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ALTERNATING OPTIMIZATION METHOD FOR ISOGEOMETRIC TOPOLOGY OPTIMIZATION WITH STRESS CONSTRAINTS

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Journal of Computational Mathematics 2024年第1期42卷 134-155页

作者： Xiaoya zhai School of Mathematical Sciences University of Science and Technology of ChinaHefei 230071China

Topology optimization (TO) has developed rapidly recently. However, topology optimization with stress constraints still faces many challenges due to its highly non-linear properties which will cause inefficient computation, iterative oscillation, and convergence guarantee problems. At the same time, isogeometric analysis (IGA) is accepted by more and more researchers, and it has become one important tool in the field of topology optimization because of its high fidelity. In this paper, we focus on topology optimization with stress constraints based on isogeometric analysis to improve computation efficiency and stability. A new hybrid solver combining the alternating direction method of multipliers and the method of moving asymptotes (ADMM-MMA) is proposed to solve this problem. We first generate an initial feasible point by alternating direction method of multipliers (ADMM) in virtue of the rapid initial descent property. After that, we adopt the method of moving asymptotes (MMA) to get the final results. Several benchmark examples are used to verify the proposed method, and the results show its feasibility and effectiveness.

关键词： Isogeometric topology optimization Stress constraints The ADMM-MMA solver

Fixed-effect and random-effects models in meta-analysis

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维普期刊数据库

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Chinese Medical Journal 2024年第1期137卷 1-4页

作者： Chunjuan zhai Gordon Guyatt Department of Cardiology Shandong Provincial Hospital Affiliated to Shandong First Medical UniversityJinanShandong 250021China Department of Health Research Methods Evidence and ImpactMcMaster UniversityHamilton L8S 4L8OntarioCanada

A systematic review synthesizes evidence addressing a structured clinical question using systematic and explicit methods to search,identify,screen,critically appraise,and extract and analyze data from relevant studies.[1]A meta-analysis,on the other hand,is a statistical method that combines the quantitative results of available primary studies addressing the same question to generate a pooled summary estimate and confidence interval(CI).[2]Meta-analyses can increase statistical power,enhance precision,and answer questions that single trials are underpowered to address.Although there are systematic reviews where meta-analyses are not appropriate,when they are well-conducted and reported,systematic reviews combined with meta-analyses provide valuable information for clinicians,researchers,and policymakers.

关键词： Fixed analysis effect

DCVAE-adv:A Universal Adversarial Example Generation Method for White and Black Box Attacks

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Tsinghua Science and Technology 2024年第2期29卷 430-446页

作者： Lei Xu Junhai zhai College of Mathematics and Information Science Hebei UniversityBaoding 071002China

Deep neural network(DNN)has strong representation learning ability,but it is vulnerable and easy to be fooled by adversarial examples.In order to handle the vulnerability of DNN,many methods have been proposed.The general idea of existing methods is to reduce the chance of DNN models being fooled by observing some designed adversarial examples,which are generated by adding perturbations to the original images.In this paper,we propose a novel adversarial example generation method,called DCVAE-adv.Different from the existing methods,DCVAE-adv constructs adversarial examples by mixing both explicit and implicit perturbations without using original images.Furthermore,the proposed method can be applied to both white box and black box attacks.In addition,in the inference stage,the adversarial examples can be generated without loading the original images into memory,which greatly reduces the memory overhead.We compared DCVAE-adv with three most advanced adversarial attack algorithms:FGSM,AdvGAN,and AdvGAN++.The experimental results demonstrate that DCVAE-adv is superior to these state-of-the-art methods in terms of attack success rate and transfer ability for targeted attack.Our code is available at https://***/xzforeverlove/DCVAE-adv.

关键词： deep neural network adversarial examples white box attack black box attack robustness

Deciphering the cellular heterogeneity of the insect brainwith single-cell RNA sequencing

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Insect Science 2024年第2期31卷 314-327页

作者： Xiaofei Wang Yifan zhai Hao Zheng Institute of Plant Protection Shandong Academy of Agricultural SciencesJinanChina College of Food Science and Nutritional En-gineering China Agricultural UniversityBeijingChina Key Laboratory of Natural Enemies Insects Ministry of Agriculture and RuralAffairsJinanChina Shandong Provincial Engineering Technology Research Center on Biocontrol of Crops Diseases and In-sectPests JinanChina

