This article examines the influence of annealing temperature on fracture toughness and forming limit curves of dissimilar aluminum/silver sheets.In the cold roll bonding process,after brushing and acid washing,the pre...This article examines the influence of annealing temperature on fracture toughness and forming limit curves of dissimilar aluminum/silver sheets.In the cold roll bonding process,after brushing and acid washing,the prepared surfaces are placed on top of each other and by rolling with reduction more than 50%,the bonding between layers is established.In this research,the roll bonding process was done at room temperature,without the use of lubricants and with a 70%thickness reduction.Then,the final thickness of the Ag/Al bilayer sheet reached 350μm by several stages of cold rolling.Before cold rolling,it should be noted that to decrease the hardness created due to plastic deformation,the roll-bonded samples were subjected to annealing heat treatment at 400℃for 90 min.Thus,the final samples were annealed at 200,300 and 400℃for 90 min and cooled in a furnace to examine the annealing temperature effects.The uniaxial tensile and microhardness tests measured mechanical properties.Also,to investigate the fracture mechanism,the fractography of the cross-section was examined by scanning electron microscope(SEM).To evaluate the formability of Ag/Al bilayer sheets,forming limit curves were obtained experimentally through the Nakazima test.The resistance of composites to failure due to cracking was also investigated by fracture toughness.The results showed that annealing increases the elongation and formability of the Ag/Al bilayer sheet while reduces the ultimate tensile strength and fracture toughness.However,the changing trend is not the same at different temperatures,and according to the results,the most significant effect is obtained at 300℃and aluminum layers.It was also determined that by increasing annealing temperature,the fracture mechanism from shear ductile with small and shallow dimples becomes ductile with deep cavities.展开更多
A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment ...A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.展开更多
Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accura...Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.展开更多
Traditional manufacturing processes for lightweight curved profiles are often associated with lengthy procedures,high costs,low efficiency,and high energy consumption.In order to solve this problem,a new staggered ext...Traditional manufacturing processes for lightweight curved profiles are often associated with lengthy procedures,high costs,low efficiency,and high energy consumption.In order to solve this problem,a new staggered extrusion(SE)process was used to form the curved profile of AZ31 magnesium alloy in this paper.The study investigates the mapping relationship between the curvature,microstructure,and mechanical properties of the formed profiles by using different eccentricities of the die.Scanning electron microscopy(SEM)and electron backscatter diffraction techniques are employed to examine the effects of different eccentricity values(e)on grain morphology,recrystallization mechanisms,texture,and Schmid factors of the products.The results demonstrate that the staggered extrusion method promotes the deep refinement of grain size in the extruded products,with an average grain size of only 15%of the original billet,reaching 12.28μm.The tensile strength and elongation of the curved profiles after extrusion under the eccentricity value of 10 mm,20 mm and 30 mm are significantly higher than those of the billet,with the tensile strength is increased to 250,270,235 MPa,and the engineering strain elongation increased to 10.5%,12.1%,15.9%.This indicates that staggered extrusion enables curvature control of the profiles while improving their strength.展开更多
On-machine measurement(OMM)stands out as a pivotal technology in complex curved surface adaptive machining.However,the complex structure inherent in workpieces poses a significant challenge as the stylus orientation f...On-machine measurement(OMM)stands out as a pivotal technology in complex curved surface adaptive machining.However,the complex structure inherent in workpieces poses a significant challenge as the stylus orientation frequently shifts during the measurement process.Consequently,a substantial amount of time is allocated to calibrating pre-travel error and probe movement.Furthermore,the frequent movement of machine tools also increases the influence of machine errors.To enhance both accuracy and efficiency,an optimization strategy for the OMM process is proposed.Based on the kinematic chain of the machine tools,the relationship between the angle combination of rotary axes,the stylus orientation,and the calibration position of pre-travel error is disclosed.Additionally,an OMM efficiency optimization model for complex curved surfaces is developed.This model is solved to produce the optimal efficiency angle combinations for each to-be-measured point.Within each angle combination,the effects of positioning errors on measurement results are addressed by coordinate system offset and measurement result compensation method.Finally,the experiments on an impeller are used to demonstrate the practical utility of the proposed method.展开更多
