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big data in support of the Sustainable Development Goals:a celebration of the establishment of the International research center of big data for Sustainable Development Goals(CBAS)

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big Earth data 2021年第3期5卷 259-262页

作者： Huadong Guo Heide Hackmann Ke Gong International research center of big data for SDGs International Science Council World Federation of Engineering Organizations

In the last century the impacts of human activity on natural processes that sustain the Earth’s biosphere,atmosphere,hydrosphere and lithosphere and that provide the bed-rock of human life support systems,have grown to the extent that they pose a credible existential threat to humanity.Today,the biggest challenge for science,technology and innovation(STI)is to contribute to the pursuit of global sustainability as exemplified in the Sustainable Development Goals(SDGs)that were adopted by the United Nations(UN)in 2015.

关键词： Sustainable pursuit humanity

big data in support of the sustainable development goals(continued):a celebration of the establishment of the International research center of big data for Sustainable Development Goals(CBAS)

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big Earth data 2021年第4期5卷 443-444页

作者： Huadong Guo Heide Hackmann Ke Gong International research center of big data for Sustainable Development Goals World Federation of Engineering Organizations

The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals(SDGs).big data improves the extent to which scientific evidence and innova-tive technological solutions can be adopted to meet these goals.However,the tools and methods of big data are still somewhat of a novelty in this respect.

关键词： Sustainable continued enable

big earth data for achieving the sustainable development goals in the belt and road region

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big Earth data 2022年第1期6卷 1-2页

作者： Fang Chen Zhongchang Sun International research center of big data for Sustainable Development Goals

The Belt and Road(B&R)is an important region with historical,economic,cultural,and political significance,including 75%of the global population and numerous social activ-ities.However,many countries along the B&R region are experiencing developmental challenges such as rapid urbanization,land degradation,water shortages,water and food security,frequent disasters,and large-scale ecosystem changes.The UN’s 17 Sustainable Development Goals(SDGs)provide a universal call to action to end poverty,protect the planet,and ensure that all people enjoy peace and prosperity by 2030 and to achieve economic,social,and environmental sustainability at global,regional,and national scales.

关键词： planet sustainable goals

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

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Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma JunWang Wenchang Yu Qinli Zhang School of big data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education big data Intelligent Perception and Application Engineering research center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

Method for wheat ear counting based on frequency domain decomposition of MSVF-ISCT

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Information Processing in Agriculture 2023年第2期10卷 240-255页

作者： Wenxia Bao Ze Lin Gensheng Hu Dong Liang Linsheng Huang Xin Zhang National Engineering research center for Agro-ecological big data Analysis and Application Anhui UniversityHefei 230601China

Wheat ear counting is a prerequisite for the evaluation of wheat yield.A wheat ear counting method based on frequency domain decomposition is proposed in this study to improve the accuracy of wheat yield estimation.The frequency domain decomposition of wheat ear image is completed by multiscale support value filter(MSVF)combined with improved sampled contourlet transform(ISCT).Support Vector Machine(SVM)is the classic classification and regression algorithm of machine learning.MSVF based on this has strong frequency domain filtering and generalization ability,which can effectively remove the complex background,while the multi-direction characteristics of ISCT enable it to represent the contour and texture information of wheat ears.In order to improve the level of wheat yield prediction,MSVF-ISCT method is used to decompose the ear image in multiscale and multi direction in frequency domain,reduce the interference of irrelevant information,and generate the sub-band image with more abundant information components of ear feature information.Then,the ear feature is extracted by morphological operation and maximum entropy threshold segmentation,and the skeleton thinning and corner detection algorithms are used to count the results.The number of wheat ears in the image can be accurately counted.Experiments show that compared with the traditional algorithms based on spatial domain,this method significantly improves the accuracy of wheat ear counting,which can provide guidance and application for the field of agricultural precision yield estimation.

关键词： Wheat ear counting Frequency domain decomposition Multiscale support value filter Improved sampled contourlet transform Image segmentation Morphological processing

big data research in Chronic Kidney Disease

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Chinese Medical Journal 2018年第22期131卷 2647-2650页

作者： Xiao-Xi Zeng Jing Liu Liang Ma Ping Fu West China Biomedical big data center Sichuan UniversityChengduSichuan 610041China Division of Nephrology West China School of MedicineSichuan UniversityChengduSichuan 610041China Division of Nephrology Kidney Research InstitutionWest China Hospital of Sichuan UniversityChengduSichuan 610041China

With a worldwide estimated prevalence of 8–16%, chronic kidney disease (CKD) is a major noncommunicable disease:it substantially contributes to premature mortality and loss of disability?adjusted life years.The variety in terms of causes,progression mechanisms,and histopathological manifestations creates challenges for early diagnosis and

