通过交叉验证堆栈和VAD信息检测Windows代码注入

Windows 32/64位代码注入攻击是恶意软件常用的攻击技术,在内存取证领域,现存的代码注入攻击检测技术在验证完整性方面不能处理动态内容,并且在解析内存中数据结构方面无法兼容不同版本的Windows系统。因此提出了通过交叉验证进程堆栈和VAD信息定位注入代码方法,将基于遍历栈帧得到的函数返回地址、模块名等信息结合进程VAD结构来检测函数返回地址、匹配文件名以定位注入代码,并且研发了基于Volatility取证框架的Windows代码注入攻击检测插件codefind。测试结果表明,即使在VAD节点被恶意软件修改,方法仍能够有效定位Windows 32/64位注入代码攻击。

Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

To enhance the comprehensive performance of artillery internal ballistics—encompassing power,accuracy,and service life—this study proposed a multi-stage multidisciplinary design optimization(MS-MDO)method.First,the comprehensive artillery internal ballistic dynamics(AIBD)model,based on propellant combustion,rotation band engraving,projectile axial motion,and rifling wear models,was established and validated.This model was systematically decomposed into subsystems from a system engineering perspective.The study then detailed the MS-MDO methodology,which included Stage I(MDO stage)employing an improved collaborative optimization method for consistent design variables,and Stage II(Performance Optimization)focusing on the independent optimization of local design variables and performance metrics.The methodology was applied to the AIBD problem.Results demonstrated that the MS-MDO method in Stage I effectively reduced iteration and evaluation counts,thereby accelerating system-level convergence.Meanwhile,Stage II optimization markedly enhanced overall performance.These comprehensive evaluation results affirmed the effectiveness of the MS-MDO method.

基于Windows/RTX的实时仿测软件设计

针对传统测试软件实时性有限、传统仿真接口软件通用性程度低的问题,为满足半实物仿真软件需兼顾单元测试和控制系统仿真验证的需求,设计了基于Windows/RTX的实时仿测软件。仿测软件采用模块化的设计原则,开发了GUI层人机交互界面和RTX层实时运行程序。为保证实时性,采用无锁循环缓冲区+双线程技术,解决了RTX环境下仿真步长为1 ms时串口数据收发的超时问题;提出一种超时检测算法监测仿真节点的实时状态。借助cJSON优化了测试用例配置文件,用户可以更灵活地编辑测试用例,利用RTW自动代码生成将弹体模型编译集成到RTX仿测软件工程。实验结果表明:该仿测软件有效兼顾了单测与仿真,提高了仿测软件的通用性和二次开发效率,降低了开发难度。

Research on Regulation Method of Energy Storage System Based on Multi-Stage Robust Optimization

To address the scheduling problem involving energy storage systems and uncertain energy,we propose a method based on multi-stage robust optimization.This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method,which helps overcome the limitations of traditional methods in terms of time scale.The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day.To achieve this,a mathematical model is constructed to represent uncertain energy sources such as photovoltaic and wind power.The generalized Benders Decomposition method is then employed to solve the multi-stage objective optimization problem.By decomposing the problem into a series of sub-objectives,the system scale is effectively reduced,and the algorithm’s convergence ability is improved.Compared with other algorithms,the multi-stage robust optimization model has better economy and convergence ability and can be used to guide the power dispatching of uncertain energy and energy storage systems.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

快速拓展Windows实时性!史上最小的PLC运动控制器发布

为了解决Windows系统下可视化界面友好、软件开发容易、兼容性好,但无法实现实时性控制;其它带有实时控制功能的系统存在软件平台搭建困难、开发难度高、兼容性差等问题。立迈胜研发了一种NIMC301-A微型运动控制器,可以做到开箱即用,极大地增加了用户的易用性。其自带CANopen主站,具备PLC控制功能,也可为WindoWs平台提供实时性拓展,而体积仅相当于“U盘”大小!

