TOWARDS BUILDING AN INTELLIGENT INTEGRATED MULTI-MODE TIME DIARY SURVEY FRAMEWORK

Enabling true responses is an important characteristic in surveys; where the responses are free from bias and satisficing. In this thesis, we examine the current state of surveys, briefly touching upon questionnaire surveys, and then on time diary surveys (TDS). TDS are open-ended conversational surveys of a free-form nature with both, the interviewer and the respondent, playing a part in its progress and successful completion. With limited research available on how intelligent and assistive components can affect TDS respondents, we explore ways in which intelligent systems such as Computer Adaptive Testing, Intelligent Tutoring Systems, Recommender Systems, and Decision Support Systems can be leveraged for use in TDS. The motivation for this work is from realizing the opportunity that an enhanced web based instrument can offer the survey domain to unite the various facets of web based surveys to create an intelligent integrated multi-mode TDS framework. We envision the framework to provide all the advantages of web based surveys and interviewer assisted surveys. The two primary challenges are in determining what data is to be used by the system and how to interact with the user – specifically integrating the (1) Interviewer-assisted mode, and (2) Self-administered mode. Our proposed solution – the intelligent integrated multi-mode framework – is essentially the solution to a set of modeling problems and we propose two sets of overreaching mechanisms: (1) Knowledge Engineering Mechanisms (KEM), and (2) Interaction Mechanisms (IxM), where KEM serves the purpose of understanding what data can be created, used and stored while IxM deals with interacting with the user. We build and study a prototype instrument in the interviewer-assisted mode based on the framework. We are able to determine that the instrument improves the interview process as intended and increases the data quality of the response data and is able to assist the interviewer. We also observe that the framework’s mechanisms contribute towards reducing interviewers’ cognitive load, data entry times and interview time by predicting the next activity. Advisor: Leenkiat So

Effect of cryogenic grinding on fatigue life of additively manufactured maraging steel

Additive manufacturing (AM) is replacing conventional manufacturing techniques due to its ability to manufacture complex structures with near-net shape and reduced material wastage. However, the poor surface integrity of the AM parts deteriorates the service life of the components. The AM parts should be subjected to post-processing treatment for improving surface integrity and fatigue life. In this research, maraging steel is printed using direct metal laser sintering (DMLS) process and the influence of grinding on the fatigue life of this additively manufactured material was investigated. For this purpose, the grinding experiments were performed under two different grinding environments such as dry and cryogenic conditions using a cubic boron nitride (CBN) grinding wheel. The results revealed that surface roughness could be reduced by about 87% under cryogenic condition over dry grinding. The fatigue tests carried out on the additive manufactured materials exposed a substantial increase of about 170% in their fatigue life when subjected to cryogenic grinding

Next-generation sequencing-based method shows increased mutation detection sensitivity in an Indian retinoblastoma cohort

Purpose: Retinoblastoma (Rb) is the most common primary intraocular cancer of childhood and one of the major causes of blindness in children. India has the highest number of patients with Rb in the world. Mutations in the RB1 gene are the primary cause of Rb, and heterogeneous mutations are distributed throughout the entire length of the gene. Therefore, genetic testing requires screening of the entire gene, which by conventional sequencing is time consuming and expensive. Methods: In this study, we screened the RB1 gene in the DNA isolated from blood or saliva samples of 50 unrelated patients with Rb using the TruSight Cancer panel. Next-generation sequencing (NGS) was done on the Illumina MiSeq platform. Genetic variations were identified using the Strand NGS software and interpreted using the StrandOmics platform. Results: We were able to detect germline pathogenic mutations in 66% (33/50) of the cases, 12 of which were novel. We were able to detect all types of mutations, including missense, nonsense, splice site, indel, and structural variants. When we considered bilateral Rb cases only, the mutation detection rate increased to 100% (22/22). In unilateral Rb cases, the mutation detection rate was 30% (6/20). Conclusions: Our study suggests that NGS-based approaches increase the sensitivity of mutation detection in the RB1 gene, making it fast and cost-effective compared to the conventional tests performed in a reflex-testing mode