Objective:To explore the prognosis-predictive influence of human epidermal growth factor receptor 2(HER2)-low status in breast cancer patients after neoadjuvant therapy(NAT).Methods:Consecutive patients with invasive ...Objective:To explore the prognosis-predictive influence of human epidermal growth factor receptor 2(HER2)-low status in breast cancer patients after neoadjuvant therapy(NAT).Methods:Consecutive patients with invasive breast cancer who underwent NAT and surgery from January 2009 to December 2020 at multiple centers were included.A modified CPS+EG scoring system that integrates HER2-low status,CPS+EGH_(low)was developed.Multiple scoring systems were compared via receiver operating characteristic curves with the area under curve(AUC),the Akaike information criterion,the C-index,and calibration curves.Results:A total of 2,141 patients were included:1,074,640,and 427 patients in the training,internal validation,and external validation groups,respectively.HER2-low patients had a significantly better breast cancer-specific survival(BCSS,P=0.008)and recurrence-free interval(RFI,P=0.030)compared to HER2-zero patients(P=0.038)but inferior outcomes than HER2-amplified ones(BCSS,P=0.002;RFI,P<0.001).The CPS+EGH_(low)(AUC:0.846,0.817,0.901)could stratify patients according to BCSS in training,internal validation,and external validation group,respectively,overperforming pathological stage(PS)(AUC:0.746,0.779,0.754),CPS+EG(AUC:0.771,0.752,0.748),and Neo-Bioscore(AUC:0.783,0.777,0.786,all P<0.05).Conclusions:HER2-low status showed a significant prognostic value in breast cancer patients after NAT.The CPS+EGH_(low)model significantly outperformed PS,CPS+EG,and Neo-Bioscore in clinical outcome prediction,which may guide further therapy targeting HER2-low.展开更多
Paeonia suffruticosa Andr.is an endemic shrub flower in China with 2n=10.This study used 228 cultivars from four populations,i.e.,Jiangnan,Japan,Northwest,and Zhongyuan,as materials to explore the genetic diversity le...Paeonia suffruticosa Andr.is an endemic shrub flower in China with 2n=10.This study used 228 cultivars from four populations,i.e.,Jiangnan,Japan,Northwest,and Zhongyuan,as materials to explore the genetic diversity levels among different populations of tree peony varieties.The results showed that 34 bands were amplified using five pairs of cp SSR primers,with an average of 6.8 bands per primer pair.The average number of different alleles(N_(a)),effective alleles(N_(e)),Shannon's information index(I),diversity(H),and polymorphic information content(PIC)were 3.600,2.053,0.708,0.433,and 0.388,respectively.The PIC value was between 0.250 and 0.500,indicating a moderate level of polymorphism for the five cp SSR primer pairs.The genetic diversity levels of peony cultivars varied among different populations,with the Northwest population showing relatively lower levels(I=0.590,H=0.289,and PIC=0.263).A total of 52 haplotypes were identified in the four examined populations,and the number of haplotypes per population ranged from 11 to 22.Forty-four private haplotypes were detected across populations,and the Northwest population exhibiting the highest count of private haplotypes with 17.The mean number of effective number of haplotypes(N_(eh)),haplotypic richness(R_(h)),and diversity(H)were 8.351,6.824,and 0.893,respectively.Analysis of molecular variance indicated that genetic variation within tree peony germplasm was greater than that between germplasm resources,and the main variation was found within individuals of peony germplasm.Cluster analysis,principal coordinate analysis,and genetic structure analysis classified tree peonies from different origins into two groups,indicating a certain degree of genetic differentiation among these four tree peony cultivation groups.This study provides a theoretical basis for the exploration,utilization,and conservation of peony germplasm resources,as well as for research on the breeding of excellent varieties.展开更多