The present work investigates higher order stress,strain and deformation analyses of a shear deformable doubly curved shell manufactures by a Copper(Cu)core reinforced with graphene origami auxetic metamaterial subjec...The present work investigates higher order stress,strain and deformation analyses of a shear deformable doubly curved shell manufactures by a Copper(Cu)core reinforced with graphene origami auxetic metamaterial subjected to mechanical and thermal loads.The effective material properties of the graphene origami auxetic reinforced Cu matrix are developed using micromechanical models cooperate both material properties of graphene and Cu in terms of temperature,volume fraction and folding degree.The principle of virtual work is used to derive governing equations with accounting thermal loading.The numerical results are analytically obtained using Navier's technique to investigate impact of significant parameters such as thermal loading,graphene amount,folding degree and directional coordinate on the stress,strain and deformation responses of the structure.The graphene origami materials may be used in aerospace vehicles and structures and defence technology because of their low weight and high stiffness.A verification study is presented for approving the formulation,solution methodology and numerical results.展开更多
针对目前EPCglobal ONS(Object Name Service)系统存在的信息容易被偷听、篡改、欺骗等威胁,提出一种基于DNSCurve协议和椭圆曲线加密机制的ONS系统来实现产品动态信息在查询过程中的安全性和隐私性,并通过实验验证此系统的安全性能,能...针对目前EPCglobal ONS(Object Name Service)系统存在的信息容易被偷听、篡改、欺骗等威胁,提出一种基于DNSCurve协议和椭圆曲线加密机制的ONS系统来实现产品动态信息在查询过程中的安全性和隐私性,并通过实验验证此系统的安全性能,能够抵御偷听、篡改、欺骗等攻击,有效地加强了ONS系统安全,对整个物联网的发展具有很重要的现实意义。展开更多
密钥交换是一种在非保护信道中建立共享密钥的方法,被广泛运用在包括TLS/SSL等网络安全协议中。基于椭圆曲线密码算法的Diffie-Hel man算法(ECDH)由于其出色的计算效率,在密钥交换协议中被逐步推广使用。目前主流的ECDH中采用的椭圆曲线...密钥交换是一种在非保护信道中建立共享密钥的方法,被广泛运用在包括TLS/SSL等网络安全协议中。基于椭圆曲线密码算法的Diffie-Hel man算法(ECDH)由于其出色的计算效率,在密钥交换协议中被逐步推广使用。目前主流的ECDH中采用的椭圆曲线是NIST P系列曲线,但随着对性能的迫切要求以及对其安全性的怀疑,IETF在2016年1月正式将Curve25519曲线用于密钥交换,称为X25519密钥交换协议;随后各大开源安全软件也进行跟进,优先推荐使用X25519。X25519的主要计算瓶颈是Curve25519的椭圆曲线点乘算法,文章提出了一种利用GPU的Curve25519的椭圆曲线点乘算法,通过对有限域算术优化和曲线算法优化,在NVIDIA Ge Force GTX 780Ti达到了每秒138万次的吞吐率,相较之前基于GPU的实现获得了5.6倍的性能提升。展开更多
基金Project(4013311)supported by the National Science Foundation of Iran(INSF)。
文摘This article examines the influence of annealing temperature on fracture toughness and forming limit curves of dissimilar aluminum/silver sheets.In the cold roll bonding process,after brushing and acid washing,the prepared surfaces are placed on top of each other and by rolling with reduction more than 50%,the bonding between layers is established.In this research,the roll bonding process was done at room temperature,without the use of lubricants and with a 70%thickness reduction.Then,the final thickness of the Ag/Al bilayer sheet reached 350μm by several stages of cold rolling.Before cold rolling,it should be noted that to decrease the hardness created due to plastic deformation,the roll-bonded samples were subjected to annealing heat treatment at 400℃for 90 min.Thus,the final samples were annealed at 200,300 and 400℃for 90 min and cooled in a furnace to examine the annealing temperature effects.The uniaxial tensile and microhardness tests measured mechanical properties.Also,to investigate the fracture mechanism,the fractography of the cross-section was examined by scanning electron microscope(SEM).To evaluate the formability of Ag/Al bilayer sheets,forming limit curves were obtained experimentally through the Nakazima test.The resistance of composites to failure due to cracking was also investigated by fracture toughness.The results showed that annealing increases the elongation and formability of the Ag/Al bilayer sheet while reduces the ultimate tensile strength and fracture toughness.However,the changing trend is not the same at different temperatures,and according to the results,the most significant effect is obtained at 300℃and aluminum layers.It was also determined that by increasing annealing temperature,the fracture mechanism from shear ductile with small and shallow dimples becomes ductile with deep cavities.
