Doctor of Philosophy

dissertationCurrent scaling trends in transistor technology, in pursuit of larger component counts and improving power efficiency, are making the hardware increasingly less reliable. Due to extreme transistor miniaturization, it is becoming easier to flip a bit stored in memory elements built using these transistors. Given that soft errors can cause transient bit-flips in memory elements, caused due to alpha particles and cosmic rays striking those elements, soft errors have become one of the major impediments in system resilience as we move towards exascale computing. Soft errors escaping the hardware-layer may silently corrupt the runtime application data of a program, causing silent data corruption in the output. Also, given that soft errors are transient in nature, it is notoriously hard to trace back their origins. Therefore, techniques to enhance system resilience hinge on the availability of efficient error detectors that have high detection rates, low false positive rates, and lower computational overhead. It is equally important to have a flexible infrastructure capable of simulating realistic soft error models to promote an effective evaluation of newly developed error detectors. In this work, we present a set of techniques for efficiently detecting soft errors affecting control-flow, data, and structured address computations in an application. We evaluate the efficacy of the proposed techniques by evaluating them on a collection of benchmarks through fault-injection driven studies. As an important requirement, we also introduce two new LLVM-based fault injectors, KULFI and VULFI, which are geared towards scalar and vector architectures, respectively. Through this work, we aim to make contributions to the system resilience community by making our research tools (in the form of error detectors and fault injectors) publicly available

Influence of emotions on consumer buying behavior

Consumer behavior is always uncertain and rational. The complex black box of consumer is a chal-lenge to the marketers and researchers across the world for its nature and tendency to change within no time. Consumer responses towards different marketing programs cannot be same and hence differ by means of its actions which are emotional and always stated as irrational. There are different emo-tional chords of consumers targeted by marketers in order to generate sales and managing effectively the marketing program. Present study is an attempt to investigate the role of emotions on FMCG pur-chases. The study highlighted the relevant emotions in consumer purchases of FMCG products with the help of factor analysis and was conducted in north India to serve the purpose of analyzing the role of emotions in FMCG purchases. Research paper Reference to this paper should be made as follows: Soodan, V., Pandey, A. C.(2016). “Influence of emotions on consumer buying behavior: A study on FMCG purchases in Uttarakhand, India”, Journal of Entrepreneurship, Business and Economics, Vol. 4, No. 2, pp. 163–18

Incorporating Biobehavioral Architecture into Car-Following Models: A Driving Simulator Study

Mathematical models of car-following, lane changing, and gap acceptance are mostly descriptive in nature and lack decision making or error tolerance. Including additional driver-related information with respect to behavior and cognitive characteristics would account for these lacking parameters and incorporate a human aspect to these models. Car-following, particularly in relation to the Intelligent Driver Model (IDM), was the primary component of this research. The major objectives of this research were to investigate how psychophysiological constructs can be modeled to replicate car-following behavior, and to correlate subjective measures of behavior with actual car-following behavior. This dissertation presents a thorough literature review into car-following models and existing driving and biobehavioral relationships that can be capitalized to improve the calibration and predicting capabilities of these models. A framework was theorized to utilize the task-capability interface to incorporate biobehavioral parameters such as cognitive workload, situation awareness, and level of activation in order to better predict changes in driving performance. Ninety drivers were recruited to validate the framework by participating in virtual scenarios within a driving simulator environment. The scenarios were created to capture all the necessary parameters by varying the situation complexity of individual tasks. A biobehavioral extension to the IDM was developed to easily calibrate predicted and observed values by grouping individual driver performance and behavioral traits. The model was validated and found to be an effective way of utilizing behavioral and performance variables to efficiently predict car-following behavior

Abundance of Secondary Metabolites in Human Microbiome

Digitized for IUPUI ScholarWorks inclusion in 2021.Human body harbors the most complicated microbial ecosystem. Bacteria that have co-evolved within a human context have barely been explored for secondary metabolites. These secondary metabolites are hypothesized to possess biological activities significant within the human host context. In our study, we studied conservation profiles of 203 secondary metabolite gene clusters across 16 human body sites and found that gastrointestinal tract and oral sites show the highest conservation for secondary metabolic gene clusters. We observed that majority of highly conserved metabolites belong to pathway type NRPS. Our phylogenetic analysis of highly conserved stool and oral samples revealed abundance of firmicutes, bacteroidetes and actinobacteria phylum

