Authors: Ilija Barukčić
Comments: 9 Pages. Copyright © 2018 by Ilija Barukčić, Jever, Germany. Published by:.
BACKGROUND: Several observational studies investigated the relationship between human papillomavirus (HPV) infection and the risk of prostate cancer (PC) and have suggested conflicting results about this relationship. However, the relationship between HPV infection and PC remains unclear. The aim of the present meta-analysis study is to investigate whether HPV serves as a cause of PC.
METHODS: The PubMed database was searched for suitable articles. Previously published expert review and systematic review were used as an additional source to identify appropriate articles. Articles selected for this meta-analysis should fulfil the following inclusion criteria: (a) no data access barrier (b) PCR DNA based identification of HPV.
RESULTS: The studies analysed were able provide evidence that without being married no (HPV infection of a men/prostate cancer).The X² value of the total 20 articles indicated a significant causal relationship between HPV and PC. In other words, if HPV infection of human prostate, then prostate cancer.
CONCLUSION: In conclusion, Human papillomavirus is the cause of prostate cancer.
KEYWORDS: Human papillomavirus, prostate cancer, causal relationship, causality
We study the boundary value problem of a partial differential-integral equations that have
many applications in finance and insurance. We will solve a boundary value problem of the
partial differential-integral equations by using the solution of conjugate equation and
reflection method and apply it to determine the probability of company bankruptcy in
Authors: Kim Ju Gyong，Ju Kwang Son
Comments: 7 Pages.
In this paper we prove Girsanov theorem for fractional Brownian motion and jump
measures and consider representation form for the stochastic differential equations in
transfer Probability space.
Authors: Russell Leidich
Comments: 13 Pages.
Successive real-valued measurements of any physical chaotic oscillator can serve as entropy inputs to a random number generator (RNG) with correspondingly many whole numbered outputs of arbitrarily small bias, assuming that no correlation exists between successive such measurements apart from what would be implied by their probability distribution function (AKA the oscillator’s analog “generator”, which is constant over time and thus asymptotically discoverable).
Given some historical measurements (a “snapshot”) of such an oscillator, we can then train the RNG to expect inputs distributed uniformally among the real intervals defined by those measurements and spanning the entire real line. Each interval thus implies an index in sorted order, starting with the leftmost which maps to zero; the RNG does nothing more than to perform this mapping. We can then replace that first oscillator with a second one presumed to abide by the same generator. It would then be possible to characterize the accuracy of that presumption by quantifying the ensuing change in quality of the RNG.
Randomness quality is most accurately expressed via dyspoissonism, which is a normalized equivalent of the log of the number of ways in which a particular distribution of frequencies (occurrence counts) of masks (whole numbers) can occur. Thus the difference in dyspoissonism between the RNG output sets will serve to estimate the information divergence between theirrespective generators, which in turn constitutes a ranking quantifier for the purpose of anomaly detection.