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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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20212
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Author: Agarwal, Shweta × Start date: 2021 ×
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    ThesisItemOpen Access
    Stochastic alternating renewal processes models for reliability analysis of multi-component systems incorporating queuing delay
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-10) Agarwal, Shweta; Singh, S.B.
    In the real-world scenario, most of the systems are comprised of more than one component and hence it becomes inevitable to estimate the reliability and availability of multi-component systems. Also, in most of the complex repair models the system fluctuates between up-state and down-state and the failure is revealed at the time of inspection. Keeping the aforementioned facts in view, the present research is centered to propose the reliability and availability models on the basis of derived proposition, Markov process, renewal theory. In this study, six models are introduced. Model [1] aims to propose a notion of alternating renewal process to mathematically model a multi-failure complex system to develop its availability and maintenance measures. In the study, M/E2/1 queueing model with infinite waiting space during service times of components has been worked out under FCFS discipline. The primary objective of the paper is about obtaining the system’s reliability, availability, and the optimal interval period with minimum maintenance cost. Model [2] analyzes the reliability characteristics of batch service queuing system with a single server model which envisages Poisson input process and exponential service times under First Come First Served (FCFS) queue discipline. With the help of renewal theory and stochastic processes the model has been designed to discuss the reliability and its characteristics. Model [3] considers the reliability characteristics of a multi-component system envisaged with Poisson arrivals and analyzed service in bulk. In the general bulk service rule the repair process is initialized when a threshold of “a” number of failed components is reached with the maximum capacity “b”. Considering the above facts reliability and availability expressions of the considered model has been derived. Model [4] treats a risk system in which at the occurrence of failure due to any of the mode of failures the failed component joins a M/G/1 queue. After the completion of the repair, whenever the serviceman becomes idle it starts an exponential classical vacation in which the serviceman does not serve the failed unit. Incorporating the above cited facts, expressions for estimating the reliability and its other measures are derived. Model [5] investigates the reliability and other measures related to it for a periodically inspected system. Whenever any of the components gets failed it joins the M/G/1 queue with a waiting threshold. While if the service of the failed components is commenced in the threshold amount of time, then it remains to its completion. Various reliability measures like availability, maintenance, long run maintenance cost rate have been estimated for the considered system. Model [6] analyses a periodically inspected system subject to imperfect maintenance policy. The considered system is inspected and maintained periodically and passes through a fixed number of imperfect repairs before being replaced. Incorporating the given facts, the reliability of the periodically inspected system is evaluated
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    ThesisItemOpen Access
    Stock price prediction using LSTM approach
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2021-09) Agarwal, Shweta; Singh, B.K.
    In today's economy, the stock market, often known as the equity market, has a significant impact. The rise or decline in the share price has a significant impact on the investor's profit. The proposed method used Long short-Term Memory (LSTM) Approach. Here I am considering multi-column LSTM model which takes more than one column to analyse and train the model and based on that it will predict the values for future days. More than one features helps the model to predict the values more accurately than providing the single feature. Here the dataset is taken from Yahoo Finance website which provides historical data to almost all of the companies listed in the stock market. The dataset is taken for a particular company PETRONET LNG from 2004 to 2018. Next 30 days values are being predicted based on that historical data. The values for 2019 is not being considered as this time was affected by corona virus and every sector of the industry was affected by this pandemic. So taking these values may provide wrong predictions as there was sudden fall and rise in the stock values during this time. I have also added 2 more features to the given historical data i.e. volatility and momentum. Volatility is basically used to capture fluctuation in the market. Momentum tells us what is the changes in the price as compare to past days. Result showa that adding these features helps model to predict more accurately.