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A satellite-derived,ground-measurement-independent monthly PM_(2.5)mass concentration dataset over China during 2000-2015

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Big Earth data 2022年第4期6卷 633-649页

作者： Ying Zhang Zhengqiang Li Yuanyuan Wei Zongren Peng State Environmental Protection Key Laboratory of Satellite Remote Sensing Aerospace Information Research InstituteChinese Academy of SciencesBeijingChina

Following the accelerated development of urbanization and industrialization,atmospheric particulate matter has become a significant threat to public health globally.Environmental health studies usually use the mass concentration of fine particles(PM_(2.5))as a base data to predict the health risks of particulate exposure.However,PM_(2.5)data from ground monitoring stations in China has not been provided until January 2013 by the Ministry of Environmental Protection of China.Hence,an alternative dataset of PM_(2.5)spatiotemporal distributions extending to years earlier than 2013 is urgently needed,which is of great significance to atmospheric environment assessment and pollution prevention and control.Atmospheric aerosol products by the moderate-resolution imaging spectroradiometer(MODIS)have been released since 2000,which provides the possibility to reconstruct historical PM_(2.5).However,most current methods do not have the ability to estimate PM_(2.5)mass concentration independently of ground observations.The PM_(2.5)mass concentration data set produced by PM_(2.5)remote sensing(PMRS)model based on physical processes does not depend on the ground observations,and also is not affected by the uncertainty of model emission sources or the completeness of chemical reaction mechanism.These ensure that the point-by-point validation for PM_(2.5)mass concentration data is more convincing,and the dataset can also be further used for model assimilation and artificial intelligence training to improve their predictions.In this study,we calculate the monthly PM_(2.5)mass concentration near the ground over land of China using aerosol inversion products(aerosol optical depth and fine-mode fraction)of MODIS and meteorological data(boundary layer height&relative humidity)provided by the Modern-Era Retrospective Analysis for Research and Applications Version 2(MERRA-2)data set.The results show that,in China,6 pollution centers mainly concentrated in the central and eastern regions.The highest PM_(2.5)mass concentration occurred i

关键词： PM_(2.5) data set satellitederived China monthly

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Prediction of nature of band gap of perovskite oxides (ABO_(3)) using a machine learning approach

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Journal of Materiomics 2022年第5期8卷 937-948页

作者： Sudha Priyanga G Manoj N.Mattur N.Nagappan Smarak Rath Tiju Thomas Department of Physics Research Institute for Natural Scienceand Institute for High Pressure at Hanyang UniversityHanyang University222 WangsimniroSeongdong-KuSeoul 04763Republic of Korea Department of Metallurgical and Materials Engineering Indian Institute of Technology BhubaneswarOdisha752050India Department of Metallurgical and Materials Engineering Indian Institute of Technology MadrasChennai600036India

A material's electronic properties and technological utility depend on its band gap value and the nature of band gap(i.***.direct or indirect).This nature of band gaps is notoriously difficult to compute from first principles.In fact it is computationally intense to approximate and also rather time consuming.Hence its prediction represents a challenging problem.Machine learning based approach offers a promising and computationally efficient means to address this problem.Here we predict the nature of band gap for perovskite oxides(ABO_(3))with elemental composition,ionic radius,ionic character and electronegativity.We do this by training machine learning models on computationally generated datasets.Knowing the nature of the band gap of the perovskite oxides(whether direct or indirect)plays a pivotal role in determining whether the perovskite can be used for photovoltaic or photocatalytic applications.A total of 5329 perovskite oxides are considered in this study.Here,we determine the correlation between the nature of band gap and the composition of the perovskite oxide.A Random Forest algorithm is used for predicting the same since it yielded higher accuracy(~91%)compared to the other Machine Learning models.The approach suggested here can be used to predict the nature of bandgap and can also aid in novel materials discovery within the family of perovskites.This is a robust,quick,and low-cost strategy to find novel materials for light harvesting applications in particular.Also we present feature ranking as it pertains to prediction of nature of bandgap and also discuss correlation between the features.We also show feature importance graphs and SHapley Additive exPlanations(SHAP)as is relevant for prediction of nature of band gaps.Using the approach reported,NaPuO_(3) and VPbO_(3) are discovered to be good candidates for solar cell materials(direct band gap~1.5 eV).Novel composition predictions for targeted applications are the future and our model is a step ahead in this direction.

关键词： Perovskites Machine learning Random forest algorithm Nature of band gap data set

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Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

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Computer Systems Science & Engineering 2023年第5期45卷 1985-2000页

作者： S.Sudha A.Srinivasan T.Gayathri Devi Department of ECE SRCSASTRA Deemed UniversityKumbakonam612001TamilnaduIndia

The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.

关键词： CNN networking segmentation hybrid classifier data set crossvalidation fundus image

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Early Nutrient Diagnosis of Kentucky Bluegrass Combining Machine Learning and Compositional Methods

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American Journal of Plant Sciences 2022年第9期13卷 1247-1260页

作者： Abdo Badra Léon Etienne Parent Department of Soils and Agri-Food Engineering Université Laval Québec Canada

Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.

关键词： Centered Log Ratio data set Machine Learning Turfgrass Foliage Color Turfgrass Shoot Density Xgboost

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