Phase Change Materials (PCMs) are integrated to metallic heat sinks to evaluate their performance to cool electronic packaging. The heat sinks were prepared as metallic containments with vertically aligned fins with optimized inter-fin spacing. The heat sink filled with PCM is subjected to various heat loads at 4W, 6W and 8W. The heat generating surface temperatures are plotted against time at each power input for both the PCM and for all the four modes of operation. It is found that in all cases, the inclusion of PCM into heat sink maintained lower temperature on heat generating surface compared to heat sink alone which shows the effectiveness of PCMs for electronic packaging.
Rapidly rising concentration levels of carbon dioxide in atmosphere has made the researchers and scientists to find an effective way to capture and sequester it. One of the most sought after sequestration technique is to store the CO2 in deep geological formations where it can stay in supercritical form. Deep saline aquifers stand out of all the other geological formations due to their high storage capacities and wide availability. In this study, a potential site for geologic sequestration of carbon dioxide in UAE has been studied. A parametric study has been performed by varying the parameters such as salinity, pore compressibility, Corey residual gas saturation, Corey residual liquid saturation and van Genuchten m value. The results for total CO2 mass are plotted as a function of simulated time. The results show that salinity, Corey residual gas and liquid saturations and van Genuchten m parameter are the most influential parameters.
Waste to Energy can be pursued biologically, chemically, or thermally, result in production of fuel or sensible heat. The petroleum industry has been generating an alarming amount of solid waste in the form of oily sludge. It is a hazardous complex emulsion of various petroleum hydrocarbons (PHCs), solid particles, water and heavy metals. Recovery of PHCs and thermochemical has been widely investigated, however biological treatment for recovery and safe disposal is less fortunate. This work focuses on the anaerobic PHC decomposition in a well-controlled landfill bioreactor for the generation of landfill gas (CO2 and CH4). It is found that on the basis of 100kg of PHC, nearly 4.5 kg and 11.8 kg of CH4 and CO2 are generated. This is fairly equal to what would be generated from MSW. Practically, co-digestion with MSW can enhance the biodegradation and the yield, contrary to WWTP sludge which only enhances the biodegradation.
An intrusion Detection System (IDS) monitors and analyzes network activity and data for potential vulnerabilities and attacks in progress. A knowledge-based IDS (KB-IDS) references a database of previous attack profiles and known system vulnerabilities to identify active intrusion attempts. A KBIDS is the most widely accepted due to their model-free properties such as learnability and adaptability. However, as a network grows in size,the efficiency and the scalability of IDS become critical. In this paper, we develop a state-of-the-art KB-IDS on a biophysically motivated intelligent voting-model through the use of a minimum and optimal feature set only for instant feedback and improved system efficiency. Existing KB-IDSs reach about 95?98?curacy, and about 1?2?lsealarm rate on KDD-99 benchmark datasets. However, our approach that utilizes ensemble-voting model achieves about 99.9?curacy and 0.1?lse-alarm rate on the same benchmark datasets.
Working with datasets that consist of millions of records is becoming increasingly necessary for scientific research. In the present work, two such datasets are used to predict student performance in test sections. In particular, the goal is to predict a student's ability to answer questions correctly based on historical results. A linear model that features a plain stochastic gradient descent learning routine produces quite satisfactory predictions for this problem. However, mixing the existing linear model with factorization machines trained with three different collaborative filtering learning algorithms can yield even better results. By offering actionable insights, it is hoped that the findings of this study can be used to improve the design of educational resources such as distance learning portals and course delivery platforms.
Mobile and wearable computing have witnessed increasing interest with new applications becoming an important part of people's daily life. Recently developed mobile wearable glasses (such as Google Glass) advances the idea that most of the smartphone functionalities would eventually move to wearable portable devices. The availability of such pervasive device creates new research opportunities and challenges. One directions is to utilize wearable camera and sensors to record and understand the user activities. The camera can be used to record a video of the user moments. However, none would have the time to watch it. Hence, automatic gathering, analysis, indexing and retrieval of multimedia has seen a great interest over the past decade. This paper discusses design of algorithms and system for keypoint based keyframe extraction from egocentric videos. And show our results of using an implementation of keypoints extraction for basic keyframe assessment based on the number of keypoints.
Google scholar is a search engine that helps users search for content that has been published in the scholarly literature. In this paper, initial impressions of the data collected during our crawl of Google Scholar are presented and discussed.
Cloud computing is an emerging paradigm that provides scalable computing capabilities where resources are accessed on a pay-as-you-go basis. Software as a Service (SaaS) applications are hosted in the cloud and made available as services for tenants' organizations over a network. To achieve reusability in the cloud, software and hardware resources are shared among tenants. Conventional multi-tenant SaaS applications provide the same set of services for all the subscribing tenants, thus resulting in one-size-fits-all applications. However, as tenants may have different requirements, customizable SaaS solutions are needed. To accommodate evolving tenants' requirements, the SaaS instance should evolve systematically. In this paper, we present a multi-tenant single instance SaaS evolution platform based on Software Product Lines (SPLs) and Model Driven Architecture (MDA) concepts. The platform specifies a set of evolution rules, based on feature modeling, that govern evolution decisions. We also present a proof of concept tool for the proposed approach.
Biological remediation of pollutants are a novel set of biotechnology approaches in which the pollutants are removed using either microorganisms or enzymes. Specifically, the use of enzymes to degrade organic pollutant is at the forefront of this exciting field and has attracted much interest due to its efficiency and potential ease. Various diverse classes of peroxidases have been commonly used for the enzymatic degradation of organic pollutants, however, studies showing how different classes of peroxidases may degrade specific dyes have not yet been reported. In this study, Soybean Peroxidase and Choloroperoxidase were used to degrade a specific aromatic azo dye, Amido Black. Specifically, we examined the differences in the pH profiles and the products produced during the dye degradation by these two peroxidases. The results show that the two peroxidases have different optimum pH and produce different products, suggesting that different mechanisms are involved when different peroxidases are used.
A modified co-precipitation method in air was used to prepare pure and coated magnetite nanoparticles. A maximum of 1.0 % of dextran was sufficient to coat the nanoparticles surfaces, after that they were bound to the crocin via a cross-linker. Crocin was reported to have anticancer effect in different in vivo and in vitro settings. The aim of this study was to synthesize magnetite nanoparticles formulations containing crocin with a higher therapeutic index for the hepatocellular carcinoma (HCC) treatment. The nanoparticles with crocin were tested in vitro and in vivo for their anticancer effects. In HepG2 cells, crocin-conjugated nanoparticles decreased cells proliferation compared with groups treated with crocin, saline, and nanoparticles alone. Crocin-loaded nanoparticles' antitumor effect was also evaluated in Balb/c mice that were chemically induced to develop HCC. The immunohistochemistry and HepG2 cells results were similar. The results indicated that nanoparticles conjugation of crocin improve its anti-tumorigenic activity.