The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the ...The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems.There is a lack of explainability,challenges with imbalanced attack classes,and limited consideration of practical edge–cloud deployment strategies in prior works.In the proposed study,we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations(SHAP)-based Explainable AI(XAI)to attack detection and classification in IIoT-CPS settings.It includes not only unsupervised clustering(K-Means and DBSCAN)to extract latent traffic patterns but also supervised classification based on taxonomy to classify 33 different kinds of attacks into seven high-level categories:Flood Attacks,Botnet/Mirai,Reconnaissance,Spoofing/Man-In-The-Middle(MITM),Injection Attacks,Backdoors/Exploits,and Benign.The three machine learning algorithms,Random Forest,XGBoost,and Multi-Layer Perceptron(MLP),were trained on a realworld dataset of more than 1 million network traffic records,with overall accuracy of 99.4%(RF),99.5%(XGBoost),and 99.1%(MLP).Rare types of attacks,such as injection attacks and backdoors,were examined even in the case of extreme imbalance between the classes.SHAP-based XAI was performed on every model to help gain transparency and trust in the model and identify important features that drive the classification decisions,such as inter-arrival time,TCP flags,and protocol type.A workable edge-computing implementation strategy is proposed,whereby lightweight computing is performed at the edge devices and heavy,computation-intensive analytics is performed at the cloud.This framework is highly accurate,interpretable,and has real-time application,hence a robust and scalable solution to securing IIoT-CPS infrastructure against dynamic cyber-attacks.展开更多
Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications compli...Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.展开更多
将工程教育融入基础科学教育,可贯通科学知识与基础工程知识学习的通道,但当前初中科学教育与工程教育的融合仍处于起步阶段。鉴于此,教师可基于CPS(Creative Problem Solving,即创造性问题解决)模型,建构包含立项、展项、评项三个阶段...将工程教育融入基础科学教育,可贯通科学知识与基础工程知识学习的通道,但当前初中科学教育与工程教育的融合仍处于起步阶段。鉴于此,教师可基于CPS(Creative Problem Solving,即创造性问题解决)模型,建构包含立项、展项、评项三个阶段的初中科学工程教育实施框架。实践中,教师可在立项阶段设立学习目标并创设真实问题情境,在展项阶段引导学生经历“析—设—验”环节以推进思维进阶,并在评项阶段前置多维评价以发展学生的批判性思维,帮助学生建立“需求—设计—产品”的工程转化逻辑,实现核心概念的意义建构与工程思维的本质提升,进而达成发展学生核心素养的育人目标。展开更多
A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on ...A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.展开更多
在教育信息化的时代背景下,探索线上线下融合的新型教学模式已然成为高等教育信息化改革的重要内容。CPS(Creative Problem Solving,创造性问题解决)模型是一种结合发散思维和聚敛思维的问题解决教学模型,能有效提升学生的创造性思维和...在教育信息化的时代背景下,探索线上线下融合的新型教学模式已然成为高等教育信息化改革的重要内容。CPS(Creative Problem Solving,创造性问题解决)模型是一种结合发散思维和聚敛思维的问题解决教学模型,能有效提升学生的创造性思维和创造力。通过对该模型在国内外教学中应用现状的分析,在结合高职学前教育专业基础课程教学实践的基础上,提出基于CPS模型的线上线下融合教学模式,旨在为实现课程教学模式的创新,以及培养学生创新意识与创造力提供指引。展开更多
基金supported by the National Natural Science Foundation of China(No.82072937 and 82072897)Interdisciplinary Program of Shanghai Jiao Tong University(No.YG2024QNB05)。
文摘Objective:To explore the prognosis-predictive influence of human epidermal growth factor receptor 2(HER2)-low status in breast cancer patients after neoadjuvant therapy(NAT).Methods:Consecutive patients with invasive breast cancer who underwent NAT and surgery from January 2009 to December 2020 at multiple centers were included.A modified CPS+EG scoring system that integrates HER2-low status,CPS+EGH_(low)was developed.Multiple scoring systems were compared via receiver operating characteristic curves with the area under curve(AUC),the Akaike information criterion,the C-index,and calibration curves.Results:A total of 2,141 patients were included:1,074,640,and 427 patients in the training,internal validation,and external validation groups,respectively.HER2-low patients had a significantly better breast cancer-specific survival(BCSS,P=0.008)and recurrence-free interval(RFI,P=0.030)compared to HER2-zero patients(P=0.038)but inferior outcomes than HER2-amplified ones(BCSS,P=0.002;RFI,P<0.001).The CPS+EGH_(low)(AUC:0.846,0.817,0.901)could stratify patients according to BCSS in training,internal validation,and external validation group,respectively,overperforming pathological stage(PS)(AUC:0.746,0.779,0.754),CPS+EG(AUC:0.771,0.752,0.748),and Neo-Bioscore(AUC:0.783,0.777,0.786,all P<0.05).Conclusions:HER2-low status showed a significant prognostic value in breast cancer patients after NAT.The CPS+EGH_(low)model significantly outperformed PS,CPS+EG,and Neo-Bioscore in clinical outcome prediction,which may guide further therapy targeting HER2-low.
