Paser

Abstract: Google Forms works as a form of professionalism in the midst of the COVID-19 pandemic in making students keep learning. Preparation to work from home is anticipated with socialization activities using Google Forms as a first step to the success of the education process through online learning. This research was conducted online with data sources of 41 MIN 1 Paser educational community with data collection techniques through interviews, non-participant observation, and document studies. Using a qualitative approach in the form of case studies, data are collected interactively according to the Miles and Huberman models. The results show: MIN 1 Paser teachers during work from home are using Google Forms as an education room and assignment bag during online learning activities. Teacher professionalism is built to always be creative and innovate in the Google Form space. Present meaningful teaching with learning videos and quizzes that motivate students. Create a new cultural tradition of teaching from paper to online form sheets.

Abstract: During the last few decades, many efforts have been made to produce automatic inspection systems to meet the specific requirements in assessing distress on road surfaces using video cameras and image processing algorithms. However, due to the noisy images from pavement surfaces, limited success was accomplished. One major issue with pure video based systems is their inability to discriminate dark areas not caused by pavement distress, such as, tire marks, oil spills, shadows, and recent fillings. To overcome the limitation of the conventional imaging based methods, a probabilistic relaxation technique based on 3-dimensional (3D) information is proposed in this report. The primary goal of this technique is to integrate conventional image processing techniques with stereovision technology to obtain an accurate topological structure of the road defects. In addition, a road scene often contains other objects such as grass, trees and buildings which should be separated from the pavement. Therefore the earlier algorithm has been enhanced to extract the pavement region from a road scene using a Support Vector Machine (SVM). Various types of cracks are then obtained from the pavement surface images and classified using a feed-forward neural network. The proposed algorithms are implemented in MATLAB and the results are presented. The second half of the document includes a report detailing the development of a software package that would allow the user to review digital photographs of pavement, evaluate that pavement by the PASER method, store the results in a database, and then make decisions based on the results of that analysis. The computer interface and analysis portion of the software was written in Microsoft Visual BASIC 2008. The long-term goals of this project include linking the evaluation results to a geographical information system database and developing various reporting strategies.

Abstract: The PharmAccess African Studies to Evaluate Resistance (PASER) network was established as a collaborative partnership of clinical sites, laboratories, and research groups in 6 African countries; its purpose is to build research and laboratory capacity in support of a coordinated effort to assess population-level acquired and transmitted human immunodeficiency virus type-1 drug resistance (HIVDR), thus contributing to the goals of the World Health Organization Global HIV Drug Resistance Network. PASER disseminates information to medical professionals and policy makers and conducts observational research related to HIVDR. The sustainability of the network is challenged by funding limitations, constraints in human resources, a vulnerable general health infrastructure, and high cost and complexity of molecular diagnostic testing. This report highlights experiences and challenges in the PASER network from 2006 to 2010.