Effectiveness of childbirth education on knowledge and childbirth experience among Primigravida Women: A quasi-experimental study
Introduction: Pregnancy is the term used to describe the period in which a fetus develops inside a woman's womb or uterus. Pregnancy usually lasts about 40 weeks or just over 9 months as measured from the last menstrual period to delivery. The experience of childbirth is always linked with emotional feelings and expectations. Objective: The aim of the study was to assess the effectiveness of childbirth education on knowledge and childbirth experience among primigravida women Methods: A quasi-experimental design was used to assess the effectiveness of childbirth education on knowledge and childbirth experience among primigravida women in Kamla Nehru Hospital, Shimla. In this study Pre-test and Post-test control group design, 50 primigravida women (25 in the experimental group and 25 in the control group), gestational age of 36- 38 weeks. Self Structured knowledge questionnaire to assess the knowledge and childbirth expectancy was measured by using Wijma Delivery expectancy questionnaire (scale-A) before the intervention in both group and childbirth experience was measured by using Wijma delivery experience questionnaire (scale-B) was used for data collection. Data was collected by structure interview schedule and analyzed by using descriptive and inferential statistics. Results- In experimental group pre-test mean±SD knowledge score was (8.60±1.38) and post-test mean±SD knowledge score was (14.12±1.81). The difference between pre-test and post-test mean±SD knowledge score was highly significant p=0.001. In the control group pre-test mean±SD knowledge score was (9.00±1.38) and post-test mean±SD knowledge score was (9.24±1.39). The difference between pre-test and post-test mean knowledge score was statistically non-significant. childbirth expectancy score of the experimental group was found to be (75.00±7.11) and the childbirth expectancy score of the control group was found to be (81.12±3.27). Whereas the childbirth experience score of the experimental group was found to be (60.40±2.87) and childbirth experience score of the control group was found to be (80.68±2.97) which was statistically significant at the level of 0.001. It was concluded that Childbirth education had an impact on knowledge and childbirth experience among primigravida women. Conclusion: The study findings implied that the implementation of childbirth education has an essential role to increase knowledge and improved childbirth experience of primigravida women.
Published by: Rohini Rajpoot, Jaswinder Kaur
Author: Rohini Rajpoot
Paper ID: V6I3-1618
Paper Status: published
Published: June 26, 2020
Permutation series and combination series
The Permutation Series and Combination Series will ensure the readers to understand the proper calculation of Permutation and Combination and will also help them to grow their knowledge on the topic. It is a very helpful topic for the students to strengthen their roots in mathematics. The magic that Mathematics has in it will be exposed to the readers to understand their Mathematics thoroughly and will also help them to enlarge their thinking capabilities. Hence, this topic is very interesting as it provides a link between A.P, G.P with Permutation, and Combination. It will help to calculate many selections and others in a very small time.
Published by: Swarnav Majumder
Author: Swarnav Majumder
Paper ID: V6I3-1617
Paper Status: published
Published: June 26, 2020
Performance and emission characteristics of a VCR SI engine fueled with ethanol/gasoline blends
Alternative fuels have become a centre of attraction these days due to its capability of reducing the dependency on fossil fuels and harm to the environment. Alcohol such as ethanol is considered as a clean and alternative fuel for SI engines when it is used in blends with gasoline in different fractions to increase oxygen content. In this experimental investigation, ethanol obtained from sugarcane waste was used in fuels prepared by blending it in increasing ratios (5, 10, 15 and 20 vol.%) with oxygen free gasoline. These ethanol/gasoline blended fuels were used to assess the combustion and emission characteristics of a VCR SI engine. Constant speed of 1300 rpm was maintained during the whole experimentation and the compression ratios were varied as 6, 7 and 9 respectively. Results showed that the ethanol - gasoline blends with 20 vol.% presented the highest volumetric efficiency, torque and brake power, whereas, the ethanol - gasoline blends of 5 vol.% presented the lowest volumetric efficiency, torque and brake power among other blends. Gasoline on the other hand showed the lowest volumetric efficiency, torque and brake power among all the test fuels. Also, the CO and UHC emissions were significantly reduced with the increase of ethanol content in the fuel blends, which indicates an efficient combustion. The impacts of compression ratios on the engine were also observed. The lowest values of CO and UHC emissions were observed at the compression ratio of 6 and with E20 blends. Ethanol – gasoline blends comprising 20 vol.% ethanol and 80 vol.% gasoline provided a better performance among all the blends used for the experimentation.
Published by: Ridon Bagra, Dr. P. Tamilselvam, G. Tharanitharan, Rolin Sorum
Author: Ridon Bagra
Paper ID: V6I3-1606
Paper Status: published
Published: June 26, 2020
End to end CI/CD pipeline for Machine Learning
In every industry machine learning applications are becoming popular, however, compared to traditional software applications the process fo developing, deploying, and continuously improving for machine learning applications is more complex. In industry practice continuous integration, delivery, and deployment enable organizations to release new features in their products frequently. For engineering processes of developing and designing secure pipelines to support continuous practices, how machine learning systems should be architected to gain a deep understanding in the process, and how to capture, improve and report data into different aspects of continuous integration, delivery, and deployment. Without proper pipeline for machine learning it is hard to predict, test, explain, and improve data workflow behavior. Pipelining in machine learning bringing different principles and practices to machine learning applications to work in a proper manner. In the industrial sector consequences of an irregular pipeline can cause financial, resource, and time will get wasted and some times it can indirectly influence companies' personal reputation in the market. This paper discusses the problems experience while building a machine learning pipeline and ultimately describe the framework to implement the problems in the workflow. Methodically reviewing the state of the art of continuous execution to organize approaches and tools, recognize challenges and practices. As a result, the machine learning pipeline reduces the gaps and increases the speed of experimentation in the workflow.
Published by: Ram Mohan Vadavalasa
Author: Ram Mohan Vadavalasa
Paper ID: V6I3-1581
Paper Status: published
Published: June 26, 2020
Machine Cloning
Middleware systems which integrate with front end and back-end systems in any enterprise solutions play an important role in a monolithic setup of an enterprise. Most of the middleware systems are file or XML based and not RPM packages as Linux or other Operating Systems define. These file and XML based softwares gives us opportunity to Clone a system and build a new one which saves time and money. Many existing tools and solutions in the market right now talk about Operating System cloning but rarely talk about software cloning. We are going to address few such methods and procedures to achieve this.
Published by: Mallikarjuna Akkinapalli
Author: Mallikarjuna Akkinapalli
Paper ID: V6I3-1568
Paper Status: published
Published: June 26, 2020
Alternate Medicine Recommendation
On the Internet, where the number of choices is overwhelming, there is a need to filter, prioritize and efficiently deliver relevant information to alleviate the problem of information overload, which has created a potential problem for many Internet users. Alternate Medicine System solves this problem by searching through a large volume of medical information to provide users with filters and services. This project explores the different characteristics and potentials of different recommendation techniques in recommendation systems to serve as a compass for research and practice in the field of medical recommendation systems. In this project, we have used a dataset over 100+ medicines from different companies and brands to do recommendation based on the content of medicine and then filter it based on rating and cost-based analysis. Through experimental results, we have found that more than 95% of the medicines have a lower cost based alternative available with a higher rating.
Published by: Roshani R. Zamare, Shital P. Dhok, Sampada V. Babhulkar, Richa S. Singh, Jayshri G. Marbade, Nayan D. Bawane, Abhishek M. Shukla
Author: Roshani R. Zamare
Paper ID: V6I3-1621
Paper Status: published
Published: June 26, 2020
