1,032,438 Jersey and 1,162,578 Holstein official Dairy Herd Improvement Association (DHIA) records from 20,380 and 34,000 herd-years, respectively, were used to compute herd-year means and within herd-year standard deviations for individual mature equivalent (ME) milk, fat, and fat percent. These herd-year means and within standard deviations were used to stratify records into five classes. Regressions for individual daughter's modified contemporary deviation (MCD) on sire's predicted difference (PD) were calculated for each class. The within herd-year standard deviations were also used in some of the six different MCD calculations used to compute six different cow indexes (CI) for each cow and trait. The six MCDs calculated were either the current deviation, log adjusted deviation, or the deviation standardized to a constant variance in combination with either the current correction for contemporaries merit or an adjusted correction. The six different CI for each trait were compared by how accurately they predicted the son's MCD trait and the daughter's MCD trait. / Master of Science

The population of the world is increasing exponentially each year with a large population base. Agricultural fields are facing the pressure of dealing with food insufficiency, whereas the challenges of limited resources of arable land and fresh water on the earth should be taken into account at the same time. Smart farming was born at the right time to cope with the problem and has become one of the most powerful approaches to reducing the ecological footprint of farming and improving agricultural yield. The four most important variables that impact crop yield are soil productivity, the accessibility of water, climate, and pests or diseases. This thesis emphasizes the application of chemical fertilizers to corn and disregards the impact of water, pests, and disease for the moment. In this study, three scenarios are explored deeper one by one. The only factor that varies among the three scenarios is the nitrogen amount available to the plant. Fertilizers have outstanding performance in improving the yield and quality of plants in agricultural fields, and this is the emphasis of this thesis. Compared with the fertilizer properties and characteristics of frequently used commercial fertilizers, polymer-coated urea was selected as the fertilizer in this study because the feature of nitrogen can be released into the soil slowly and in a controlled manner. Scenario 1 created an ideal condition where unlimited nitrogen was provided to the corn. Scenario 2 assumed that a fixed amount of polymer-coated urea was applied at the beginning of the sowing season only. Scenario 3 figured out an optimal yield by separating the fertilizer application at the beginning and in the middle of the growing days with the same amounts of fertilizer used in Scenario 2. The model was performed based on historical data from Oklahoma and Ottawa using IoT sensors. The simulation model generated with Python figured out that approximately the end of June to the start of July is the best time to apply the remaining fertilizer, assuming that the sowing stage starts on May 1. The percentage of polymer-coated urea applied initially was found to usually be around 10% in the tested regions. The model was used to predict the yield in Ottawa using from 40.94 g/(m^2) in Scenario 2 to 55.43 g/(m^2) in Scenario 3, achieving an outstanding increasing rate of 35.38%.

Isotopic abundances of the elements xenon, krypton, and cesium formed in the 14-Mev neutron fission of 238U have been measured using the mass-spectrometric method. The relative yields of some isotopes of krypton, strontium, zirconium, molybdenum, ruthenium, iodine, xenon, barium, cerium and neodymium were measured using a Ge(Li) detector. The ratios were normalized through isobaric nuclides, and absolute yields were obtained by normalizing the sum of the heavy-mass yields to 100%. A semi-empirical method has been developed for constructing neutron yield curves. Such a curve was used to obtain a primary-yield curve from the cumulative yields reported here. The results indicate that considerable structure might exist in the primary-yield curve at the higher excitation energy. / Thesis / Doctor of Philosophy (PhD)

No abstract Provided. / Thesis / Bachelor of Arts (BA)

<p> The relative cumulative yields of krypton and xenon isotopes from the thermal neutron fission of 237Np and Np238 and from the fast neutron fission of Np237 have been measured by means of a mass spectrometer. These are the first fission yield results for 238Np, and the first for 237Np at thermal neutron energies. The results are compared with those from other fissioning nuclides.</p> <p> Independent yields of the shielded nuclides 80Br, 82Br, 128I, and 130I from the thermal neutron fission of 235U, 233U, 239Pu, and 238Np have been determined by mass spectrometric analyses of the krypton and xenon β-decay products. The results are discussed in terms of conventional charge distribution theories, and also in terms of neutron emission effects.</p> / Thesis / Doctor of Philosophy (PhD)

<p> The effects of engineering operating variables in the sulphide precipitation of Cu+2, Cd+2 and Zn+2 have been examined with a statistical experimental design. The independent (operating) variables studied were 1) the level of sulphide added, 2) the level of Fe+2 added as scavenger for excess HS- ion and 3) the pH level. The dependent variables were the concentrations of dissolved, suspended and total metals in the supernatant after treatment and the zone settling velocities of the suspensions.</p> <p> The major effect of the operating variables was to yield a precipitate which was either a stable colloid or a suspension which coagulated and settled. Stable colloid formation was associated with an excess of HS- ion. For those conditions which produced coagulant suspensions, 1) ten of the thirteen correlations between dependent and independent variables were not statistically significant at the 95% confidence level, 2) for practical purposes, variations in engineering operating variables did not alter the levels of dissolved and suspended Cu+2, Cd+2 and Zn+2 .</p> <p> When the precipitates coagulated and settled, concentrations of approximately 100 mg/l of each of Cu+2, Cd+2 and Zn+2 were reduced to mean values of 0.41, 0.33 and 0.62 mg/l total metals and 0.03, 0.01 and 0.37 mg/l dissolved metals respectively.</p> / Thesis / Master of Engineering (MEngr)

