most of data in multi-attribute decision making (madm) problems are changeable rather than constant and stable. therefore, sensitivity analysis after problem solving can effectively contribute to making accurate decisions. in this paper, we offer a new method for sensitivity analysis in multi-attribute decision making problems in which if the weights of one attribute changes, then we can deter...

spectral decomposition is a powerful tool for analysis of seismic data. fourier transform determines the frequency contents of a signal. but for analysis of non-stationary signals, 1-d transform to frequency domain is not sufficient. in early years, transforming of seismic traces into time and frequency domain was done via windowed fourier transform, called a short time fourier transform (stft)...

We resum the recently calculated second order kernel of the BFKL equation. That kernel can be viewed as the sum of a conformally invariant part and a running coupling part. The conformally invariant part leads to a corrected BFKL intercept as found earlier. The running coupling part of the kernel leads to a non-Regge term in the energy dependence of high energy hard scattering, as well as a Q2−...

Explicit expressions for the non-singlet and singlet structure functions g1 at the small x-region are obtained. They include the total resummation of doublelogarithmic contributions and accounting for the running QCD coupling effects. We predict that asymptotically the singlet g1 ∼ x−∆S , with the intercept ∆S = 0.86, which is approximately twice larger than the non-singlet intercept ∆NS = 0.4....

Random intercept model and random intercept & random slope model carrying two-levels of hierarchy in the population are presented and compared with the traditional regression approach. The impact of students’ satisfaction on their grade point average (GPA) was explored with and without controlling teachers influence. The variation at level-1 can be controlled by introducing the higher levels of...

Attribute reduction is an important inductive learning issue addressed by the Rough Sets society. Most existing works on this issue use the minimal attribute bias, i.e., searching for reducts with the minimal number of attributes. But this bias does not work well for datasets where different attributes have different sizes of domains. In this paper, we propose a more reasonable strategy called ...