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

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

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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 fields.Recently,solving CO problems on graphs through learning methods has attracted great attention.Advanced deep learning methods,e.g.,graph neural networks(GNNs),have been used to effectively assist the process of solving COs.However,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 graphs.Moreover,simply using original graphs to model COs only captures the direct correlations among objects,which does not consider the mathematical logicality and properties of COs.In this paper,we propose a unified pre-training and adaptation framework for COs on graphs with the help of the maximum satisfiability(Max-SAT)problem.We 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 information.Then we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them.In the pre-training stage,Max-SAT instances are generated to initialize the parameters of the model.In the fine-tuning stage,instances from CO and Max-SAT problems are used for adaptation so that the transferable ability can be further improved.Numerical 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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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 Hunger.This 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 effect.This 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 2019.The 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 micronutrients.The 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 income.The 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 countries.These 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 areas.However,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

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Low-Rank Optimal Transport for Robust Domain Adaptation

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1667-1680页

作者： Bingrong Xu Jianhua Yin Cheng Lian Yixin Su Zhigang zeng IEEE the School of Automation Wuhan University of TechnologyWuhan 430070China Intelligent Transportation Systems Research Center Wuhan University of TechnologyWuhan 430063 Chongqing Research Institute Wuhan University of TechnologyChongqingChina the School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074 the Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan 430074China

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

关键词： Domain adaptation low-rank constraint noise corruption optimal transport

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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 reserves.However,large volume change upon cycling and poor conductivity of Si cause rapid capacity decay and poor fast‐charging capability limiting its commercial applications.Here,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 stability.The 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 transport.The 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 density.Consequently,C/VGSs@Si–C delivers excellent Li‐ion storage performances under industrial electrode conditions.In 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

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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)scenes.In 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 patterns.Integration 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 practice.MIA 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

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Transglutaminase 2 serves as a pathogenic hub gene of KRAS mutant colon cancer based on integrated analysis

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World Journal of Gastrointestinal Oncology 2024年第5期16卷 2074-2090页

作者： Wei-Bin Peng Yu-Ping Li Yong zeng Kai Chen First People’s Hospital of Foshan Foshan 528000Guangdong ProvinceChina Department of Radiation Oncology Cancer Hospital of Shantou University Medical CollegeShantou 515031Guangdong ProvinceChina

BACKGROUND Colon cancer is acknowledged as one of the most common malignancies worldwide,ranking third in United States regarding incidence and mortality.Notably,approximately 40%of colon cancer cases harbor oncogenic KRAS mutations,resulting in the continuous activation of epidermal growth factor receptor signaling.AIM To investigate the key pathogenic genes in KRAS mutant colon cancer holds considerable importance.METHODS Weighted gene co-expression network analysis,in combination with additional bioinformatics analysis,were conducted to screen the key factors driving the progression of KRAS mutant colon cancer.Meanwhile,various in vitro experiments were also conducted to explore the biological function of transglutaminase 2(TGM2).RESULTS Integrated analysis demonstrated that TGM2 acted as an independent prognostic factor for progression-free survival.Immunohistochemical analysis on tissue microarrays revealed that TGM2 was associated with an elevated probability of perineural invasion in patients with KRAS mutant colon cancer.Additionally,biological roles of the key gene TGM2 was also assessed,suggesting that the downregulation of TGM2 attenuated the proliferation,invasion,and migration of the KRAS mutant colon cancer cell line.CONCLUSION This study underscores the potential significance of TGM2 in the progression of KRAS mutant colon cancer.This insight not only offers a theoretical foundation for therapeutic approaches but also highlights the need for additional clinical trials and fundamental research to support our preliminary findings.

关键词： Colon cancer KRAS mutation Transglutaminase 2 Weighted gene co-expression network analysis

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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 the School of Electrical Engineering and Automation Wuhan University

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) process. Unlike 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 methanation. This 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 scenarios. Temperature 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 flowsheet. Simulation 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： Biological system modeling Hydrogen Resistance heating Stochastic processes Production Methane Inductors

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Rational Design of Cost-Effective Metal-Doped ZrO_(2)for Oxygen Evolution Reaction

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Nano-Micro Letters 2024年第9期16卷 385-396页

作者： Yuefeng Zhang Tianyi Wang Liang Mei Ruijie Yang Weiwei Guo Hao Li Zhiyuan zeng Department of Materials Science and Engineering and State Key Laboratory of Marine PollutionCity University of Hong Kong83 Tat Chee AvenueKowloon 999077Hong KongPeople’s Republic of China Advanced Institute for Materials Research(WPI‑AIMR) Tohoku UniversitySendai 980‑8577Japan Shenzhen Research Institute City University of Hong KongShenzhen 518057People’s Republic of China Shanxi Supercomputing Center Lvliang 033000ShanxiPeople’s Republic of China

The design of cost-effective electrocatalysts is an open challenging for oxygen evolution reaction(OER)due to the“stable-oractive”dilemma.Zirconium dioxide(ZrO_(2)),a versatile and low-cost material that can be stable under OER operating conditions,exhibits inherently poor OER activity from experimental observations.Herein,we doped a series of metal elements to regulate the ZrO_(2)catalytic activity in OER via spin-polarized density functional theory calculations with van der Waals interactions.Microkinetic modeling as a function of the OER activity descriptor(G_(O*)-G_(HO*))displays that 16 metal dopants enable to enhance OER activities over a thermodynamically stable ZrO_(2)surface,among which Fe and Rh(in the form of single-atom dopant)reach the volcano peak(i.***.the optimal activity of OER under the potential of interest),indicating excellent OER performance.Free energy diagram calculations,density of states,and ab initio molecular dynamics simulations further showed that Fe and Rh are the effective dopants for ZrO_(2),leading to low OER overpotential,high conductivity,and good stability.Considering cost-effectiveness,single-atom Fe doped ZrO_(2)emerged as the most promising catalyst for OER.This finding offers a valuable perspective and reference for experimental researchers to design cost-effective catalysts for the industrial-scale OER production.

关键词： Oxygen evolution reaction Metal oxide Electrocatalysis Surface Pourbaix analysis Doping

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Data-driven Anomaly Detection Method Based on Similarities of Multiple Wind Turbines

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 803-818页

作者： Xiangjun zeng Ming Yang Chen Feng Mingqiang Wang Lingqin Xia the College of Electrical Engineering & New Energy China Three Gorges University the Key Laboratory of Power System Intelligent Dispatch and Control Shandong University

The operating conditions of wind turbines(WTs) in the same wind farm(WF) may share similarities due to their shared manufacturing process, control strategy, and operating environment. However, the similarities of WTs are seldom considered in WT anomaly detection, resulting in the disregard of useful information. This paper proposes a method to improve the reliability and accuracy of WT anomaly detection using the supervisory control and data acquisition(SCADA) data of multiple WTs in the same WF. First, a similarity assessment method based on a comparison of different observation time series is proposed, which objectively quantifies the similarities of WT operating conditions. Then, the SCADA data of the target WT and selected WTs that are similar are used to establish several estimation models through a long short-term memory(LSTM) algorithm. LSTM models that exhibit good estimation performance are used to construct a combined estimation model that estimates the variations in the monitored variables of the target WT. Finally, an anomaly detection method that jointly compares the effective value and information entropy of the residuals is proposed to identify anomalies. The effectiveness and accuracy of the proposed method are verified using the data of two actual WFs.

关键词： Estimation Anomaly detection Data models Fault detection Time series analysis Training data Signal processing algorithms

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