NEAR-GRAZING AND NOISE-INFLUENCED DYNAMICS OF ELASTIC CANTILEVERS WITH NONLINEAR TIP INTERACTION FORCES

Within this dissertation work, numerical, analytical, and experimental studies are conducted with macro-scale and micro-scale elastic structures in the presence of nonlinear force interactions. The specific physical systems explored within this work are an atomic force microscope (AFM) micro-cantilever and a macro-scale cantilever experiencing similar tip interaction forces as the AFM cantilever operated in tapping mode. The tip sample forces in an AFM operation are highly nonlinear, with long-range attractive forces and short-range repulsive forces. In the macro-scale case, magnetic attractive forces and repulsive forces, which arise due to impacts with a compliant surface are used to generate similar nonlinear tip interaction forces. For elastic structures subjected to off-resonance base excitations, bifurcations close to grazing events are studied in detail, and the observed nonlinear phenomena are found to be common across the considered length scales. The dynamics of the considered systems are studied with a reduced-order computational model based on Galerkin projection with a single mode approximation. Along with studies on the bifurcation behavior, the effects of added Gaussian white noise on the system dynamics are also examined. Non-smooth system dynamics is studied by constructing local maps near the discontinuity. Period-doubling events are examined by using Poincaré maps and discontinuity mapping analysis. An important component of this dissertation research is the investigations into the effects of noise on the dynamics of these structures. Experimental and numerical efforts are used to examine the stochastic dynamics of the cantilever structures when a random component is added to the harmonic input. The noise effects are studied when the excitation frequency is close to a system resonance as well as when it is off-resonance. An analytical-numerical method with moment evolution equations is used to study the effects of noise. The effects of noise on contact and adhesion phenomena are explored. Through this dissertation work, the importance of considering noise-influenced dynamics in micro-scale applications such as AFM operations is illustrated. In addition, this work helps shed light on universality of nonlinear phenomenon across different length scales

Three Essays on the Role of Unstructured Data in Marketing Research

This thesis studies the use of firm and user-generated unstructured data (e.g., text and videos) for improving market research combining advances in text, audio and video processing with traditional economic modeling. The first chapter is joint work with K. Sudhir and Minkyung Kim. It addresses two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, we develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, we address the problem of missing attributes in text in constructing attribute sentiment scores---as reviewers write only about a subset of attributes and remain silent on others. We develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, we show superior accuracy in converting text to numerical attribute sentiment scores with our model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings. The second essay, which is joint work with Aniko Oery and Joyee Deb is an information-theoretic model to study what causes selection in valence in user-generated reviews. The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the strength of brand image (dispersion of consumer beliefs about quality) and the informativeness of good and bad experiences impacts selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. If the brand image is strong (consumer beliefs are homogeneous), only negative WOM can arise. With a weak brand image or heterogeneous beliefs, positive WOM can occur if positive experiences are sufficiently informative. Using data from Yelp.com, we show how strong brands (chain restaurants) systematically receive lower evaluations controlling for several restaurant and reviewer characteristics. The third essay which is joint work with K.Sudhir and Khai Chiong studies success factors of persuasive sales pitches from a multi-modal video dataset of buyer-seller interactions. A successful sales pitch is an outcome of both the content of the message as well as style of delivery. Moreover, unlike one-way interactions like speeches, sales pitches are a two-way process and hence interactivity as well as matching the wavelength of the buyer are also critical to the success of the pitch. We extract four groups of features: content-related, style-related, interactivity and similarity in order to build a predictive model of sales pitch effectiveness

Identification of internalin-A-like virulent proteins in Leishmania donovani

Abstract Background An active immune surveillance and a range of barriers to infection allow the host to effectively eliminate microbial pathogens. However, pathogens may use diverse strategies to subdue such host defences. For instance, one such mechanism is the use of leucine-rich repeat (LRR) proteins by pathogens (microbial) to cause infection. In this study, we aimed at identifying novel virulence factor(s) in Leishmania donovani, based on the possibility of lateral gene transfers of bacterial virulence factor(s) to L. donovani. Methods Rigorous homology searching protocols including Hidden Markov Model (HMM) and BLASTp based searches were employed to detect remote but significant similarities between L. donovani proteins and bacterial virulence factors. Results We found that some L. donovani proteins are similar to internalin-A (Inl-A) protein of Listeria monocytogenes, a surface LRR protein that helps mediate host cell invasion by interacting with E-cadherin on the cell membrane. However, to date, no such invasion mechanism has been reported in Leishmania donovani, the causative agent of visceral leishmaniasis. Moreover, a comparative LRR motif analysis of L. donovani Inl-A-like proteins against the Inl-A protein of L. monocytogenes revealed existence of characteristic consensus LRR regions, suggesting a reliable evolutionary relationship between them. Further, through rigorous three dimensional (3D) modeling of L. donovani Inl-A-like proteins and subsequent molecular docking studies we suggest the probability of human E-cadherin binding with the L. donovani Inl-A-like proteins. Conclusions We have identified three potential candidates (UniProt ID: E9B7L9, E9BMT7 and E9BUL5) of Inl-A-like LRR containing proteins in L. donovani with the help of systematic whole genome sequence analysis. Thus, herein we propose the existence of a novel class of Inl-A-like virulence factor proteins in L. donovani and other Leishmania species based on sequence similarity, phylogenetic analysis and molecular modelling studies in L. donovani

Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings

Attribute Sentiment Scoring with Online Text Reviews: Accounting for Language Structure and Missing Attributes

The authors address two significant challenges in using online text reviews to obtain fine-grained attribute level sentiment ratings. First, they develop a deep learning convolutional-LSTM hybrid model to account for language structure, in contrast to methods that rely on word frequency. The convolutional layer accounts for the spatial structure (adjacent word groups or phrases) and LSTM accounts for the sequential structure of language (sentiment distributed and modified across non-adjacent phrases). Second, they address the problem of missing attributes in text in construct-ing attribute sentiment scores—as reviewers write only about a subset of attributes and remain silent on others. They develop a model-based imputation strategy using a structural model of heterogeneous rating behavior. Using Yelp restaurant review data, they show superior accuracy in converting text to numerical attribute sentiment scores with their model. The structural model finds three reviewer segments with different motivations: status seeking, altruism/want voice, and need to vent/praise. Interestingly, our results show that reviewers write to inform and vent/praise, but not based on attribute importance. Our heterogeneous model-based imputation performs better than other common imputations; and importantly leads to managerially significant corrections in restaurant attribute ratings

Attribute Sentiment Scoring With Online Text Reviews : Accounting for Language Structure and Attribute Self-Selection

The authors address two novel and significant challenges in using online text reviews to obtain attribute level ratings. First, they introduce the problem of inferring attribute level sentiment from text data to the marketing literature and develop a deep learning model to address it. While extant bag of words based topic models are fairly good at attribute discovery based on frequency of word or phrase occurrences, associating sentiments to attributes requires exploiting the spatial and sequential structure of language. Second, they illustrate how to correct for attribute self-selection—reviewers choose the subset of attributes to write about—in metrics of attribute level restaurant performance. Using Yelp.com reviews for empirical illustration, they find that a hybrid deep learning (CNN-LSTM) model, where CNN and LSTM exploit the spatial and sequential structure of language respectively provide the best performance in accuracy, training speed and training data size requirements. The model does particularly well on the “hard” sentiment classification problems. Further, accounting for attribute self-selection significantly impacts sentiment scores, especially on attributes that are frequently missing

When Do Consumers Talk?

The propensity of consumers to engage in word-of-mouth (WOM) differs after good versus bad experiences, which can result in positive or negative selection of user-generated reviews. We show how the dispersion of consumer beliefs about quality (brand strength), informativeness of good and bad experiences, and price can affect selection of WOM in equilibrium. WOM is costly: Early adopters talk only if they can affect the receiver’s purchase. Under homogeneous beliefs, only negative WOM can arise. Under heterogeneous beliefs, the type of WOM depends on the informativeness of the experiences. We use data from Yelp.com to validate our predictions

IMACULAT - an open access package for the quantitative analysis of chromosome localization in the nucleus

The alteration in the location of the chromosomes within the nucleus upon action of internal or external stimuli has been implicated in altering genome function. The effect of stimuli at a whole genome level is studied by using two-dimensional fluorescence in situ hybridization (FISH) to delineate whole chromosome territories within a cell nucleus, followed by a quantitative analysis of the spatial distribution of the chromosome. However, to the best of our knowledge, open access software capable of quantifying spatial distribution of whole chromosomes within cell nucleus is not available. In the current work, we present a software package that computes localization of whole chromosomes - Image Analysis of Chromosomes for computing localization (IMACULAT). We partition the nucleus into concentric elliptical compartments of equal area and the variance in the quantity of any chromosome in these shells is used to determine its localization in the nucleus. The images are pre-processed to remove the smudges outside the cell boundary. Automation allows high throughput analysis for deriving statistics. Proliferating normal human dermal fibroblasts were subjected to standard a two-dimensional FISH to delineate territories for all human chromosomes. Approximately 100 images from each chromosome were analyzed using IMACULAT. The analysis corroborated that these chromosome territories have non-random gene density based organization within the interphase nuclei of human fibroblasts. The ImageMagick Perl API has been used for pre-processing the images

When do consumers talk?

The propensity of consumers to engage in word-of-mouth (WOM) can differ after good versus bad experiences, resulting in positive or negative selection of user-generated reviews. We study how the propensity to engage in WOM depends on information available to customers through different marketing channels. We develop a model of WOM in which a target customer makes a purchase decision based on his private brand association, public product-specific information (e.g. from advertising or past reviews) and WOM content, and an early adopter of the new product engages in WOM only if her information is instrumental to the target customer’s purchase decision. We define brand image to be the distribution of the customers’ brand associations, and strength of the brand image to be the precision of this distribution. We show that if the brand image is strong, then in equilibrium only negative WOM can arise. In contrast, with a weak brand image, positive WOM must occur. Moreover, holding product quality fixed, a positive advertising signal realization leads to a more positive WOM selection. We use restaurant review data from Yelp.com to motivate our model assumptions and validate the predictions. For example, a textual analysis of reviews is consistent with prevalence of an instrumental motive for WOM. Further, a review rating for national established chain restaurant locations, where the brand image is strong, is almost 1-star lower (on a 5-star scale) than a review rating for a comparable independent restaurant, controlling for reviewer and restaurant characteristics