The world’s population is expected to grow to 9.6 billion by 2050. This exponential growth imposes a significant challenge on food security making the development of efficient crop production a growing concern. The traditional methods of analyzing soil and crop yield rely on manual field surveys and the use of expensive instruments. This process is not only time-consuming but also requires a team of specialists making this method of prediction expensive. Prediction of yield is an integral part of smart farming as it enables farmers to make timely informed decisions and maximize productivity while minimizing waste. Traditional statistical approaches fall short in optimizing yield prediction due to the multitude of diverse variables that influence crop production. Additionally, the interactions between these variables are non-linear which these methods fail to capture. Recent approaches in machine learning and data-driven models are better suited for handling the complexity and variability of crop yield prediction. Maize, also known as corn, is a staple crop in many countries and is used in a variety of food products, including bread, cereal, and animal feed. In 2021-2022, the total production of corn was around 1.2 billion tonnes superseding that of wheat or rice, making it an essential element of food production. With the advent of remote sensing, Unmanned aerial vehicles or UAVs are widely used to capture high-quality field images making it possible to capture minute details for better analysis of the crops. By combining spatial features, such as topography and soil type, with crop growth information, it is possible to develop a robust and accurate system for predicting crop yield. Convolutional Neural Networks (CNNs) are a type of deep neural network that has shown remarkable success in computer vision tasks, achieving state-of-the-art performance. Their ability to automatically extract features and patterns from data sets makes them highly effective in analyzing complex and high-dimensional datasets, such as drone imagery. In this research, we aim to build an effective crop yield predictor using data fusion and deep learning. We propose several Deep CNN architectures that can accurately predict corn yield before the end of the harvesting season which can aid farmers by providing them with valuable information about potential harvest outcomes, enabling them to make informed decisions regarding resource allocation. UAVs equipped with RGB (Red Green Blue) and multi-spectral cameras were scheduled to capture high-resolution images for the entire growth period of 2021 of 3 fields located in Ottawa, Ontario, where primarily corn was grown. Whereas, the ground yield data was acquired at the time of harvesting using a yield monitoring device mounted on the harvester. Several data processing techniques were employed to remove erroneous measurements and the processed data was fed to different CNN architectures, and several analyses were done on the models to highlight the best techniques/methods that lead to the most optimal performance. The final best-performing model was a 3-dimensional CNN model that can predict yield utilizing the images from the Early(June) and Mid(July) growing stages with a Mean Absolute Percentage error of 15.18% and a Root Mean Squared Error of 17.63 (Bushels Per Acre). The model trained on data from Field 1 demonstrated an average Correlation Coefficient of 0.57 between the True and Predicted yield values from Field 2 and Field 3. This research provides a direction for developing an end-to-end yield prediction model. Additionally, by leveraging the results from the experiments presented in this research, image acquisition, and computation costs can be brought down.

The ability to predict wood properties would aid in the growing of southern pine timber for specific end uses. Three wood properties, specific gravity, shrinkage, and knottiness, were chosen as the focus of this study. Silvicultural studies focusing on southern pine management were researched for any information on their impacts on wood properties. The information from silvicultural studies was then used to evaluate growth and yield models for ease of adaptation to predict wood properties. The information necessary to predict all wood properties is not currently available. Although, specific gravity has significantly more information available than the other properties and it is probably the most predictable.

Field corn, Zea mays L., is a commonly grown crop in Mississippi. Brown stink bug, Euschistus servus Say, is an insect that can infest field corn. Growers and consultants have expressed concerns of the difficulty in detecting infestations and estimating yield loss potential once damage is found in a field. The results of these experiments showed a relationship between damage severity, plant height, and yield loss. As damage severity increased, plant height and yield were significantly reduced. On a per area basis, yields were reduced when ≥ 10% plants were damaged. Mean plant heights were reduced when ≥ 20% plants were damaged. Results from simulated damage experiments were similar to those of the natural infestation damage; however, target damage severities (damage ratings) were not achieved. Further methodology refinement is needed.

High-voltage SiC Schottky barrier diodes have been fabricated with 1mm square contacts. The SBD?s were fabricated using both an argon implant and a field plate overlap for edge termination. The current-voltage characterization of the diodes is presented with statistical yield information on the first set of diodes produced from the Mississippi Center for Advanced Semiconductor Prototyping. After packaging, reverse bias breakdown voltages over 500V at 0.1 A/cm2 and an on-state forward voltage drop of less than 2.5V at 100 A/cm2 were demonstrated. A 0.65-0.85 eV barrier height was extracted from the SBD?s using I-V measurements. Field plate terminated devices demonstrated consistent, low standard deviation breakdown voltages and low leakage currents. The argon implanted devices demonstrated a higher breakdown voltage with higher leakage currents and a higher standard deviation. It was proven that the diodes followed the thermionic field emission model for up to one third of the breakdown voltage. Over 15,000 diodes have been tested and results analyzed in this work.