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Impact of Antecedent Soil Moisture Anomalies over the Indo-China Peninsula on the Super Meiyu Event in 2020

Journal of Meteorological Research 2023年第2期37卷 234-247页

作者： Yinshuo dong Haishan Chen Xuan Dong Key Laboratory of Meteorological Disaster Ministry of Education/Joint International Research Laboratory of Climate and Environment Change/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science&TechnologyNanjing210044 School of Atmospheric Sciences Nanjing University of Information Science&TechnologyNanjing210044

In the summer of 2020,a super Meiyu event occurred in the Yangtze River basin(YRB),causing enormous economic losses and human casualties.Recent studies have investigated the possible causes of this super Meiyu event from the perspective of anomalous atmospheric circulation activities and sea surface temperature(SST)anomalies;however,the influence of land surface processes has not garnered considerable attention.This study investigates the possible contributions of land surface processes to this extreme event based on observational analysis and numerical simulations,and shows that antecedent soil moisture(SM)anomalies over the Indo-China Peninsula(ICP)may have had a vital influence on the super Meiyu in 2020.Negative SM anomalies in May over the ICP increased the surface temperature and sensible heat flux.The“memory”of soil allowed the anomalies to persist into the Meiyu period.The heating of the lower atmosphere by the surface strengthened the western Pacific subtropical high,which caused an anomalous anticyclone from the ICP to Northwest Pacific and thus enhanced the southwesterly winds and vertical motion over the YRB.Consequently,the water vapor flux and convergence were strengthened.Sensitivity experiments based on the Weather Research and Forecasting(WRF)model further confirmed the results of observational analysis and indicated that the warm air heated by the ICP surface significantly warmed the lower troposphere from the ICP to Northwest Pacific under the influence of the background wind,thus increasing the geopotential height and inducing an anticyclone.The results of the sensitivity experiments showed that the SM anomalies in May over the ICP increased the precipitation by 10.6%from June to July over the YRB.These findings can improve our understanding of the mechanism of the super Meiyu event in 2020 and facilitate the prediction of extreme Meiyu events.

关键词： super Meiyu soil moisture Indo-China Peninsula surface heating

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Low lighting image enhancement using local maximum color value prior

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Frontiers of Computer Science 2016年第1期10卷 147-156页

作者： Xuan Dong Jiangtao WEN Department of Computer Science and Technology Tsinghua University Beijing 100084 China

We study the problem of low lighting image en- hancement. Previous enhancement methods for images un- der low lighting conditions usually fail to consider the factor of image degradation during image formation. As a result, the lost contrast could not be recovered after enhancement. This paper will adaptively recover the contrast and adjust the exposure for low lighting images. Our first contribution is the modeling of image degradation in low lighting con- ditions. Then, the local maximum color value prior is pro- posed, i.e., in most regions of well exposed images, the lo- cal maximum color value of a pixel will be very high. By combining the image degradation model and local maximum color value prior, we propose to recover the un-degraded im- ages under low lighting conditions. Last, an adaptive expo- sure adjustment module is proposed to obtain the final result. We show that our approach enables better enhancement com- paring with popular image editing tools and academic algo- rithms.

关键词： low lighting enhancement image degradationmodel

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A mechanically robust egg white hydrogel scaffold with excellent biocompatibility by three-step green processing

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Science China(Technological Sciences) 2022年第7期65卷 1599-1612页

作者： WEI Zhen-Zhen dong xuan ZHANG Yu-Qing School of Biology and Basic Medical Sciences Medical CollegeSoochow UniversitySuzhou 215123China College of Chemistry Chemical Engineering and Materials ScienceSoochow UniversitySuzhou 215123China

Egg white(EW) is one of the most common and high-quality nutritional proteins. A novel three-step green processing technology is constructed to process homogeneous EW into a mechanically robust EW hydrogel scaffold(EWHS) with excellent biocompatibility. The homogeneous EW is first treated with stream to form a hydrogel, then slowly dehydrated to form a fragile and almost transparent EW glass, and finally annealed into an EWHS. The EWHS is water-insoluble, wet soft and translucent and has a porous network structure, and its pore size distribution is between 0.1–1.0 μm. The tensile strength in wet state is as high as 5.0 MPa, the elongation at break is about 93%, and the swelling rate is about 85%. The order of the enzymatically residual rate of these EWHSs is pepsin > neutral protease > papain> trypsin > alkaline protease. Mouse L929 cells can adhere,proliferate, and grow well on the EWHSs. After EWHS was implanted in rats for three weeks, the levels of IL-6, TNF-α, and NF-κB inflammatory factors did not change significantly in comparison with the pseudo-operated and normal groups. This novel EW hydrogel biomaterial has potential applications in medical 3 D tissue engineering materials, such as tendons, soft tissues, and in vivo implants.

