IN SILICO MOLECULAR MODELING AND DOCKING STUDIES OF NANO COMPOSITES COMPOUND TO REGULATION, INHIBITION AND TREATMENT LEAF AND STEM WHEAT RUST

Puccinia graminis f. sp. tritici (Pgt) and P. triticina (Pt), the causal agents of stem and leaf rust, respectively form new physiological races that significantly reduce growth and yield of wheat cultivars. Therefore, seeking for exploring if there an inhibition effect of the Nano Composites compound on leaf and stem rust to regulation, inhibition and treatment leaf and stem wheat rust objectives to continuously produce new wheat pesticides resistant to stem and leaf rust. The aim of the study was to finding natural and Nano compounds to control, treatment and regulation of wheat rust. In this study we used molecular modeling and docking for the two vital proteins in stem and leaf wheat rust MAP kinase 1 [Puccinia triticina] and PGTG Puccinia graminis f. sp. Tritici. In the silico analysis, the two vital proteins activity is suppressed and inhibited In this work the chitosan and chitosan –Cu which selected for the study are considered as safe compounds the compounds showed interaction with the MAPK1 and PGAT proteins Thus the bioactive compounds that are interacting with the target can be used as a potent inhibitor to block the action of our proteins

The Role of Financial Analysis in Measuring the Continuity of Enterprises from the Viewpoint of the Auditor

Abstract The study aims to try to design a framework to guide auditors when assessing the assumption of continuity as one of the accounting assumptions that builds upon Auditor to his opinion. Was reached many of the results of this study the most important; The enterprises that are experiencing problems in repaying its obligations in the short term means the inability to continuity in the term, but does not necessarily mean that their inability to continuity in the long term as it is possible to continue to engage in business in the long term, The International Auditing Standard No. (570) and U.S. auditing standards Bulletin No. (59) contain clear guidance for the auditors can be guided them to identify the extent of continuity of enterprises to engage in the activity

Robust Crop and Weed Segmentation under Uncontrolled Outdoor Illumination

An image processing algorithm for detecting individual weeds was developed and evaluated. Weed detection processes included were normalized excessive green conversion, statistical threshold value estimation, adaptive image segmentation, median filter, morphological feature calculation and Artificial Neural Network (ANN). The developed algorithm was validated for its ability to identify and detect weeds and crop plants under uncontrolled outdoor illuminations. A machine vision implementing field robot captured field images under outdoor illuminations and the image processing algorithm automatically processed them without manual adjustment. The errors of the algorithm, when processing 666 field images, ranged from 2.1 to 2.9%. The ANN correctly detected 72.6% of crop plants from the identified plants, and considered the rest as weeds. However, the ANN identification rates for crop plants were improved up to 95.1% by addressing the error sources in the algorithm. The developed weed detection and image processing algorithm provides a novel method to identify plants against soil background under the uncontrolled outdoor illuminations, and to differentiate weeds from crop plants. Thus, the proposed new machine vision and processing algorithm may be useful for outdoor applications including plant specific direct applications (PSDA)

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Anti-Black Racism Lesson Plan

Anti-Black Racism Lesson Pla

Improved Computation of Object Skeleton

Improved Computation of Object Skeleton................

Offline Signature Verification Using Machine Vision

Signature forgery still represents a great challenge to financial institutions, which makes accurate signature verification inevitable. On the other hand, computer technology and information processing areas witness remarkable qualitative improvements associated with significant costs reduction. Thisboosted the usage of machine vision techniques. In this research, an intensive work was carried out on offline signatures to establish a system for verifying them using their digital images. Signature morphological structure was utilized to explore characteristics associated with different signatures.Signature verification algorithms were developed using binary images of signatures employing two different verification approaches, one was based on statistical techniques, while the other was based on neural networks (NN) techniques. A signature database was built by collecting 840 signatures from 66 volunteers, and was used for training the statistical and NN classifiers and subsequently for testing purposes. Research results indicated that the statistical classifiers' outcomes were highly satisfactory whereas the NN classifiers' outcomes were not of the same quality. The statistical classifiers outperformed their NN counterparts in terms of both Correct Classification Rates (CCRs) and Misclassification rates(CCRs). The CCRs of genuine signatures for the statistical and NN classifiers were 84.4% and 51.1%, respectively, while the CCRs of the forged signatures for the statistical and NN classifiers were 82.8% and 66.1%, respectively