Characterizing Popularity Dynamics of User-generated Videos: A Category-based Study of YouTube

Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is also important for the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis of the popularity pattern of YouTube videos is performed, considering the categories of videos. Metadata and request patterns were collected by employing category-specific YouTube crawlers. The request patterns were observed for a period of five months. Results confirm that the time varying popularity of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and Film. While for some categories views at early ages can be used to predict future popularity, for some others predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution technique along with a new video recommendation method

Women’s fear of normal delivery and their decision on the mode of delivery: a cross-sectional study

Background: Caesarean section, C-section, or caesarean birth is the surgical delivery of a baby through a cut (incision) in the mother's abdomen and uterus. It's a surgical procedure utilized when a vaginal delivery isn't possible or safe, or when the health of the mother or infant is at risk. Caesarean section rates range from 0.4 to 40% in different countries, and the trend has been growing over the previous 30 years. These increases have been linked to a number of factors. The purpose of this study was to learn more about these factors in the context of Bangladeshi demographics. Aim of the study was to investigate the women’s Fear of normal delivery and their decision on the mode of deliveryMethods: This cross-sectional observational study was conducted at the department of obstetrics and gynecology in Uttara Adhunik medical college and hospital, Bangladesh. The study duration was from 1February 2021 to 30 November 2021. The present study was carried out with a total of 63 women who had undergone caesarean section during the study period. A convenient sample selection method was done for the selection of the participantsResult: Among the participants of the present study, 47.62% were aged between 25-29 years, 76.19% were Muslims, and 76.19% stayed at the hospital for 3-4 days. 90.48% had been pregnant for 36-40 weeks. Lower abdominal pain and labor pain was observed in 20.63% of the participants. The majority of the participants underwent caesarean delivery due to fear of labor pain and episiotomy.Conclusion: The fear of childbirth or labor pain is the most common cause in patients undergoing caesarean section, followed by doctor advice due to medical comorbidities and fear of episiotomy

Characterizing Videos and Users in YouTube: A Survey

Abstract—Web 2.0 has reshaped the way people interact with Web sites. People are now able to view content created by other users as well as publish their own content on Web 2.0 sites, instead of downloading content created by a single author. Understanding the characteristics of the Web 2.0 sites has become a subject of immense interest to the Internet service providers, content makers and on-line advertisers. This understanding is also important for the sustainable development of the content distribution systems. As an approach to comprehend the characteristics of Web 2.0, significant amount of research has been done in investigating the characteristics of YouTube, the most popular web 2.0 site. In this paper, the characteristics of YouTube, based on earlier works, are studied from both video and user perspectives along with some open research issues. This kind of study is instrumental to understand the driving aspects of YouTube and other similar user generated content (UGC) sites. Keywords-YouTube videos; YouTube users; Web 2.0; User generated conten

Revisiting the debate: Are code metrics useful for measuring maintenance effort?

Evaluating and predicting software maintenance effort using source code metrics is one of the holy grails of software engineering. Unfortunately, previous research has provided contradictory evidence in this regard. The debate is still open: as a community we are not certain about the relationship between code metrics and maintenance impact. In this study we investigate whether source code metrics can indeed establish maintenance effort at the previously unexplored method level granularity. We consider ∼ 730K Java methods originating from 47 popular open source projects. After considering seven popular method level code metrics and using change proneness as a maintenance effort indicator, we demonstrate why past studies contradict one another while examining the same data. We also show that evaluation context is king. Therefore, future research should step away from trying to devise generic maintenance models and should develop models that account for the maintenance indicator being used and the size of the methods being analyzed. Ultimately, we show that future source code metrics can be applied reliably and that these metrics can provide insight into maintenance effort when they are applied in a judiciously context-sensitive manner.</p

In vitro protein digestibility of different feed ingredients in Thai koi (Anabas testudineus)

The study was carried out to determine relative protein digestibility (RPD) of different feed ingredients for Thai koi (Anabas. testudineus; n=22) using in vitro digestibility technique. Gut crude enzyme extracted from the experimental species was used to assay RPD using pH drop method. The RPD of fish meal (FM), meat & bone meal (M&B), shrimp meal (SM), soybean meal (SM), mustard oilcake (MOC) and rice polish (RP) were 78.08%, 72.82%, 20.65%, 76.08%, 67.39% and 35.86%, respectively when the respective ingredients were hydrolyzed by the gut crude enzyme extract of A. testudineus and caesin was used as the standard. The highest relative protein digestibility was found in fish meal (78.08 %) and the lowest was found in shrimp meal (35.65 %). The determined RPD of different feed ingredients can be used as the base information for the feed preparation of A. testudineus

Revisiting the debate: Are code metrics useful for measuring maintenance effort?

Evaluating and predicting software maintenance effort using source code metrics is one of the holy grails of software engineering. Unfortunately, previous research has provided contradictory evidence in this regard. The debate is still open: as a community we are not certain about the relationship between code metrics and maintenance impact. In this study we investigate whether source code metrics can indeed establish maintenance effort at the previously unexplored method level granularity. We consider ∼ 730K Java methods originating from 47 popular open source projects. After considering seven popular method level code metrics and using change proneness as a maintenance effort indicator, we demonstrate why past studies contradict one another while examining the same data. We also show that evaluation context is king. Therefore, future research should step away from trying to devise generic maintenance models and should develop models that account for the maintenance indicator being used and the size of the methods being analyzed. Ultimately, we show that future source code metrics can be applied reliably and that these metrics can provide insight into maintenance effort when they are applied in a judiciously context-sensitive manner.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Software Engineerin