With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Further...With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.展开更多
In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the effor...In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.展开更多
Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the ...Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indicators.Experimental validation using three-dimensional(3 D)-printed constructs and micro-computed tomography(μCT)reconstructions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations.展开更多
目的分析二维斑点追踪成像(two-dimensional speckle-tracking imaging,2D-STI)参数、QT间期离散度(dispersion of QT interval,QTd)与AMI(acute myocardial infarction,AMI)患者经皮冠状动脉介入(percutaneous coronary intervention,P...目的分析二维斑点追踪成像(two-dimensional speckle-tracking imaging,2D-STI)参数、QT间期离散度(dispersion of QT interval,QTd)与AMI(acute myocardial infarction,AMI)患者经皮冠状动脉介入(percutaneous coronary intervention,PCI)治疗后主要不良心血管事件(major adverse cardiovascular events,MACE)的关系。方法选取2022年3月至2024年11月邢台市中心医院收治的115例AMI患者作为病例组,同时选取在本院进行体检的108名健康志愿者为对照组。根据是否发生MACE将病例组患者分为MACE组(n=36)和无MACE组(n=79)。比较病例组和对照组、MACE组和无MACE组患者的2D-STI参数、QTd,分析影响AMI患者PCI治疗后MACE发生的独立危险因素,并利用Spearman相关性分析对2D-STI参数、QTd与AMI患者PCI治疗后MACE发生的关系进行分析。结果与对照组比较,病例组患者的NN间期标准差(standard deviation of NN interval,SDNN)、震荡斜率(slope of oscillation,TS)、左心房平均峰值应变率(overall systolic mean peak strain,mSs)、计算左心房被动射血分数(left atrial passive ejection fraction,LAPEF)、左心房总射血分数(total left atrial ejection fraction,LATEF)、左心房收缩期平均峰值应变率(left atrial strain rate in the left ventricular systolic,mSRs)、左心房主动射血分数(left atrial active ejection fraction,LAAEF)、左心房舒张早期平均峰值应变率(left atrial strain rate in the early left ventricular diastole,mSRe)、左心房舒张晚期平均峰值应变率(left atrial strain rate in the late left ventricular diastole,mSRa)下降,校正QTd(correct of QTd,QTcd)、QTd、震荡初始(oscillatory inception,TO)、最小左心房容积(minimum left atrial volume,LAV_(min))、最大左心房容积(maximum left atrial volume,LAV_(max))、左心房收缩前容积(left atrial presystolic volume,LAVp)上升,差异均有统计学意义(P<0.05)。另外,MACE组和无MACE组患者的病变支数、肌酸激酶同工酶(creatine kinase isoenzyme MB,CK-MB)、氨基末端脑钠肽前体(N-terminal pro-B type natriuretic peptide,NT-proBNP)、mSs、LAPEF、LATEF、mSRs、LAAEF、mSRe、mSRa、LAV_(min)、LAV_(max)、LAVp、SDNN、TS、QTcd、QTd、TO比较,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示以上指标均是影响患者PCI治疗后MACE发生的危险因素。Spearman相关性分析结果显示,AMI患者PCI治疗后MACE的发生与QTd、LAV_(min)、LAV_(max)、LAVp、mSRa呈正相关(r=0.447、0.319、0.407、0.441、0.339,P<0.05),与mSs、LAPEF、LATEF、mSRs、LAAEF、mSRe呈负相关(r=-0.228、-0.319、-0.333、-0.282、-0.317、-0.337,P<0.05)。结论AMI患者2D-STI参数、QTd存在差异,并与PCI治疗后MACE发生有关,可作为患者PCI治疗后MACE发生的临床检测指标。展开更多
基金supported by the National Natural Science Foundation of China under Grant 62471493supported by the Natural Science Foundation of Shandong Province under Grant ZR2023LZH017,ZR2024MF066。
文摘With the rapid development of intelligent cyber-physical systems(ICPS),diverse services with varying Quality of Service(QoS)requirements have brought great challenges to traditional network resource allocation.Furthermore,given the open environment and a multitude of devices,enhancing the security of ICPS is an urgent concern.To address these issues,this paper proposes a novel trusted virtual network embedding(T-VNE)approach for ICPS based combining blockchain and edge computing technologies.Additionally,the proposed algorithm leverages a deep reinforcement learning(DRL)model to optimize decision-making processes.It employs the policygradient-based agent to compute candidate embedding nodes and utilizes a breadth-first search(BFS)algorithm to determine the optimal embedding paths.Finally,through simulation experiments,the efficacy of the proposed method was validated,demonstrating outstanding performance in terms of security,revenue generation,and virtual network request(VNR)acceptance rate.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2024-02-01176).
