k J mol^(-1).X光电子能谱(XPS)分析表明Bi_(2)Mo O_(6)/CNTs与Mg在球磨过程中发生固相反应生成Bi单质.密度泛函理论(DFT)计算揭示Bi原子掺杂可改变Mg的局域电荷分布,增强Mg对H_(2)O的吸附能,并降低H_(2)O解离后H原子的吸附能,促进水解反应进行.

Remarkable catalysis of spinel ferrite XFe2O4(X=Ni,Co,Mn,Cu,Zn)nanoparticles on the dehydrogenation properties of LiAlH_(4):An experimental and theoretical study

Safe,compact,lightweight and cost-effective hydrogen storage is one of the main challenges that need to be addressed to effectively deploy the hydrogen economy.LiAlH_(4),as a solid-state hydrogen storage material,presents several advantages such as high hydrogen storage capacity,low price and abundant sources.Unfortunately,neither thermodynamic nor kinetic properties of dehydrogenation for LiAlH_(4)can fulfill the requirements of practical application.Thus,a series of spinel ferrite nanoparticles such as XFe_(2)O_(4)(X=Ni,Co,Mn,Cu,Zn,Fe)were prepared by using the modified thermal decomposition method,and then doped into LiAlH_(4)by using ball milling.Our results show that LiAlH_(4)doped with 7 wt%NiFe_(2)O_(4)starts to release hydrogen at 69.1°C,and the total amount of hydrogen released is 7.29 wt%before 300°C.The activation energies of the two-step hydrogen release reactions of LiAlH_(4)doped with 7 wt%NiFe_(2)O_(4)are 42.32 kJ mol^(-1)and 71.42 k J mol,which are 59.0%and 63.6%lower than those of as-received LiAlH_(4),respectively.Combining the density functional theory(DFT)calculations,we reveal that both the presence of Ni FeOand in-situ formed AlNiin ball-milling decrease the desorption energy barrier of Al-H bonding in LiAlH_(4)and accelerate the breakdown of Al-H bonding through the interfacial charge transfer and the dehybridization of the Al-H cluster.Thus,the experimental and theoretical results open a new avenue toward designing high effective catalysts applied to LiAlH_(4)as a candidate for hydrogen storage.

Topological phononic materials:Computation and data

Considering the current volume of materials data,it is impossible to investigate each compound by trial-and-error experiments involving labor-intensive efforts.The scientists in the Shenyang National Laboratory for Materials Science developed a home-made software,HT-PHONON,selecting over 5,000 topological phononic(TP)materials out of 13,000 materials within high-throughput computational materials design combined with a big data analysis.Furthermore,an online database for TP materials has been constructed,which is now freely open to the public community through the website www.phonon.synl.ac.cn.In this perspective,we review this platform and discuss exciting consequences of TP materials that are expected from manipulating the phonons,linking fundamental research to potential applications.