Design of Collection Systems and Semi-Autonomous Controls for a Tick Collection Robot

The overarching goal of this research was to simultaneously design, manufacture, and test different mechanisms for robotic tick collection, along with designing and developing an electronics package and code that would allow the robot to behave in a semi-autonomous fashion. The tick collection mechanisms were designed with the intention of providing an optimal efficiency for physically collecting ticks, minimizing their impact on the overall maneuverability of the robot, being as cost effective as possible, and being as easy to replicate as possible. A total of seven different prototypes were developed and tested under real world conditions. Of these seven, four demonstrated very promising results regarding the functional requirements of the project. The electronics package and accompanying code are being developed with the intention of allowing the robot to autonomously navigate environments which would make manual control difficult, such as dense shrubbery that would impair a direct line of sight.https://digitalworks.union.edu/srps_2020/1010/thumbnail.jp

“We Believe in Diversity”: Brian Flores vs. The NFL and Structural Racism, Shortcomings of the Rooney Rule

Master\u27s thesis completed at Trinity College Hartford CT for the degree of Master of Arts in American Studies

GENOME-WIDE ASSOCIATION STUDIES AT THE INTERFACE OF ALZHEIMER’S DISEASE AND EPIDEMIOLOGICALLY RELATED DISORDERS

Genome-wide association studies (GWAS)s provide an unbiased means of exploring the landscape of complex genetic disease. As such, these studies have identified genetic variants that are robustly associated with a multitude of conditions. I hypothesize that these genetic variants serve as excellent tools for evaluation of the genetic interface between epidemiologically related conditions. Herein, I test the association between SNPs associated with either (i) plasma lipids, (ii) rheumatoid arthritis (RA) or (iii) diabetes mellitus (DM) and late-onset Alzheimer’s disease (AD) to identify shared genetic variants. Regarding the most significantly AD-associated variants, I have also attempted to elucidate their molecular function. Only cholesterol-associated SNPs, as a group, are significantly associated with AD. This association remains after excluding APOE SNPs and suggests that peripheral and or central cholesterol metabolism contribute to AD risk. The general lack of association between RA-associated SNPs and AD is also significant in that these data challenge the hypothesis that genetic variants that increase risk of RA confer protection against AD. Functional studies of variants exhibiting novel associations with AD reveal that the lipid-associated SNP rs3846662 modulates HMGCR exon 13 splicing differentially in different cell types. Although less clear, trends were also observed between the RA-associated rs2837960 and the expression of several BACE2 isoforms, and between the DM-associated rs7804356 and expression of a rare SKAP2 isoform, respectively. In conclusion, the overlap of lipid-, RA- or DM-associated SNPs with AD is modest but in several instances significant. Continued analysis of the interface between GWAS of separate conditions will likely facilitate novel associations missed by conventional GWAS. Furthermore, the identification of functional variants associated with multiple conditions should provide insight into novel mechanisms of disease and may lead to the identification of new therapeutic targets in an era of personalized genomic medicine

Two-dimensional numerical experiments of convection.

PhDI report the results of numerical studies of 2-dimensional fully compressible convection of a fluid in which the dependence of the radiative conductivity on temperature T is taken as K, (V + aT -5)1 so that central regions of the layer are unstable to convection whereas the surrounding layers are stable. Calculations have been undertaken for Rayleigh numbers at the centre of the unstable layer Rc,, = 4.78 106 -9-56 107 and Prandtl numbers a=0.05 - 1. The main result found is that in a (statistically) stationary state the viscous dissipation decreases with decreasing Prandtl number, and that the equilibrium of the whole layer is governed by a substantial penetrative region in which the convective flux is negative. The results found here suggest that the so called "Roxburgh criterion" can be used to give a good estimate of convective penetration at small Prandtl numbers. I also report the results of three sets of numerical experiments involving the interaction of magnetic fields with 2-dimensional fully compressible convection, were the fluid has the same conductivity. For these experiments R9subscript cen)= 4.78 106 and [sigma]=1.0, typically. For one set of experiments a simple model of the evolution of a toroidal flux tube is considered. The purpose of these numerical experiments was to test the role played by magnetic buoyancy in the rise of the magnetic flux tube. It was found that magnetic buoyancy was not important, except possibly when the initial field strength was large (Chandrasekhar number Q= 10'). In another set of numerical experiments the initial magnetic field was an uniform horizontal field. One of the results found was that the magnetic field did not significantly reduce convective penetration, even when the total (integrated) magnetic energy was of the same order as the total kinetic energy. The general behaviour found was that magnetic field was expelled from the convective region, until Q- 10'. Then the initial field strength was strong enough to suppress convection completely. No oscillatory solutions were found. Finally experiments were made for initially vertical magnetic fields. An oscillatory solution is presented. Also reported are preliminary calculations of 2-dimensional penetrative convection, for a model were the effects of energy generation and self gravity are included

