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Task success in co-located and distributed collaborative work settings is characterized by clear and efficient communication between participating members. Communication issues like 1) Unwanted interruptions and 2) Delayed feedback in collaborative work based distributed scenarios have the potential to impede task coordination and significantly decrease the probability of accomplishing task objective. Research shows that 1) Interrupting tasks at random moments can cause users to take up to 30% longer to resume tasks, commit up to twice the errors, and experience up to twice the negative effect than when interrupted at boundaries 2) Skill retention in collaborative learning tasks improves with immediate feedback dissemination. To address the negative impact of these communication issues, this dissertation presents two multi-user, multi-tasking collaborative work scenarios and illustrates respective real-time fully functional computer supported cooperative work (CSCW) based prototypes. ACE-IMS leverages lexical affirmation cues which are indicative of task boundaries to intelligently identify “the right time to interrupt” and ReadMI assesses Motivational Interviewing (MI) based clinician-client dialogue in collaborative learning environment to identify speaker intents like open-ended questions, close-ended questions, reflective statements and scale enquiring statements and provide quantitative feedback to assist the facilitator in comprehensive practitioner skill assessment. To implement these functionalities both systems leverage task-oriented dialogues as datasets and utilize natural language processing with latest developments in ubiquitous technologies like mobile-cloud computing, computational linguistics, and deep learning. This research goes a step further in demonstrating the usability of CSCW based system designs by reporting qualitative and quantitative user feedback data by deploying ReadMI in an actual collaborative learning environment. The participants agree that ReadMI based metrics provide a tangible way to measure practitioner progress and offsets facilitator workload, showing a strong potential to enhance collaborative work experience.

Abstract: Cellulose nanofibers are fibers of high aspect ratios with exceptional barrier properties and are manufactured from renewable source. In this study, cellulose from various sources such as Kraft pulp and bleached chemical thermomechanical pulp (BCTMP) with different dispersion methods was used to make cellulose nanofibers using 2,2,6,6,Tetramethylpiperidine-1-oxyl (TEMPO) mediated selective oxidation method. The oxidation levels of the fibers from different cellulose sources were correlated with the amount of primary oxidizer used. The self-assembly of the nanofibers due to freeze-drying was analysed and the thermal degradation properties of these freeze-dried fibers were studied. The nanofibers produced form the TEMPO oxidation was used as a template for the growth of calcium hydroxide particles. A novel heterogeneous chemical precipitation method was used to deposit calcium hydroxide on the surface of the nanofibers. This research was aimed at developing a carbon dioxide absorber in active food packaging applications, as calcium hydroxide could absorb the carbon dioxide to form calcium carbonate to prevent the excess carbon dioxide damage in packaged food produces, especially climacteric fruits like apple, bananas etc. The metal-fibre composite was studied under thermal degradation at high temperature before and after carbonation of the calcium hydroxide particles. The crystal formation was analysed using x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy. The flow properties of cellulose nanofibers at different dilutions, cellulose nanofiber blends and hydroxyethyl cellulose blends with metal-fiber composite at different concentrations were measured and the respective films made from the blends were studied using dynamic mechanical analysis.

