دانلود گزارش کارورزی و کار آموزی

Abstract

Abstract : 

This paper discusses the possibility of wealth adjustment through a credit network. The discussed credit network in this paper is a kind of loaning with no interest rate (its value is zero). It explains the influence of existence or inexistence of a cooperation originated from the credit network on wealth distribution and adjustment in an artificial society. To show how the wealth may distribute, environment agents in terms of their obtained wealth have been classified into ten wealth categories; thus, the share of each category in terms of population has been determined. In addition, the survival of population in the environment has been studied. Findings and results show more balanced distribution of agents among the categories of wealth and higher survival of the population in the existence of the credit network. More over, the curve of population has fewer fluctuations. In other words, the population is more stable due to the ability of credit network in making more survival and stability in the population of environment in periods of time by providing the possibility of cooperation and wealth better distribution.

Abstract

Navigation methods for mobile robots need to take various sources of uncertainty into account in order to get robust performance. The ability to improve performance with experience and to adapt to new circumstances is equally important for long-term operation. Real-time constraints, limited computation and memory, as well as the cost of collecting training data also need to be accounted for. In this paper, we discuss our evolving architecture for mobile robot navigation that we use as a test-bed for evaluating methods for dealing with uncertainty in the face of real-time constraints and limited computational resources. The architecture is based on POMDP models that explicitly represent actuator uncertainty, sensor uncertainty, and approximate knowledge of the environment (such as uncertain metric information). Using this model, the robot is able to track its likely location as it navigates through a building. Here, we discuss additions to the architecture: a learning component that allows the robot to improve the POMDP model from experience, and a decision-theoretic path planner that takes into account the expected performance of the robot as well as probabilistic information about the state of the world. A key aspect of both additions is the efficient allocation of computational resources and their practical application to real-world robots.

DOI

10.1007/BFb0013970

Print ISBN

978-3-540-61376-3

Online ISBN

978-3-540-68506-7

Abstract:

 Diosgenin is a steroidal sapogenin found in plants such as Dioscorea nipponoca, Solanum incanum, Solanum

xanthocarpum and Trigonella foenum graecum. Diosgenin, biologically active phytochemicals have been used for the treatment of

various types of disorder such as leukemia, inflammation, hypercholesterolemia and cancer. It is also able to prevent bone loss to

the same extent as that of oestrogen. It is a typical initial intermediate for synthesis of steroidal compounds, oral contraceptives and

sex hormones. Dioscorea, Costus and Trigonella are mainly used for the production of diosgenin. On the basis of literature survey it

divulges that diosgenin has very impressive pharmacological profile and could be used as a medicine for the treatment of

different types of disorders in the future. Thus, the present work aims to provide collective information in concern with its

pharmacological activity and phytoanalytical techniques. This review will be beneficial to researches for the development of an

alternative method for the treatment of innumerable diseases from diosgenin.

S. M. Sadat Kiai • M. Elahi • S. Adlparvar • E. Shahhoseini • S. Sheibani • H. Ranjber akivaj •

S. Alhooie • A. Safarien • S. Farhangi • N. Aghaei • S. Amini • M. M. Khalaj • A. R. Zirak •

A. A. Dabirzadeh • J. Soleimani • F. Torkzadeh • M. M. Mousazadeh • K. Moradi •

M. Abdollahzadeh • A. Talaei • A. A. Zaeem • A. Moslehi • A. Kashani • A. R. Babazadeh •

F. Bagiyan • M. Ardestani • A. Roozbahani • H. Pourbeigi • H. Tajik Ahmadi •

M. A. Ahmadifaghih • M. S. Mahlooji • B. N. Mortazavi • F. Zahedi