Synthesis and Characterization of Segmented Fluorescent Conjugated Polymers via Acyclic Diene Metathesis (ADMET)

This doctoral thesis is focused on the novel and facile synthesis and characterization of segmented conjugated polymers featuring various electro-optically active segments, with or without heteroatom linkages. The polymers were synthesized, via acyclic diene metathesis (ADMET) using ruthenium-based Grubbs-type catalysts. All products are soluble, and have a well-defined all-trans microstructure without defects. Some of the polymers were also synthesized via Suzuki polycondensation for comparison purposes. All monomers utilized were designed and synthesized in the laboratory. Segmented conjugated polymers have received a great deal of attention in organic electronics, such as organic light emitting-diodes, organic field-effect transistors and organic solar cells due to that fact they are more flexible, lightweight and processable than their inorganic counterparts. ADMET allowed us access to luminescent conjugated polymers exhibiting different emission characteristics by systematically varying electro-optically active segments in the polymer backbone. The effects of alternating segments (incl. donor-acceptor systems), directly linked and bridged by heteroatoms or vinylene groups, were studied with regard to opto-electronic properties of the polymers. Characterizations included UV-vis, fluorescence spectroscopy, and cyclic voltammetry. Si was found to effectively disrupt the π-conjugation resulting in a well-defined blue emission. The HOMO-LUMO energy levels could be tuned by careful selection of aromatic segments in the polymer backbone. E.g. systems with alternating functionalized phenylene vinylene and benzothiadiazole segments exhibited strong electronic interactions between segments, resulting in broad absorptions and lower HOMO-LUMO band gaps, which are important for higher power conversion efficiencies in solar cells. The experimental results obtained were consistent with calculated data obtained from density functional theory (DFT) calculation

A Provable Defense for Deep Residual Networks

We present a training system, which can provably defend significantly larger neural networks than previously possible, including ResNet-34 and DenseNet-100. Our approach is based on differentiable abstract interpretation and introduces two novel concepts: (i) abstract layers for fine-tuning the precision and scalability of the abstraction, (ii) a flexible domain specific language (DSL) for describing training objectives that combine abstract and concrete losses with arbitrary specifications. Our training method is implemented in the DiffAI system

Reduce the Rooming Time

The aim of the project Reducing the Rooming Time by the Medical Assistant is to reduce the rooming time from thirteen minutes to ten minutes. In the Department of Adult and Family Medicine, there are about eighty physicians who are supported by eighty medical assistants. The appointment length is twenty minutes. The rooming procedures take about thirteen minutes to complete which leaves only seven minutes for physicians to see their patients. This has led to patients, physicians, and staff unsatisfied. A team was created which include physicians, medical assistants, and receptionists. Data was obtained by the system’s analyst that looked at the time spent by each MA in the patient’s chart to document the rooming procedure. The team utilized performance improvement tools such as SWOT analysis, fishbone diagram, and a process map to identify areas of opportunities. The test of change was identified by the team that will aid in reducing the rooming time. The first test of change was delayed due to data issues and staff adherence. Few changes were made to test the change again. Rooming project has the potential to improve quality of care and patient satisfaction. It also has the potential to improve staff and physicians morale

EEG Source Localization: A Machine Learning Approach

Inimaju aktiivsuse salvestamise jaoks on olemas mitmeid meetodeid. Üks nendest on EEG, mis suudab ajusignaali mõõta peaaegu samal hetkel, kui see signaal ajus tekib.Samas selle ruumiline täpsus on väga madal. Konkureeriv tehnoloogia on fMRI, mille ruumiline täpsus on hea, kuid ajaline täpsus madal. Mõõtes ajusignaale kasutades mõlemat tehnoloogiat korraga saab kätte signaali, mis on rikas ja täpne aju aktiivsuse kirjeldus nii ruumis kui ka ajas. Signaali allika järeldamist EEG andmetest nimetatakse allika lokaliseerimise probleemiks. Antud uuringus me demonstreerime uut lokaliseerimise meetodit, mis kasutab masinõpet. Uue meetodi suutlikkuse hindamiseks kasutame andmestikku, kus EEG ja fMRI signaalid olid salvestatud samaaegselt. Samuti võrdleme antud töös väljatöötatud meetodit teiste allika lokaliseerimise meetoditega.There are different techniques for recording human brain activity. One of them EEG can capture brain activity in the time frame at which the activity occurs, but has a poor spatial resolution. Another technology fMRI, captures brain activity with high spatial resolution compared to EEG, but with poor temporal resolution. Simultaneously recording brain activity using these two techniques helps us capture a richer, spatio-temporally more precise description of human brain activity. Inferring the source location within the brain from an EEG signal is defined as EEG source localization problem. In this thesis, a new method that is based on machine learning for solving EEG source localization problem isproposed and its performance is evaluated on a simultaneously recorded EEG and fMRIdata set. This method’s performance is also compared to a commonly used method