多媒体课件在Windows系统中的个性化创意设计

文章从Windows桌面操作系统中图形界面应用出发,探讨通过个性化的设计方法创建自己的Windows Space,给用户创造出虚拟的桌面和软件界面应用,以多媒体课件的设计为实例,分析了多媒体课件界面设计的组成要素以及运用Flash和Dreamweaver进行个性化创意设计的基本步骤。

基于强化学习的自动化Windows域渗透方法

Windows域为用户之间的资源共享及信息交互提供统一的系统服务,在便利内网管理的同时带来了巨大的安全隐患。近年来,针对域控制器的各式攻击层出不穷,实现自动化渗透能够灵活检测Windows域中存在的漏洞威胁,保障办公网络安全稳定地持久运行,其核心是高效挖掘环境内可行的攻击路径。为此,将渗透测试过程进行强化学习建模,通过智能体与域环境的真实交互发现漏洞组合,进而验证有效的攻击序列;基于主机对渗透进程的贡献差异,减少强化学习模型中非必要的状态与动作,优化路径选择策略,提升实际攻击效率;使用状态动作删减、探索策略优化的Q学习算法筛选最优攻击路径,自动验证域环境中所有可能的安全隐患,为域管理员提供防护依据。实验针对典型内网业务场景展开测试,从生成的13种高效攻击路径中筛选最优路径,通过与相关研究成果对比,突出了所提方法在域控权限获取、主机权限获取、攻击步长、收敛性以及时间代价等方面的性能优化效果。

Trace Elements of Multi-stage Minerals and Titanite U-Pb Dating for the Gneisses from Liansan Island,Sulu UHPM Belt

Gneisses with anatectic characteristics from the Liansan island in the Sulu UHPM(ultra-high pressure metamorphic)belt were studied for petrography,titanite U-Pb dating and mineral geochemistry.Three origins of garnets are distinguished:metamorphic garnet,peritectic garnet and anatectic garnet,which are formed in the stages of peak metamorphism,retrograde anatexis and melt crystallization,respectively.The euhedral titanite has a high content of REE and high Th/U ratios,which is interpreted as indicating that it was newly-formed from an anatectic melt.The LA-ICP-MS titanite U-Pb dating yields 214-217 Ma ages for the titanite(melt)crystallization.The distribution of trace elements varies in response to the different host minerals at different stages.At the peak metamorphic stage,Y and HREE are mainly hosted by garnet,Ba and Rb by phengite,Sr,Nb,Ta,Pb,Th,U and LREE by allanite and Y,U and HREE by zircon.During partial melting,Y,Pb,Th,U and REE are released into the melt,which causes a dramatic decline of these element contents in the retrograde minerals.Finally,titanite absorbs most of the Nb,U,LREE and HREE from the melt.Therefore,the different stages of metamorphism have different mineral assemblages,which host different trace elements.

Overview of multi-stage charging strategies for Li-ion batteries

To reduce the carbon footprint in the transportation sector and improve overall vehicle efficiency,a large number of electric vehicles are being manufactured.This is due to the fact that environmental concerns and the depletion of fossil fuels have become significant global problems.Lithium-ion batteries(LIBs)have been distinguished themselves from alternative energy storage technologies for electric vehicles(EVs) due to superior qualities like high energy and power density,extended cycle life,and low maintenance cost to a competitive price.However,there are still certain challenges to be solved,like EV fast charging,longer lifetime,and reduced weight.For fast charging,the multi-stage constant current(MSCC) charging technique is an emerging solution to improve charging efficiency,reduce temperature rise during charging,increase charging/discharging capacities,shorten charging time,and extend the cycle life.However,there are large variations in the implementation of the number of stages,stage transition criterion,and C-rate selection for each stage.This paper provides a review of these problems by compiling information from the literature.An overview of the impact of different design parameters(number of stages,stage transition,and C-rate) that the MSCC charging techniques have had on the LIB performance and cycle life is described in detail and analyzed.The impact of design parameters on lifetime,charging efficiency,charging and discharging capacity,charging speed,and rising temperature during charging is presented,and this review provides guidelines for designing advanced fast charging strategies and determining future research gaps.

Multi-Stage Improvement of Marine Predators Algorithm and Its Application

The metaheuristic algorithms are widely used in solving the parameters of the optimization problem.The marine predators algorithm(MPA)is a novel population-based intelligent algorithm.Although MPA has shown a talented foraging strategy,it still needs a balance of exploration and exploitation.Therefore,a multi-stage improvement of marine predators algorithm(MSMPA)is proposed in this paper.The algorithm retains the advantage of multistage search and introduces a linear flight strategy in the middle stage to enhance the interaction between predators.Predators further away from the historical optimum are required to move,increasing the exploration capability of the algorithm.In the middle and late stages,the searchmechanism of particle swarmoptimization(PSO)is inserted,which enhances the exploitation capability of the algorithm.This means that the stochasticity is decreased,that is the optimal region where predators jumping out is effectively stifled.At the same time,self-adjusting weight is used to regulate the convergence speed of the algorithm,which can balance the exploration and exploitation capability of the algorithm.The algorithm is applied to different types of CEC2017 benchmark test functions and threemultidimensional nonlinear structure design optimization problems,compared with other recent algorithms.The results show that the convergence speed and accuracy of MSMPA are significantly better than that of the comparison algorithms.