文摘In recent years,ransomware attacks have become one of the most common and destructive types of cyberattacks.Their impact is significant on the operations,finances and reputation of affected companies.Despite the efforts of researchers and security experts to protect information systems from these attacks,the threat persists and the proposed solutions are not able to significantly stop the spread of ransomware attacks.The latest remarkable achievements of large language models(LLMs)in NLP tasks have caught the attention of cybersecurity researchers to integrate thesemodels into security threat detection.Thesemodels offer high embedding capabilities,able to extract rich semantic representations and paving theway formore accurate and adaptive solutions.In this context,we propose a new approach for ransomware detection based on an ensemblemethod that leverages three distinctLLMembeddingmodels.This ensemble strategy takes advantage of the variety of embedding methods and the strengths of each model.In the proposed solution,each embedding model is associated with an independently trainedMLP classifier.The predictions obtained are then merged using a weighted voting technique,assigning each model an influence proportional to its performance.This approach makes it possible to exploit the complementarity of representations,improve detection accuracy and robustness,and offer a more reliable solution in the face of the growing diversity and complexity of modern ransomware.
基金supported by the National Natural Science Foundation of China(Nos.52305314 and U21A20394)the Beijing Natural Science Foundation(Nos.7252285 and L246001)the National Key Research and Development Program of China(No.2023YFB4605800)。
文摘Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath.However,the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths.Here,we present an artificial intelligence(AI)-driven framework that interprets and predicts embedded printability using rheological data.Using a standardized workflow,we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity.Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress,support bath zero shear viscosity,flow behavior index,and time constant.To enable the prediction of printability in a generalizable manner,we further developed a cascaded neural network,which achieved mean relative prediction errors below 15%across all indicators.Experimental validation using three-dimensional(3 D)-printed constructs and micro-computed tomography(μCT)reconstructions confirmed a strong correlation between predicted and actual fidelity.This work establishes a physics-informed,data-driven paradigm for decoding and optimizing embedded printing,offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations.
文摘目的分析二维斑点追踪成像(two-dimensional speckle-tracking imaging,2D-STI)参数、QT间期离散度(dispersion of QT interval,QTd)与AMI(acute myocardial infarction,AMI)患者经皮冠状动脉介入(percutaneous coronary intervention,PCI)治疗后主要不良心血管事件(major adverse cardiovascular events,MACE)的关系。方法选取2022年3月至2024年11月邢台市中心医院收治的115例AMI患者作为病例组,同时选取在本院进行体检的108名健康志愿者为对照组。根据是否发生MACE将病例组患者分为MACE组(n=36)和无MACE组(n=79)。比较病例组和对照组、MACE组和无MACE组患者的2D-STI参数、QTd,分析影响AMI患者PCI治疗后MACE发生的独立危险因素,并利用Spearman相关性分析对2D-STI参数、QTd与AMI患者PCI治疗后MACE发生的关系进行分析。结果与对照组比较,病例组患者的NN间期标准差(standard deviation of NN interval,SDNN)、震荡斜率(slope of oscillation,TS)、左心房平均峰值应变率(overall systolic mean peak strain,mSs)、计算左心房被动射血分数(left atrial passive ejection fraction,LAPEF)、左心房总射血分数(total left atrial ejection fraction,LATEF)、左心房收缩期平均峰值应变率(left atrial strain rate in the left ventricular systolic,mSRs)、左心房主动射血分数(left atrial active ejection fraction,LAAEF)、左心房舒张早期平均峰值应变率(left atrial strain rate in the early left ventricular diastole,mSRe)、左心房舒张晚期平均峰值应变率(left atrial strain rate in the late left ventricular diastole,mSRa)下降,校正QTd(correct of QTd,QTcd)、QTd、震荡初始(oscillatory inception,TO)、最小左心房容积(minimum left atrial volume,LAV_(min))、最大左心房容积(maximum left atrial volume,LAV_(max))、左心房收缩前容积(left atrial presystolic volume,LAVp)上升,差异均有统计学意义(P<0.05)。另外,MACE组和无MACE组患者的病变支数、肌酸激酶同工酶(creatine kinase isoenzyme MB,CK-MB)、氨基末端脑钠肽前体(N-terminal pro-B type natriuretic peptide,NT-proBNP)、mSs、LAPEF、LATEF、mSRs、LAAEF、mSRe、mSRa、LAV_(min)、LAV_(max)、LAVp、SDNN、TS、QTcd、QTd、TO比较,差异有统计学意义(P<0.05)。多因素Logistic回归分析结果显示以上指标均是影响患者PCI治疗后MACE发生的危险因素。Spearman相关性分析结果显示,AMI患者PCI治疗后MACE的发生与QTd、LAV_(min)、LAV_(max)、LAVp、mSRa呈正相关(r=0.447、0.319、0.407、0.441、0.339,P<0.05),与mSs、LAPEF、LATEF、mSRs、LAAEF、mSRe呈负相关(r=-0.228、-0.319、-0.333、-0.282、-0.317、-0.337,P<0.05)。结论AMI患者2D-STI参数、QTd存在差异,并与PCI治疗后MACE发生有关,可作为患者PCI治疗后MACE发生的临床检测指标。
