Study of carbon nanotube film and shape memory alloys treatment for sensing and structural vibration control

Structural vibration control is of great importance. Current active and passive vibration control strategies usually employ individual elements to fulfill this task, such as viscoelastic patches for providing damping, transducers for picking up signals and actuators for inputting actuating forces. The goal of this dissertation work is to design, manufacture, investigate and apply a new type of multifunctional composite material for structural vibration control. This new composite, which is based on multi-walled carbon nanotube (MWCNT) film, is potentially to function as free layer damping treatment and strain sensor simultaneously. That is, the new material integrates the transducer and the damping patch into one element. The multifunctional composite was prepared by sandwiching the MWCNT film between two adhesive layers. Static sensing test indicated that the MWCNT film sensor resistance changes almost linearly with the applied load. Sensor sensitivity factors were comparable to those of the foil strain gauges. Dynamic test indicated that the MWCNT film sensor can outperform the foil strain gage in high frequency ranges. Temperature test indicated the MWCNT sensor had good temperature stability over the range of 237 K-363 K. The Young’s modulus and shear modulus of the MWCNT film composite were acquired by nanoindentation test and direct shear test, respectively. A free vibration damping test indicated that the MWCNT composite sensor can also provide good damping without adding excessive weight to the base structure. A new model for sandwich structural vibration control was then proposed. In this new configuration, a cantilever beam covered with MWCNT composite on top and one layer of shape memory alloy (SMA) on the bottom was used to illustrate this concept. The MWCNT composite simultaneously serves as free layer damping and strain sensor, and the SMA acts as actuator. Simple on-off controller was designed for controlling the temperature of the SMA so as to control the SMA recovery stress as input and the system stiffness. Both free and forced vibrations were analyzed. Simulation work showed that this new configuration for sandwich structural vibration control was successful especially for low frequency system.

Frameworks for Systemic and Structural Analysis of Financial Innovations in Infrastructure

Financial innovations have emerged globally to close the gap between the rising global demand for infrastructures and the availability of financing sources offered by traditional financing mechanisms such as fuel taxation, tax-exempt bonds, and federal and state funds. The key to sustainable innovative financing mechanisms is effective policymaking. This paper discusses the theoretical framework of a research study whose objective is to structurally and systemically assess financial innovations in global infrastructures. The research aims to create analysis frameworks, taxonomies and constructs, and simulation models pertaining to the dynamics of the innovation process to be used in policy analysis. Structural assessment of innovative financing focuses on the typologies and loci of innovations and evaluates the performance of different types of innovative financing mechanisms. Systemic analysis of innovative financing explores the determinants of the innovation process using the System of Innovation approach. The final deliverables of the research include propositions pertaining to the constituents of System of Innovation for infrastructure finance which include the players, institutions, activities, and networks. These static constructs are used to develop a hybrid Agent-Based/System Dynamics simulation model to derive propositions regarding the emergent dynamics of the system. The initial outcomes of the research study are presented in this paper and include: (a) an archetype for mapping innovative financing mechanisms, (b) a System of Systems-based analysis framework to identify the dimensions of Systems of Innovation analyses, and (c) initial observations regarding the players, institutions, activities, and networks of the System of Innovation in the context of the U.S. transportation infrastructure financing.

Solving satisfiability, *implication, and equivalence problems in database systems

Satisfiability, implication and equivalence problems are important and widely-encountered database problems that need to be efficiently and effectively solved. We provide a comprehensive and systematic study of these problems. We consider three popular types of arithmetic inequalities, (X op C), (X op Y), and (X op Y + C), where X and Y are attributes, C is a constant of the domain of X, and op $\in\ \{{<},\ {\le},\ {=},\ {\not=},\ {>},\ {\ge}\}.$ These inequalities are most frequently used in a database system, since the first type of inequalities represents $\theta$-join, the second type represents selection, and the third type is popular in deductive databases. We study the problems under the integer domain and the real domain, as well as under two different operator sets.^ Our results show that solutions under different domains and/or different operator sets are quite different. In this dissertation, we either report the first necessary and sufficient conditions as well as their efficient algorithms with complexity analysis, or provide improved algorithms. ^

