Natural convection in porous triangular enclosures with a solid adiabatic fin attached to the horizontal wall

A numerical analysis has been performed to investigate the effects of fin location onto the bottom wall of a triangular enclosure filled with porous media whose height base ratio is 1. The temperature of the bottom wall is higher than that of the inclined wall while the vertical wall is insulated. Thus, the fin divides the heated bottom wall to two separate regions. Finite difference method was applied to solve governing equations which are written using Darcy method. Solutions of algebraic equations were made by Successive Under Relaxation (SUR) technique. The effective parameters on flow and temperature fields are: Rayleigh number, location center of fin, dimensionless fin height, and dimensionless fin width. The obtained results indicated that the fin can be used as a control element for heat transfer and fluid flow.     

Using the KBTA method for inferring computer and network security alerts from time-stamped,raw system metrics

In this study, we propose a new approach for detecting previously unencountered instances of known classes of malicious software based on their temporal behavior. In the proposed approach, time-stamped security data are continuously monitored within the target computer system or network and then processed by the knowledge-based temporal abstraction (KBTA) methodology. Using KBTA, continuously measured data (e.g., the number of running processes) and events (e.g., installation of a software) are integrated with a security-domain, temporal-abstraction knowledge-base (i.e., a security ontology for abstracting meaningful patterns from raw, time-oriented security data), to create higher-level, time-oriented concepts and patterns, also known as temporal abstractions. Automatically-generated temporal abstractions can be monitored to detect suspicious temporal patterns. These patterns are compatible with a set of predefined classes of malware as defined by a security expert employing a set of time and value constraints. The new approach was applied for detecting worm-related malware using two different ontologies. Evaluation results demonstrated the effectiveness of the new approach. The approach can be used for detecting other types of malware by updating the security ontology with new definitions of temporal patterns.     

Approximating the Degree-Bounded Minimum Diameter Spanning Tree Problem

We consider the problem of finding a minimum diameter spanning tree with maximum node degree $B$ in a complete undirected edge-weighted graph. We provide an $O(\sqrt{\log_Bn})$-approximation algorithm for the problem. Our algorithm is purely combinatorial, and relies on a combination of filtering and divide and conquer.     

Visible light-induced charge retention and photocatalysis with hybrid CdSe-Au nanodumbbells

Visible light photocatalysis is a promising route for harnessing of solar energy to perform useful chemical reactions and to convert light to chemical energy. Nanoscale photocatalytic systems used to date were based mostly on oxide semiconductors aided by metal deposition and were operational only under UV illumination. Additionally, the degree of control over particle size and shape was limited. We report visible light photocatalysis using highly controlled hybrid gold-tipped CdSe nanorods (nanodumbbells). Under visible light irradiation, charge separation takes place between the semiconductor and metal parts of the hybrid particles. The charge-separated state was then utilized for direct photoreduction of a model acceptor molecule, methylene blue, or alternatively, retained for later use to perform the reduction reaction in the dark.     

An expert diagnosis system for classification of human parasite eggs based on multi-class SVM

In this paper, it is proposed a new methodology based on invariant moments and multi-class support vector machine (MCSVM) for classification of human parasite eggs in microscopic images. The MCSVM is one of the most used classifiers but it has not used for classification of human parasite eggs to date. This method composes four stages. These are pre-processing stage, feature extraction stage, classification stage, and testing stage. In pre-processing stage, the digital image processing methods, which are noise reduction, contrast enhancement, thresholding, and morphological and logical processes. In feature extraction stage, the invariant moments of pre-processed parasite images are calculated. Finally, in classification stage, the multi-class support vector machine (MCSVM) classifier is used for classification of features extracted feature extraction stage. We used MATLAB software for estimating the success classification rate of proposed approach in this study. For this aim, proposed approach was tested by using test data. At end of test, 97.70% overall success rates were obtained.