基金supported by the National Social Science Fund of China(Grand No.21XTJ001).
文摘A Receiver Operating Characteristic(ROC)analysis of a power is important and useful in clinical trials.A Classical Conditional Power(CCP)is a probability of a classical rejection region given values of true treatment effect and interim result.For hypotheses and reversed hypotheses under normal models,we obtain analytical expressions of the ROC curves of the CCP,find optimal ROC curves of the CCP,investigate the superiority of the ROC curves of the CCP,calculate critical values of the False Positive Rate(FPR),True Positive Rate(TPR),and cutoff of the optimal CCP,and give go/no go decisions at the interim of the optimal CCP.In addition,extensive numerical experiments are carried out to exemplify our theoretical results.Finally,a real data example is performed to illustrate the go/no go decisions of the optimal CCP.
文摘Objective Primary liver cancer,predominantly hepatocellular carcinoma(HCC),is a significant global health issue,ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality.Accurate and early diagnosis of HCC is crucial for effective treatment,as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma(ICC)exhibit different prognoses and treatment responses.Traditional diagnostic methods,including liver biopsy and contrast-enhanced ultrasound(CEUS),face limitations in applicability and objectivity.The primary objective of this study was to develop an advanced,lightweighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images.The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions.Methods This retrospective study encompassed a total of 161 patients,comprising 131 diagnosed with HCC and 30 with non-HCC malignancies.To achieve accurate tumor detection,the YOLOX network was employed to identify the region of interest(ROI)on both B-mode ultrasound and CEUS images.A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images.These curves provided critical data for the subsequent analysis and classification process.To analyze the extracted brightness change curves and classify the malignancies,we developed and compared several models.These included one-dimensional convolutional neural networks(1D-ResNet,1D-ConvNeXt,and 1D-CNN),as well as traditional machine-learning methods such as support vector machine(SVM),ensemble learning(EL),k-nearest neighbor(KNN),and decision tree(DT).The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics:area under the receiver operating characteristic(AUC),accuracy(ACC),sensitivity(SE),and specificity(SP).Results The evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM,0.56 for ensemble learning,0.63 for KNN,and 0.72 for the decision tree.These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves.In contrast,the deep learning models demonstrated significantly higher AUCs,with 1D-ResNet achieving an AUC of 0.72,1D-ConvNeXt reaching 0.82,and 1D-CNN obtaining the highest AUC of 0.84.Moreover,under the five-fold cross-validation scheme,the 1D-CNN model outperformed other models in both accuracy and specificity.Specifically,it achieved accuracy improvements of 3.8%to 10.0%and specificity enhancements of 6.6%to 43.3%over competing approaches.The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification.Conclusion The 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies,surpassing both traditional machine-learning methods and other deep learning models.This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’diagnostic capabilities.By improving the accuracy and efficiency of clinical decision-making,this tool has the potential to positively impact patient care and outcomes.Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.
基金Project(JQ2022E004)supported by the Natural Science Foundation of Heilongjiang Province,China。
文摘Traditional manufacturing processes for lightweight curved profiles are often associated with lengthy procedures,high costs,low efficiency,and high energy consumption.In order to solve this problem,a new staggered extrusion(SE)process was used to form the curved profile of AZ31 magnesium alloy in this paper.The study investigates the mapping relationship between the curvature,microstructure,and mechanical properties of the formed profiles by using different eccentricities of the die.Scanning electron microscopy(SEM)and electron backscatter diffraction techniques are employed to examine the effects of different eccentricity values(e)on grain morphology,recrystallization mechanisms,texture,and Schmid factors of the products.The results demonstrate that the staggered extrusion method promotes the deep refinement of grain size in the extruded products,with an average grain size of only 15%of the original billet,reaching 12.28μm.The tensile strength and elongation of the curved profiles after extrusion under the eccentricity value of 10 mm,20 mm and 30 mm are significantly higher than those of the billet,with the tensile strength is increased to 250,270,235 MPa,and the engineering strain elongation increased to 10.5%,12.1%,15.9%.This indicates that staggered extrusion enables curvature control of the profiles while improving their strength.