Mixed spatial and nonspatial problems in location based services

With hundreds of millions of users reporting locations and embracing mobile technologies, Location Based Services (LBSs) are raising new challenges. In this dissertation, we address three emerging problems in location services, where geolocation data plays a central role. First, to handle the unprecedented growth of generated geolocation data, existing location services rely on geospatial database systems. However, their inability to leverage combined geographical and textual information in analytical queries (e.g. spatial similarity joins) remains an open problem. To address this, we introduce SpsJoin, a framework for computing spatial set-similarity joins. SpsJoin handles combined similarity queries that involve textual and spatial constraints simultaneously. LBSs use this system to tackle different types of problems, such as deduplication, geolocation enhancement and record linkage. We define the spatial set-similarity join problem in a general case and propose an algorithm for its efficient computation. Our solution utilizes parallel computing with MapReduce to handle scalability issues in large geospatial databases. Second, applications that use geolocation data are seldom concerned with ensuring the privacy of participating users. To motivate participation and address privacy concerns, we propose iSafe, a privacy preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. iSafe combines geolocation data extracted from crime datasets and geosocial networks such as Yelp. In order to enhance iSafe's ability to compute safety recommendations, even when crime information is incomplete or sparse, we need to identify relationships between Yelp venues and crime indices at their locations. To achieve this, we use SpsJoin on two datasets (Yelp venues and geolocated businesses) to find venues that have not been reviewed and to further compute the crime indices of their locations. Our results show a statistically significant dependence between location crime indices and Yelp features. Third, review centered LBSs (e.g., Yelp) are increasingly becoming targets of malicious campaigns that aim to bias the public image of represented businesses. Although Yelp actively attempts to detect and filter fraudulent reviews, our experiments showed that Yelp is still vulnerable. Fraudulent LBS information also impacts the ability of iSafe to provide correct safety values. We take steps toward addressing this problem by proposing SpiDeR, an algorithm that takes advantage of the richness of information available in Yelp to detect abnormal review patterns. We propose a fake venue detection solution that applies SpsJoin on Yelp and U.S. housing datasets. We validate the proposed solutions using ground truth data extracted by our experiments and reviews filtered by Yelp.

Patterns of local R&D cooperation of foreign subsidiaries in an intermediate country: innovative and structural factors

We attempt to contribute to a better understanding of cooperative innovation patterns of foreign subsidiaries (FS) in Spain as a representative intermediate country, going deeply into three main aspects: firstly, a sectoral taxonomy which combines international technological dynamism and revealed technological advantage as a way to understand such patterns. Secondly we focus our attention on innovative intensive subsidiaries, assuming they are the most important ones for hosting countries. Thirdly, we combine innovation and structural-competitive variables to explain local cooperation. We found more intense cooperation of FS with local agents in dynamic specialization sectors, as well as the fact that this is mostly carried out in a complementary mode with inner knowledge capabilities of the companies. Cooperative activities are influenced by economicstructural factors of the Spanish economy, particularly in highly innovative companies. Cooperative strategies of domestic firms might also have an influence on those of foreign subsidiaries.

Sedimentary and structural controls on seismogenic slumping within mass transport deposits from the Dead Sea basin

GIA acknowledges funding from the Carnegie Trust to undertake fieldwork for this project. SM acknowledges the Israel Science Foundation (ISF grant no. 1436/14) and the Ministry of National Infrastructures, Energy and Water Resources (grant no. #214-17-027). RW was supported by the Israel Science Foundation (ISF grant no. 1245/11). We thank Hugo Ortner and Pedro Alfaro for careful and constructive reviews.

Simultaneous material and structural optimization by multiscale topology optimization

Acknowledgments The authors acknowledge the support from Engineering and Physical Sciences Research Council, grant number EP/M002322/1. The authors would also like to thank Numerical Analysis Group at the Rutherford Appleton Laboratory for their FORTRAN HSL packages (HSL, a collection of Fortran codes for large-scale scientific computation. See http://www.hsl.rl.ac.uk/).

Biochemical and Structural Studies on PrfA, the Transcriptional Regulator of Virulence in Listeria monocytogenes

Abstract

Listeria monocytogenes is a gram-positive soil saprophytic bacterium that is capable of causing fatal infection in humans. The main virulence regulator PrfA, a member of the Crp/FNR family of transcriptional regulators, activates the expression of essential proteins required for host cell invasion and cell-to-cell spread. The mechanism of PrfA activation and the identity of its small molecule coactivator have remained a mystery for more than 20 years, but it is hypothesized that PrfA shares mechanistic similarity to the E. coli cAMP binding protein, Crp. Crp activates gene expression by binding cAMP, increasing the DNA binding affinity of the protein and causing a significant DNA bend that facilitates RNA polymerase binding and downstream gene activation. Our data suggests PrfA activates virulence protein expression through a mechanism distinct from the canonical Crp activation mechanism that involves a combination of cysteine residue reduction and glutathione (GSH) binding.