基金supported by Innovation Scientists and Technicians Troop Construction Projects of Henan Province(Grant No.212101510003)the Central Plains Scholar Workstation Project(Grant No.224400510002)+1 种基金the Youth Science Foundation of Henan Province(Grant No.202300410136)the Experimental Development Foundation of Henan University of Science and Technology(Grant No.SY2324004)。
文摘Paeonia suffruticosa Andr.is an endemic shrub flower in China with 2n=10.This study used 228 cultivars from four populations,i.e.,Jiangnan,Japan,Northwest,and Zhongyuan,as materials to explore the genetic diversity levels among different populations of tree peony varieties.The results showed that 34 bands were amplified using five pairs of cp SSR primers,with an average of 6.8 bands per primer pair.The average number of different alleles(N_(a)),effective alleles(N_(e)),Shannon's information index(I),diversity(H),and polymorphic information content(PIC)were 3.600,2.053,0.708,0.433,and 0.388,respectively.The PIC value was between 0.250 and 0.500,indicating a moderate level of polymorphism for the five cp SSR primer pairs.The genetic diversity levels of peony cultivars varied among different populations,with the Northwest population showing relatively lower levels(I=0.590,H=0.289,and PIC=0.263).A total of 52 haplotypes were identified in the four examined populations,and the number of haplotypes per population ranged from 11 to 22.Forty-four private haplotypes were detected across populations,and the Northwest population exhibiting the highest count of private haplotypes with 17.The mean number of effective number of haplotypes(N_(eh)),haplotypic richness(R_(h)),and diversity(H)were 8.351,6.824,and 0.893,respectively.Analysis of molecular variance indicated that genetic variation within tree peony germplasm was greater than that between germplasm resources,and the main variation was found within individuals of peony germplasm.Cluster analysis,principal coordinate analysis,and genetic structure analysis classified tree peonies from different origins into two groups,indicating a certain degree of genetic differentiation among these four tree peony cultivation groups.This study provides a theoretical basis for the exploration,utilization,and conservation of peony germplasm resources,as well as for research on the breeding of excellent varieties.
基金funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan(Grant No.AP23489127)。
文摘The Industrial Internet of Things(IIoT),combined with the Cyber-Physical Systems(CPS),is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems.There is a lack of explainability,challenges with imbalanced attack classes,and limited consideration of practical edge–cloud deployment strategies in prior works.In the proposed study,we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations(SHAP)-based Explainable AI(XAI)to attack detection and classification in IIoT-CPS settings.It includes not only unsupervised clustering(K-Means and DBSCAN)to extract latent traffic patterns but also supervised classification based on taxonomy to classify 33 different kinds of attacks into seven high-level categories:Flood Attacks,Botnet/Mirai,Reconnaissance,Spoofing/Man-In-The-Middle(MITM),Injection Attacks,Backdoors/Exploits,and Benign.The three machine learning algorithms,Random Forest,XGBoost,and Multi-Layer Perceptron(MLP),were trained on a realworld dataset of more than 1 million network traffic records,with overall accuracy of 99.4%(RF),99.5%(XGBoost),and 99.1%(MLP).Rare types of attacks,such as injection attacks and backdoors,were examined even in the case of extreme imbalance between the classes.SHAP-based XAI was performed on every model to help gain transparency and trust in the model and identify important features that drive the classification decisions,such as inter-arrival time,TCP flags,and protocol type.A workable edge-computing implementation strategy is proposed,whereby lightweight computing is performed at the edge devices and heavy,computation-intensive analytics is performed at the cloud.This framework is highly accurate,interpretable,and has real-time application,hence a robust and scalable solution to securing IIoT-CPS infrastructure against dynamic cyber-attacks.