关键词： egg white hydrogel scaffolds dehydration heat treatment mechanical properties swelling rate

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Contribution of phenylpropanoid metabolism to plant development and plant–environment interactions

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Journal of Integrative Plant Biology 2021年第1期63卷 180-209页

作者： Nai-Qian dong Hong-xuan Lin National Key Laboratory of Plant Molecular Genetics CAS Centre for Excellence in Molecular Plant Sciences and Collaborative Innovation Center of Genetics and DevelopmentShanghai Institute of Plant Physiology and Ecologythe Chinese Academy of SciencesShanghai 200032China University of the Chinese Academy of Sciences Beijing 100049China School of Life Science and Technology ShanghaiTech UniversityShanghai 201210China

Phenylpropanoid metabolism is one of the most important metabolisms in plants, yielding more than 8,000 metabolites contributing to plant development and plant-environment interplay.Phenylpropanoid metabolism materialized during the evolution of early freshwater algae that were initiating terrestrialization and land plants have evolved multiple branches of this pathway, which give rise to metabolites including lignin, flavonoids, lignans, phenylpropanoid esters, hydroxycinnamic acid amides, and sporopollenin.Recent studies have revealed that many factors participate in the regulation of phenylpropanoid metabolism, and modulate phenylpropanoid homeostasis when plants undergo successive developmental processes and are subjected to stressful environments. In this review, we summarize recent progress on elucidating the contribution of phenylpropanoid metabolism to the coordination of plant development and plant–environment interaction, and metabolic flux redirection among diverse metabolic routes. In addition, our review focuses on the regulation of phenylpropanoid metabolism at the transcriptional, post-transcriptional, post-translational,and epigenetic levels, and in response to phytohormones and biotic and abiotic stresses.

关键词： flavonoids lignin metabolic flux phenylpropanoid secondary metabolites

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Prior austenite grain boundary recognition in martensite microstructure based on deep learning

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Journal of Iron and Steel Research(International) 2023年第5期30卷 1050-1056页

作者： xuan-dong Wang Nan Li Hang Su Hui-min Meng Institute for Advanced Materials and Technology University of Science and Technology BeijingBeijing 100083China Department of Structural Steel Central Iron and Steel Research Institute Co.Ltd.Beijing 100081China Material Digital R&D Center China Iron&Steel Research Institute Group Co.Ltd.Beijing 100081China Central Laboratory Central Iron and Steel Research Institute Co.Ltd.Beijing 100081China

Grain size determination is essential in producing and testing iron and steel materials.Grain size determination of martensitic steels usually requires etching with picric acid to reveal the prior austenite grain boundaries.However,picric acid is toxic and explosive and belongs to hazardous chemicals,which makes it difficult for laboratories and testing institutions to obtain.A new experimental method was developed to use Nital etchant instead of picric acid.The deep learning method was used to recognize the prior austenite grain boundaries in the etched martensite microstructure,and the grain size could be determined according to the recognition result.Firstly,the polished martensite specimen was etched twice with Nital etchant and picric acid,respectively,and the same position was observed using an optical microscope.The images of the martensitic structure and its prior austenite grain boundary label were obtained,and a data set was constructed.Secondly,based on this data set,a convolutional neural network model with a semantic segmentation function was trained,and the accuracy rate of the test set was 87.53%.Finally,according to the recognition results of the model,the grain size rating can be automatically determined or provide a reference for experimenters,and the difference between the automatic determination results and the measured results is about 0.5 level.

