Browsing by Author "Kaur, Jaspreet"

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Efficacy of p-coumaric acid in chronic constriction injury induced neuropathic pain in rats
(Indian Drug Manufacturers' Association, 2021-12-14T00:00:00) Bharti, Akash; Kaur, Jaspreet; Kumar, Amit; Singh, Simranjit; Kumar, Deepak
The present research work has been designed to evaluate the effect of p-coumaric acid in chronic constriction injury (CCI) of sciatic nerve induced neuropathic pain in rats. In addition, biochemical tests such as thiobarbituric acid reactive substances (TBARS), reduced glutathione (GSH) and total protein were performed in sciatic nerve tissue sample. The neuropathic pain has been effi ciently and successfully induced in rat by the performance of CCI. The battery of behavioural test showed the development of neuropathic pain as an index of rising the paw and tail thermal and mechanical pain sensitivity. The treatment of p-coumaric acid at dose 50 and 100 mg kg-1, p.o. for 15 consecutive days have been shown to produce signifi cant ameliorative effect on CCI of sciatic nerve induced neuropathic pain sensitivity. In addition, CCI of sciatic nerve also induces the oxidative stress in nervous system by rising TBARS, decrease GSH and proteins levels in sciatic nerve tissue and these effects are reversed via administration of p-coumaric acid and statistically equivalent to standard drug. Hence, it may be concluded that, p-coumaric acid can be useful in the management of neuropathic pain symptoms. � 2021 Indian Drug Manufacturers' Association. All rights reserved.
India’s Trade with Central Asia with special reference to Kazakhstan in post-cold war era. In: Malhotra, R. Gill, S. S. and Gaur, N. (eds.)
(Centre for Rural and Industrial Developments, 2013) Kaur, Sandeep; Kaur, Jaspreet
Indo-kazakhstan trade : Trends and protocols in the post cold war era
(Central University of Punjab, 2012) Kaur, Jaspreet; Kaur, Sandeep
Though economic relations between India and Kazakhstan have been strengthening; still the current size of trade and investment between the two countries is relatively less than potential. In this context, the present study is an endeavour to analyze the existing trends in bilateral trade and also to highlight the future prospects for India and Kazakhstan. It has been found that the increase in merchandise trade between the two countries is mainly because of the changing demand structure and comparative advantages of both the economies in complementary sectors in recent years. The trade specialization indices (RCA and Michaely) emphasize that while Kazakhstan has been specializing in a few energy products; India's exports have been more diversified. Also, both the countries have comparative advantages in different products in the same industry, revealing the opportunity for higher intra-industry trade (IIT) in future, which would reduce cost and enhance the benefits for both the countries. However, it has been revealed that India's trade with Kazakhstan is much below than the rest of the world. Thus, there are enormous complementarities in bilateral trade that need to be tapped. It is mandatory to overcome the geographical, political and other hurdles to increase two way flow of goods. Just before fully utilizing India's potential to contribute in transition of Kazakhstan and Kazakhstan's ability to provide the energy resources to India, it is required that the process of bilateral cooperation gets a boost
Indo-Kazakhstan Trade: Barriers and Prospects
(Research journal of Area Study Center, Univeristy of Peshawar, Pakistan, 2014) Kaur, Jaspreet; Kaur, Sandeep
The degree of Indo-Kazakhstan bilateral trade is not so relevant but it has been growing fast in last few years. This increase is exhibiting increasing role of the republic in India’s trade. As per Indicative Trade Potential (ITP), there exists a vast scope to increase and diversify this bilateral trade. India has major potential in machinery and transport while Kazakhstan can expand the exports from its traditional sectors of mineral and metals. Potential in different products reveals the opportunity for diversify the trade in future. It would enhance the benefits for both the countries in long term.
Neural network based refactoring area identification in Software System with object oriented metrics
(Indian Society for Education and Environment, 2016) Kaur, Jaspreet; Singh, Satwinder
Objectives of the Study (a) To study previously designed models for identification of refactoring area in Object Oriented Software Systems. (b) To design a general framework or model that helps to easily identify the software code smells for a good quality of coding. (c) To identify the bad smells in the code with a design of neural network based model with the help of object-oriented metrics and further to predict the performance of the proposed model using various evaluation parameters of confusion matrix. Analysis/Methods: In this study, two different versions of Rhino (1.7r1 and 1.7r2) were taken as dataset. Object-Oriented metrics were taken as input data and the probability factor (occurrence or non-occurrence of a bad smell as output. Presence of a bad smell was considered as 1 and 0 means absence of bad smell. If there was at least one bad smell present in the code in a class, it was marked as smelly class. The tool used to extract the databases for collected object-oriented metrics and bad smells of these Rhino versions is PTIDEJ. Further, the data was tested on neural networks for different epochs to predict their performance. Findings: a) Bad Smell Analysis: Twelve design smells were considered to detect the presence of bad smell in code. If there was at least one bad smell present in the code in a class, it was marked as smelly class. b) Neural Network Model Table: Weight and bias factor for various predictors were calculated for different epochs (500, 1000, and 2000). It shows the weights assigned from input layer to hidden layer and from hidden layer to output neurons layer. After the training, the weights were tested on various datasets. C) Performance Tables and Graphs: In this, the Neural network proposed model was trained using different number of epochs to examine if the number of epochs used in training has any impact on the results or not. Further, the results for the accuracy of these models were shown. Novelty/Improvement: When the data was highly trained then the results were better. When the data was trained with 500 epochs, it was suitable for only with-in company projects but when the data was more trained than the model was also appropriate for cross projects. It was seen that when the data was trained with 1000 and 2000 epochs, the results of the proposed model were improved.
Revealed Comparative Advantages: India and Kazakhstan in Post-Cold War Era
(Korea University of International Studies, 2018) Kaur, Jaspreet; Kaur, Sandeep
The structure of Comparative advantages enjoyed by India and Kazakhstan in each others market has been examined for the period of 1995-2010.