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Identification of ductile fracture model parameters for three ASTM structural steels using particle swarm optimization

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第6期23卷 421-442页

作者： Ya-zhi ZHU shi-ping huang Hao HONG Department of Structural Engineering Tongji UniversityShanghai 200092China School of Civil Engineering and Transportation South China University of TechnologyGuangzhou 510640China China-Singapore International Joint Research Institute Guangzhou 510700China Shanghai Municipal Engineering Design Institute(Group) Co.Ltd.Shanghai 200092China

Accurate prediction of ductile fracture requires determining the material properties,including the parameters of the constitutive and ductile fracture model,which represent the true material response.Conventional calibration of material parameters often relies on a trial-and-error approach,in which the parameters are manually adjusted until the corresponding finite element model results in a response matching the experimental global response.The parameter estimates are often subjective.To address this issue,in this paper we treat the identification of material parameters as an optimization problem and introduce the particle swarm optimization(PSO)algorithm as the optimization approach.We provide material parameters of two uncoupled ductile fracture models—the Rice and Tracey void growth model(RT-VGM)and the micro-mechanical void growth model(MM-VGM),and a coupled model—the gurson-Tvergaard-Needleman(GTN)model for ASTM A36,A572 Gr.50,and A992 structural steels using an automated PSO method.By minimizing the difference between the experimental results and finite element simulations of the load-displacement curves for a set of tests of circumferentially notched tensile(CNT)bars,the calibration procedure automatically determines the parameters of the strain hardening law as well as the uncoupled models and the coupled GTN constitutive model.Validation studies show accurate prediction of the load-displacement response and ductile fracture initiation in V-notch specimens,and confirm the PSO algorithm as an effective and robust algorithm for seeking ductile fracture model parameters.PSO has excellent potential for identifying other fracture models(e.‍g.,shear modified GTN)with many parameters that can give rise to more accurate predictions of ductile fracture.Limitations of the PSO algorithm and the current calibrated ductile fracture models are also discussed in this paper.

关键词： Parameter calibration Void growth model(VGM) Gurson-Tvergaard-Needleman(GTN)model A36 steel A572 Gr.50 steel A992 steel Particle swarm optimization(PSO)

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Glass Formation of n-Butanol： Coarse-grained Molecular Dynamics Simulations Using Gay-Berne Potential Model

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Chinese Journal of Chemical Physics 2012年第2期25卷 177-185,I0003页

作者： Gui-long Xie Yong-hong Zhang shi-ping huang Division of Molecule and Materials Simulation State Key Laboratory of Organic-Inorganic CompositesBeijing University of Chemical TechnologyBeijing 100029China Department of Applied Physics Tianjin Polytechnic UniversityTianjin 300160China

用模拟基于快乐伯恩的潜力建模的纹理粗糙的分子的动力学，我们模仿了液体 n-butanol 的冷却过程。GB 参数的一个新集合被适合密度的结果获得功能的理论计算。模拟与 10 K 的温度减少在 290-50 K 的范围被执行。冷却特征与温度根据密度... 详细信息

用模拟基于快乐伯恩的潜力建模的纹理粗糙的分子的动力学，我们模仿了液体 n-butanol 的冷却过程。GB 参数的一个新集合被适合密度的结果获得功能的理论计算。模拟与 10 K 的温度减少在 290-50 K 的范围被执行。冷却特征与温度根据密度，势能和 orientational 顺序参数的变化被决定，其斜坡都显示出断绝。光线的分发功能曲线和功能曲线展出在第二座山峰切开的秒等级 orientational 关联。用这些的不连续的变化热力学并且结构性质，我们在温度 Tg=120 的估计获得玻璃转变 ?????

关键词：分子动力学模拟势模型同性恋正丁醇玻璃形成粗粒玻璃化转变温度密度泛函理论

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Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 405-417页

作者： Yi Yu Guo-ping Liu Yi huang Peng shi IEEE Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and TechnologyShenzhen 518055China School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China School of Electrical and Mechanical Engineering the University of AdelaideSA 5005Australia Research and Innovation Centre Obuda University1034 BudapestHungary

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

关键词： DC microgrids distributed control high-order fully actuated system approach linear quadratic regulator microgrid modeling secondary control

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Physiological and pathological functions of circular RNAs in the nervous system

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Neural Regeneration Research 2024年第2期19卷 342-349页

作者： Min Zhou shi Li Chuan huang School of Life Sciences Chongqing UniversityChongqingChina

Circular RNAs(circRNAs)are a class of covalently closed single-stranded RNAs that are expressed during the development of specific cells and tissues.CircRNAs play crucial roles in physiological and pathological processes by sponging microRNAs,modulating gene transcription,controlling the activity of certain RNA-binding proteins,and producing functional peptides.A key focus of research at present is the functionality of circRNAs in the nervous system and several advances have emerged over the last 2 years.However,the precise role of circRNAs in the nervous system has yet to be comprehensively reviewed.In this review,we first summarize the recently described roles of circRNAs in brain development,maturity,and aging.Then,we focus on the involvement of circRNAs in various diseases of the central nervous system,such as brain cancer,chronic neurodegenerative diseases,acute injuries of the nervous system,and neuropathic pain.A better understanding of the functionality of circRNAs will help us to develop potential diagnostic,prognostic,and therapeutic strategies to treat diseases of the nervous system.

