Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally...Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally,approximately 15 million PTB cases are reported annually,posing a huge burden on individual families and the community economy[2].In the context of climate warming,O_(3) pollution has continuously increased in many countries in recent years,including China;therefore,scientific communities and government agencies must strive to mitigate ozone pollution.展开更多
Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security...Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security threats.Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments,yet they are fundamentally limited in computational and memory resources.Accurate and memoryefficient persistent flow detection on programmable switches is therefore essential.However,existing approaches often rely on fixed-window sketches or multiple sketches instances,which either suffer from insufficient temporal precision or incur substantial memory overhead,making them ineffective on programmable switches.To address these challenges,we propose SP-Sketch,an innovative sliding-window-based sketch that leverages a probabilistic update mechanism to emulate slot expiration without maintaining multiple sketch instances.This innovative design significantly reduces memory consumption while preserving high detection accuracy across multiple time intervals.We provide rigorous theoretical analyses of the estimation errors,deriving precise error bounds for the proposed method,and validate our approach through comprehensive implementations on both P4 hardware switches(with Intel Tofino ASIC)and software switches(i.e.,BMv2).Experimental evaluations using real-world traffic traces demonstrate that SP-Sketch outperforms traditional methods,improving accuracy by up to 20%over baseline sliding window approaches and enhancing recall by 5%compared to non-sliding alternatives.Furthermore,SP-Sketch achieves a significant reduction in memory utilization,reducing memory consumption by up to 65%compared to traditional methods,while maintaining a robust capability to accurately track persistent flow behavior over extended time periods.展开更多
Memory is a cognitive process through which past experiences are encoded,stored,and retrieved,playing a crucial role in intelligent behavior.It is well established that the hippocampus continues to reactivate memories...Memory is a cognitive process through which past experiences are encoded,stored,and retrieved,playing a crucial role in intelligent behavior.It is well established that the hippocampus continues to reactivate memories for several days after learning,and this process primarily occurs during sleep[1,2].The prevailing view suggests that sharp-wave ripples(SWRs)during non-rapid eye movement(NREM)sleep serve as key electrophysiological signatures of memory replay[3,4].However,only a small portion of SWRs contain memory replay[5].The direct relationship among SWRs,memory replay,and memory consolidation remains an open question.Another unresolved issue is how the hippocampus simultaneously reactivates both new and old memories while preventing interference.展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing opti...This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center,focusing on minimizing vehicle dispatch,loading and unloading,operating,and time window penalty costs.The model incorporates constraints such as vehicle capacity,time windows,and travel distance,and is solved using a genetic algorithm to ensure optimal route planning.Through MATLAB simulations,34 customer points are analyzed,demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%,increases vehicle loading rates by 20.04%,and decreases travel distance compared to delivery-only or pickup-only models.The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization,offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.展开更多
This year summarizes the experience of industrialization of vacuum glazing in the past twenty years.A series of technical difficulties have been solved to start the first global mass production of high-quality vacuum ...This year summarizes the experience of industrialization of vacuum glazing in the past twenty years.A series of technical difficulties have been solved to start the first global mass production of high-quality vacuum glass.High quality means high performance and long life which are interrelated.A mass production line must be able to achieve these two requirements if it is to produce vacuum glazing products that can be accepted by the society.With a U-value of 0.4 W/m²·K based on Low-E(low emissivity)with an emissivity of 0.03 the door is wide open for further solutions.Time,gradually to improve costs,maximizes output and develops innovative solutions of advanced window and façade systems combining complete new features like smart glasses,intelligent lamella systems in hybrid VG-IG solutions changing the building world towards“Energy plus Houses”.Market demand will rapidly increase with completely new options.Cost saving means to balance additional advantages for savings against system costs of window or façade elements.Due to promotion of energy saving and emission reduction,both,subjective and objective conditions for industrialization of vacuum glasses are perfect;the building world is waiting for it,since long.There is a lot to investigate and to gain for business success.展开更多
