The purpose of this thesis was to understand the importance of supply chain visibility (SCV) and to provide an analysis of the technology available for achieving SCV. Historical events where companies lacked efficient SCV were assessed to understand how errors in the supply chain can have detrimental effects on a company and their reputation. Environmental, social, and governance standards within the supply chain were defined along with the importance of meeting the legal and consumer expectations of a supply chain. There are many different organizations dedicated to helping companies meet ESG standards to achieve ethical, sustainable supply chains. Examples such as the Responsible Business Association and the Organization for Economic Co-Operation and Development were considered. A government solution to SCV, called the Freight Logistics Optimization Works Initiative, considered the importance of data sharing for large companies with complex supply chains, and this solution was assessed for understanding. Current companies and technologies available to achieve SCV were examined for understanding as to how the issue of SCV is currently addressed in the industry. A case study on the company Moses Lake Industries looked at how their complicated chemical manufacturing supply chain has adapted to achieve SCV. This included understanding supplier location, manufacturing processes, and risks. Future technologies that are currently being developed which could further benefit the supply chain industry were considered. Other future considerations, such as the movement of manufacturing out of high risk areas and the need for centralization of SCV solution, were also discussed.

Rotary drums are used to manufacture pharmaceuticals, cement, food, and other particulate products because of their high heat and mass transfer rates. These processes are governed by particle parameters, such as particle size, particle distribution, and shape, and operating parameters, such as rotation rate and fill level. Enormous energy savings are possible with further research in rotary drums due to potential increases in operating efficiency. This study investigates the drum rotation rate on particle bed temperature at temperatures above 500 °C to see the role that radiation heat transfer plays in this process. 2 mm silica beads and a stainless steel rotary drum were used at a fill level of 25% with rotation rates from 2-10 rpm. A new setup and procedure were developed using heating coils and an IR camera to reach high temperatures. The inner drum wall temperature exceeded the outer drum wall temperature because the steel transmitted more heat into the drum at higher temperatures. Although it was unclear whether the heat transfer rate was affected by the increasing rotation rate, the highest final average particle temperature was obtained at 5 rpm. The particle bed temperature distribution narrowed as the rotation rate increased because, at higher rotation rates, more particles are in contact with the drum wall than at lower rotation rates.

In this project, we will argue that the Kardashian women influence beauty standards by portraying themselves as beauty moguls to control the interests of their audience and promoting themselves as the modern day standard of beauty. This affects their audience by creating unattainable and unrealistic standards of beauty that negatively impacts their sense of self and mental health. We will explore the influence they have created and why their influence remains preferred by their audience over time, explaining how they maintain control of the industry.

In this project, we will argue that the Kardashian women influence beauty standards by portraying themselves as beauty moguls to control the interests of their audience and promoting themselves as the modern day standard of beauty. This affects their audience by creating unattainable and unrealistic standards of beauty that negatively impacts their sense of self and mental health. We will explore the influence they have created and why their influence remains preferred by their audience over time, explaining how they maintain control of the industry.

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and time scales and enabling large-scale MD simulations with DFT-level accuracy. In this work, we investigate the feasibility of GNNs to learn the HK map from the external potential approximated as Gaussians to the electron density 𝑛(𝑟), and the mapping from 𝑛(𝑟) to the energy density 𝑒(𝑟) using Pytorch Geometric. We develop a graph representation for densities on radial grid points and determine that a k-nearest neighbor algorithm for determining node connections is an effective approach compared to a distance cutoff model, having an average graph size of 6.31 MB and 32.0 MB for datasets with 𝑘 = 10 and 𝑘 = 50 respectively. Furthermore, we develop two GNNs in Pytorch Geometric, and demonstrate a decrease in training losses for a 𝑛(𝑟) to 𝑒(𝑟) of 8.52 · 10^14 and 3.10 · 10^14 for 𝑘 = 10 and 𝑘 = 20 datasets respectively, suggesting the model could be further trained and optimized to learn the electron density to energy functional.

Ketone levels give an insight into the bodies metabolism. People with epilepsy or people dieting may want to keep their levels high, whereas type one diabetics or those recovering from eating disorders may want to keep their levels low. Current ketone detection methods involve blood samples or urinalysis. A ketone (acetone) biosensor was fabricated to detect levels in human breath, providing a noninvasive way to quickly and accurately detect ketone levels in the body.

In this thesis, six experiments which were computer simulations were conducted in order to replicate the negative association between sample size and accuracy that is repeatedly found in ML literature by accounting for data leakage and publication bias. The reason why it is critical to understand why this negative association is occurring is that in published studies, there have been multiple reports that the accuracies in ML models are overoptimistic leading to cases where the results are irreproducible despite conducting multiple trials and experiments. Additionally, after replicating the negative association between sample size and accuracy, parametric curves (learning curves with the parametric function) were fitted along the empirical learning curves in order to evaluate the performance. It was found that there is a significant variance in accuracies when the sample size is small, but little to no variation when the sample size is large. In other words, the empirical learning curves with data leakage and publication bias were able to achieve the same accuracy as the learning curve without data leakage at a large sample size.

Metal oxides are crucial materials that can be applied to sustainable processes for heat storage or oxygen pumping. In order to be able to apply metal oxides to industrial processes, an effective model of the metal oxide’s reduction thermodynamics is required. To do this, Wilson et al., (2023) developed a compound energy formulism (CEF) algorithm to form these models. The algorithm in its current form can effectively form model thermodynamics; however, the data set required for this model is extensive and large, leading to high costs of modeling a metal oxide. Furthermore, the algorithm faces further difficulties with uneven data densities within the set, leading to poorer fits for low density data. To assist in alleviating the cost associated with data collection, data-omitting strategies were performed to find unimportant points, or points that formed models that had good fits to the original model when removed. After conducting these tests, many points and trends were found to be crucial to keep within the data set, but due to uneven data density, no definitive conclusions could be made on how to reduce the algorithm’s data set. The tests gave evidence that points in high data density regions could be removed from the data set due to only the fact that there existed nearby points to provide essential information to closely interpolate/extrapolate the missing data. Although this project currently did not meet the goal of reducing the data set, preliminary findings of what points could be non-crucial to the data set were identified. Future testing with the proposed weighting methods will be conducted to determine what data can be safely removed from the set to form models that properly reflect the metal oxide’s properties.

Using DFT calculations and GAMESS computational software, porphine and its derivatives were analyzed for unique sites to accept the adsorbates As(III), As(V) and P(V) in order to compare resulting adsorption energies and determine if any of these molecules prefer arsenic oxyanions over phosphate. Pure porphine preferred As(III) over P(V) with a resulting adsorption energy of -0.7974 eV. Of the functionalized porphyrins tested, carboxyl porphyrin preferred As(V) over P(V) with a total adsorption energy of -0.7345 eV. Ethyl, methyl, chlorine and amino porphyrin all preferred As(III), with energies of -0.7934, -0.8239, -0.7602, and -0.8508 eV, respectively. Of the metalated porphyrins tested, copper and vanadium porphyrin preferred As(V) over P(V) with adsorption energies of -0.7645 and -2.0915 eV. Chromium, iron and magnesium porphyrin all preferred As(III) over P(V) with energies of -0.5993, -1.4539, and - 1.0790 eV, respectively.