Listeria lacking glutathione synthase (ΔgshF) is avirulent in mice; however virulence is rescued when the bacterium expresses the constitutively active PrfA mutant G145S. Interestingly, Listeria expressing a PrfA mutant in which its four cysteines are mutated to alanine (Quad PrfA), demonstrate a 30-fold decrease in virulence. The Quad and ΔgshF double mutant strains are avirulent. DNA-binding affinity, measured through fluorescence polarization assays, indicate reduction of the cysteine side chains is sufficient to allow PrfA to binds its physiological promoters Phly and PactA with low nanomolar affinity. Oxidized PrfA binds the promoters poorly.

Unexpectedly, Quad also binds promoter DNA with nanomolar affinity, suggesting that the cysteines play a role in transcription efficiency in addition to DNA binding. Both PrfA and Quad bind GSH at physiologically relevant and comparable affinities, however GSH did not affect DNA binding in either case. Thermal denaturation assays suggest that Quad and wild-type PrfA differ structurally upon binding GSH, which supports the in vivo difference in infection between the regulator and its mutant.

Structures of PrfA in complex with cognate DNA, determined through X-ray crystallography, further support the disparity between PrfA and Crp activation mechanisms as two structures of reduced PrfA bound to Phly (PrfA-Phly30 and PrfA-Phly24) suggest the DNA adopts a less bent DNA conformation when compared to Crp-cAMP- DNA. The structure of Quad-Phly30 confirms the DNA-binding data as the protein-DNA complex adopts the same overall conformation as PrfA-Phly.

From these results, we hypothesize a two-step activation mechanism wherein PrfA, oxidized upon cell entry and unable to bind DNA, is reduced upon its intracellular release and binds DNA, causing a slight bend in the promoter and small increase in transcription of PrfA-regulated genes. The structures of PrfA-Phly30 and PrfA-Phly24 likely visualize this intermediate complex. Increasing concentrations of GSH shift the protein to a (PrfA-GSH)-DNA complex which is fully active transcriptionally and is hypothesized to resemble closely the transcriptionally active structure of the cAMP-(Crp)-DNA complex. Thermal denaturation results suggest Quad PrfA is deficient in this second step, which explains the decrease in virulence and implicates the cysteine residues as critical for transcription efficiency. Further structural and biochemical studies are on-going to clarify this mechanism of activation.

Sequence and Structural Determinants of Specificity Differences between Paralogous Transcription Factors

Transcription factors (TFs) control the temporal and spatial expression of target genes by interacting with DNA in a sequence-specific manner. Recent advances in high throughput experiments that measure TF-DNA interactions in vitro and in vivo have facilitated the identification of DNA binding sites for thousands of TFs. However, it remains unclear how each individual TF achieves its specificity, especially in the case of paralogous TFs that recognize distinct target genomic sites despite sharing very similar DNA binding motifs. In my work, I used a combination of high throughput in vitro protein-DNA binding assays and machine-learning algorithms to characterize and model the binding specificity of 11 paralogous TFs from 4 distinct structural families. My work proves that even very closely related paralogous TFs, with indistinguishable DNA binding motifs, oftentimes exhibit differential binding specificity for their genomic target sites, especially for sites with moderate binding affinity. Importantly, the differences I identify in vitro and through computational modeling help explain, at least in part, the differential in vivo genomic targeting by paralogous TFs. Future work will focus on in vivo factors that might also be important for specificity differences between paralogous TFs, such as DNA methylation, interactions with protein cofactors, or the chromatin environment. In this larger context, my work emphasizes the importance of intrinsic DNA binding specificity in targeting of paralogous TFs to the genome.

Essays in Empirical Operations Management: Bayesian Learning of Service Quality and Structural Estimation of Complementary Product Pricing and Inventory Management

This dissertation contributes to the rapidly growing empirical research area in the field of operations management. It contains two essays, tackling two different sets of operations management questions which are motivated by and built on field data sets from two very different industries --- air cargo logistics and retailing.

The first essay, based on the data set obtained from a world leading third-party logistics company, develops a novel and general Bayesian hierarchical learning framework for estimating customers' spillover learning, that is, customers' learning about the quality of a service (or product) from their previous experiences with similar yet not identical services. We then apply our model to the data set to study how customers' experiences from shipping on a particular route affect their future decisions about shipping not only on that route, but also on other routes serviced by the same logistics company. We find that customers indeed borrow experiences from similar but different services to update their quality beliefs that determine future purchase decisions. Also, service quality beliefs have a significant impact on their future purchasing decisions. Moreover, customers are risk averse; they are averse to not only experience variability but also belief uncertainty (i.e., customer's uncertainty about their beliefs). Finally, belief uncertainty affects customers' utilities more compared to experience variability.