Insects show highly complicated adaptive and sophisticated behaviors, includ-ing spatial orientation skills, learning ability, and social interaction. These behaviors arecontrolled by the insect brain, the central part of the nervous system. The tiny insect brainconsists of millions of highly differentiated and interconnected cells forming a complexnetwork. Decades of research has gone into an understanding of which parts of the insectbrain possess particular behaviors, but exactly how they modulate these functional conse-quences needs to be clarified. Detailed description of the brain and behavior is required todecipher the complexity of cell types, as well as their connectivity and function. Single-cell RNA-sequencing (scRNA-seq) has emerged recently as a breakthrough technologyto understand the transcriptome at cellular resolution. With scRNA-seq, it is possible touncover the cellular heterogeneity of brain cells and elucidate their specific functions andstate. In this review, we first review the basic structure of insect brains and the links toinsect behaviors mainly focusing on learning and memory. Then the scRNA applicationson insect brains are introduced by representative studies. Single-cell RNA-seq has allowedresearchers to classify cell subpopulations within different insect brain regions, pinpointsingle-cell developmental trajectories, and identify gene regulatory networks. These devel-opments empower the advances in neuroscience and shed light on the intricate problemsin understanding insect brain functions and behaviors.

关键词： behavior brain cell type insect single-cell RNA sequencing

Mechanism of microarc oxidation on AZ91D Mg alloy induced byβ-Mg_(17)Al_(12) phase

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International Journal of Minerals,Metallurgy and Materials 2024年第4期31卷 712-724页

作者： Dajun zhai Xiaoping Li Jun Shen College of Materials Science and Engineering Chongqing UniversityChongqing 400044China

This work proposed a strategy of indirectly inducing uniform microarc discharge by controlling the content and distribution ofβ-Mg_(17)Al_(12)phase in AZ91D Mg alloy.Two kinds of nano-particles(ZrO_(2)and TiO_(2))were designed to be added into the substrate of Mg alloy by friction stir processing(FSP).Then,Mg alloy sample designed with different precipitated morphology ofβ-Mg_(17)Al_(12)phase was treated by microarc oxidation(MAO)in Na_(3)PO_(4)/Na2SiO3electrolyte.The characteristics and performance of the MAO coating was analyzed using scanning electron microscopy(SEM),energy dispersive spectrometer(EDS),X-ray diffraction(XRD),X-ray photoelectron spectroscopy(XPS),contact angle meter,and potentiodynamic polarization.It was found that the coarseα-Mg grains in extruded AZ91D Mg alloy were refined by FSP,and theβ-Mg_(17)Al_(12)phase with reticular structure was broken and dispersed.The nano-ZrO_(2)particles were pinned at the grain boundary by FSP,which refined theα-Mg grain and promoted the precipitation ofβ-Mg_(17)Al_(12)phase in grains.It effectively inhibited the“cascade”phenomenon of microarcs,which induced the uniform distribution of discharge pores.The MAO coating on Zr-FSP sample had good wettability and corrosion resistance.However,TiO_(2)particles were hardly detected in the coating on TiFSP sample.

关键词： AZ91D Mg alloy microarc oxidation friction stir processing ZrO_(2) TiO_(2) β-Mg_(17)Al_(12)

Dynamics modeling and electromechanical coupling characteristics analysis of cage induction motors

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Science China(Technological Sciences) 2024年第3期67卷 709-724页

作者： ZHOU ZiWei CHEN ZaiGang zhai WanMing State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610031China

Induction motors, as typical electromechanical energy conversion devices, have received limited attention in previous studies on electromechanical coupling vibrations, precise modeling, and the use of electromechanical coupling effects for fault diagnosis and condition assessment in motor drive systems. This study proposes a comprehensive model of cage induction motors that integrates the multiple coupled circuit model with a rotor-bearing dynamics model. The model accounts for the linear increase in the magnetomotive force across the slot and incorporates the skidding characteristics of bearings in the rotor-bearing system. By calculating the time-varying mutual inductance parameters based on the air-gap distribution in the vibration environment, the electromechanical coupling vibration of the cage motor is investigated. Furthermore, this study examines the electromechanical coupling vibration characteristics influenced by various factors, including bearing clearances, radial loads, and the vertical excitation frequencies of the stators. The results show that the proposed model improves the excitation inputs for the electrical and mechanical systems of the motor compared with conventional models. Increased bearing clearance and radial load affect the current and torque similarly but have opposite effects on the slip ratio. This study provides a deeper understanding of electromechanical coupling mechanisms and facilitates the use of such phenomena for fault diagnosis and condition assessment in motor-driven systems.

