Search results for: Don Buddika Oshadi Malaweera
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 3

Search results for: Don Buddika Oshadi Malaweera

3 Orotic Acid-Induced Fatty Liver in Mink: Characterization and Testing of Bioactive Peptides for Prevention and Treatment

Authors: Don Buddika Oshadi Malaweera, Lora Harris, Bruce Rathgeber, Chibuike C. Udenigwe, Kirsti Rouvinen-Watt

Abstract:

Fatty liver disease is among the three most severe health concerns for mink and believed to occur through the same mechanism as nursing sickness. In North America, nursing sickness affects about 45% of mink farms and in Canada, approximately 50,000 mink females is affected annually. Orotic acid (OA) plays a critical role in lipid metabolism and can increase hepatic lipids by enhancing Sterol regulatory element binding protein-1c expression and decreasing Carnitine palmitoyl transferase I activity. This study was conducted to identify particular pathways and regulatory control points involved in fatty liver development, and evaluate the effectiveness of arginine and bioactive peptides for prevention and treatment of fatty liver disease in mink. A total of 45 mink were used in 9 treatments. The experimental diets consisted of 1% OA, 2% L-arginine and 5% of whey protein hydrolysates. At the end of 10 days of experimental period, the mink were anaesthetized, sampled for blood and euthanized, samples were obtained for histological, biochemical and molecular assays. The blood samples will be analyzed for clinical chemistry and triacylglycerol. The liver samples will be analyzed for total lipid content and analyzed for 6 genes of interest involved in adipogenic transformation, ER stress, and liver inflammation.

Keywords: fatty liver, L-arginine, mink, orotic acid, whey protein hydrolysates

Procedia PDF Downloads 273
2 Analyzing the Quality of Cloud-Based E-Learning Systems on the Perception of the Learners and the Teachers

Authors: R. W. C. Devindi, S. M. Buddika Harshanath

Abstract:

E-learning is a widely used technology for learning in the modern world. With the pandemic situation the popularity of using e-learning has been increased in a larger capacity. The e-learning educational systems require software resources as well as hardware usually but it is hard for most of the education institutions to afford those resources. Also with the massive user load e-learning has to broaden the server side resources as well. Therefore, in the present cloud computing was implemented in order to make the e – learning systems more efficient. The researcher has analyzed the quality of the e-learning systems on the perception of the learners and the teachers with the aid of hypothesis and has given the analyzed results and the discussion in this report. Therefore, the future research will be able to get some steps to increase the quality of the online learning systems furthermore. In the case of e-learning, quality assurance and cost effectiveness are essential. A complex quality assurance system is used in the stated project. There are no well-defined standard evaluation measures in this field. As a result, accurately assessing the e-learning system's overall quality is challenging. The researcher has done the analysis with the aid of standard methods and software.

Keywords: LMS–learning management system, SPSS–statistical package for social sciences (software), eigen value, hypothesis

Procedia PDF Downloads 79
1 Monitor Student Concentration Levels on Online Education Sessions

Authors: M. K. Wijayarathna, S. M. Buddika Harshanath

Abstract:

Monitoring student engagement has become a crucial part of the educational process and a reliable indicator of the capacity to retain information. As online learning classrooms are now more common these days, students' attention levels have become increasingly important, making it more difficult to check each student's concentration level in an online classroom setting. To profile student attention to various gradients of engagement, a study is a plan to conduct using machine learning models. Using a convolutional neural network, the findings and confidence score of the high accuracy model are obtained. In this research, convolutional neural networks are using to help discover essential emotions that are critical in defining various levels of participation. Students' attention levels were shown to be influenced by emotions such as calm, enjoyment, surprise, and fear. An improved virtual learning system was created as a result of these data, which allowed teachers to focus their support and advise on those students who needed it. Student participation has formed as a crucial component of the learning technique and a consistent predictor of a student's capacity to retain material in the classroom. Convolutional neural networks have a plan to implement the platform. As a preliminary step, a video of the pupil would be taken. In the end, researchers used a convolutional neural network utilizing the Keras toolkit to take pictures of the recordings. Two convolutional neural network methods are planned to use to determine the pupils' attention level. Finally, those predicted student attention level results plan to display on the graphical user interface of the System.

Keywords: HTML5, JavaScript, Python flask framework, AI, graphical user

Procedia PDF Downloads 61