Effect of False Windows on Light Exposure and Sleep Quality in Hospitalized Patients

Purpose: We aimed to investigate the effects of installing false windows next to hospital beds without windows on the amount of light received by patients and their sleep quality. Methods: The study included patients admitted to the Department of Neurology at our hospital between September 2020 and August 2021. An Actigraph device was fitted to patients’ wrist and their beds to measure the amount of light received and sleep quality. Patients were divided into three groups: bed with a window, aisle bed with a false window, and aisle bed without a window. Mean sleep efficiency (%), mean steps (per day), and the amount of light (lux) received by the patients and beds were measured. Results: Valid data were obtained for 48 participants (median age, 66.5 years). There were 23 patients in beds with a window, 13 patients in aisle beds without a false window, and 12 in aisle beds with a false window. No statistically significant differences were found in terms of mean sleep efficiency, number of steps taken, and the amount of light received by the patients (P > 0.05);however, difference in the mean amount of light received by the beds at the location of the bed was statistically significant (P Conclusion: The amount of light that the patient receives is not necessarily affected by the location of the bed or the presence of a false window.

基于Windows服务器的网络安全问题研究

针对Windows服务器网络安全框架,分析了Windows服务器网络安全功能及应用,指出了Windows服务器网络安全的常见问题,包括操作系统漏洞、病毒攻击、恶意软件、黑客攻击、暴力破解及系统攻击等。提出了Windows服务器网络安全常用算法,包括Windows进程自动加载技术、端口隐藏、密码算法。分析了经典Windows服务器网络安全框架,需将网络监控系统放在同一个网络中,采用加密数据传输,以保证客户通信安全。

基于Windows服务器终端的远程信息获取研究

分析了基于Windows服务器终端的远程信息获取技术,包括Microsoft Windows Server远程桌面服务、远程管理服务及网络管理服务。研究表明,使用Windows Server远程终端可实现信息获取与交互,通过套接字技术及TCP/IP等多种协议可完成Windows服务器终端通信,结合完成端口可搭建较为完成的系统框架。目前,搭建的系统框架能够满足客户的日常需求、服务器终端信息获取,采用加密技术可完成对隐私信息的网络保护。

基于Windows平台的故障转移集群研究与应用

针对目前铁路客运营销辅助决策系统的资料库存在单点故障的问题,研究Microsoft SQL Server数据库。在Windows Server服务器上利用微软公司内置的故障转移集群功能来提高应用的稳定性和可用性;基于故障转移集群实现SQL Server的Always On功能;通过研究集群相关技术,完成基于Windows Server 2016的无域控故障转移集群部署实践。实践表明,利用Windows故障转移特性,可以实现SQL Server数据库的高可用性及应用或服务的高可用性。

Development of RF windows for 650 MHz multibeam klystron

Radio frequency windows are developed and evaluated for a 650 MHz continuous-wave multibeam klystron.Thin-pillbox windows with alumina and beryllia disks are designed with an average RF power of CW 400 kW.Results of a cold test and tuning procedures are described.The final measured S11 curves under the required bandwidth are less than-32.0 and-26.9 dB for alumina and beryllia windows,respectively.The windows are tested up to CW 143 kW for traveling waves and CW 110 kW for standing waves using a solid-state amplifier as an RF power source.Multipactor simulations for windows and benchmark studies for the thermal analysis of ceramic disks are introduced.