RNA Viral Prophylaxis: Problems and Potential Solutions

Over 80% of the newly emerging infectious diseases are caused by RNA viruses. Major global problems associated with the development of vaccines against the RNA virus are their high genetic and antigenic diversity. Hence, effective control of epidemics with newly emerging RNA viruses require improved vaccines which are either specific to the new strain or broadly effective even when new viral strains emerge. The main focus of this dissertation is to develop epidemic vaccines using these two approaches. Using a newly emerged swine enteric virus called porcine epidemic diarrhea virus (PEDV) as a model, our first goal was to develop a quick and easy method for rapid response vaccines with potential applicability to a range of RNA viruses. We hypothesized that the methods which can disrupt genomic RNA without impacting the structural integrity of the virus would result in attenuated vaccine with minimum replication in the host while inducing immune responses. As hypothesized, developed rapid response PEDV vaccine induced complete protection against the virulent challenge virus, while vaccine viral shedding was not detected in vaccinated pigs. To address the second problem of rapid viral evolution leading to vaccines becoming obsolete, we used swine influenza virus (SIV) as a model to develop and test a universal vaccine composed of peptides encoding conserved antigenic epitopes which are present in most influenza A viruses. Importantly, a novel amphiphilic invertible polymer (AIP) was used to address the well-recognized problem of poor antigenicity of peptides. We hypothesized that peptides encoding conserved epitopes when conjugated with an AIP will induce strong immune responses and protect against challenge virus. While the conserved epitopes were previously tested by others in mice, we were the first to test a combination of these epitopes in pigs. Pigs vaccinated with the peptide polymer vaccine mounted strong antibody responses against the epitopes indicating that the delivery system was effective. However, protection against replication of the challenge virus was delayed. In summary, the methods developed and tested in this body of work significantly contribute to the area of emergency response management in infectious disease outbreaks.United States Department of Agriculture, National Institute of Food and Agriculture (USDA-NIFA)North Dakota State Agricultural Products Utilization Committee (ND APUC)North Dakota State Board of Agricultural Research (ND SABRE

A subclass of strongly close-to-convex functions associated with Janowski function

The aim of this paper is to introduce a new subclass of strongly close-to-convex functions by subordinating to Janowski function. Certain properties such as coefficient estimates, distortion theorem, argument theorem, inclusion relations and radius of convexity are established for this class. The results obtained here will generalize various earlier known results

Competition in the IndianAutomobile Industry

ABSTRACT “Following India's growing openness, the arrival of new and existing models, easy availability of finance at relatively low rate of interest and price discounts offered by the dealers and manufacturers all have stirred the demand for vehicles and a strong growth of the Indian automobile industry”. The main focus of my dissertation will be the Indian automobile industry due to its rich diversity and ever-changing patterns. The Research Question which I would follow would be the growing competitiveness of the Indian Automobile Industry. The competition levels in the Indian automobile Industry have been growing and every manufacturer has been coming out new products in a quick span of time. Everyone wants to launch their new model in the specific range first in order to gain monopoly in that group. The Major players in the automobile industry have been focusing a lot in the R&D department in the process of coming out with new products meeting the consumers preferences. This study analyses the determinants of competitiveness in the Indian auto industry. It is based on a Porter five force model and a qualitative analysis of data. A complete industry analysis will be done in the dissertation of the Indian automobile industry using the five force model. The Qualitative Analysis will be done with the help of a questionnaire based on the competition in the Indian automobile industry. The automobile manufacturing sector of India involves assembling of automobile components, comprises two-wheelers, three-wheelers, four-wheelers, passenger cars, light commercial vehicles, heavy trucks and buses/coaches. In India, mopeds, scooters and motorcycles constitute the two-wheeler industry