Economic potential of forest resources of Nepal

Nepal's forest resources underpin the livelihoods of rural people in important ways. During the country's 'planned development' over the past 50 years, the government, donors and policy makers have viewed these resources as a key vehicle for ushering in economic growth and for meeting basic needs. They underscore the potential value of forest resources for achieving conservation and socio-economic objectives. To what extent have economic incentives been generated to effectively harness these resources to meet the said objectives is an open question. To address this question, this paper reviews briefly and broadly the economic potential of the country's forest resources in terms of forest goods and services. Estimates of economic potential of timber and non-timber forest products and environmental services have been assessed. A number of recommendations for realizing the potential for achieving development and poverty reduction objectives is provided

Cusp-shaped Elastic Creases and Furrows

The surfaces of growing biological tissues, swelling gels, and compressed rubbers do not remain smooth, but frequently exhibit highly localized inward folds. We reveal the morphology of this surface folding in a novel experimental setup, which permits to deform the surface of a soft gel in a controlled fashion. The interface first forms a sharp furrow, whose tip size decreases rapidly with deformation. Above a critical deformation, the furrow bifurcates to an inward folded crease of vanishing tip size. We show experimentally and numerically that both creases and furrows exhibit a universal cusp-shape, whose width scales like $y^{3/2}$ at a distance $y$ from the tip. We provide a similarity theory that captures the singular profiles before and after the self-folding bifurcation, and derive the length of the fold from large deformation elasticity.Comment: 5 pages, 4 figure

A comprehensive classification of deep learning libraries

Deep Learning (DL) networks are composed of multiple processing layers that learn data representations with multiple levels of abstraction. In recent years, DL networks have significantly improved the state-of-the-art across different domains, including speech processing, text mining, pattern recognition, object detection, robotics and big data analytics. Generally, a researcher or practitioner who is planning to use DL networks for the first time faces difficulties in selecting suitable software tools. The present article provides a comprehensive list and taxonomy of current programming languages and software tools that can be utilized for implementation of DL networks. The motivation of this article is hence to create awareness among researchers, especially beginners, regarding the various languages and interfaces that are available to implement deep learning, and to provide a simplified ontological basis for selecting between them

A new clustering method using an augmentation to the self organizing maps

A technique is developed using Self Organizing Maps (SOM) to efficiently cluster the data and it is compared with existing clustering Techniques such as K-Means clustering, Hierarchical clustering and SOM Clustering. The proposed technique is used to cluster an Earthquake dataset and the performance is compared with the other existing clustering technique. The experimental results show that the proposed clustering method demonstrated better results as compared to other clustering methods