The impact of therapy on the quality of life in asymptomatic patients with freshly detected hypertension

Background: Hypertension is an commonly encountered disease which adversely affect  all aspects of quality of life (QoL). The existing studies are confounded by the presence of multiple comorbidities and inclusion of elderly, which by themselves impairs the QoL. There is thus a need to study the impact of hypertension on QoL, in isolation.Method: This is a single center, prospective, intention to treat, observation study. The aim of the study is to evaluate the change in the QoL over six months, in newly diagnosed asymptomatic patients of hypertension. The tools used to assess the QoL included World Health Organisation’s Quality of Life Questionnaire (WHOQOL- BREF) and Short Form-36 (SF-36).Result: A total of 232 patients (172 males and 60 females) were enrolled in the study. The mean age was 44.66 years. A total of 102 patients (43.97%) had stage-1 and 130 patients (56.03%) had stage-2 hypertension. The female gender is associated with a higher likelihood of presentation with stage-2 hypertension. The male cohort had a better baseline QoL. The desired blood pressures was achieved in 40.52%. With therapy, the QoL improved significantly; sub-hoc analysis showed, the improvement was higher in males and those with stage-1 hypertension. There is an inverse relationship between the QoL and requirement for higher number of antihypertensive mediations.Conclusions: In patients with asymptomatic primary hypertension, treatment improves all aspects of QoL. The factors adversely affecting the QoL include female gender, higher stage of hypertension, poor blood pressure control and requirement of higher numbers of antihypertensive medicine

Bayesian Formulation of Time-Dependent Carrier-Borne Epidemic Model with a Single Carrier

In this paper, the time dependent carrier-borne epidemic model defined by Weiss in 1965 has been adopted into a Bayesian framework for the estimation of its parameters. A complete methodological structure has been proposed for estimating the relative infection rate and probability of survival of k out of m susceptibles after time t from the start of the epidemic. The methodology has been proposed assuming a single carrier to simplify the study of the behavioral validity of the fitted Bayesian model with respect to time and relative infection rate. Further, the proposed model has been implemented on two real data sets- the typhoid epidemic data from Zermatt in Switzerland and the Covid-19 epidemic data from Kerala in India. Results show that the proposed methodology produces reliable predictions which are consistent with those of the maximum likelihood estimates and with expected epidemiological patterns

Effect of cabinet tray dryer on Tomato (Lycopersicon Esculentum) slices during drying process and storage study of dehydrated tomato powder

The effected dehydrated tomato (Lycopersicon Esculentum) samples determining by acidity, pH, ascorbic acid, lycopene content and microbial growth. The highest acidity value 7.42 of untreated samples (4mm) in LDPE packaging material and 8.12 of untreated samples (8 mm) in aluminum foil packaging and the lowest pH value 2.05 of untreated samples (6mm) in LDPE packaging material and 2.07 of untreated samples (8 mm) in aluminum foil package under cabinet tray dryer (65 0C) after 120 days. The lowest lycopene content value 50.71 of untreated sam-ples (6 mm) in LDPE packaging material and 60.24 of untreated samples (4 mm) in aluminum foil packaging under cabinet tray dryer at 65 0C after 120 days. The lowest vitamin C value 8.54 of untreated samples (4mm) in LDPE packaging and 9.50 of untreated samples (6 mm) in aluminum foil packaging under cabinet tray dryer after 120 days. We can see that microbial growth not detect in the starting 30 days but after one month we can easily see microbial growth. When considering growth rates of microbial pathogens, in addition to temperature, time is a critical consideration. Food producers or manufacturers address the concept of time as it relates to microbial growth when a product's shelf life is determined. The highest microbial growth 4.55×102 of untreated samples (6mm) in LDPE pack-aging and 4.49×102 of untreated samples (8 mm) in aluminum foil packaging under cabinet tray dryer after 120 days