The landscape of energy generation and utilization is witnessing an unprecedented change. We are at the threshold of a major shift in electricity generation from utilization of conventional sources of energy like coal to sustainable and renewable sources of energy like solar and wind. On the other hand, electricity consumption, especially in the field of transportation, due to advancements in the field of battery research and exponential technologies like vehicle telematics, is seeing a shift from carbon based to Lithium based fuel. Encouraged by 1. Decrease in the cost of Li – ion based batteries 2. Breakthroughs in battery chemistry research - resulting in increased drive range 3. Government incentives and tariff concessions by utilities for EV owners in the form of tax credits, EV – only parking spaces, free charging equipment etc., the automobile market, especially the passenger vehicle market, is witnessing a steady growth in the sale of electric vehicles. This has resulted in Electric Vehicles contributing to the electricity load resulting in two challenges 1. At the supply end, it contributes as a potential micro energy storage system to fit the time gap between the demand for electricity and the supply of renewable and/or low cost electricity generation; and, 2. At the consumer-end, it creates a necessity to make energy consumption as sustainable and renewable as possible, while preserving battery life.In this thesis work we attempt to provide multiple practical solutions to address these needs by advancing existing technologies in the industry. Firstly, we have developed a “Joint EV-Grid Solution for Robust and Low-Complexity Smart Charing”, where we have designed and implemented a distributed smart charging algorithm, which runs in the EV with load and pricing information collected from Grid through the charging station. It is responsible for optimizing the charge plan of the user’s vehicle based on his/her preference and ensure a full charge before departure. The objective could be minimizing the electricity cost per charge session or maximizing the renewable energy usage. For instance, by setting the preference to optimize the algorithm according to “Price”, the additional demand is scheduled to off-peak hours (i.e., incurring the least cost). Alternatively, by setting the preference to “Renewables” the EV charges based on the maximum availability of renewable energy sources, thereby maximizing the utilization of renewable energy resources which may lead to reduced cost, if not minimize it.Furthermore, we have improved on our initial approach by introducing “Smart Charging Solution through Usage/Charging Pattern Learning” where we have used machine learning algorithms like Logistic regression and Fuzzy Logic to enable EVs to learn the usage and charging pattern of users and prepare a charging plan that is personalized at the users’ end and prevents potential smart-changing-caused demand peaks by distributing the net load throughout the day.Through our experiment studies we were successful in creating a distributed Charging algorithm and a Machine Learning system that could cater to the said requirements through innovative charging strategies. Consequently, helping us create a sustainable, win – win situation for both electricity consumer and producer.

Polysialic acid (PSA) is a developmentally regulated glycan made of repeating sialic acid monomers with α2-8 linkages. PSA has very limited expression in adults, and modifies only a few cell-surface proteins. However, PSA is overexpressed in several human cancers and is associated with metastasis and poor prognosis. We have described a derivative of PSA containing a mixture of de-N-acetyl and N-acetyl neuraminic acid residues (dPSA) found intracellularly in many normal human tissues but expressed at much higher levels on the cell surface of many human cancer cell lines. The proteins modified with dPSA and dPSA function in normal and abnormal human biology are unknown. The purpose of this study was to identify protein(s) modified with PSA and possible dPSA-dependent functions in cancer cell lines that express dPSA antigens. Using co-immunoprecipitation with the anti-dPSA monoclonal antibody SEAM 2 and mass spectroscopy, we identified membrane-associated nucleolin that is either directly modified or associated with dPSA. In addition, knocking down expression of the polysialyltransferase ST8SiaII (STX) in SK-MEL-28 human melanoma cells nearly eliminated dPSA and nucleolin from membranes but had no effect on the levels of nuclear nucleolin, and resulted in aberrant cell morphology, cell adhesion, and motility. The data suggest that cell-surface nucleolin depends on modification with dPSA, and that dPSA-modified nucleolin has an important role in cell adhesion and migration.

In this thesis we examine three different models in the MSSM context, all of which have significant supergravity anomaly contributions to their soft masses. These models are the so-called Minimal, Hypercharged, and Gaugino Anomaly Mediated Supersymmetry Breaking models. We explore some of the string theoretical motivations for these models and proceed by understanding how they would appear at the Large Hadron Collider (LHC). Our major results include calculating the LHC reach for each model's parameter space and prescribing a method for distinguishing the models after the collection of 100 fb^(−1) at s^(1/2) = 14 TeV. AMSB models are notorious for predicting too low a dark matter relic density. To counter this argument we explore several proposed mechanisms for non-thermal dark matter production that act to augment abundances from the usual thermal calculations. Interestingly, we find that future direct detection dark matter experiments potentially have a much better reach than the LHC for these models.

