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Predicting the Mechanical Behavior of a Bioinspired Nanocomposite through Machine Learning

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Computer Modeling in Engineering & Sciences 2024年第8期140卷 1299-1313页

作者： Xingzi Yang Wei Gao Xiaodu Wang Xiaowei zeng Department of Mechanical Engineering University of Texas at San AntonioSan AntonioTX78249USA Department of Mechanical Engineering Texas A&M UniversityCollege StationTX77843USA

The bioinspired nacre or bone structure represents a remarkable example of tough,strong,lightweight,and multifunctional structures in biological materials that can be an inspiration to design bioinspired high-performance *** bioinspired structure consists of hard grains and soft material *** the material interface has a very low volume percentage,its property has the ability to determine the bulk material *** learning technology nowadays is widely used in material science.A machine learning model was utilized to predict the material response based on the material interface properties in a bioinspired *** model was trained on a comprehensive dataset of material response and interface properties,allowing it to make accurate *** results of this study demonstrate the efficiency and high accuracy of the machine learning *** successful application of machine learning into the material property prediction process has the potential to greatly enhance both the efficiency and accuracy of the material design process.

关键词： Bioinspired nanocomposite computational model machine learning finite element material interface

A unified pre-training and adaptation framework for combinatorial optimization on graphs

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Science China Mathematics 2024年第6期67卷 1439-1456页

作者： Ruibin zeng Minglong Lei Lingfeng Niu Lan Cheng School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Economics and Management University of Chinese Academy of SciencesBeijing100190China School of Mathematics and Statistics Central South UniversityChangsha410075China

Combinatorial optimization(CO)on graphs is a classic topic that has been extensively studied across many scientific and industrial ***,solving CO problems on graphs through learning methods has attracted great *** deep learning methods,e.g.,graph neural networks(GNNs),have been used to effectively assist the process of solving ***,current frameworks based on GNNs are mainly designed for certain CO problems,thereby failing to consider their transferable and generalizable abilities among different COs on ***,simply using original graphs to model COs only captures the direct correlations among objects,which does not consider the mathematical logicality and properties of *** this paper,we propose a unified pre-training and adaptation framework for COs on graphs with the help of the maximum satisfiability(Max-SAT)*** first use Max-SAT to bridge different COs on graphs since they can be converted to Max-SAT problems represented by standard formulas and clauses with logical *** we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from *** the pre-training stage,Max-SAT instances are generated to initialize the parameters of the *** the fine-tuning stage,instances from CO and Max-SAT problems are used for adaptation so that the transferable ability can be further *** experiments on several datasets show that features extracted by our framework exhibit superior transferability and Max-SAT can boost the ability to solve COs on graphs.

关键词： combinatorial optimization graph neural networks domain adaptation maximum satisfiability problem

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When smoke meets gut:deciphering the interactions between tobacco smoking and gut microbiota in disease development

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Science China(Life Sciences) 2024年第5期67卷 854-864页

作者： Bo Chen Guangyi zeng Lulu Sun Changtao Jiang Center of Basic Medical Research Institute of Medical Innovation and ResearchPeking University Third HospitalBeijing 100191China Department of Physiology and Pathophysiology School of Basic Medical SciencesState Key Laboratory of Vascular Homeostasis and RemodelingPeking UniversityBeijing 100191China Center for Obesity and Metabolic Disease Research School of Basic Medical SciencesPeking UniversityBeijing 100191China State Key Laboratory of Women’s Reproductive Health and Fertility Promotion Peking UniversityBeijing 100191China Department of Endocrinology and Metabolism Peking University Third HospitalBeijing 100191China

Tobacco smoking is a prevalent and detrimental habit practiced worldwide,increasing the risk of various diseases,including chronic obstructive pulmonary disease(COPD),cardiovascular disease,liver disease,and *** previous research has explored the detrimental health effects of tobacco smoking,recent studies suggest that gut microbiota dysbiosis may play a critical role in these *** tobacco smoke components,such as nicotine,are found in the gastrointestinal tract and interact with gut microbiota,leading to lasting impacts on host health and *** review delves into the ways tobacco smoking and its various constituents influence gut microbiota composition and *** also summarize recent advancements in understanding how tobacco smoking-induced gut microbiota dysbiosis affects host ***,this review introduces a novel perspective on how changes in gut microbiota following smoking cessation may contribute to withdrawal syndrome and the degree of health improvements in smokers.

