Healing double vacancy defects on graphene: reconstruction by C2 adsorption.

Graphene has emerged as an exciting material because of its widespread applications resulting from its unique properties. Nano-scale engineering of graphene's structure is one of the most active research areas aimed at introducing functionalities to improve the performance or endow the graphene lattice with novel properties. In this regard, conversion between the hexagon and non-hexagon rings becomes an exciting tool to tune the electronic structure of graphene due to the distinct electronic structure and functionalities induced in graphene by each type of ring. This Density Functional Theory (DFT) study is an in-depth look at the adsorption-induced conversion of pentagon-octagon-pentagon rings to hexagon rings, and systematically investigates the possibility of the conversion of pentagon-octagon-pentagon rings to pentagon-heptagon pair rings. Moreover, the bottlenecks for these atomic-level conversions in the lattice structure of graphene and the influence of heteroatom doping on the mechanisms of these transformations are established.

Quantification of heavy metal pollution for environmental assessment of soil condition.

The aim of this study was to quantify heavy metal pollution for environmental assessment of soil quality using a flexible approach based on multivariate analysis. The study was conducted using 241 soil samples collected from agricultural, urban and rangeland areas in northwestern Iran. The heavy metals causing soil pollution (SP) in the study area were determined. The efficiency of principal component analysis (PCA) and discriminate analysis (DA) were compared to identify the critical heavy metals causing SP. Fourteen soil pollution indices were developed using non-linear and linear scoring equations and different integration methods. The indices were validated using the integrated pollution and potential ecological risk indices and by comparing their ability to detect soil pollution risk levels. Chromium (Cr), lead (Pb), Zinc (Zn) and copper (Cu) were identified as the significant pollutant elements using PCA, and the main pollutant elements identified using DA comprised cadmium (Cd), Zn and Pb. DA yielded a better data set for indexing SP and indicated high pollution risks for Cd > Pb > Zn. Sources of heavy metals were reliably identified using PCA, variation assessment and interrelationship evaluation of soil variables. Cr, nickel (Ni) and cobalt (Co) were found to have geogenic sources, and anthropogenic sources controlled the accumulation of Pb, Zn, Cd and Cu in soil. Linear function and additive integration were the best scoring and integrating methods for indexing HMP. The multivariate analysis provided a reliable and rapid method for indexing and mapping soil HMP.

Detecting Diseases in Medical Prescriptions Using Data Mining Tools and Combining Techniques.

Data about the prevalence of communicable and non-communicable diseases, as one of the most important categories of epidemiological data, is used for interpreting health status of communities. This study aims to calculate the prevalence of outpatient diseases through the characterization of outpatient prescriptions. The data used in this study is collected from 1412 prescriptions for various types of diseases from which we have focused on the identification of ten diseases. In this study, data mining tools are used to identify diseases for which prescriptions are written. In order to evaluate the performances of these methods, we compare the results with Naïve method. Then, combining methods are used to improve the results. Results showed that Support Vector Machine, with an accuracy of 95.32%, shows better performance than the other methods. The result of Naive method, with an accuracy of 67.71%, is 20% worse than Nearest Neighbor method which has the lowest level of accuracy among the other classification algorithms. The results indicate that the implementation of data mining algorithms resulted in a good performance in characterization of outpatient diseases. These results can help to choose appropriate methods for the classification of prescriptions in larger scales.