Comparison of Cheng's Index-and SSR Marker-based Classification of Asian Cultivated Rice

A total of 100 cultivated rice accessions,with a clear isozyme-based classification,were analyzed based on Cheng＇s index and simple sequence repeat （SSR） marker.The results showed that the isozyme-based classification was in high accordance with that based on Cheng＇s index and SSR markers.Mantel-test revealed that the Euclidean distance of Cheng＇s index was significantly correlated with Nei＇s unbiased genetic distance of SSR markers （r=0.466,P ≤ 0.01）.According to the model-based group and cluster analysis,the Cheng＇s index-and SSR-based classification coincided with each other,with the goodness of fit of 82.1% and 84.7% in indica,97.4% and 95.1% in japonica,respectively,showing higher accordance than that within subspecies.Therefore,Cheng＇s index could be used to classify subspecies,while SSR marker could be more efficient to analyze the subgroups within subspecies.

Peak extraction and classification from digital elevation models based on the relationship between morphological characteristics and spatial position

A peak is an important topographic feature crucial in quantitative geomorphic feature analysis,digital geomorphological mapping,and other fields.Most peak extraction methods are based on the maximum elevation in a local area but ignore the morphological characteristics of the peak area.This paper proposes three indices based on the morphological characteristics of peaks and their spatial relationship with ridge lines:convexity mean index(CM-index),convexity standard deviation(CSD-index),and convexity imbalance index(CIBindex).We develop computation methods to extract peaks from digital elevation model(DEM).Subsequently,the initial peaks extracted by neighborhood statistics are classified using the proposed indices.The method is evaluated in the Qinghai Tibet Plateau and the Loess Plateau in China.An ASTER Global DEM(ASTGTM2 DEM)with a grid size of 30 m is chosen to assess the suitability of the proposed mountain peak extraction and classification method in different geomorphic regions.DEM data with grid sizes of 30 m and 5 m are used for the Loess Plateau.The mountain peak extraction and classification results obtained from the different resolution DEM are compared.The experimental results show that:(1)The CM-index and the CSDindex accurately reflect the concave or convex morphology of the surface and can be used as supplements to existing surface morphological indices.(2)The three indices can identify pseudo mountain peaks and classify the remaining peaks into single ridge peak(SR-Peak)and multiple ridge intersection peak(MRI-Peak).The visual inspection results show that the classification accuracy in the different study areas exceeds 75%.(3)The number of peaks is significantly higher for the 5 m DEM than for the 30 m DEM because more peaks can be detected at a finer resolution.

A Novel Metadata Based Multi-Label Document Classification Technique

From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

Classification of Hourly Clearness Index of Solar Radiation in the District of Yamoussoukro

The exploitation of systems using solar energy as a source of energy is not fluctuations free because of short passage of clouds on solar radiation. The amplitude, the persistence and the frequency of these fluctuations should be analyzed with appropriate tools, instead of focusing on their location over time. The analysis of these fluctuations should use the instantaneous clearness index whose distribution is given as a first approximation which is independent not only of the season but also of the site. It is important to evaluate the potential solar energy in a region. Indeed such evaluation helps the decision-makers in their reflections on agricultural or photovoltaic solar projects. Then this study was conducted for a predictive purpose. The method used in our work combines the classification method which is the hierarchical ascending classification and two partitioning methods, the principal component?analysis and the K-means method. The partitioning method enabled to?achieve a number of well-known situations (in advance) that are representative of the day. The study was based on the data of a climatic weather station in the district of Yamoussoukro located in the center region of Côte d’Ivoire during the 2017 year. Using the clearness index, the study allowed the classification of the solar radiation in the region. Thus, it showed that only 346 days of the 365 days in 2017 were classified (95%). We identified three clusters of days, the cloudy sky (29%), the partly cloudy sky?(32%) and the clear sky (39%). The statistical tests used for the characterization?of these clusters will be detailed in a future study.

