Pressure the Universe

In physics, mass–energy equivalence is a concept formulated by Albert Einstein that explains the relationship between mass and energy. It states every mass has an energy equivalent and vice versa. The recognition of the concept of mass–energy equivalence requires the recognition of the existence of force of pressure of the universe.

Pressure of the universe is the cause of all the movements taking place in the real world. The greatest it proves itself within the corpuscle, where under his influence shaped portion of energy that defines its mass. Let's deduce concept Universe pressure from the formula of mass-energy equivalence.

Control of Road Traffic Using Learning Classifier System

Road traffic control through proper control of junction signals is one of the complicated control issues. A conventionally used system is the rule-based system which is often employed in designing systems with deterministic states. In this paper, we have tried to study the control issue with the idea of distributed control through using Learning Classifier Systems (LCS). It means, controlling signal of any junction is done separately from other junctions through an independent Learning Classifier System and with the purpose of decreasing the lines of automobiles queue in the conduced streets to the junction. Furthermore, learning classifier systems have been used in order to control junction traffic within distributed control system.

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The Use of Molecular and Imaging Biomarkers in Lung Cancer Risk Prediction

In hypothesis testing, p-value is routinely used as a measure of statistical evidence against the null hypothesis, where a smaller p-value indicates stronger evidence substantiating the alternative hypothesis. P-value is the probability of type-I error made in a hypothesis testing, namely, the chance that one falsely reject the null hypothesis when the null holds true. 

In a disease genome wide association study (GWAS), p-value potentially tells us how likely a putative disease associated variant is due to random chance. For a long time p-values have been taken seriously by the GWAS community as a safeguard against false positives. Every disease-associated mutation reported in a GWAS must reach a stringent p-value cutoff (e.g., 10-8) in order to survive the multiple testing corrections. This is reasonable because after testing millions of variants in the genome, some random variants ought to yield small p-values purely by chance.

On the Use of P-Values in Genome Wide Disease Association Mapping

In hypothesis testing, p-value is routinely used as a measure of statistical evidence against the null hypothesis, where a smaller p-value indicates stronger evidence substantiating the alternative hypothesis. P-value is the probability of type-I error made in a hypothesis testing, namely, the chance that one falsely reject the null hypothesis when the null holds true. In a disease genome wide association study (GWAS), p-value potentially tells us how likely a putative disease associated variant is due to random chance. For a long time p-values have been taken seriously by the GWAS community as a safeguard against false positives. 

Every disease-associated mutation reported in a GWAS must reach a stringent p-value cutoff in order to survive the multiple testing corrections. This is reasonable because after testing millions of variants in the genome, some random variants ought to yield small p-values purely by chance. Despite of p-value’s theoretical justification, however, it has become increasingly evident that statistical p-values are not nearly as reliable as it was believed. 

A mathematical model that can describe the human emotion communications

Recently scientists developed a mathematical pattern to describe the human emotion communications and interactions required for the perception and recognition of environment. This mathematical model is capable of defining functional distortion during activity emotions.

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On Discretizations of the Generalized Boole Type Transformations and their Ergodicity

The Frobenius-Perron Operator and Its Discretization:

We consider an m-dimensional; not necessary compact; C1- manifold Mm, endowed with a Lebesgue measure μ determined on the σ-algebra of Borel subsets of Mm and ϕ: Mm→Mm being an almost everywhere smooth mapping. The related Frobenius-Perron operator

is defined by means of the integral relationship

for any and all μ-measurable subsets A⊂M^m Equivalently it can be defined as a mapping on the measure space (M^m)

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Properties of Nilpotent Orbit Complexification

Real and complex nilpotent orbits have received considerable attention in the literature. The former have been studied in a variety of contexts, including differential geometry, symplectic geometry, and Hodge theory.  Also, there has been some interest in concrete descriptions of the poset structure on real nilpotent orbits in specific cases. By contrast, complex nilpotent orbits are studied in algebraic geometry and representation theory — in particular, Springer Theory.

Attention has also been given to the interplay between real and complex nilpotent orbits, with the Kostant-Sekiguchi Correspondence being perhaps the most famous instance. Accordingly, the present article provides additional points of comparison between real and complex nilpotent orbits. Specifically, let g be a finite-dimensional semisimple real Lie algebra with complexification g Each real nilpotent orbit.

About the ggplot2 Package

"ggplot2 is an R package for producing statistical, or data, graphics, but it is unlike most other graphics packages because it has a deep underlying grammar. This grammar, based on the Grammar of Graphics, is composed of a set of independent components that can be composed in many different ways. Plots can be built up iteratively and edited later. A carefully chosen set of defaults means that most of the time you can produce a publication-quality graphic in seconds, but if you do have special formatting requirements, a comprehensive theming system makes it easy to do what you want.

ggplot2 is designed to work in a layered fashion, starting with a layer showing the raw data then adding layers of annotation and statistical summaries."

"ggplot2 is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. It takes care of many of the fiddly details that make plotting a hassle (like drawing legends) as well as providing a powerful model of graphics that makes it easy to produce complex multi-layered graphics."

Statistical Methods in Trials with Sequential Parallel Design for Trials with High Placebo Response

Strong placebo response has been problematic in central nervous system (CNS) clinical trials, leading to a reduced drug effect and thus resulting in decrease in probability of finding an effective drug. The ideal situation is to have comparative data collected only from subjects who are placebo non-responders. Stringent trial procedures together with enrichment of placebo non-responders are some of the ways to decrease placebo response in clinical trials. 

Fava et al. (2003) proposed a SPD where subjects are only randomized during Period 1. Accordingly, some placebo non-responders in Period 1 continue on placebo in Period 2 and others switch to drug in Period 2; and subjects who are treated with drug in Period 1 would continue to receive drug in Period 2. Treatment sequences for all subjects are all pre-specified prior to trial start; and data from Period 2 for subjects who are on drug in both periods are for safety evaluations only.