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Expanding on Basis Risk Estimates for Pasture, Rangeland, and Forage Insurance
Get PDFBasis risk or residual risk arising from disparity between an index’s estimate of losses and actual losses is inherent in index-based insurance products. We approximate basis risk as the false negative probability (FNP) within pasture, rangeland, and forage (PRF) rainfall index insurance for the south-central coastal region of California. We estimate the FNP on average that at least one of two selected coverage intervals will fail to provide an indemnity when a loss is realized at 48%. The average FNP is reduced to only 11% when considering whether both selected intervals fail to provide an indemnity when a loss is realized
Cellulose Nano Fibers Infused Polylactic Acid Using the Process of Twin Screw Melt Extrusion for 3D Printing Applications
No full textIn this thesis, cellulose nanofiber (CNF) reinforced polylactic acid (PLA) filaments were produced for 3D printing applications using melt extrusion. The use of CNF reinforcement has the potential to improve the mechanical properties of PLA, making it a more suitable material for various 3D printing applications. To produce the nanocomposites, a master batch with a high concentration of CNFs was premixed with PLA, and then diluted to final concentrations of 1, 3, and 5 wt% during the extrusion process. The dilution was carried out to assess the effects of varying CNF concentrations on the morphology and mechanical properties of the composites. The results showed that the addition of 3 wt.% CNF significantly enhanced the mechanical properties of the PLA composites. Specifically, the tensile strength increased by 77.7%, the compressive strength increased by 62.7%, and the flexural strength increased by 60.2%. These findings demonstrate that the melt extrusion of CNF reinforced PLA filaments is a viable approach for producing nanocomposites with improved mechanical properties for 3D printing applications. In conclusion, the study highlights the potential of CNF reinforcement in improving the mechanical properties of PLA for 3D printing applications. The results can provide valuable information for researchers and industries in the field of 3D printing and materials science, as well as support the development of more advanced and sustainable 3D printing materials
Three Essays Evaluating Long-Term Agricultural Projections
No full textThis dissertation consists of three essays that evaluate long-term agricultural projections. The first essay focus on evaluating Congressional Budget Office’s (CBO) baseline projection of United States Department of Agriculture (USDA) mandatory farm and nutrition programs. The second essay examine USDA soybean ending stock projections, and the third essay investigate impact of macroeconomic assumptions on USDA’s baseline farm income projections. The three essays are summarized as follows:Essay1: AnEvaluation of Congressional Budget Office’s Baseline Projections of USDA Mandatory Farm and Nutrition Programs.The CBO projections of USDA’s mandatory farm and nutrition program outlays play a vital role in shaping agricultural policy and in agricultural policy debates. Using CBO projections and observed outcomes from 1985 through 2020, we examine the degree to which projections of farm, supplemental nutrition assistance, and child nutrition program outlays are unbiased, efficient, and informative. We find that projections for farm program and child nutrition program outlays are unbiased. Supplemental nutrition assistance program outlays are unbiased at short horizons but are downward bias beyond a three-year horizon. We find that all three series of projections are inefficient. The projections for supplemental nutrition assistance program and child nutrition program outlays are informative up to a five-year projection horizon, but the farm program outlay projections are informative for only a one-year horizon. Disaggregated farm program outlay projections since 2008 further suggest that the uninformativeness principally stems from conservation program projections. The findings may provide valuable insights for CBO to improve future projections and for projection users, including policymakers, to adjust expectations and future Farm Bill discussions.Essay 2: Evaluation of USDA’s soybean ending stock baseline projection.The carryover inventory of a specific commodity – ending stock, which summarizes a commodity market’s supply and demand components – is an important measure of level of scarcity in a market. Using USDA baseline projections and realized values from 1997 through 2020, we examine bias and informativeness of soybean ending stock projection. We decompose soybean ending stock projection errors using a machine learning algorithm, Extreme Gradient Boosting (XGBoost). Our results indicate that soybean ending stock projections are unbiased (except for nowcast), however ending stock projections are informative for only one-year horizon. The decomposition of ending stock projection indicates that demand components (crushing, seed and residual, and exports) are primary sources of ending stock projection error for a majority of projection horizons. US soybean market is directly linked with global soybean import and exports, thus, we further investigate USDA projection of soybean export and import country-by-country. Results indicate that USDA’s foreign soybean import projections are barely informative for most of countries including China whereas export projection from Argentina, Brazil, and Other South American countries are informative for four years, two years, and not informative at all, respectively, under our conservative estimates. Finally, an analysis of US soybean export projection error to foreign export/import destinations indicates that errors on export projection from Brazil and other South American Countries (except Argentina) cost USDA the most. Results may help market participants form expectation when making plan and business decisions
The Strategic Logic of Arms Purchasing