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ...The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.展开更多
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
CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet...CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet the industrial demands.Herein,the nanorod-like bimetallic ln_(2)O_(3)/Bi_(2)O_(3)catalysts were successfully synthesized by pyrolysis of bimetallic InBi-MOF precursors.The abundant oxygen vacancies generated from the lattice mismatch of Bi_(2)O_(3)and ln_(2)O_(3)reduced the activation energy of CO_(2)to*CO_(2)·^(-)and improved the selectivity of*CO_(2)·^(-)to formate simultaneously.Meanwhile,the carbon skeleton derived from the pyrolysis of organic framework of InBi-MOF provided a conductive network to accelerate the electrons transmission.The catalyst exhibited an ultra-broad applied potential window of 1200 mV(from-0.4 to-1.6 V vs RHE),relativistic high Faradaic efficiency of formate(99.92%)and satisfactory stability after 30 h.The in situ FT-IR experiment and DFT calculation verified that the abundant oxygen vacancies on the surface of catalysts can easily absorb CO_(2)molecules,and oxygen vacancy path is dominant pathway.This work provides a convenient method to construct high-performance bimetallic catalysts for the industrial application of CO_(2)RR.展开更多
Energy density,the Achilles’heel of aqueous supercapacitors,is simultaneously determined by the voltage window and specific capacitance of the carbon materials,but the strategy of synchronously boosting them has rare...Energy density,the Achilles’heel of aqueous supercapacitors,is simultaneously determined by the voltage window and specific capacitance of the carbon materials,but the strategy of synchronously boosting them has rarely been reported.Herein,we demonstrate that the rational utilization of the interaction between redox mediators(RMs)and carbon electrode materials,especially those with rich intrinsic defects,contributes to extended potential windows and more stored charges concurrently.Using 4-hydroxy-2,2,6,6-tetramethylpiperidinyloxyl(4OH-TEMPO)and intrinsic defect-rich carbons as the RMs and electrode materials,respectively,the potential window and capacitance are increased by 67%and sixfold in a neutral electrolyte.Moreover,this strategy could also be applied to alkaline and acid electrolytes.The first-principle calculation and experimental results demonstrate that the strong interaction between 4OH-TEMPO and defectrich carbons plays a key role as preferential adsorbed RMs may largely prohibit the contact of free water molecules with the electrode materials to terminate the water splitting at elevated potentials.For the RMs offering weaker interaction with the electrode materials,the water splitting still proceeds with a thus sole increase of the stored charges.The results discovered in this work could provide an alternative solution to address the low energy density of aqueous supercapacitors.展开更多
Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the comp...Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.展开更多
To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,...To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.展开更多
基金supported by the Natural Science Foundation of Henan Province[grant number:242300420115]Key Scientific Research Projects in Universities of Henan Province[grant number:23A330006].
文摘Preterm birth(PTB)is defined as delivery before 37 weeks of gestation.PTB is associated with increased cardiovascular risk,neurodevelopmental disorders,and other diseases in infancy,childhood,and adulthood[1].Globally,approximately 15 million PTB cases are reported annually,posing a huge burden on individual families and the community economy[2].In the context of climate warming,O_(3) pollution has continuously increased in many countries in recent years,including China;therefore,scientific communities and government agencies must strive to mitigate ozone pollution.
基金supported by the National Undergraduate Innovation and Entrepreneurship Training Program of China(Project No.202510559076)at Jinan University,a nationwide initiative administered by the Ministry of Educationthe National Natural Science Foundation of China(NSFC)under Grant No.62172189.
