Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

Meta-analysis of bivariate P values

AIM： To propose a new meta-analysis method for bi-variate P value which account for the paired structure. METHODS： Studies that look to test two different fea-tures from the same sample gives rise to bivariate Pvalue. A relevant example of this is testing for periodici-ty as well expression from time-course gene expressionstudies. Kocak et al （2010） uses George and Mudholkar’（1983） “Difference of Two Logit-Sums” method to poolbivariate P value across independent experiments, as-suming independence within a pair. As bivariate P valueneed not to be independent within a given study, wepropose a new meta-analysis approach for pooling bi-variate P value across independent experiments, whichaccounts for potential correlation between paired P-val-ues. We compare the “Difference of Two Logit Sums”method with our novel approach in terms of their sen-sitivity and specifcity through extensive simulations by generating P value samples from most commonly used tests namely, Z test, t test, chi-square test, and F test, with varying sample sizes and correlation structure. RESULTS： The simulations results showed that our new meta-analysis approach for correlated and uncor-related bivariate P value has much more desirable sen-sitivity and specifcity features compared to the existing method, which treats each member of the paired P value as independent. We also compare these meta-analysis approaches on bivariate P value from periodici-ty and expression tests of 4936 S.Pombe genes from 10 independent time-course experiments and we showed that our new approach ranks the periodic, conserved, and cycling genes significantly higher, and detects many more periodic, “conserved” and “cycling” genes among the top 100 genes, compared to the ‘Difference of Two Logit-Sums’ method. Finally, we used our meta-analytic approach to compare the relative evidence in the association of pre-term birth with preschool wheez-ing versus pre-school asthma.CONCLUSION： The new meta-analysis method has much better sensitivity and specifc characteristics com-pared to the “Difference of Two-Logit Sums” method and it is not computationally more expensive.

High-Speed EMU TCMS Design and LCC Technology Research

This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.

HOW TO AVOID GROUNDHOG DAY...AGAIN!

Albert Einstein is credited with saying, “Insanity: doing the same thing over and over again and expecting different results.” Then in the 1980s, we watched as poor Bill Murray kept waking up to the very same day, very same activities, and same exact results in the comedy “Groundhog Day”. We challenge you to look at the process of design, delivery, and operations for our buildings. Are we all stuck in “insanity” or “Groundhog Day?” We keep doing things the same way and expecting different results. This article explores how the utilization of facility data from design to operations can not only break the paradigm of “insanity,” it can make our facilities more efficient and sustainable to construct and operate. Our basic premise is that if you do not have accurate “data” about your facility it will be very difficult, if not impossible, to operate your facility effectively.

A fast arc fault detection method for AC solid state power controllers in MEA

Arc fault detection is desperately required in Solid State Power Controllers（SSPC） in addition to their fundamental functions because arcs will provoke growing harm and threat to aircraft safety. Experimental study has been done to obtain the faulted current data. In order to improve the detection speed and accuracy, two fast arc fault detection methods have been proposed in this paper with the analysis of only half cycle data. Both Fast Fourier Transform（FFT） and Wavelet Packets Decomposition（WPD） have been adopted to distinguish arc fault currents from normal operation currents. Analysis results show that Alternating Current（AC） arcs can be effectively and accurately detected with the proposed half cycle data based methods. Moreover,experimental verification results have also been provided.