Construction of the Classification and Grading Index System of Cultivated Land Based on the Viewpoint of Sustainable Development

In order to objectively and reasonably evaluate the actual and potential value of cultivated land, both social and ecological values are introduced into the classification and grading index system of cultivated land based on the viewpoint of sustainable development, after considering the natural and economic values of cultivated land. Index system construction of the sustainable utilization of cultivated land should follow the principles of economic viability, social acceptability, and ecological protection. Classification of cultivated land should take into account the soil fertility of cultivated land. Then, grading of cultivated land is carried out from the practical productivity (or potential productivity) of cultivated land. According to the existing classification index system of cultivated land, the soil, natural and environmental factors in plains, mountains and hills are mainly modified in the classification index system of cultivated land. And index systems for the cultivated land classification in plains, mountains and hills are set up. The grading index system of cultivated land is established based on the economic viability (economic value), social acceptability (social value) and protection of cultivated land (ecological value). Quantitative expression of cultivated land grading index is also carried out.

Genetic analysis of morphological index and its related taxonomic traits for classification of indica

The Cheng index distinguishes indica andjaponica rice based on six taxonomic traits.This index has been widely used for classifi- cation of indica and japonica varieties in China.In this study,a double haploid(DH)popula-tion derived from anther culture of ZYQ8/JX17 F,a typical inter-subspecies hybrid,was used to investigate the six taxonomictraits,i.e.leaf hairiness(LH),color of hullwhen heading(CHH),hairiness of hull(HH),length of the first and second panicle internode(LPI),length/width of grain(L/W),andphenol reaction(PH).The morphological in- dex(MI)was also calculated.Based on themolecular linkage map constructed from this

Mathe matical Subject Classification在数学文献信息分类中的应用

美国《数学评论》的分类体系《数学主题分类表》(MathematicalSubjectClassification) ,是国际数学界影响最大使用最广的数学专业分类表。数学工作者可以使用美国数学会在网站上提供的MathematicalSubjectClassification为自己的数学论文标引分类号 ,也可从该分类途径检索网上的数学信息。

An Embedded Feature Selection Method for Imbalanced Data Classification

Imbalanced data is one type of datasets that are frequently found in real-world applications, e.g., fraud detection and cancer diagnosis. For this type of datasets, improving the accuracy to identify their minority class is a critically important issue.Feature selection is one method to address this issue. An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class. A decision tree is a classifier that can be built up by using different splitting criteria. Its advantage is the ease of detecting which feature is used as a splitting node. Thus, it is possible to use a decision tree splitting criterion as a feature selection method. In this paper, an embedded feature selection method using our proposed weighted Gini index(WGI) is proposed. Its comparison results with Chi2, F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected. As the number of selected features increases, our proposed method has the highest probability of achieving the best performance. The area under a receiver operating characteristic curve(ROC AUC) and F-measure are used as evaluation criteria. Experimental results with two datasets show that ROC AUC performance can be high, even if only a few features are selected and used, and only changes slightly as more and more features are selected. However, the performance of Fmeasure achieves excellent performance only if 20% or more of features are chosen. The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.

Durability classification of red beds rocks in central Yunnan based on particle size distribution and slaking procedure

Moisture induced disintegration of soft rock in Red Beds is common all over the world. The slake durability index test is most useful to quantify durability of the soft rocks. Based on a series of slaking test, this article aims to develop a durability classification involving particle size and slaking procedure. To describe the slaking procedure in detail,the Relative Slake Durability Index(Id_i) is proposed. The Id_i is the percentage ratio of the i^(th) weight of oven-dry retained portion to the(i-1)^(th) weight of ovendry retained portion. Results show that the Id_i of samples have a large difference in certain slaking procedure, whereas the traditional Durability Slake Index(Id) is almost constant. Considering this limitation of Id in durability classification, an advanced classification by applying the Id_i and disintegration ratio(DR) is further established in this article. Compared to the durability classification based on Slake Durability Index(Id), the new classification accounts for the particle size of the slaked material and the slaking procedure, so it provides a better measure of the degree of slaking. The classification recommended in this article divide the slake durability into three classes(i.e., low, medium and high class). Furthermore, it divides both the low class and the medium class into 3 subclasses.