关键词： cage induction motors rotor-bearing dynamics mutual inductance electromechanical testing current signal analysis

Bivariate attribution of the compound hot and dry summer of 2022 on the Tibetan Plateau

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Science China Earth Sciences 2024年第7期67卷 2122-2136页

作者： Baiquan ZHOU Panmao zhai Zhen LIAO State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081China

The extraordinarily high temperatures experienced during the summer of 2022 on the Tibetan Plateau(TP)demand attention when compared with its typical climatic conditions.The absence of precipitation alongside the elevated temperatures resulted in 2022 being the hottest and driest summer on record on the TP since at least 1961.Recognizing the susceptibility of the TP to climate change,this study employed large-ensemble simulations from the HadGEM3-A-N216 attribution system,together with a copula-based joint probability distribution,to investigate the influence of anthropogenic forcing,primarily global greenhouse gas emissions,on this unprecedented compound hot and dry event(CHDE).Findings revealed that the return period for the 2022 CHDE on the TP exceeds 4000 years,as determined from the fitted joint distributions derived using observational data spanning 1961-2022.This CHDE was directly linked to large-scale circulation anomalies,including the control of equivalent-barotropic high-pressure anomalies and the northward displacement of the subtropical westerly jet stream.Moreover,anthropogenic forcing has,to some extent,promoted the surface warming and increased variability in precipitation on the TP in summer,establishing conditions conducive for the 2022 CHDE from a long-term climate change perspective.The return period for a 2022-like CHDE on the TP was estimated to be approximately 283 years(142-613 years)by the large ensemble forced by both anthropogenic activities and natural factors.Contrastingly,ensemble simulations driven solely by natural forcing indicated that the likelihood of occurrence of a 2022-like CHDE was almost negligible.These outcomes underscore that the contribution of anthropogenic forcing to the probability of a 2022-like CHDE was 100%,implying that without anthropogenically induced global warming,a comparable CHDE akin to that observed in 2022 on the TP would not be possible.

关键词： Compound hot and dry event Tibetan Plateau Bivariate event attribution Anthropogenic forcing

Not in Control,but Liable?Attributing Human Responsibility for Fully Automated Vehicle Accidents

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Engineering 2024年第2期33卷 121-132页

作者： Siming zhai Lin Wang Peng Liu Center for Psychological Sciences Zhejiang UniversityHangzhou 310063China Department of Library and Information Science Incheon National UniversityIncheon 22012Republic of Korea

Human agency has become increasingly limited in complex systems with increasingly automated decision-making capabilities.For instance,human occupants are passengers and do not have direct vehicle control in fully automated cars(i.e.,driverless cars).An interesting question is whether users are responsible for the accidents of these cars.Normative ethical and legal analyses frequently argue that individuals should not bear responsibility for harm beyond their control.Here,we consider human judgment of responsibility for accidents involving fully automated cars through three studies with seven experiments(N=2668).We compared the responsibility attributed to the occupants in three conditions:an owner in his private fully automated car,a passenger in a driverless robotaxi,and a passenger in a conventional taxi,where none of these three occupants have direct vehicle control over the involved vehicles that cause identical pedestrian injury.In contrast to normative analyses,we show that the occupants of driverless cars(private cars and robotaxis)are attributed more responsibility than conventional taxi passengers.This dilemma is robust across different contexts(e.g.,participants from China vs the Republic of Korea,participants with first-vs third-person perspectives,and occupant presence vs absence).Furthermore,we observe that this is not due to the perception that these occupants have greater control over driving but because they are more expected to foresee the potential consequences of using driverless cars.Our findings suggest that when driverless vehicles(private cars and taxis)cause harm,their users may face more social pressure,which public discourse and legal regulations should manage appropriately.

关键词： Fully automated vehicle accidents Responsibility attribution Controllability Foreseeability

Knowledge-driven material design platform based on the whole-process simulation and modeling

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第2期13卷 1-15页

作者： Gongzhuang Peng Tie Li xiang zhai Wenzheng Liu Heming Zhang National Engineering Research Center for Advanced Rolling Technology University of Science and Technology Beijing Beijing 100083 P.R.China Shanghai Electro-Mechanical Engineering Institute Shanghai 201109 P.R.China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing 100854 P.R.China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing 100854P.R.China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854P.R.China National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China

In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this study.In this platform,the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level,the thermo-mechanical coupling field level and the microstructure evolution level.The design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model,including customers’requirement knowledge,material component knowledge,process design knowledge and quality inspection knowledge,and utilizes the case-based reasoning approach to reuse the knowledge.The data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties,material components,and process parameters from historical samples,and utilizes multi-objective optimiza-tion algorithms to find the optimal combination of process parameters.Application of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

关键词： Product development knowledge-based engineering multi-scale simulation performance prediction model