ELECTRONEGATIVITY TOPOLOGICAL INDEX AND ITS APPLICATION IN CORRELATION WITH MX,MX_2,MX_3,MX_4 MOLECULAR PROPERTIES

A New electronegativity topological index is defined and used to correlate with properties of MX,MX_2,MX_3,MX_4.

FIXED POINT INDEXES AND ITS APPLICATIONS TO NONLINEAR INTEGRAL EQUATIONS MODELLING INFECTIOUS DISEASES

In this paper the fixed point index problem for a class of positive operators with boundary control conditions is discussed,and some sufficient conditions for the fixed pointindex to be equal to1 or 0 are given.Moreover,a general fixed point theorem of expansions and compressions for cone is obtained,which generalizes and improves the corresponding results of[3,8,9].As an application,we utilize the results presented above to study the existence conditions of positive solutions of nonlinear integral equations modelling infectious diseases.

Evaluation of the applicability of multiple drought indices in the core zone of “westerlies-dominated climatic regime”

Drought indices are frequently used to measure the intensity,start and end of droughts.However,the performances of these indices depend on regions and the type of droughts.Therefore,it is necessary to evaluate whether these indices are applicable for a given region.This study evaluated the ability of the self-calibrating Palmer Drought Severity Index(sc PDSI),the Standardized Precipitation Evapotranspiration Index(SPEI),and the Standardized Moisture Anomaly Index(SZI) to describe the dry-wet conditions and drought events in the core zone of the “westerlies-dominated climatic regime”.The results showed that the 12-month SZI(SZI12) and SPEI(SPEI12) have good correlations with precipitation and PET respectively,while the sc PDSI can capture the changes in precipitation and soil moisture.In Xinjiang region of China,the SZI12 and sc PDSI showed that this region gradually became wetter during 1961–2014,which was consistent with increased precipitation,decreased PET,and improved vegetation.However,the SPEI12 showed significant drying due to the strong influence of the PET,suggesting that this index exaggerated the drought conditions in this region.Precipitation in Kazakhstan and the southern Central Asian regions has increased slightly over the past 50 years,but the PET has greatly increased,altogether,all three indices suggested a drying trend in Central Asia.The evaluation of the ability of these drought indices to identify typical drought events in Xinjiang region of China suggested that the sc PDSI can better detect the typical drought events compared with the SZI12.In conclusion,the sc PDSI is the most suitable index for characterizing the long-term trend of hydroclimate conditions and drought events in the core zone of the “westerlies-dominated climatic regime”.This study provides a theoretical basis for the rational utilization and improvement of the drought index,which is critical for monitoring,attributing,and predicting of drought events in arid areas.

Incomplete data management： a survey

Incomplete data accompanies our life processes and covers almost all fields of scientific studies, as a result of delivery failure, no power of battery, accidental loss, etc. However, how to model, index, and query incomplete data in- curs big challenges. For example, the queries struggling with incomplete data usually have dissatisfying query results due to the improper incompleteness handling methods. In this pa- per, we systematically review the management of incomplete data, including modelling, indexing, querying, and handling methods in terms of incomplete data. We also overview sev- eral application scenarios of incomplete data, and summa- rize the existing systems related to incomplete data. It is our hope that this survey could provide insights to the database community on how incomplete data is managed, and inspire database researchers to develop more advanced processing techniques and tools to cope with the issues resulting from incomplete data in the real world.

An Effective Density Based Approach to Detect Complex Data Clusters Using Notion of Neighborhood Difference

The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.