Sol-gel-based porous Ti-doped tungsten oxide films for high-performance dual-band electrochromic smart windows

Dual-band electrochromic smart windows(DESWs)with independent control of the transmittance of near-infrared and visible light show great potential in the application of smart and energy-saving buildings.The current strategy for building DESWs is to screen materials for composite or prepare plasmonic nanocrystal films.These rigorous preparation processes seriously limit the further development of DESWs.Herein,we report a facile and effective sol-gel strategy using a foaming agent to achieve porous Ti-doped tungsten oxide film for the high performance of DESWs.The introduction of foaming agent polyvinylpyrrolidone during the film preparation can increase the specific surface area and free carrier concentration of the films and enhance their independent regulation ability of near-infrared electrochromism.As a result,the optimal film shows excellent dual-band electrochromic properties,including high optical modulation(84.9%at 633 nm and 90.3%at 1200 nm),high coloration efficiency(114.9 cm^(2) C^(-1) at 633 nm and 420.3 cm^(2) C^(-1) at 1200 nm),quick switching time,excellent bistability,and good cycle stability(the transmittance modulation losses at 633 and 1200 nm were 11%and 3.5%respectively after 1000 cycles).A demonstrated DESW fabricated by the sol-gel film showed effective management of heat and light of sunlight.This study represents a significant advance in the preparation of dual-band electrochromic films,which will shed new light on advancing electrochromic technology for future energy-saving smart buildings.

基于Windows平台的轻量级快速启动程序设计与开发

数以亿计的Windows平台用户在间隔启用多个软件时常因“找图标”而被迫打断工作思路,进而影响工作效率并引发烦躁情绪。针对上述痛点,研究出一种基于Windows系统运行功能的轻量级直达式快速启动方法,通过修改环境变量、添加快捷方式、自定义启动名称,设计并开发了一款满足Windows用户日常办公需求的轻量级快速启动程序。与同类程序相比,该程序兼具软件启动便捷、个性化定制和无后台资源占用等优势,可显著提升办公效率以及改善Windows系统使用体验。

Prediction of the Wastewater’s pH Based on Deep Learning Incorporating Sliding Windows

To protect the environment,the discharged sewage’s quality must meet the state’s discharge standards.There are many water quality indicators,and the pH(Potential of Hydrogen)value is one of them.The natural water’s pH value is 6.0–8.5.The sewage treatment plant uses some data in the sewage treatment process to monitor and predict whether wastewater’s pH value will exceed the standard.This paper aims to study the deep learning prediction model of wastewater’s pH.Firstly,the research uses the random forest method to select the data features and then,based on the sliding window,convert the data set into a time series which is the input of the deep learning training model.Secondly,by analyzing and comparing relevant references,this paper believes that the CNN(Convolutional Neural Network)model is better at nonlinear data modeling and constructs a CNN model including the convolution and pooling layers.After alternating the combination of the convolutional layer and pooling layer,all features are integrated into a full-connected neural network.Thirdly,the number of input samples of the CNN model directly affects the prediction effect of the model.Therefore,this paper adopts the sliding window method to study the optimal size.Many experimental results show that the optimal prediction model can be obtained when alternating six convolutional layers and three pooling layers.The last full-connection layer contains two layers and 64 neurons per layer.The sliding window size selects as 12.Finally,the research has carried out data prediction based on the optimal CNN deep learning model.The predicted pH of the sewage is between 7.2 and 8.6 in this paper.The result is applied in the monitoring system platform of the“Intelligent operation and maintenance platform of the reclaimed water plant.”

Active and passive modulation of solar light transmittance in a uniquely multifunctional dual-band single molecule for smart window applications

Functional materials may change color by heat and electricity separately or simultaneously in smart windows.These materials have not only demonstrated remarkable potential in the modulation of solar radiation but are also leading to the development of indoor environments that are more comfortable and conducive to improving individuals'quality of life.Unfortunately,dual-responsive materials have not received ample research attention due to economic and technological challenges.As a consequence,the broader utilization of smart windows faces hindrances.To address this new generational multistimulus responsive chromic materials,our group has adopted a developmental strategy to create a poly(NIPAM)n-HV as a switchable material by anchoring active viologen(HV)onto a phase-changing poly(NIPAM)n-based smart material for better utility and activity.These constructed smart windows facilitate individualistic reversible switching,from a highly transparent state to an opaque state(thermochromic)and a red state(electrochromic),as well as facilitate a simultaneous dual-stimuli response reversible switching from a clear transparent state to a fully opaque(thermochromic)and orange(electrochromic)states.Absolute privacy can be attained in smart windows designed for exclusive settings by achieving zero transmittance.Each unique chromic mode operates independently and modulates visible and near-infrared(NIR)light in a distinct manner.Hence,these smart windows with thermal and electric dual-stimuli responsiveness demonstrate remarkable heat regulation capabilities,rendering them highly attractive for applications in building facades,energy harvesting,privacy protection,and color display.