关键词： tobacco smoking gut microbiota dysbiosis nicotine smoking cessation

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Nanotechnology-based Detection Strategies for Hypertension Biomarkers

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Chemical Research in Chinese Universities 2024年第4期40卷 564-576页

作者： WANG Zhichao zeng Yuan WANG Yaling CHEN Chunying Henan Institute of Advanced Technology Zhengzhou UniversityZhengzhou450003P.R.China CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety&CAS Center for Excellence in Nanoscience National Center for Nanoscience and Technology of ChinaChinese Academy of Sciences(CAS)Beijing100190P.R.China

Hypertension significantly increases the risk of cardiovascular diseases and seriously affects people’s *** incidence of hypertension is rising rapidly in the world and hypertension has become a significant factor influencing the global average life *** diagnosis of hypertension is generally fulfilled by measuring diastolic and systolic blood pressure,but it is insufficient to differentiate essential hypertension from secondary hypertension,so it is crucial to identifying the cause of hypertension by detecting biomarkers in *** clinical practice,five plasma biomarkers are utilized for diagnosing hypertension,and the detection tasks of a large number of cases have raised increasing demands for cost-effective,efficient,speedy,and diverse methods,which makes many traditional diagnostic technologies inadequate for meeting the needs of clinical *** nanomaterial-based detection techniques have frequently attained the qualities of quick reaction,low cost,straightforward operation,high sensitivity,and strong specificity in recent years with the advancement of nanotechnology,so they have great potential for early and rapid *** this review,we will introduce the characteristics and drawbacks of current clinical detection methods for hypertension screening,the principles and advancements of nanotechnology-based detection methods,as well as their potentials for clinical application.

关键词： Hypertension Biomarker Diagnosis Clinical detection Nanotechnology-based detection

Multilevel carbon architecture of subnanoscopic silicon for fast‐charging high‐energy‐density lithium‐ion batteries

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Carbon Energy 2024年第4期6卷 256-268页

作者： Meisheng Han Yongbiao Mu Lei Wei Lin zeng Tianshou Zhao Shenzhen Key Laboratory of Advanced Energy Storage Southern University of Science and TechnologyShenzhenChina SUSTech Energy Institute for Carbon Neutrality Southern University of Science and TechnologyShenzhenChina Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhenChina

Silicon(Si)is widely used as a lithium‐ion‐battery anode owing to its high capacity and abundant crustal ***,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial ***,we propose a multilevel carbon architecture with vertical graphene sheets(VGSs)grown on surfaces of subnanoscopically and homogeneously dispersed Si–C composite nanospheres,which are subsequently embedded into a carbon matrix(C/VGSs@Si–C).Subnanoscopic C in the Si–C nanospheres,VGSs,and carbon matrix form a three‐dimensional conductive and robust network,which significantly improves the conductivity and suppresses the volume expansion of Si,thereby boosting charge transport and improving electrode *** VGSs with vast exposed edges considerably increase the contact area with the carbon matrix and supply directional transport channels through the entire material,which boosts charge *** carbon matrix encapsulates VGSs@Si–C to decrease the specific surface area and increase tap density,thus yielding high first Coulombic efficiency and electrode compaction ***,C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode *** particular,the full cells show high energy densities of 603.5 Wh kg^(−1)and 1685.5 Wh L^(−1)at 0.1 C and maintain 80.7%of the energy density at 3 C.

关键词： fast charging high energy densities lithium‐ion batteries multilevel carbon architecture subnanoscopic silicon anode

Urban Flood Mitigation Strategies with Coupled Gray–Green Measures: A Case Study in Guangzhou City, China

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International Journal of Disaster Risk Science 2024年第3期15卷 467-479页

作者： Jiayue Li Jiajun zeng Guoru Huang Wenjie Chen School of Civil Engineering and Transportation South China University of Technology School of Architecture South China University of Technology State Key Laboratory of Subtropical Building and Urban Science South China University of Technology College of Water Conservancy and Civil Engineering South China Agricultural University

The integration of gray and green infrastructure has proven to be a feasible approach for managing stormwater in established urban areas. However, evaluating the specific contributions of such coupled strategies is challenging. This study introduced a novel integrated hydrological-hydrodynamic model that takes into account the layout of low-impact development(LID)facilities along with pipeline alignment and rehabilitation. Reliable results from modeling were used to assess the individual contribution of LID and improved drainage facilities to urban flooding mitigation. We selected a natural island in Guangzhou City, China, as the study site. The results indicate that combining three LID measures, namely green roofs, sunken green spaces, and permeable pavements, can reduce total runoff by 41.7% to 25.89% for rainfall recurrence periods ranging from 1year to 100 years, and decrease the volume of nodal overflow by nearly half during rainfall events of less than 10-year return period. By integrating LID measures with the upgraded gray infrastructure, the regional pipeline overloading condition is substantially alleviated, resulting in a significant improvement in pipeline system resilience. For urban flooding control, it is recommended to integrate sufficient green space and avoid pipe-laying structural issues during urban planning and construction. The findings may assist stakeholders in developing strategies to best utilize gray and green infrastructure in mitigating the negative eff ects of urban flooding.