关键词： Alzheimer’s disease amyotrophic lateral sclerosis brain development circRNAs neuropathic pain Parkinson’s disease

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Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

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Advances in Atmospheric Sciences 2024年第1期41卷 141-154页

作者： Tingyu WANG ping huang Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

关键词： ENSO diversity deep learning ENSO prediction dynamical forecast system

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Novel milestones for early esophageal carcinoma:From bench to bed

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World Journal of Gastrointestinal Oncology 2024年第4期16卷 1104-1118页

作者： Ji-Han Qi shi-Ling huang shi-Zhu Jin Department of Gastroenterology and Hepatology The Second Affiliated Hospital of Harbin Medical UniversityHarbin 150086Heilongjiang ProvinceChina

Esophageal cancer(EC)is the seventh most common cancer worldwide,and esophageal squamous cell carcinoma(ESCC)accounts for the majority of cases of EC.To effectively diagnose and treat ESCC and improve patient prognosis,timely diagnosis in the initial phase of the illness is necessary.This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs.Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quanti-tative polymerase chain reaction(qPCR),high-throughput sequencing technology(next-generation sequencing),and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis,treatment,and prognosis of diseases.The invest-igation of the microbiome is a swiftly progressing area in human cancer research,and microorganisms with complex functions are potential components of the tumor microenvironment.The intratumoral microbiota was also found to be connected to tumor progression.The application of endoscopy as a crucial technique for the early identification of ESCC has been essential,and with ongoing advancements in technology,endoscopy has continuously improved.With the advancement of artificial intelligence(AI)technology,the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent.The implementation of AI can effectively resolve the discrepancies among observers,improve the detection rate,assist in predicting the depth of invasion and differentiation status,guide the pericancerous margins,and aid in a more accurate diagnosis of ESCC.

关键词： Early esophageal squamous cell carcinoma Epidemiology and risk factors Molecular biology Epigenetic Microbiology Endoscopy and artificial intelligence

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Simultaneous Bulk-and Surface-initiated Living Polymerization Studied with a Heterogeneous Stochastic Reaction Model

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Chinese Journal of Polymer Science 2024年第3期42卷 364-372,I0008页

作者： Jia-Shu Ma Zhi-Ning huang Jia-Hao Li Bang-ping Jiang Yan-Da Liao shi-Chen Ji Xing-Can Shen State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources(Ministry of Education of China)School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China School of Computer Science and Engineering&School of Software Guangxi Normal UniversityGuilin 541004China

To better characterize the properties of surface-initiated polymers, simultaneous bulk-and surface-initiated polymerizations are usually carried out by assuming that the properties of the surface-initiated polymers resemble those of the bulk-initiated polymers. Through a Monte Carlo simulation using a heterogeneous stochastic reaction model, it was discovered that the bulk-initiated polymers exhibit a higher molecular weight and a lower dispersity than the corresponding surface-initiated polymers, which indicates that the equivalent assumption is invalid. Furthermore, the molecular weight distributions of the two types of polymers are also different, suggesting different polymerization mechanisms. The results can be simply explained by the heterogeneous distributions of reactants in the system. This study is helpful to better understand surface-initiated polymerization.

关键词： Surface-initiated polymerization Polymer brush Stochastic reaction model Heterogeneous polymerization Simultaneous polymerization

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Understanding the Low Predictability of the 2015/16 El Niño Event Based on a Deep Learning Model

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Advances in Atmospheric Sciences 2024年第7期41卷 1313-1325页

作者： Tingyu WANG ping huang Xianke YANG Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National Space Science Center Chinese Academy of SciencesBeijing 100190China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China College of Plant Science and Technology Huazhong Agricultural UniversityWuhan 430070China

The 2015/16 El Niño event ranks among the top three of the last 100 years in terms of intensity,but most dynamical models had a relatively low prediction skill for this event before the summer months.Therefore,the attribution of this particular event can help us to understand the cause of super El Niño–Southern Oscillation events and how to forecast them skillfully.The present study applies attribute methods based on a deep learning model to study the key factors related to the formation of this event.A deep learning model is trained using historical simulations from 21 CMIP6 models to predict the Niño-3.4 index.The integrated gradient method is then used to identify the key signals in the North Pacific that determine the evolution of the Niño-3.4 index.These crucial signals are then masked in the initial conditions to verify their roles in the prediction.In addition to confirming the key signals inducing the super El Niño event revealed in previous attribution studies,we identify the combined contribution of the tropical North Atlantic and the South Pacific oceans to the evolution and intensity of this event,emphasizing the crucial role of the interactions among them and the North Pacific.This approach is also applied to other El Niño events,revealing several new precursor signals.This study suggests that the deep learning method is useful in attributing the key factors inducing extreme tropical climate events.

