introduction: brain-derived neurotrophic factor (bdnf) and its receptor, trkb, in the hippocampus are targets for adverse effects of stress paradigms in addition, bdnf and its receptor play key role in the pathology of brain diseases like depression. in the present study, we evaluated the possible role of hippocampal bdnf in depression during pregnancy, methods: to achieve the purpose, repeated...

Abstract The data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability labels the examples. This poses a variety challenges existing machine learning methods, related to analyzing datasets with large that described in high-dimensional space, where not all have provided. For example, when investigating toxicity che...

The soil roughness at the field level is an easy visually perceptible notion, but difficult to describe numerically. The objective of this paper is to propose a method to establish quantitative and descriptive soil roughness indices. A measurement system was developed using a laptop-computer and a laser cell. The elevation data are measured on the ground along a square array. They are treated w...

System identification takes a space of possible models and a stream of observational data of a physical system, and attempts to identify the element of the model space that best describes the observed system. In traditional approaches, the model space is specified by a parameterized differential equation, and identification selects numerical parameter values so that simulation of the model best...

Comparative analysis (CA) of dynamical systems is an important problem in qualitative reasoning. CA techniques predict differences in the behavior of two systems as a consequence of differences in the initial conditions or structural differences. A disadvantage of these techniques is the imprecision of the possible answers due to their qualitative nature. This report presents SQCA, an implement...

System identiication takes a space of possible models and a stream of observational data of a physical system, and attempts to identify the element of the model space that best describes the observed system. In traditional approaches, the model space is speciied by a parameterized diierential equation, and identiication selects numerical parameter values so that simulation of the model best mat...

Feature extraction is a very important preprocessing step for classification of hyperspectral images. The linear discriminant analysis (LDA) method fails to work in small sample size situations. Moreover, LDA has poor efficiency for non-Gaussian data. LDA is optimized by a global criterion. Thus, it is not sufficiently flexible to cope with the multi-modal distributed data. We propose a new fea...