Using Synoptic Classification and Trajectory Analysis to Assess Air Quality during the Winter Heating Period in rümqi, China

Synoptic patterns identified by an automated procedure employing principal- component analysis and a two-stage cluster analysis, and backward trajectory analysis clustered by the HYSPLIT4.9 model were used to examine air quality patterns over¨ Uru¨mqi, China, one of the most heavily polluted cities in the world. Six synoptic patterns representing different atmospheric circulation patterns and air-mass characteristics were classified during the winter heating periods from 2001 to 2008, and seven trajectory clusters representing different paths of air masses arriving at ürümqi were calculated during the winter heating periods from 2005 to 2008. Then air quality was evaluated using these two approaches, and significant variations were found across both synoptic patterns and trajectory clusters. The heaviest air-pollution episodes occurred when ürümqi was either in an extremely cold, strong anticyclone or at the front of a migrating cyclone. Both conditions were characterized by with light winds, cold, wet surface air, and relatively dry upper air. ürümqi was predominately influenced by air masses from the southwest and from local areas. Air pollution index （API） levels were highest for air masses originating from the southwest with a longer path or for the local area, because of transport from semi-desert/desert regions by strong winds and because of local heavy pollution emissions, respectively. The interactions between these two analytical approaches showed that poor diffusion conditions, together with local circulation, enhanced air pollution, besides, regional air-mass transport caused by strong winds contributed to serious air quality under relatively good diffusion conditions.

Influence of data analysis when exploiting DFN model representation in the application of rock mass classification systems

Discrete fracture network(DFN) models have been proved to be effective tools for the characterisation of rock masses by using statistical distributions to generate realistic three-dimensional(3 D) representations of a natural fracture network. The quality of DFN modelling relies on the quality of the field data and their interpretation. In this context, advancements in remote data acquisition have now made it possible to acquire high-quality data potentially not accessible by conventional scanline and window mapping. This paper presents a comparison between aggregate and disaggregate approaches to define fracture sets, and their role with respect to the definition of key input parameters required to generate DFN models. The focal point of the discussion is the characterisation of in situ block size distribution(IBSD) using DFN methods. An application of IBSD is the assessment of rock mass quality through rock mass classification systems such as geological strength index(GSI). As DFN models are becoming an almost integral part of many geotechnical and mining engineering problems, the authors present a method whereby realistic representation of 3 D fracture networks and block size analysis are used to estimate GSI ratings, with emphasis on the limitations that exist in rock engineering design when assigning a unique GSI value to spatially variable rock masses.

Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain, China

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.

Presenting an engineering classification system for coal spontaneous combustion potential

The phenomenon of coal spontaneous combustion is one of the common hazards in coal mines and also one of the important reasons for the loss of coal in piles and mines. Based on previous researches, different types of coals have different spontaneous combustion characteristics. For coal loss prevention, a measure is necessary for prediction of coal spontaneous combustion. In this study, a new engineering classification system called ＂Coal Spontaneous Combustion Potential Index （CSCPI）＂ is presented based on the Fuzzy Delphi Analytic Hierarchy Process （FDAHP） approach. CSCPI classifies coals based on their spontaneous combustion capability. After recognition of the roles of the effective parameters influencing the initiation of a spontaneous combustion, a series of intrinsic, geological, and mining characteristics of coal seams are investigated. Then, the main stages of the implementation of the FDAHP method are studied and the weight of each parameter involved is calculated. A classification list of each parameter is formed, the CSCPI system is described, and the engineering classifying system is subsequently presented. In the CSCPI system, each coal seam can be rated by a number from 0 to 100; a higher number implies a greater ease for the coal spontaneous combustion capability. Based on the CSCPI system, the propensity of spontaneous combustion of coal can be classified into three potential levels： low, medium, and high. Finally, using the events of coal spontaneous combustion occurring in one of the Iranian coal mines, Eastern Alborz Coal Mines, an initial validation of the mentioned systematic approach is conducted. Comparison of the results obtained in this study illustrate a relatively good agreement.