The second essay is based on a data set obtained from a large Chinese supermarket chain, which contains sales as well as both wholesale and retail prices of un-packaged perishable vegetables. Recognizing the special characteristics of this particularly product category, we develop a structural estimation model in a discrete-continuous choice model framework. Building on this framework, we then study an optimization model for joint pricing and inventory management strategies of multiple products, which aims at improving the company's profit from direct sales and at the same time reducing food waste and thus improving social welfare.

Collectively, the studies in this dissertation provide useful modeling ideas, decision tools, insights, and guidance for firms to utilize vast sales and operations data to devise more effective business strategies.

Seawater carbonate chemistry and structural integrity of the coralline algae Lithothamnion glaciale in a laboratory experiment

The uptake of anthropogenic emission of carbon dioxide is resulting in a lowering of the carbonate saturation state and a drop in ocean pH. Understanding how marine calcifying organisms such as coralline algae may acclimatize to ocean acidification is important to understand their survival over the coming century. We present the first long-term perturbation experiment on the cold-water coralline algae, which are important marine calcifiers in the benthic ecosystems particularly at the higher latitudes. Lithothamnion glaciale, after three months incubation, continued to calcify even in undersaturated conditions with a significant trend towards lower growth rates with increasing pCO2. However, the major changes in the ultra-structure occur by 589 µatm (i.e. in saturated waters). Finite element models of the algae grown at these heightened levels show an increase in the total strain energy of nearly an order of magnitude and an uneven distribution of the stress inside the skeleton when subjected to similar loads as algae grown at ambient levels. This weakening of the structure is likely to reduce the ability of the alga to resist boring by predators and wave energy with severe consequences to the benthic community structure in the immediate future (50 years).

Effects of introspection on meta and structural attitude bases

Introspection is the process by which individuals question their attitudes; either questioning why they hold their attitudes (Why introspection), or how they feel about a particular attitude object (How introspection). Previous research has suggested that Why-introspection induces attitude change, and that Why and How introspection influence attitude-behaviour consistency,persuasion, and other effects. Generally, psychologists have assumed that affective and cognitive attitude bases are the mechanism by which introspection leads to these effects. Leading perspectives originating from these findings suggest that either Why introspection changes the content of cognitive attitude bases (the skewness hypothesis), or increases the salience of cognitive attitude bases (the dominance hypothesis); whereas How introspection may increase the salience of affective attitude bases (another part of the dominance hypothesis). However, direct evidence for these mechanisms is lacking, and the distinction between structural and meta bases has not been considered. Two studies investigated this gap in the existing literature. Both studies measured undergraduate students’ attitudes and attitude bases (both structural and meta, affective and cognitive) before and after engaging in an introspection manipulation (Why introspection / How introspection / control), and after reading a (affective / cognitive) persuasive passage about the attitude object. No evidence was found supporting either the skewness or dominance hypotheses. Furthermore, previous introspection effects were not replicated in the present data. Possible reasons for these null findings are proposed, and several unexpected effects are examined.

Axis Mapping: The Estimation of Surface Orientations and its Applications in Vehicle Localization and Structural Geology

The map representation of an environment should be selected based on its intended application. For example, a geometrically accurate map describing the Euclidean space of an environment is not necessarily the best choice if only a small subset its features are required. One possible subset is the orientations of the flat surfaces in the environment, represented by a special parameterization of normal vectors called axes. Devoid of positional information, the entries of an axis map form a non-injective relationship with the flat surfaces in the environment, which results in physically distinct flat surfaces being represented by a single axis. This drastically reduces the complexity of the map, but retains important information about the environment that can be used in meaningful applications in both two and three dimensions. This thesis presents axis mapping, which is an algorithm that accurately and automatically estimates an axis map of an environment based on sensor measurements collected by a mobile platform. Furthermore, two major applications of axis maps are developed and implemented. First, the LiDAR compass is a heading estimation algorithm that compares measurements of axes with an axis map of the environment. Pairing the LiDAR compass with simple translation measurements forms the basis for an accurate two-dimensional localization algorithm. It is shown that this algorithm eliminates the growth of heading error in both indoor and outdoor environments, resulting in accurate localization over long distances. Second, in the context of geotechnical engineering, a three-dimensional axis map is called a stereonet, which is used as a tool to examine the strength and stability of a rock face. Axis mapping provides a novel approach to create accurate stereonets safely, rapidly, and inexpensively compared to established methods. The non-injective property of axis maps is leveraged to probabilistically describe the relationships between non-sequential measurements of the rock face. The automatic estimation of stereonets was tested in three separate outdoor environments. It is shown that axis mapping can accurately estimate stereonets while improving safety, requiring significantly less time and effort, and lowering costs compared to traditional and current state-of-the-art approaches.