文摘Persistent flows are defined as network flows that persist over multiple time intervals and continue to exhibit activity over extended periods,which are critical for identifying long-term behaviors and subtle security threats.Programmable switches provide line-rate packet processing to meet the requirements of high-speed network environments,yet they are fundamentally limited in computational and memory resources.Accurate and memoryefficient persistent flow detection on programmable switches is therefore essential.However,existing approaches often rely on fixed-window sketches or multiple sketches instances,which either suffer from insufficient temporal precision or incur substantial memory overhead,making them ineffective on programmable switches.To address these challenges,we propose SP-Sketch,an innovative sliding-window-based sketch that leverages a probabilistic update mechanism to emulate slot expiration without maintaining multiple sketch instances.This innovative design significantly reduces memory consumption while preserving high detection accuracy across multiple time intervals.We provide rigorous theoretical analyses of the estimation errors,deriving precise error bounds for the proposed method,and validate our approach through comprehensive implementations on both P4 hardware switches(with Intel Tofino ASIC)and software switches(i.e.,BMv2).Experimental evaluations using real-world traffic traces demonstrate that SP-Sketch outperforms traditional methods,improving accuracy by up to 20%over baseline sliding window approaches and enhancing recall by 5%compared to non-sliding alternatives.Furthermore,SP-Sketch achieves a significant reduction in memory utilization,reducing memory consumption by up to 65%compared to traditional methods,while maintaining a robust capability to accurately track persistent flow behavior over extended time periods.
基金supported by the National Natural Science Foundation of China(32371028,32300822,U24A20373,and 82071177)the Shanghai Rising-Star Program(24QA2704800)+2 种基金the Shanghai Jiao Tong University 2030 InitiativeShanghai Municipal Health Commission(202340046)the Fund for Excellent Young Scholars of Shanghai Ninth People's Hospital,Shanghai Jiao Tong University School of Medicine.
文摘Memory is a cognitive process through which past experiences are encoded,stored,and retrieved,playing a crucial role in intelligent behavior.It is well established that the hippocampus continues to reactivate memories for several days after learning,and this process primarily occurs during sleep[1,2].The prevailing view suggests that sharp-wave ripples(SWRs)during non-rapid eye movement(NREM)sleep serve as key electrophysiological signatures of memory replay[3,4].However,only a small portion of SWRs contain memory replay[5].The direct relationship among SWRs,memory replay,and memory consolidation remains an open question.Another unresolved issue is how the hippocampus simultaneously reactivates both new and old memories while preventing interference.
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
文摘This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center,focusing on minimizing vehicle dispatch,loading and unloading,operating,and time window penalty costs.The model incorporates constraints such as vehicle capacity,time windows,and travel distance,and is solved using a genetic algorithm to ensure optimal route planning.Through MATLAB simulations,34 customer points are analyzed,demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%,increases vehicle loading rates by 20.04%,and decreases travel distance compared to delivery-only or pickup-only models.The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization,offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.
文摘This year summarizes the experience of industrialization of vacuum glazing in the past twenty years.A series of technical difficulties have been solved to start the first global mass production of high-quality vacuum glass.High quality means high performance and long life which are interrelated.A mass production line must be able to achieve these two requirements if it is to produce vacuum glazing products that can be accepted by the society.With a U-value of 0.4 W/m²·K based on Low-E(low emissivity)with an emissivity of 0.03 the door is wide open for further solutions.Time,gradually to improve costs,maximizes output and develops innovative solutions of advanced window and façade systems combining complete new features like smart glasses,intelligent lamella systems in hybrid VG-IG solutions changing the building world towards“Energy plus Houses”.Market demand will rapidly increase with completely new options.Cost saving means to balance additional advantages for savings against system costs of window or façade elements.Due to promotion of energy saving and emission reduction,both,subjective and objective conditions for industrialization of vacuum glasses are perfect;the building world is waiting for it,since long.There is a lot to investigate and to gain for business success.
文摘The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.