Autosomal Dominant Polycystic Kidney Disease a monogenic inherited human disease causing renal epithelial cells to proliferate forming fluid-filled cysts resulting in renal failure. Currently, no treatment exists to prevent or slower cyst formation, and most ADPKD patients require renal transplantation or life-long hemodialysis for survival. Mutations in either PKD1 or PKD2, are the underlying cause of ADPKD, with PKD1 mutations accounting for over 85% of the cases. Polycystin-1 (PC1) is an integral membrane glycoprotein containing 4302 amino acids with putative extracytoplasmic ligand-binding domains, but no physiological ligand has been identified to date. It has 11 transmembrane domains with an extracytoplasmic region of ~3000 amino acids and a cytoplasmic tail of ~200 amino acids. Many signaling pathways has been implicated to play a causative role for ADPKD but none show a clear mechanism. The study of STAT6, P100 and the PC1 tail using reporter assays, binding experiments, and immunohistochemistry suggested a model where the PC1 tail is cleaved due to loss of fluid flow, which triggers the nuclear translocation the constitutive trimeric complex comprising of the cleaved PC1 tail, STAT6 and P100 in renal epithelial cells. Introduction of C-terminus and the complete PC1 tail mRNA in zebra fish embryos resulted in cyst formation in their pronephric duct. Increased STAT6, P100 and PC1 tail staining in ADPKD kidney tissue sections also proved the importance of this pathway in stimulating cystogenesis. The membrane anchored PC1 tail was sufficient to activate constitutive pY-STAT3 and whereas the cleaved PC1 tail stimulated IFN-gamma stimulated reporter. This brings out a model where the PC1 can act as a molecular switch whereby it can regulate different STAT pathways depending on cellular conditions. Constitutive activation of STAT3 by normal PC1 may suggest the active STAT pathway under normal condition. Cleavage of the PC1 tail due to renal injury changes the function of the tail as a transcriptional co-activator which may now either activate STAT6 which would lead to proliferation or STAT1 or STAT3 leading to apoptosis. Inhibitors of the pathways that are activated in ADPKD can prove to be potential therapies for the disease.

Aggressive technology scaling, rising on-chip integration, and the continued increase in microprocessor power and thermal density threaten both the hard and soft error reliability of future microprocessor designs. Therefore, designing low overhead mechanisms for improving reliability will be a critical requirement at future technologies. Technology constraints of wire-delay and power consumption, and limits on deep pipelining, have impelled a shift to distributed architectures that rely on modularity in design, and on-chip interconnection networks for communication, and place a greater burden on software for exploiting concurrency from the application to achieve high performance on the distributed substrate [1]. The focus of this dissertation is on architectural techniques for improving the hard and soft error reliability of future technology-scalable distributed architectures. We make the key observation that these underlying principles of distributed architectures have important synergies that can be exploited to improve the hard and soft error reliability of microprocessors at low overhead. Using a detailed end-to-end model for chip yield, we demonstrate that with just redundant rows and columns in memory arrays and caches the yield of chip multiprocessors drops substantially from 85% at 250nm to 60% at 50nm. We exploit the three principles of modern and future distributed architectures: the abundant microarchitectural redundancy provided by modular design, the natural redundancy in communication paths provided by multi-hop, routed, on-chip networks, and the availability of greater software assistance; for efficiently managing the redundancy to improve yield at low performance overhead. Using just modular redundancy at the intra- and inter-processor granularity, we improve the yield of chip multiprocessors to 99.6% at 50nm, with a maximum reduction in performance in any chip of less than 20%. Further, we extend this technique to take advantage of the block-atomic, and static-placement-dynamic-issue execution model in the TRIPS architecture to efficiently manage the redundancy provided by modular design and on-chip networks. Our evaluation of this compiler-assisted yield enhancement technique in the TRIPS architecture shows significant yield improvement with less than 4% impact on performance. This dissertation also quantitatively demonstrates through detailed modeling that the raw soft error rate, especially that of combinational logic, will increase substantially at future technologies. This emphasizes the need for innovative solutions that extend soft error protection to latches, and combinational logic, while appropriately balancing the power consumption, area, and complexity overhead. We propose a new class of better-than-worst-case soft error reliability techniques called AVF throttling, that trade concurrency for reducing the amount of processor state vulnerable to soft errors. Since future architectures must increasingly rely on exploiting concurrency for achieving high performance, they aggressively bring future program state into the processor and mine them for available parallelism, thus increasing the amount of vulnerable state. AVF throttling is based on the key observation that while exploiting concurrency on the critical path can significantly improve performance, the majority of the program has abundant slack and can be deferred to substantially reduce the amount of vulnerable state with negligible effect on the execution time. Our evaluation in the TRIPS architecture shows that around 90% of the vulnerable state is due to slack. We design a hybrid AVF throttling technique that uses the compiler to estimate slack and the hardware to dynamically exploit it. Using the compiler for static slack estimation considerably reduces the complexity of the technique. Further, it takes advantage of the TRIPS execution model and on-chip networks to exploit slack more efficiently, and significantly improves reliability by 25-42% for a set of SPEC and EEMBC benchmarks. We also present a detailed comparison of AVF throttling with prior approaches including redundant execution, and selective redundant execution. Based on the comparison, we argue that while AVF throttling may provide a smaller absolute reliability improvement, it significantly reduces the power consumption and complexity overhead, making the three techniques appropriate in systems with different reliability requirements. Overall, this dissertation establishes that distributed architectures provide a good foundation for building a reliable system from unreliable components, and our results set a good starting point for further innovative research in this area.

