using the electrostatic capacity of a condenser, the existence of a distance function on a finsler space is discussed. this distance function divides finsler spaces into the two classes, denoted here by i and ii. the topology generated by this distance on the finsler spaces of class ii coincides with its intrinsic topology. this work provides a natural extension of mathematical analysis tools n...

This is a concept paper which introduces a dynamic extension to a collaborative network relationship analysis approach. The extension is based on the MetaMatrix approach known from the Dynamic Network Analysis field. Several entity classes representing agents, tasks, resources and knowledge are introduced and possible relations between entities of the different classes are analysed. All entity ...

We discuss those classes K of relational structures closed under unions that are de…ned by excluding certain …nite structures. We characterize such classes and show they contain an in…nite family of pair-wise non-embeddable members. We investigate structures in K satisfying extension conditions and construct universal structures in K satisfying only …nitely many of the extension conditions.

This paper aims to study the role of women in agricultural promotion and its effect on rural development. Research method in this study was based on a survey and the required information was collected through questionnaire from a sample of women especially rural women. The data were analyzed using Logit model. The results showed that women had a major role in farming including producion, conser...

Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a cost-sensitive perceptron learning rule for non-se...

Learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We derive a costsensitive perceptron learning rule for non-sep...