INTEGRATED VEGETATION CLASSIFICATION AND MAPPINGUSING REMOTE SENSING AND GIS TECHNIQUES

NOAA-AVHRR data have been more and more used by scientists because of its short temporal resolution,large scope, inexpensive cost and broad wave bands. On macro and middle scale of vegetation remote sensing, NOAAAVHRR possesses an advantage when compared with other satellites. However, because NOAA-AVHRR also problem of low resolution, data distortion and geometrical distortion, in the area of application of NOAA-AVHRR in largescale vegetation - mapping, the accuracy of vegetation classification should be improved. This paper discuss the feasibilityof integrating the geographic data in GIS(Geographical Information System) and remotely sensed data in GIS. Under theenvironment of GIS, temperature, precipitation and elevation, which serve as main factors affecting vegetation growth,were processed by a mathematical model and qualified into geographic image under a certain grid system. The geographicimage were overlaid to the NOAA-AVHRR data which had been compressed and processed. In order to evaluate the usefulness of geographic data for vegetation classification, the area under study was digitally classified by two groups of interpreter: the proposed methodology using maximum likelihood classification assisted by the geographic database and a conventional maximum likelihood classification only. Both result were compared using Kappa statistics. The indices to both theproposed and the conventional digital classification methodology were 0. 668(yew good) and 0. 563(good), respetively.The geographic database rendered an improvement over the conventional digital classification. Furthermore, in this study,some problems related to multi-sources data integration are also discussed.

Research on Protecting Information Security Based on the Method of Hierarchical Classification in the Era of Big Data

Big data is becoming increasingly important because of the enormous information generation and storage in recent years.It has become a challenge to the data mining technique and management.Based on the characteristics of geometric explosion of information in the era of big data,this paper studies the possible approaches to balance the maximum value and privacy of information,and disposes the Nine-Cells information matrix,hierarchical classification.Furthermore,the paper uses the rough sets theory to proceed from the two dimensions of value and privacy,establishes information classification method,puts forward the countermeasures for information security.Taking spam messages for example,the massive spam messages can be classified,and then targeted hierarchical management strategy was put forward.This paper proposes personal Information index system,Information management platform and possible solutions to protect information security and utilize information value in the age of big data.

RS and GIS-based Analysis of Landscape Pattern Changes in Urban-rural Ecotone:A Case Study of Daiyue District,Tai' an City,China

Based on RS data of Daiyue District,Tai'an City in 2000,2005 and 2010,changes of land use types in urban-rural ecotone of Tai'an City from 2000 to 2010 were analyzed.Ecological theories,ArcGIS techniques and landscape structure analysis software Fragstats were applied to select relevant landscape pattern indexes and analyze changes of landscape structure,and compare changes in two durations(2000-2005,2005-2010).The results showed that area of natural landscapes in the study area declined from 2000 to 2010,construction land expanded,land use types were mainly transferred from natural landscapes to man-made landscapes.In terms of landscape level,number of patches(NP),patch density(PD),patch shape index(SHAPE),Shannon's diversity index(SHDI) and Shannon's evenness index(SHEI) increased,the largest patch index(LPI) declined.In terms of type level,arable land were influenced by the most human interventions,large-scale patches turned fragmented,and landscape dominance degraded;woodland landscapes were concentrated in mountainous areas,waterscape indexes showed slight changes.Dominance of regional dominant landscape types degraded,landscape fragmentation and landscape heterogeneity increased,and landscape stability declined.

Classification of Vegetation in North Tibet Plateau Based on MODIS Time-Series Data

Based on the 16d-composite MODIS （moderate resolution imaging spectroradiometer）-NDVI（normalized difference vegetation index） time-series data in 2004, vegetation in North Tibet Plateau was classified and seasonal variations on the pixels selected from different vegetation type were analyzed. The Savitzky-Golay filtering algorithm was applied to perform a filtration processing for MODIS-NDVI time-series data. The processed time-series curves can reflect a real variation trend of vegetation growth. The NDVI time-series curves of coniferous forest, high-cold meadow, high-cold meadow steppe and high-cold steppe all appear a mono-peak model during vegetation growth with the maximum peak occurring in August. A decision-tree classification model was established according to either NDVI time-series data or land surface temperature data. And then, both classifying and processing for vegetations were carried out through the model based on NDVI time-series curves. An accuracy test illustrates that classification results are of high accuracy and credibility and the model is conducive for studying a climate variation and estimating a vegetation production at regional even global scale.