AVOIDIT IRS: An Issue Resolution System To Resolve Cyber Attacks

Cyber attacks have greatly increased over the years and the attackers have progressively improved in devising attacks against specific targets. Cyber attacks are considered a malicious activity launched against networks to gain unauthorized access causing modification, destruction, or even deletion of data. This dissertation highlights the need to assist defenders with identifying and defending against cyber attacks. In this dissertation an attack issue resolution system is developed called AVOIDIT IRS (AIRS). AVOIDIT IRS is based on the attack taxonomy AVOIDIT (Attack Vector, Operational Impact, Defense, Information Impact, and Target). Attacks are collected by AIRS and classified into their respective category using AVOIDIT.Accordingly, an organizational cyber attack ontology was developed using feedback from security professionals to improve the communication and reusability amongst cyber security stakeholders. AIRS is developed as a semi-autonomous application that extracts unstructured external and internal attack data to classify attacks in sequential form. In doing so, we designed and implemented a frequent pattern and sequential classification algorithm associated with the five classifications in AVOIDIT. The issue resolution approach uses inference to educate the defender on the plausible cyber attacks. The AIRS can work in conjunction with an intrusion detection system (IDS) to provide a heuristic to cyber security breaches within an organization. AVOIDIT provides a framework for classifying appropriate attack information, which is fundamental in devising defense strategies against such cyber attacks. The AIRS is further used as a knowledge base in a game inspired defense architecture to promote game model selection upon attack identification. Future work will incorporate honeypot attack information to improve attack identification, classification, and defense propagation.In this dissertation, 1,025 common vulnerabilities and exposures (CVEs) and over 5,000 lines of log files instances were captured in the AIRS for analysis. Security experts were consulted to create rules to extract pertinent information and algorithms to correlate identified data for notification. The AIRS was developed using the Codeigniter [74] framework to provide a seamless visualization tool for data mining regarding potential cyber attacks relative to web applications. Testing of the AVOIDIT IRS revealed a recall of 88%, precision of 93%, and a 66% correlation metric

Validating Predictions of Unobserved Quantities

The ultimate purpose of most computational models is to make predictions, commonly in support of some decision-making process (e.g., for design or operation of some system). The quantities that need to be predicted (the quantities of interest or QoIs) are generally not experimentally observable before the prediction, since otherwise no prediction would be needed. Assessing the validity of such extrapolative predictions, which is critical to informed decision-making, is challenging. In classical approaches to validation, model outputs for observed quantities are compared to observations to determine if they are consistent. By itself, this consistency only ensures that the model can predict the observed quantities under the conditions of the observations. This limitation dramatically reduces the utility of the validation effort for decision making because it implies nothing about predictions of unobserved QoIs or for scenarios outside of the range of observations. However, there is no agreement in the scientific community today regarding best practices for validation of extrapolative predictions made using computational models. The purpose of this paper is to propose and explore a validation and predictive assessment process that supports extrapolative predictions for models with known sources of error. The process includes stochastic modeling, calibration, validation, and predictive assessment phases where representations of known sources of uncertainty and error are built, informed, and tested. The proposed methodology is applied to an illustrative extrapolation problem involving a misspecified nonlinear oscillator

Large Language Models as Subpopulation Representative Models: A Review

Of the many commercial and scientific opportunities provided by large language models (LLMs; including Open AI's ChatGPT, Meta's LLaMA, and Anthropic's Claude), one of the more intriguing applications has been the simulation of human behavior and opinion. LLMs have been used to generate human simulcra to serve as experimental participants, survey respondents, or other independent agents, with outcomes that often closely parallel the observed behavior of their genuine human counterparts. Here, we specifically consider the feasibility of using LLMs to estimate subpopulation representative models (SRMs). SRMs could provide an alternate or complementary way to measure public opinion among demographic, geographic, or political segments of the population. However, the introduction of new technology to the socio-technical infrastructure does not come without risk. We provide an overview of behavior elicitation techniques for LLMs, and a survey of existing SRM implementations. We offer frameworks for the analysis, development, and practical implementation of LLMs as SRMs, consider potential risks, and suggest directions for future work

Consumers' consumption practices of ready-to-eat foods and food safety knowledge

The purpose of this study was to examine consumers’ consumption practices of ready-to-eat foods and their food safety knowledge. Convenience sampling was done by giving questionnaires to 258 ready-to-eat food consumers in Klang Valley, Kuala Lumpur, Malaysia. Based on the results, the biggest motivator for consumers to purchase RTE foods was convenience (47.3%). Most respondents (40.7%) consume RTE foods more than twice a week, and purchased RTE food at restaurants and cafes (36.8%). A majority (54.3%) of the respondents purchased RTE foods for their lunch, and spent RM11-RM20 per person (48.1%). Overall, the respondents had good food safety knowledge. A majority (81%) knew that diarrhea is the common symptom of foodborne disease, and knew about the correct temperature for refrigeration (42.2%). Most of the respondents knew that bacteria will grow quickly at body temperature (36.4%), and that room temperature encourages multiplication of bacteria (66.7%). Respondents knew that raw and cooked food should be separated to prevent transfer of bacteria (60.9%), and that placing raw meat in the plastic bag before putting it into the grocery basket decreases chance of foodborne illness (62%). However, not many respondents (10.1%) knew that the best way to clean a cutting board after using it for raw meat is by washing with bleach and water. This study could help RTE food marketers gain better insights into consumers’ attitude and behavior patterns towards RTE food. Knowing the baseline knowledge of consumers in food safety is essential for the development of effective educational health

b \rightarrow s \gamma AND Z \rightarrow b \overline{b} IN TECHNICOLOR WITH SCALARS

We consider the radiative decay $b\rightarrow s \gamma$, and the correction to the $Zb\overline{b}$ vertex in technicolor models with scalars. In these models, the scalar develops a vacuum expectation value when the technifermions condense, and the ordinary fermions develop masses via Yukawa couplings. Since the symmetry breaking sector involves both a fundamental scalar doublet and an isotriplet of composite scalars (the technipions), the phenomenology associated with the charged scalars is similar to that found in a type-I two-Higgs doublet model. We show that the correction to the $Zb\overline{b}$ vertex is small over the allowed parameter space of the model in the two limits that we consider, and that there can be large, potentially observable, contributions to the $b\rightarrow s \gamma$ branching fraction.Comment: 9pp. LaTeX, 4 figures in a uuencoded compressed .ps file We remove nonstandard LaTeX fonts from the previous versio