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...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。展开更多
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 ...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.展开更多
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 ...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.展开更多
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 malwar...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.展开更多
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...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.展开更多
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...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.展开更多
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...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.展开更多
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 ...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.展开更多
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Window...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。展开更多
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...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.展开更多
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 st...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.展开更多
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...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.”展开更多
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 ...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.展开更多
基金supported by the“National Natural Science Foundation of China”(Grant Nos.52105106,52305155)the“Jiangsu Province Natural Science Foundation”(Grant Nos.BK20210342,BK20230904)the“Young Elite Scientists Sponsorship Programby CAST”(Grant No.2023JCJQQT061).
文摘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.
文摘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.
基金This researchwork is supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R411),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘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.
基金supported by funds from the National Natural Science Foundation of China(Grant Nos.42172067,41972064,U1906207)the SDUST Research Fund。
文摘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.
文摘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.
基金supported in part byNationalNatural Science Foundation of China(No.62066001)Natural Science Foundation of Ningxia Province(No.2021AAC03230)Program of Graduate Innovation Research of North Minzu University(No.YCX22111).
文摘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.
文摘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.
文摘分析了基于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。
文摘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.
基金supported by the National Natural Science Foundation of China(51902064)the Natural Science Foundation of Guangxi(2022GXNSFFA0350325)+2 种基金the Scientific and Technological Bases and Talents of Guangxi(Guike AD20159073)the special fund for“Guangxi Bagui Scholars”the“Guangxi HundredTalent Program”。
文摘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.
基金This research was funded by the National Key R&D Program of China(No.2018YFB2100603)the Key R&D Program of Hubei Province(No.2022BAA048)+2 种基金the National Natural Science Foundation of China program(No.41890822)the Open Fund of National Engineering Research Centre for Geographic Information System,China University of Geosciences,Wuhan 430074,China(No.2022KFJJ07)The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Centre of Wuhan University.
文摘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.”
基金supported by the National Research Foundation (NRF)grants funded by the Ministry of Education (2020R1A6A1A03038817),Republic of Korea。
文摘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.