Machine Learning Algorithms in Network Security

This starred paper aimed to analyze different machine learning algorithms using security log data and to identify the best algorithm, which is both accurate and fastest in detecting the attacks by analyzing security data. In this paper, we reviewed different security risk assessments and machine learning algorithms and code. We brought together the security risk and machine learning algorithms to analyze security data by creating a test environment. For any organization detecting the attacks accurately and quickly is an essential factor in reducing the risk of a security breach. No amount of systems, standards, compliance guidelines can assure a complete hundred percent guarantee of avoiding the security breach. The assumption is security breaches will happen, and the best way to reduce the risk is to detect the attack early and implement the mitigation procedures. The early detection of the attack will provide security professionals the time to reduce the impact and safeguard the organization. We have discussed in risk assessment how different security guidelines are implemented within the organization, which slow and provide more time and increase the effort of hackers in getting access to core organization systems. This will be achieved by making sure the attack is detected early and once creating multiple layers of security so that it becomes difficult for attackers as risk procedures prevent the Kill chain of attackers by slowing and stopping the attack at different level

A manufacturer-service provider model with remanufacturability and variable product life considerations

There has been a growing emphasis on remanufacturing as a profitable means to reduce wastage, conserve energy and costs. An alternate approach to obtain similar environmental and economic benefits is to increase the product life. We model and analyze the economic relationships among the level of remanufacturing, product life and economic consequences under the framework of a manufacturer/remanufacturer and a service provider who utilizes the manufacturer\u27s product to provide service to his/her customers. In our framework, the remanufacturability is defined as the fraction of used products that can be economically remanufactured, and it is assumed that the remanufacturability can be increased via fixed cost investment in product and process design technologies. We also assume that the product life which is defined to be the number of units of service that is provided from the product can be increased by utilizing higher quality components with corresponding higher variable cost. Under these assumptions, we formulate three distinct supply chain scenarios. Namely, a manufacturer driven supply chain, a centrally coordinated supply chain and a service provider driven supply chain. From the subsequent equilibrium and optimality analysis, we derive several interesting managerial insights. For example, there are several conditions under which a higher technology investment in remanufacturability leads to a shorter product life

Gibbs Dividing Surface and Helium Adsorption

All adsorption data is based on the definition of Gibbs dividing surface, which is a purely mathematical transformation. Adsorption measurements in microporous solids necessitate experimental determination of the dividing surface. An international protocol does not exist on how to perform this important measurement. Commonly, helium is assumed not to adsorb and used as a probe molecule for this measurement. Each experimentalist chooses an arbitrary set of conditions, often without even disclosing them, which adds to the confusion in adsorption literature. Here, a self-consistent method for the analysis of helium data is proposed which does not assume non-adsorbing helium. The method is compared to others using the extensive set of helium/silicalite data. The Gibbs dividing surface and hence the helium isotherms at all temperatures are determined

Two-phase numerical model for thermal conductivity and convective heat transfer in nanofluids

Due to the numerous applications of nanofluids, investigating and understanding of thermophysical properties of nanofluids has currently become one of the core issues. Although numerous theoretical and numerical models have been developed by previous researchers to understand the mechanism of enhanced heat transfer in nanofluids; to the best of our knowledge these models were limited to the study of either thermal conductivity or convective heat transfer of nanofluids. We have developed a numerical model which can estimate the enhancement in both the thermal conductivity and convective heat transfer in nanofluids. It also aids in understanding the mechanism of heat transfer enhancement. The study reveals that the nanoparticle dispersion in fluid medium and nanoparticle heat transport phenomenon are equally important in enhancement of thermal conductivity. However, the enhancement in convective heat transfer was caused mainly due to the nanoparticle heat transport mechanism. Ability of this model to be able to understand the mechanism of convective heat transfer enhancement distinguishes the model from rest of the available numerical models

Engineering Better Electric-Powered Wheelchairs To Enhance Rehabilitative and Assistive Needs of Disabled and Aged Populations

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Production of extracellular amylase from agricultural residues by a newly isolated Aspergillus species in solid state fermentation

The production of extracellular amylases by solid state fermentation (SSF) was investigated employing our laboratory isolate Aspergillus sp.MK07. Various agricultural residual substrates like wheat bran, rice bran and green gram husk were studied for enzyme production. Highest enzyme production was obtained with wheat bran as a substrate. Effects of process variables, namely: incubation period, temperature, initial moisture content, pH, supplementary carbon, nitrogen source and inoculum level on production of amylase have been studied and accordingly, optimum conditions have been determined. It was found that amylase production was highest at 120 h of incubation period at 30°C, 70% initial moisture content, 5.0 pH and 5% inoculum level. Supplementation of carbon (starch) and nitrogen source (peptone) showed an increase in amylase production and the highest amount of amylase production obtained under all optimized conditions was 164 U/g.Key words: Solid state fermentation, optimization, Aspergillus, fermentation, amylas