关键词： Chronic Kidney Disease Computational Biology databse Machine Learning

Effects of short-range attraction on Jamming transition

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Chinese Physics B 2021年第6期30卷 409-413页

作者：徐震寰王瑞崔佳梅刘彦君郑文 Institute of Public-safety and big data College of Data ScienceTaiyuan University of TechnologyTaiyuan 030060China center for big data research in Health Changzhi Medical CollegeChangzhi 046000China

Enormous progresses to understand the jamming transition have been driven via simulating purely repulsive particles which were somehow idealized in the past two decades. While the attractive systems are both theoretical and practical compared with repulsive systems. By studying the statistics of rigid clusters, we find that the critical packing fraction φ_(c) varies linearly with attraction μ for different system sizes when the range of attraction is short. While for systems with long-range attractions, however, the slope of φ_(c) appears significantly different, which means that there are two distinct jamming scenarios. In this paper, we focus our main attention on short-range attractions scenario and define a new quantity named "short-range attraction susceptibility" χ_(p), which describes the degree of response of the probability of finding jammed states pjto short-range attraction strength μ. Our central results are that χ_(p) diverges in the thermodynamic limit as χ_(p) ∝|φ-φ_(c)^(∞)|^(-γ_(p)), where φ_(c)^(∞) is the packing fraction at the jamming transition for the infinite system in the absence of attraction. χ_(p) obeys scaling collapse with a scaling function in both two and three dimensions, illuminating that the jamming transition can be considered as a phase transition as proposed in previous work.

关键词： short-range attraction Jamming transition short-range attraction susceptibility

Time-sensitive prediction of NO_(2) concentration in China using an ensemble machine learning model from multi-source data

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Journal of Environmental Sciences 2024年第3期137卷 30-40页

作者： Chenliang Tao Man Jia Guoqiang Wang Yuqiang Zhang Qingzhu Zhang Xianfeng Wang Qiao Wang Wenxing Wang Big Data Research Center for Ecology and Environment Environment Research InstituteShandong UniversityQingdao 266237China Shandong Provincial Eco-environment Monitoring center Jinan 250101China

Nitrogen dioxide(NO_(2))poses a critical potential risk to environmental quality and public health.A reliable machine learning(ML)forecasting framework will be useful to provide valuable information to support government decision-making.Based on the data from1609 air quality monitors across China from 2014-2020,this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range.The ensemble ML model incorporates a residual connection to the gated recurrent unit(GRU)network and adopts the advantage of Transformer,extreme gradient boosting(XGBoost)and GRU with residual connection network,resulting in a 4.1%±1.0%lower root mean square error over XGBoost for the test results.The ensemble model shows great prediction performance,with coefficient of determination of 0.91,0.86,and 0.77 for 1-hr,3-hr,and 24-hr averages for the test results,respectively.In particular,this model has achieved excellent performance with low spatial uncertainty in Central,East,and North China,the major site-dense zones.Through the interpretability analysis based on the Shapley value for different temporal resolutions,we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions,while the impact of meteorological conditions would be ever-prominent for the latter.Compared with existing models for different spatiotemporal scales,the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO_(2),which will help developing effective control policies.

关键词： Air quality prediction Deep learning Ensemble method Nitrogen dioxide Spatiotemporal covariates

SAND: semi-automated adaptive network defense via programmable rule generation and deployment

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Science China(Information Sciences) 2022年第7期65卷 121-138页

作者： Haoyu CHEN Deqing ZOU Hai JIN Shouhuai XU Bin YUAN National Engineering research center for big data Technology and System Services Computing Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology National Engineering research center for big data Technology and System Services Computing Technology and System LabHubei Engineering Research Center on Big Data Security School of Cyber Science and EngineeringHuazhong University of Science and Technology Department of Computer Science University of Colorado at Colorado Springs

Cyber security is dynamic as defenders often need to adapt their defense postures. The state-ofthe-art is that the adaptation of network defense is done manually(i.e., tedious and error-prone). The ideal solution is to automate adaptive network defense, which is however a difficult problem. As a first step towards automation, we propose investigating how to attain semi-automated adaptive network defense(SAND). We propose an approach extending the architecture of software-defined networking, which is centered on providing defenders with the capability to program the generation and deployment of dynamic defense rules enforced by network defense tools. We present the design and implementation of SAND, as well as the evaluation of the prototype implementation. Experimental results show that SAND can achieve agile and effective dynamic adaptations of defense rules(less than 15 ms on average for each operation), while only incurring a small performance overhead.

关键词： network defense Network defense adaptive defense automated defense programmable defense security services software-defined networking security management