关键词： Coupled hydrodynamic model Gray–green approach Guangzhou City LID Pipe renovation Urban inundation

Off-farm employment,agriculture production activities,and household dietary diversity in environmentally and economically vulnerable areas of Asia

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Journal of Integrative Agriculture 2024年第2期23卷 359-373页

作者： Yunli Bai Xuanye zeng Chao Fu Linxiu Zhang Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China United Nations Environment Programme-International Ecosystem Management Partnership(UNEP-IEMP) Beijing 100101China University of Chinese Academy of Sciences Beijing 100101China

Access to off-farm employment has been expected to be a critical approach to ending hunger and all forms of malnutrition,two important targets of achieving Zero *** study aims to investigate the role of off-farm employment in improving dietary diversity through substitution effect and complementary effect with agricultural production activities and income *** study adopts Poisson/Tobit/Probit/OLS regressions and the instrument variable method based on the primary survey data collected among 1,282 households at 12 sites in environmentally and economically vulnerable areas of China,Nepal,Cambodia,Thailand,and Myanmar in *** results show that off-farm employment is positively associated with household dietary diversity and the consumption of flesh meat,fish and other aquatic animals,fruits,and milk and dairy products,which are rich in protein and *** results of mechanism analysis show that off-farm employment contributes to household dietary diversity by improving crop diversity,especially for poor households,boosting the probability of livestock raising for households with the middle one-third disposal income,and increasing household *** positive association between off-farm employment and household dietary diversity is much higher for households with the bottom one-third disposal income,low illiteracy,and from upper-middle income *** findings imply that off-farm employment does play a vital role in achieving multiple benefits of poverty alleviation,malnutrition reduction,and agrobiodiversity conservation in environmentally and economically vulnerable ***,it may enlarge the gaps in dietary diversity between households with low human capital and from low and lower-middle income countries and those with high human capital and from middle-high countries.

关键词： off-farm employment dietary diversity income effect substitution effect complementary effect

A Local-Global Attention Fusion Framework With Tensor Decomposition for Medical Diagnosis

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1536-1538页

作者： Peishu Wu Han Li Liwei Hu Jirong Ge Nianyin zeng Department of Instrumental and Electrical Engineering Xiamen UniversityFujian 361005China Key Research Laboratory of Osteoporosis Syndrome Genomics Fujian Academy of Chinese Medical SciencesFuzhou 350003China

Dear Editor,In this letter,a novel hierarchical fusion framework is proposed to address the imperfect data property in complex medical image analysis(MIA)*** particular,by combining the strengths of convolutional neural networks(CNNs)and transformers,the enhanced feature extraction,spatial modeling,and sequential context learning are realized to provide comprehensive insights on the complex data *** of information in different level is enabled via a multi-attention fusion mechanism,and the tensor decomposition methods are adopted so that compact and distinctive representation of the underlying and high-dimensional medical image features can be accomplished[1].It is shown from the evaluation results that the proposed framework is competitive and superior as compared with some other advanced algorithms,which effectively handles the imperfect property of inter-class similarity and intra-class differences in diseases,and meanwhile,the model complexity is reduced within an acceptable level,which benefits the deployment in clinic *** has assumed a pivotal role in numerous critical clinical scenarios,where sophisticated image analysis techniques have proven instrumental in augmenting medical decision-making,facilitating individualized therapeutic interventions,and enhancing patient prognostication[2]−[4].

关键词： handle image property

Optimal Stochastic Scheduling Strategy of Multi-vector Energy Complex Integrated with Full-blown Power-to-biomethane Model

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 863-873页

作者： Guanwei zeng Chengxi Liu Minfang Liao Yongjian Luo Xuzhu Dong Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network WuhanChina the School of Electrical Engineering and Automation Wuhan UniversityWuhanChina

We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC),considering a fullblown model of the power-to-biomethane(Pt M)*** conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane,a detailed Pt M model is introduced to emphasize the reactor kinetics and chemical equilibria of *** model crystallizes the interactions between the Pt M process and MEC flexibility,allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic *** optimization and flowsheet design of the Pt M process increase the average selectivity of methane(i.e.,ratio between net biomethane production and hydrogen consumption)up to 83.7%in the proposed synthesis *** results can provide information and predictions to operators about the optimal operating conditions of a Pt M unit while improving the MEC flexibility.

关键词： Multi-vector energy complex optimal stochastic scheduling power-to-biomethane unit process synthesis natural gas