基金Projects(51775445,52175435)supported by the National Natural Science Foundation of ChinaProject(CX2023051)supported by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University,China。
文摘On-machine measurement(OMM)stands out as a pivotal technology in complex curved surface adaptive machining.However,the complex structure inherent in workpieces poses a significant challenge as the stylus orientation frequently shifts during the measurement process.Consequently,a substantial amount of time is allocated to calibrating pre-travel error and probe movement.Furthermore,the frequent movement of machine tools also increases the influence of machine errors.To enhance both accuracy and efficiency,an optimization strategy for the OMM process is proposed.Based on the kinematic chain of the machine tools,the relationship between the angle combination of rotary axes,the stylus orientation,and the calibration position of pre-travel error is disclosed.Additionally,an OMM efficiency optimization model for complex curved surfaces is developed.This model is solved to produce the optimal efficiency angle combinations for each to-be-measured point.Within each angle combination,the effects of positioning errors on measurement results are addressed by coordinate system offset and measurement result compensation method.Finally,the experiments on an impeller are used to demonstrate the practical utility of the proposed method.
基金supported by Scientific Research Project of Qiqihar University(145209130)supported by the Natural Science Foundation of Inner Mongolia Autonomous Region of China(Grant No.2023LHMS05054 and 2023LHMS05017)+3 种基金the Inner Mongolia University of Technology Natural Science Foundation of China(Grant No.DC2200000903)the Program for Innovative Research Teams in Universities of the Inner Mongolia Autonomous Region of China(Grant No.NMGIRT2213)the key technological project of Inner Mongolia(Grant No.2021GG0255 and 2021GG0259)the Fundamental Research Funds for the directly affiliated Universities of Inner Mongolia Autonomous Region(Grant No.JY20220046)。
文摘The present work investigates higher order stress,strain and deformation analyses of a shear deformable doubly curved shell manufactures by a Copper(Cu)core reinforced with graphene origami auxetic metamaterial subjected to mechanical and thermal loads.The effective material properties of the graphene origami auxetic reinforced Cu matrix are developed using micromechanical models cooperate both material properties of graphene and Cu in terms of temperature,volume fraction and folding degree.The principle of virtual work is used to derive governing equations with accounting thermal loading.The numerical results are analytically obtained using Navier's technique to investigate impact of significant parameters such as thermal loading,graphene amount,folding degree and directional coordinate on the stress,strain and deformation responses of the structure.The graphene origami materials may be used in aerospace vehicles and structures and defence technology because of their low weight and high stiffness.A verification study is presented for approving the formulation,solution methodology and numerical results.
文摘针对目前EPCglobal ONS(Object Name Service)系统存在的信息容易被偷听、篡改、欺骗等威胁,提出一种基于DNSCurve协议和椭圆曲线加密机制的ONS系统来实现产品动态信息在查询过程中的安全性和隐私性,并通过实验验证此系统的安全性能,能够抵御偷听、篡改、欺骗等攻击,有效地加强了ONS系统安全,对整个物联网的发展具有很重要的现实意义。
文摘密钥交换是一种在非保护信道中建立共享密钥的方法,被广泛运用在包括TLS/SSL等网络安全协议中。基于椭圆曲线密码算法的Diffie-Hel man算法(ECDH)由于其出色的计算效率,在密钥交换协议中被逐步推广使用。目前主流的ECDH中采用的椭圆曲线是NIST P系列曲线,但随着对性能的迫切要求以及对其安全性的怀疑,IETF在2016年1月正式将Curve25519曲线用于密钥交换,称为X25519密钥交换协议;随后各大开源安全软件也进行跟进,优先推荐使用X25519。X25519的主要计算瓶颈是Curve25519的椭圆曲线点乘算法,文章提出了一种利用GPU的Curve25519的椭圆曲线点乘算法,通过对有限域算术优化和曲线算法优化,在NVIDIA Ge Force GTX 780Ti达到了每秒138万次的吞吐率,相较之前基于GPU的实现获得了5.6倍的性能提升。