A novel approach to structural anisotropy classification for jointed rock masses using theoretical rock quality designation formulation adjusted to joint spacing

Rock quality designation(RQD)has been considered as a one-dimensional jointing degree property since it should be determined by measuring the core lengths obtained from drilling.Anisotropy index of jointing degree(AI_(jd))was formulated by Zheng et al.(2018)by considering maximum and minimum values of RQD for a jointed rock medium in three-dimensional space.In accordance with spacing terminology by ISRM(1981),defining the jointing degree for the rock masses composed of extremely closely spaced joints as well as for the rock masses including widely to extremely widely spaced joints is practically impossible because of the use of 10 cm as a threshold value in the conventional form of RQD.To overcome this limitation,theoretical RQD(TRQD_(t))introduced by Priest and Hudson(1976)can be taken into consideration only when the statistical distribution of discontinuity spacing has a negative exponential distribution.Anisotropy index of the jointing degree was improved using TRQD_(t) which was adjusted to wider joint spacing by considering Priest(1993)’s recommendation on the use of variable threshold value(t)in TRQD_(t) formulation.After applications of the improved anisotropy index of a jointing degree(AI'_(jd))to hypothetical jointed rock mass cases,the effect of persistency of joints on structural anisotropy of rock mass was introduced to the improved AI'_(jd) formulation by considering the ratings of persistency of joints as proposed by Bieniawski(1989)’s rock mass rating(RMR)classification.Two real cases were assessed in the stratified marl and the columnar basalt using the weighted anisotropy index of jointing degree(W_AI'_(jd)).A structural anisotropy classification was developed using the RQD classification proposed by Deere(1963).The proposed methodology is capable of defining the structural anisotropy of a rock mass including joint pattern from extremely closely to extremely widely spaced joints.

Automated multi-scale classification of the terrain units of the Jiaxie Guyots and their mineral resource characteristics

Given the advances in satellite altimetry and multibeam bathymetry,benthic terrain classification based on digital bathymetric models(DBMs)has been widely used for the mapping of benthic topographies.For instance,cobaltrich crusts(CRCs)are important mineral resources found on seamounts and guyots in the western Pacific Ocean.Thick,plate-like CRCs are known to form on the summit and slopes of seamounts at the 1000–3000 m depth,while the relationship between seamount topography and spatial distribution of CRCs remains unclear.The benthic terrain classification of seamounts can solve this problem,thereby,facilitating the rapid exploration of seamount CRCs.Our study used an EM122 multibeam echosounder to retrieve high-resolution bathymetry data in the CRCs contract license area of China,i.e.,the Jiaxie Guyots in 2015 and 2016.Based on the DBM construted by bathymetirc data,broad-and fine-scale bathymetric position indices were utilized for quantitative classification of the terrain units of the Jiaxie Guyots on multiple scales.The classification revealed four first-order terrain units(e.g.,flat,crest,slope,and depression)and eleven second-order terrain units(e.g.,local crests,depressions on crests,gentle slopes,crests on slopes,and local depressions,etc.).Furthermore,the classification of the terrain and geological analysis indicated that the Weijia Guyot has a large flat summit,with local crests at the southern summit,whereas most of the guyot flanks were covered by gentle slopes.“Radial”mountain ridges have developed on the eastern side,while large-scale gravitational landslides have developed on the western and southern flanks.Additionally,landslide masses can be observed at the bottom of these slopes.The coverage of local crests on the seamount is∼1000 km^(2),and the local crests on the peak and flanks of the guyots may be the areas where thick and continuous plate-like CRCs are likely to occur.