关键词： Prior austenite grain boundary Microstructure etching Image recognition Deep learning

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A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

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FRONTIERS OF ENVIRONMENTAL SCIENCE & ENGINEERING 2024年第5期18卷 54-54页

作者： Wang, xuan dong, Yan Yang, Jing Liu, Zhipeng Lu, Jinsuo Xian Univ Architecture & Technol Sch Environm & Municipal Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol Shaanxi Key Lab Environm Engn Xian 710055 Peoples R China Xian Univ Architecture & Technol State Key Lab Green Bldg West China Xian 710055 Peoples R China

Neural networks (NNs) have been used extensively in surface water prediction tasks due to computing algorithm improvements and data accumulation. An essential step in developing an NN is the hyperparameter selection. In practice, it is common to manually determine hyperparameters in the studies of NNs in water resources tasks. This may result in considerable randomness and require significant computation time; therefore, hyperparameter optimization (HPO) is essential. This study adopted five representatives of the HPO techniques in the surface water quality prediction tasks, including the grid sampling (GS), random search (RS), genetic algorithm (GA), Bayesian optimization (BO) based on the Gaussian process (GP), and the tree Parzen estimator (TPE). For the evaluation of these techniques, this study proposed a method: first, the optimal hyperparameter value sets achieved by GS were regarded as the benchmark; then, the other HPO techniques were evaluated and compared with the benchmark in convergence, optimization orientation, and consistency of the optimized values. The results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence, reasonable optimization orientation, and the highest consistency rates with the benchmark values. The optimization consistency rates via TPE for the hyperparameters hidden layers, hidden dimension, learning rate, and batch size were 86.7%, 73.3%, 73.3%, and 80.0%, respectively. Unlike the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test, the proposed benchmark-based HPO evaluation approach is feasible and robust.

关键词： Neural networks Hyperparameter optimization Surface water quality prediction Bayes optimization Genetic algorithm INPUT VARIABLE SELECTION VARIATIONAL APPROACH MODEL ARIMA

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A pixel-based outlier-free motion estimation algorithm for scalable video quality enhancement

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Frontiers of Computer Science 2015年第5期9卷 729-740页

作者： Xuan Dong Jiangtao WEN Department of Computer Science and Technology Tsinghua University Beijing 100084 China

Scalable video quality enhancement refers to the process of enhancing low quality frames using high quality ones in scalable video bitstreams with time-varying qualities. A key problem in the enhancement is how to search for correspondence between high quality and low quality frames. Previous algorithms usually use block-based motion estimation to search for correspondences. Such an approach can hardly estimate scale and rotation transforms and always in- troduces outliers to the motion estimation results. In this paper, we propose a pixel-based outlier-free motion estimation algorithm to solve this problem. In our algorithm, the motion vector for each pixel is calculated with respect to estimate translation, scale, and rotation transforms. The motion relationships between neighboring pixels are considered via the Markov random field model to improve the motion estimation accuracy. Outliers are detected and avoided by taking both blocking effects and matching percentage in scale- invariant feature transform field into consideration. Experiments are conducted in two scenarios that exhibit spatial scalability and quality scalability, respectively. Experimental results demonstrate that, in comparison with previous algorithms, the proposed algorithm achieves better correspondence and avoids the simultaneous introduction of outliers, especially for videos with scale and rotation transforms.

关键词： motion estimation scalable video coding videosuper resolution.

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Ionotropic receptor 8a is involved in the attraction of Helicoverpa armigera to acetic acid

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Insect Science 2022年第3期29卷 657-668页

作者： Xia-xuan Zhang Bin Yang dong-dong Sun Meng-Bo Guo Jie Zhang Gui-Rong Wang State Key Laboratory for Biology of Plant Diseases and Insect Pests Institute of Plant ProtectionChinese Academy of Agricultural SciencesBeijingChina Lingnan Guangdong Laboratory of Modem Agriculture Genome Analysis Laboratory of the Ministry of AgricultureAgricultural Genomics Institute at ShenzhenChinese Academy of Agricultural SciencesShenzhenGuangdong ProvinceChina College of Life Sciences Anhui Normal UniversityWuhuAnhui ProvinceChina