This simulation study was conducted to investigate the role of within-field variability in realizing economic and environmental benefits from precision farming. The objectives of the study were to (i) illustrate analytically the influence of within-field variability on the economic outcomes of a given sampling intensity and therefore, the choice of the most economical sampling scheme, (ii) develop a method to determine the minimum spatial variability (distribution of land within a field with different production capabilities) needed so the additional returns from precision farming would at least cover the costs of using the technology, (iii) illustrate the role of weather expectations in precision farming, (iv) test the hypothesis that precision farming holds the promise of environmental benefits, and (v) examine policy options to motivate farmers to adopt precision farming, if the new technology is found to reduce environmental degradation. The objectives were accomplished assuming that the farmers' main objective was profit maximization and that the technology was adopted by custom hiring the necessary services from the farm service sector. The study created four hypothetical com fields with different degrees of within-field variability on which nitrogen (N) was applied at variable rates based on soil sample tests. The results suggested, for each sampling intensity considered, that the more variability, the higher the returns above N costs with Variable Rate Technology (VRT) than with Uniform Rate Technology (URT). Further, it was indicated by the results that higher sampling intensity was economically optimal for the fields with higher variability, over a range of sampling costs considered. Precision farming need not necessarily imply grid sampling. The technology could be used to apply inputs at spatially variable rates on different land types (classified, for example, according to soil series, slopes, landscape positions, etc.) with their oAvn yield responses to applied inputs. Under such circumstances, economic feasibility of adopting VRT depends upon the existing land mix on the field. Given input and product prices, custom charges, and knowledge of yield response to applied inputs on two or more land types, the study developed a method to identify the required land proportions so the additional returns from VRT could at least cover custom charges. These proportions were referred to as spatial break-even variability proportions. It is not just economic benefits that are claimed of precision farming. The new technology is also expected to benefit the environment by matching input application to plant and soil needs. The study investigated the potential of precision farming to reduce N loading into the environment. The Environmental Policy Integrated Climate (EPIC) crop growth model was used to estimate com yield responses to applied N and predict total N losses on different soils under different rainfall scenarios. The results indicated potential of the new technology to help reduce environmental degradation. The analysis suggested increasing importance of well-informed and accurate weather expectations under precision farming. In the majority of cases examined, farmers' decisions to adopt VRT meant economic losses when their rainfall expectations went wrong. Given the evidence of environmental benefits from being precise in input application, the study analyzed policy options to motivate farmers to adopt VRT. Subsidizing custom charges and restricting N use were the two options analyzed and found to help reduce N loss. The results showed totally different effects on production and farm incomes of input use restriction with and without VRT. With farmers having access to VRT, the fall in returns due to N restriction was much less than the fall that would have occurred with the same N use restriction without precision technology. Interestingly, when N use was restricted and farmers were forced to adopt VRT, production actually increased compared to the amount produced with URT under conditions of unconstrained N supply. To sum up the findings of this study, the economic benefits from grid sampling depend upon the extent of variability; highly intensive sampling is beneficial for the fields with high variability. Farmers often have a broad idea of variability across the field based on characteristics like soil series, slope, soil depth and yield variability shown by yield monitors. Planned sampling needs to be guided by such prior experience. The land mix on the field impacts the economic outcome of VRT. The method developed here helps find the minimum spatial variability needed on fields with two or more land types so the farmer can at least offset the custom charges with VRT adoption. The method is flexible and incorporates changing input and product prices as well as custom charges. VRT holds environmental promise. However, a farmer's motive to adopt the technology is purely economic. As such, efforts are needed to make the technology attractive to farmer. Where the technology proves beneficial for the environment, government can subsidize custom charges to promote VRT adoption. Restricting input use could also promote technology adoption without much adverse effect on income and production. Farmers need to be more informed in formulating weather expectations under precision farming; the adverse effects on their economic interests due to wrong expectations can be more severe with VRT than with URT.

