Evaluating the most common mutation in BRCAI and BRCA2 genes in women who had mothers with brest cancer and controls

Background and purpose: Breast Cancer is one of the health problems in every population. The aim of this study was to determine the frequency of BRCA1 and BRCA2 common mutations in women whose mothers were diagnosed with breast cancer. Materials and methods: A case�control study was performed in 109 females (less than 40 years of age) who had mothers with breast cancer. For scanning of genomic mutations in BRCA1 and BRCA2, genes mutation analysis was done in BRCA1 (exon2, 20) and BRCA2 (exon11) using Real Time PCR test. We also studied 109 healthy controls without family history of breast cancer. Results: No any mutation was found in this population. Conclusion: This study showed no mutation in affected and control group. Therefore, other mutations and genes may have a role in breast cancer pathogenesis in our population. © 2016, Mazandaran University of Medical Sciences. All rights reserved

Machine Learning for Predictive Analytics in Social Media Data

Machine Learning (ML) has become a potent predictive analytics tool in several fields, including the study of social media data. Social media sites have developed into massive repositories of user-generated information, providing insightful data about user trends, interests, and behavior. This abstract emphasizes the use of machine learning methods for predictive analytics in social media data and examines the potential and problems unique to this field. Utilizing the capabilities of machine learning algorithms to identify significant trends and forecast user behavior from social media data is the goal of this study. The study makes use of a sizable dataset made up of user profiles, blog posts, comments, and engagement metrics gathered from well-known social networking sites. Predictive models are created using a variety of machine learning algorithms, such as ensemble methods, neural networks, decision trees, and support vector machines. As a result, this study emphasizes how important machine learning is for doing predictive analytics on social media data. The employment of diverse algorithms and preprocessing methods yields insightful information about user behavior and enables precise prediction of user behaviors. To improve the prediction powers of machine learning in this area, future research should concentrate on tackling the obstacles related to social media data, such as privacy concerns and data quality issues

Graphene -- Based Nanocomposites as Highly Efficient Thermal Interface Materials

We found that an optimized mixture of graphene and multilayer graphene - produced by the high-yield inexpensive liquid-phase-exfoliation technique - can lead to an extremely strong enhancement of the cross-plane thermal conductivity K of the composite. The "laser flash" measurements revealed a record-high enhancement of K by 2300 % in the graphene-based polymer at the filler loading fraction f =10 vol. %. It was determined that a relatively high concentration of single-layer and bilayer graphene flakes (~10-15%) present simultaneously with thicker multilayers of large lateral size (~ 1 micrometer) were essential for the observed unusual K enhancement. The thermal conductivity of a commercial thermal grease was increased from an initial value of ~5.8 W/mK to K=14 W/mK at the small loading f=2%, which preserved all mechanical properties of the hybrid. Our modeling results suggest that graphene - multilayer graphene nanocomposite used as the thermal interface material outperforms those with carbon nanotubes or metal nanoparticles owing to graphene's aspect ratio and lower Kapitza resistance at the graphene - matrix interface.Comment: 4 figure

Effect of Covalent Functionalisation on Thermal Transport Across Graphene-Polymer Interfaces

This paper is concerned with the interfacial thermal resistance for polymer composites reinforced by various covalently functionalised graphene. By using molecular dynamics simulations, the obtained results show that the covalent functionalisation in graphene plays a significant role in reducing the graphene-paraffin interfacial thermal resistance. This reduction is dependent on the coverage and type of functional groups. Among the various functional groups, butyl is found to be the most effective in reducing the interfacial thermal resistance, followed by methyl, phenyl and formyl. The other functional groups under consideration such as carboxyl, hydroxyl and amines are found to produce negligible reduction in the interfacial thermal resistance. For multilayer graphene with a layer number up to four, the interfacial thermal resistance is insensitive to the layer number. The effects of the different functional groups and the layer number on the interfacial thermal resistance are also elaborated using the vibrational density of states of the graphene and the paraffin matrix. The present findings provide useful guidelines in the application of functionalised graphene for practical thermal management.Comment: 8 figure

An efficient discrete log pseudo random generator

Abstract. The exponentiation function in a finite field of order p (a prime number) is believed to be a one-way function. It is well known that O(log log p) bits are simultaneously hard for this function. We consider a special case of this problem, the discrete logarithm with short exponents, which is also believed to be hard to compute. Under this intractibility assumption we show that discrete exponentiation modulo a prime p can hide n−ω(log n) bits(n=⌈log p ⌉ and p =2q+1, where q is also a prime). We prove simultaneous security by showing that any information about the n − ω(log n) bits can be used to discover the discrete log of g s mod p where s has ω(log n) bits. For all practical purposes, the size of s can be a constant c bits. This leads to a very efficient pseudo-random number generator which produces n − c bits per iteration. For example, when n = 1024 bits and c = 128 bits our pseudo-random number generator produces a little less than 900 bits per exponentiation.

A review on the applications of micro-/mini-channels for battery thermal management

This review of the literature explores the potentials of liquid micro-/mini-channels to reduce operating temperatures and make temperature distributions more uniform in batteries. First, a classification and an overview of the various methods of battery thermal management are presented. Then, different types of lithium-ion batteries and their advantages and disadvantages are introduced, and the components of batteries are described in detail. The studies conducted on the performance of micro-/mini-channels for cooling all types of rectangular and cylindrical batteries are reviewed, and the key finding of these studies is presented. It is shown that, in general, using counterflow configuration creates a rather uniform temperature distribution in the battery cell and keeps the maximum temperature difference below 5 ∘C. The lowest battery maximum temperature is obtained for parallel and counterflow configurations in the straight and U-turn channels, respectively. In a parallel configuration, the peak point of the battery temperature is in the outlet area. However, in the counter-flow configuration, it occurs in the central region of the battery module. The survey of the literature further reveals that proper channel paths and flow configurations keep the battery maximum temperature within the safe range of 25∘C<Tmax<40∘C. For such flow configurations, the pressure drop remains minimally affected

An Optimization of CDN Using Efficient Load Distribution and RADS Caching Algorithm

Nowadays, while large-sized multimedia objects are becoming very popular throughout the Internet, one of the important issues appears to be the acceleration of content delivery network (CDN) performance. CDN is a web system that delivers the web cached objects to the client and accelerates the web performance. Therefore the performance factor for any CDN is vital factor in determining the quality of services. The performance improvement can be achieved through load balancing technique, so the server load could be distributed to several clustered groups of machines and processed in parallel. Also the performance of CDN heavily depends on caching algorithm which is used to cache the web objects. This study investigates a method that improves the performance of delivering multimedia content through CDN while using RADS algorithm for caching large-sized objects separately from small-sized ones. We will also consider the efficient distribution of requests outgoing from local servers in order to balance the CDN load. This method uses various types of factors such as CPU processing time, I/O access time and Task Queue between nearby servers. At the end of the paper, we will see the experimental results derived from implementing the proposed optimization technique and observe how it could contribute to the effectiveness of CDN