关键词： ENSO attribution deep learning ENSO prediction extreme El Niño

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Clinical effects of nonconvulsive electrotherapy combined with mindfulness-based stress reduction and changes of serum inflammatory factors in depression

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World Journal of Psychiatry 2024年第5期14卷 653-660页

作者： Zhi-Wen Gu Chun-ping Zhang Li-ping Chen Xiong huang Department of Psychiatry Guangzhou First People's HospitalSouth China University of TechnologyGuangzhou 510180Guangdong ProvinceChina Department of Psychiatry The Affiliated Brain Hospital of Guangzhou Medical University(Guangzhou HuiAi Hospital)Guangzhou 510370Guangdong ProvinceChina

BACKGROUND Depression is a common and serious psychological condition,which seriously affects individual well-being and functional ability.Traditional treatment methods include drug therapy and psychological counseling;however,these methods have different degrees of side effects and limitations.In recent years,nonconvulsive electrotherapy(NET)has attracted increasing attention as a noninvasive treatment method.However,the clinical efficacy and potential mechanism of NET on depression are still unclear.We hypothesized that NET has a positive clinical effect in the treatment of depression,and may have a regulatory effect on serum inflammatory factors during treatment.AIM To assess the effects of NET on depression and analyze changes in serum inflammatory factors.METHODS This retrospective study enrolled 140 patients undergoing treatment for depression between May 2017 and June 2022,the observation group that received a combination of mindfulness-based stress reduction(MBSR)and NET treatment(n=70)and the control group that only received MBSR therapy(n=70).The clinical effectiveness of the treatment was evaluated by assessing various factors,including the Hamilton Depression Scale(HAMD)-17,self-rating idea of suicide scale(SSIOS),Pittsburgh Sleep Quality Index(PSQI),and levels of serum inflammatory factors before and after 8 wk of treatment.The quality of life scores between the two groups were compared.Comparisons were made using t and χ^(2) tests.RESULTS After 8 wk of treatment,the observation group exhibited a 91.43%overall effectiveness rate which was higher than that of the control group which was 74.29%(64 vs 52,χ^(2)=7.241;P<0.05).The HAMD,SSIOS,and PSQI scores showed a significant decrease in both groups.Moreover,the observation group had lower scores than the control group(10.37±2.04 vs 14.02±2.16,t=10.280;1.67±0.28 vs 0.87±0.12,t=21.970;5.29±1.33 vs 7.94±1.35,t=11.700;P both<0.001).Additionally,there was a notable decrease in the IL-2,IL-1β,and IL-6 in both groups after treatment.Furthermore,the observation

关键词： Depression Nonconvulsive electrotherapy Mindfulness-based stress reduction Serum inflammatory factors Clinical effect Hamilton Depression Scale

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Casein kinase-2 inhibition promotes retinal ganglion cell survival after acute intraocular pressure elevation

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Neural Regeneration Research 2024年第5期19卷 1112-1118页

作者： Meng Wang shi-Qi Yao Yao huang Jia-Jian Liang Yanxuan Xu Shaowan Chen Yuhang Wang Tsz Kin Ng Wai Kit Chu Qi Cui Ling-ping Cen Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong ShantouGuangdong ProvinceChina Shantou University Medical College ShantouGuangdong ProvinceChina Beijing Tongren Eye Center Beijing Tongren HospitalCapital Medical UniversityBeijingChina Department of Ophthalmology and Visual Sciences The Chinese University of Hong KongHong Kong Special Administrative RegionChina

Intraocular pressure elevation can induce retinal ganglion cell death and is a clinically reversible risk factor for glaucoma,the leading cause of irreversible blindness.We previously demonstrated that casein kinase-2 inhibition can promote retinal ganglion cell survival and axonal regeneration in rats after optic nerve injury.To investigate the underlying mechanism,in the current study we increased the intraocular pressure of adult rats to 75 mmHg for 2 hours and then administered a casein kinase-2 inhibitor(4,5,6,7-tetrabromo-2-azabenzimidazole or 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole)by intravitreal injection.We found that intravitreal injection of 4,5,6,7-tetrabromo-2-azabenzimidazole or 2-dimethylamino-4,5,6,7-tetrabromo-1H-benzimidazole promoted retinal ganglion cell survival and reduced the number of infiltrating macrophages.Transcriptomic analysis showed that the mitogen activated protein kinase signaling pathway was involved in the response to intraocular pressure elevation but was not modulated by the casein kinase-2 inhibitors.Furthermore,casein kinase-2 inhibition downregulated the expression of genes(Cck,Htrsa,Nef1,Htrlb,Prph,Chat,Slc18a3,Slc5a7,Scn1b,Crybb2,Tsga10ip,and Vstm21)involved in intraocular pressure elevation.Our data indicate that inhibition of casein kinase-2 can enhance retinal ganglion cell survival in rats after acute intraocular pressure elevation via macrophage inactivation.

关键词： casein kinase-2 glaucoma intraocular pressure elevation macrophages retinal ganglion cells

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