Stress-intensity factors, K, are obtained at the tips of radial cracks emanating from the boundary of an internal circular fastener hole in a plate loaded by a pin or rivet that just fills the hole and is pulled in the negative y-direction, in the plane of the plate. Solutions are obtained in a conformally mapped region in the form of series for different crack lengths and are based on the Muskhelishvili formulation in the two-dimensional theory of elasticity. The method presents a general technique for determining K for k symmetrically located radial cracks of equal length for a wide variety of boundary tractions. Tractions on the crack edges, both concentrated and distributed, fall within the scope of this method.

This thesis document consists of five chapters (originally three papers) examining various issues on firms' financial reporting decisions. In the second chapter (with original paper title "The impacts of product market competition on the quantity and quality of voluntary disclosure decisions", forthcoming at Review of Accounting Studies), I investigate the determinants of firms' financial disclosures. In specific, I examine how firms' voluntary disclosure decisions are influenced by the product market competition. Using separate measures to capture different dimensions of competition, I show that competition from potential entrants increases disclosure quantity while competition from existing rivals decreases disclosure quantity. I also find that competition enhances disclosure quality mainly through reducing the optimism in profit forecasts and reducing the pessimism in investment forecasts. In the third chapter (with original paper title "Accounting conservatism and the cost of capital: international analysis"), I investigate the capital market consequences of financial reporting. In specific, I examine the contracting benefits of accounting conservatism on international debt and equity markets. I show that firms domiciled in countries with more conservative financial reporting systems have significantly lower cost of debt and equity capital, after controlling for differences in legal institutions and securities regulations. In the fourth chapter (with original paper title "Corporate governance and restrictions in debt contracts", co-authored with Irem Tuna and Florin Vasvari both at London Business School), we investigate the interactions between corporate governance and lender governance, which have been traditionally regarded as important determinants of firms' financial reporting quality. Using a large sample of public bond and syndicated loan contracts and exploratory Principal Component Analysis to extract indicators for the quality of board and shareholder governance, we document evidence consistent with the substitution effect between corporate governance and lender governance.

Mandatory financial reporting is subject to generally accepted accounting principles while voluntary disclosure such as management forecasts could be used by managers to reveal their inside information to market participants. This dissertation examines the interaction between mandatory reporting and voluntary disclosure and their relevance to equity market and credit market. Chapter 1 provides the outline of the dissertation and discusses the major contributions. Chapter 2 reviews previous literature and develops the hypotheses. Management forecasts about future earnings are sometimes issued along with current quarter earnings announcements, and these bundled management forecasts have recently become more prevalent. Using a composite measure of ex-ante management forecast accuracy that takes into account forecast ability, forecast difficulty and forecast environment, Chapter 3 shows that the bundled management forecasts can mitigate investors' under-reaction to current earnings and reduce the magnitude of post-earnings announcement drift only when these forecasts have high ex-ante accuracy. Firms have the incentive to cater for capital markets' demand in their financial reporting. Prior research has provided evidence that conservatism can lower the debt cost and solve the interest conflict between bondholders and shareholders. If firms anticipate market?s demand for conservatism before they issue public bonds, they will report more conservatively before issuing bonds for the first time. Using alternative measures of accounting conservatism, Chapter 4 shows that firms do report more conservatively before bond IPO. This result highlights the incremental importance of the debt market over the equity market in inducing conservative reporting and supports the argument that conservatism is more closely related with the debt market. Besides the equity market, management forecasts are also value relevant to the credit market. Chapter 5 provides the evidence that credit default swap (CDS) spreads react significantly and negatively to management forecast news, and that these reactions are stronger than those to actual earnings news. The credit market reactions to bad management forecast news and forecasts issued by credit risky companies are larger, reflecting the asymmetric payoff of debt securities. The impact of management forecasts on CDS spreads, relative to earnings announcements, also becomes stronger during the recent credit crisis when the market uncertainty is greater.