Ionotropic receptors(IRs)were first found in Drosophila melanogaster,and derive from ionotropic glutamate receptors(iGluRs),which are implicated in detecting acids,ammonia,amine,temperature and humidity.Although IRs are involved in sensing acid odors in a few insects,such as D.melanogaster,Aedes aegypti,and Manduca sexta,the function of IRs in Helicoverpa armigera is still unknown.IR8a was confirmed to be a co-receptor associated with acid detection.From the results of phylogenetic analysis,HarmIR8a displayed high similarity compared to homologs in D.melanogaster,M.sexta,and A.aegypti,suggesting that HarmIR8a might have a consistent function as a co-receptor for acid detection.In this study,clustered regularly interspaced palindromic repeats(CRISPR)/CRISPR-associated protein 9(Cas9)-mediated genome editing was implemented to knockout HarmIR8a for in vivo functional analysis.Electrophysiological and behavioral assays were performed to compare the differences between HarmIR8a knockout mutants and wild type individuals.From electroantennogram(EAG)analysis,we found that wild type H.armigera adults could detect short-chain carboxylic acids.In addition,wind tunnel experiments showed that 1%acetic acid attracted wild type H.armigera adults.However,acid sensing and attraction were reduced or abolished in the HarmIR8a knockout mutants.Our data suggest that HarmIR8a is important for H.armigera to detect short-chain carboxylic acids and mediate attraction behavior to acetic acid.

关键词： acetic acid CRISPR/Cas9 electroantennogram Helicoverpa armigera ionotropic receptors

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Nanomechanical and Chemical Mapping of the Structure and Interfacial Properties in Immiscible Ternary Polymer Systems

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Chinese Journal of Polymer Science 2021年第6期39卷 651-658,I0004页

作者： Hao-xuan Li Thomas PRussell dong Wang State Key Laboratory of Organic-Inorganic Composites&Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical TechnologyBeijing 100029China Polymer Science and Engineering Department University of Massachusetts AmherstMassachusetts 01003United States Materials Sciences Division Lawrence Berkeley National Laboratory1 Cyclotron RoadBerkeleyCalifornia 94720United States

It is a challenge to identify each phase in a multi-component polymer system and uniquely determine the interfacial properties between the different phases.Using atomic force microscopy nanomechanical mapping(AFM-NM)and AFM-based infrared spectroscopy(AFM-IR),we identify each phase,visualize structural developments,and determine the interfacial properties in a blend of three polymers:high-density polyethylene(HDPE),polyamide(PA6)and poly(styrene-b-ethylene-co-butylene-b-styrene)(SEBS).Each phase can be identified from the Young’s modulus,along with the structural development within the phases before and after compatibilization.The interfacial widths between HDPE/PA6,HDPE/SEBS and SEBS/PA6 were determined independently in one measurement from a Young’s modulus map.The structural,mechanical property development and identity of the phases were determined by AFM-NM,while AFM-IR,providing complementary chemical information,identified interfacial reactions,showed the chemical affinity of a compatibilizer with the component phases,and mapped the distribution of the compatibilizer in the ternary polymer blends.The chemical,structural and interfacial information obtained by these measurements provide information that is essential for producing mechanically robust materials from incompatible mixtures of polymers.

关键词： AFM nanomechanical mapping AFM infrared spectroscopy Multi-component systems Interfacial structure Interfacial properties

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Scalable and adaptive log manager in distributed systems

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Frontiers of Computer Science 2023年第2期17卷 45-62页

作者： Huan ZHOU Weining QIAN xuan ZHOU Qiwen dong Aoying ZHOU Wenrong TAN The Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest Minzu UniversityChengdu 610041China School of Data Science and Engineering East China Normal UniversityShanghai 200062China

On-line transaction processing(OLTP)systems rely on transaction logging and quorum-based consensus protocol to guarantee durability,high availability and strong consistency.This makes the log manager a key component of distributed database management systems(DDBMSs).The leader of DDBMSs commonly adopts a centralized logging method to writing log entries into a stable storage device and uses a constant log replication strategy to periodically synchronize its state to followers.With the advent of new hardware and high parallelism of transaction processing,the traditional centralized design of logging limits scalability,and the constant trigger condition of replication can not always maintain optimal performance under dynamic workloads.In this paper,we propose a new log manager named Salmo with scalable logging and adaptive replication for distributed database systems.The scalable logging eliminates centralized contention by utilizing a highly concurrent data structure and speedy log hole tracking.The kernel of adaptive replication is an adaptive log shipping method,which dynamically adjusts the number of log entries transmitted between leader and followers based on the real-time workload.We implemented and evaluated Salmo in the open-sourced transaction processing systems Cedar and DBx1000.Experimental results show that Salmo scales well by increasing the number of working threads,improves peak throughput by 1.56×and reduces latency by more than 4×over log replication of Raft,and maintains efficient and stable performance under dynamic workloads all the time.

关键词： distributed database systems transaction log-ging log replication scalable adaptive

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