基金supported by the National Natural Science Foundation of China(62402415)and in part by the Natural Science Foundation of Shandong Province of China(ZR2024MF129)in part by State Key Laboratory of Massive Personalized Customization System and Technology(No.H&C-MPC-2023-02-03).
文摘Data-flow errors are prevalent in cyber-physical systems(CPS).Although various approaches based on business process modeling notation(BPMN)have been devised for CPS modeling,the absence of formal specifications complicates the verification of data-flow.Formal techniques such as Petri nets are popularly used for identifying data-flow errors.However,due to their interleaving semantics,they suffer from the state-space explosion problem.As an unfolding method for Petri nets,the merged process(MP)technique can well represent concurrency relationships and thus be used to address this issue.Yet generating MP is complex and incurs substantial overhead.By designing and applyingα-deletion rules for Petri nets with data(PNDs),this work simplifies MP,thus resulting in simplified MP(SMP)that is then used to identify data-flow errors.Our approach involves converting a BPMN into a PND and then constructing its SMP.The algorithms are developed to identify data-flow errors,e.g.,redundantdata and lost-data ones.The proposed method enhances the efficiency and effectiveness of identifying data-flow errors in CPS.It is expected to prevent the problems caused by data-flow errors,e.g.,medical malpractice and economic loss in some practical CPS.Its practicality and efficiency of the proposed method through several CPS.Its significant advantages over the state of the art are demonstrated.
文摘将工程教育融入基础科学教育,可贯通科学知识与基础工程知识学习的通道,但当前初中科学教育与工程教育的融合仍处于起步阶段。鉴于此,教师可基于CPS(Creative Problem Solving,即创造性问题解决)模型,建构包含立项、展项、评项三个阶段的初中科学工程教育实施框架。实践中,教师可在立项阶段设立学习目标并创设真实问题情境,在展项阶段引导学生经历“析—设—验”环节以推进思维进阶,并在评项阶段前置多维评价以发展学生的批判性思维,帮助学生建立“需求—设计—产品”的工程转化逻辑,实现核心概念的意义建构与工程思维的本质提升,进而达成发展学生核心素养的育人目标。
文摘A security issue with multi-sensor unmanned aerial vehicle(UAV)cyber physical systems(CPS)from the viewpoint of a false data injection(FDI)attacker is investigated in this paper.The FDI attacker can employ attacks on feedback and feed-forward channels simultaneously with limited resource.The attacker aims at degrading the UAV CPS's estimation performance to the max while keeping stealthiness characterized by the Kullback-Leibler(K-L)divergence.The attacker is resource limited which can only attack part of sensors,and the attacked sensor as well as specific forms of attack signals at each instant should be considered by the attacker.Also,the sensor selection principle is investigated with respect to time invariant attack covariances.Additionally,the optimal switching attack strategies in regard to time variant attack covariances are modeled as a multi-agent Markov decision process(MDP)with hybrid discrete-continuous action space.Then,the multi-agent MDP is solved by utilizing the deep Multi-agent parameterized Q-networks(MAPQN)method.Ultimately,a quadrotor near hover system is used to validate the effectiveness of the results in the simulation section.
文摘在教育信息化的时代背景下,探索线上线下融合的新型教学模式已然成为高等教育信息化改革的重要内容。CPS(Creative Problem Solving,创造性问题解决)模型是一种结合发散思维和聚敛思维的问题解决教学模型,能有效提升学生的创造性思维和创造力。通过对该模型在国内外教学中应用现状的分析,在结合高职学前教育专业基础课程教学实践的基础上,提出基于CPS模型的线上线下融合教学模式,旨在为实现课程教学模式的创新,以及培养学生创新意识与创造力提供指引。