基金financially supported by the National Natural Science Foundation of China(52072409)the Major Scientific and Technological Innovation Project of Shandong Province(2020CXGC010403)+1 种基金the Taishan Scholar Project(No.ts201712020)the Natural Science Foundation of Shandong Province(ZR2021QE062)
文摘CO_(2)electrochemical reduction reaction(CO_(2)RR)to formate is a hopeful pathway for reducing CO_(2)and producing high-value chemicals,which needs highly selective catalysts with ultra-broad potential windows to meet the industrial demands.Herein,the nanorod-like bimetallic ln_(2)O_(3)/Bi_(2)O_(3)catalysts were successfully synthesized by pyrolysis of bimetallic InBi-MOF precursors.The abundant oxygen vacancies generated from the lattice mismatch of Bi_(2)O_(3)and ln_(2)O_(3)reduced the activation energy of CO_(2)to*CO_(2)·^(-)and improved the selectivity of*CO_(2)·^(-)to formate simultaneously.Meanwhile,the carbon skeleton derived from the pyrolysis of organic framework of InBi-MOF provided a conductive network to accelerate the electrons transmission.The catalyst exhibited an ultra-broad applied potential window of 1200 mV(from-0.4 to-1.6 V vs RHE),relativistic high Faradaic efficiency of formate(99.92%)and satisfactory stability after 30 h.The in situ FT-IR experiment and DFT calculation verified that the abundant oxygen vacancies on the surface of catalysts can easily absorb CO_(2)molecules,and oxygen vacancy path is dominant pathway.This work provides a convenient method to construct high-performance bimetallic catalysts for the industrial application of CO_(2)RR.
基金financially supported by the National Natural Science Foundation of China(22179145,22138013,and 21975287)Shandong Provincial Natural Science Foundation(ZR2020ZD08)+1 种基金Taishan Scholar Project(no.ts201712020)the startup support grant from China University of Petroleum(East China)
文摘Energy density,the Achilles’heel of aqueous supercapacitors,is simultaneously determined by the voltage window and specific capacitance of the carbon materials,but the strategy of synchronously boosting them has rarely been reported.Herein,we demonstrate that the rational utilization of the interaction between redox mediators(RMs)and carbon electrode materials,especially those with rich intrinsic defects,contributes to extended potential windows and more stored charges concurrently.Using 4-hydroxy-2,2,6,6-tetramethylpiperidinyloxyl(4OH-TEMPO)and intrinsic defect-rich carbons as the RMs and electrode materials,respectively,the potential window and capacitance are increased by 67%and sixfold in a neutral electrolyte.Moreover,this strategy could also be applied to alkaline and acid electrolytes.The first-principle calculation and experimental results demonstrate that the strong interaction between 4OH-TEMPO and defectrich carbons plays a key role as preferential adsorbed RMs may largely prohibit the contact of free water molecules with the electrode materials to terminate the water splitting at elevated potentials.For the RMs offering weaker interaction with the electrode materials,the water splitting still proceeds with a thus sole increase of the stored charges.The results discovered in this work could provide an alternative solution to address the low energy density of aqueous supercapacitors.
基金supported by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP-2024-RS-2024-00437494)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation).
文摘Digital forensics aims to uncover evidence of cybercrimes within compromised systems.These cybercrimes are often perpetrated through the deployment of malware,which inevitably leaves discernible traces within the compromised systems.Forensic analysts are tasked with extracting and subsequently analyzing data,termed as artifacts,from these systems to gather evidence.Therefore,forensic analysts must sift through extensive datasets to isolate pertinent evidence.However,manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive.Previous studies addressed such inefficiencies by integrating artificial intelligence(AI)technologies into digital forensics.Despite the efforts in previous studies,artifacts were analyzed without considering the nature of the data within them and failed to prove their efficiency through specific evaluations.In this study,we propose a system to prioritize suspicious artifacts from compromised systems infected with malware to facilitate efficient digital forensics.Our system introduces a double-checking method that recognizes the nature of data within target artifacts and employs algorithms ideal for anomaly detection.The key ideas of this method are:(1)prioritize suspicious artifacts and filter remaining artifacts using autoencoder and(2)further prioritize suspicious artifacts and filter remaining artifacts using logarithmic entropy.Our evaluation demonstrates that our system can identify malicious artifacts with high accuracy and that its double-checking method is more efficient than alternative approaches.Our system can significantly reduce the time required for forensic analysis and serve as a reference for future studies.
基金supported by Natural Science Foundation Project of Gansu Provincial Science and Technology Department(No.1506RJZA084)Gansu Provincial Education Department Scientific Research Fund Grant Project(No.1204-13).
文摘To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.