No full textInternational arms sales play an important role in the defense of states and as a result a sizeable literature has developed that deals with this topic. The importance of arms sales to states has carried over into political science as a result, where weapons play an important role in a variety of theories particularly those that fall into the various realist camps. Most of this literature focuses upon states that produce and sell arms with a significantly smaller literature dealing with states that purchase, rather than produce, them. This unfortunate gap in the literature prevents a complete understanding of international arms sales as the decision making process of arms purchasing states is currently almost completely discounted.This dissertation develops a theory of the strategic logic of arms purchasing. The theory laid out here suggests that states that must purchase arms weigh costs and benefits before selecting certain arms suppliers over others in the greater international arms market. This decision is largely based upon the perceived need for weapons based on the likelihood of conflict in the near future versus the dependence associated with purchasing the arms. The dependence associated with arms purchases can be reduced by purchasing arms from multiple arms producers at the cost of logistical efficiency.The theory laid out here is then tested via a pair of comparative historical case studies. These case studies analyze a state’s history in terms of geopolitical alignment with its major arms suppliers and its perceived probability of conflict. Predictions are made based on the theory and these predictions are then tested by developing networks, graphs, and charts based on the Stockholm International Peace Research Institute’s Arms Transfers Database and the U.S. Department of State’s Bureau of Arms Control, Verification and Compliance’s World Military Expenditures and Arms Transfers series of reports. The first comparative historical analysis covers Iran and Iraq during roughly the time period before and during the Iran-Iraq War while the second comparative case study analyzes Finland, Czechoslovakia and the Czech Republic during the lead up and aftermath of the collapse of the Soviet Union. The theory performs well in predicting the actions of the states in the case studies suggesting that arms purchasing states balance need for weapons against the dependence associated with the arms purchases
Quasi-Two-Dimensional Halide Perovskite Materials for Photovoltaic Applications
No full textAs energy demands for the world increase, the necessity for alternate sources of energy are critical. Just in the United States alone, 92 quadrillion British thermal units (Btu) were used in 2020. As political and geographical pressures surrounding oil increase, along with the growing concern for climate, the drive to explore alternative and renewable means for harvesting energy is on the rise. Solar cells, also known as photovoltaics (PVs), are an attractive renewable source and have been developed as an alternative energy means for over 60 years. When considering losses due to atmospheric absorption and scattering, the Earth’s surface gets about 1000 W/m2 of energy from the sun, which is why there are research efforts around the world trying to maximize the efficiency of solar cells.Organic-inorganic halide perovskites provide for ideal absorbing layers that feature long carrier lifetime and diffusion lengths, strong photoluminescence, and promising tunability. Furthermore, the solution-processing methods used to make these perovskites ensure that the solar cells will remain low-cost and have easy scale-up possibilities. The main problem perovskites is that they degrade in the presence of water, thus leading to decreased device performance.In this work two approaches are investigated to increase moisture stability. The first investigates incorporation of thiols as pseudohalides into the 2D perovskite structure. Instead of the theorized perovskite, two novel 2D compounds were created, Pb2X(S-C6H5)3 (X= I, Br, Cl) and PbI1.524(S-C6H5)0.476. While not perovskites, this study gives insight into the effect that the thiol may have on determining structure when comparing –S-C6H5with –SCN groups. Future work will explore more electronegative thiols that will be used to make moisture resistant, tunable 2D perovskitesThe second approach is to incorporate longer organic ammonium cations into the perovskite structure to produce quasi-2D perovskite films fabricate them into devices. Adding in electronically insulating ligands leads to a stricter requirement for vertically aligned 2D films and special care must be taken to have efficient charge collection. The current field has successfully incorporated short ligands such as butylammonium (BA) into PVs, however the extension to larger and more beneficially hydrophobic ligands has been very scarce. In this work, a novel solvent engineering system is developed to create vertically aligned quasi-2D perovskite absorbing layers based off of a bithiophene ligand (2T). These absorbing layers are then characterized and incorporated into efficient PV devices. Generalizations to solvent conditions related to ligand choice is discussed herein, creating deep insights into incorporating more conjugated ligands into devices
Assessing Field Standard Practices for Incorporating Black Individuals in EEG Research
No full textEEG is a commonly used method in both research and medical practice that is reliant on electrode to scalp contact to record brain activity. Anti-black racism is a problem that is prevalent within EEG because of both the differences in hair texture, density, and follicle shape as well as the cultural and historical significance of Black hair and touching Black hair for Black people. The potential impact of Black people being unable to successfully receive EEG substantial including: risk of misdiagnosis, lack of representation within neurophysiological research, and negative experiences to Black patients and participants. In the current study, we began to address the gap in the literature regarding Black hair and EEG by surveying current principal investigators (PIs) who are leading laboratories using EEG as a primary method. The primary objective was to gain an initial understanding of the way in which members of laboratories primarily using EEG in various parts of the country currently engage with Black participants, and to what extent they do so at all. We utilized quantitative and qualitative questions in order to assess a variety of components for each laboratory. We used a case study method approach to data analysis. Our findings suggest that there is value in examining concerns of underrepresentation of Black people in EEG. The laboratories in our study primarily did not have tailored outreach for Black participants. Many laboratories in our sample did not alter protocols for Black participants. Eight of our nine case studies reported additional challenges when working with Black participants in comparison to Non-Black participants; Each of those laboratories reported excluding the Black participant or not using the Black participant’s data after the fact. It is essential that we continue to examine the various components of conducting EEG with Black people to gain a better understanding, and therefor inform future best practices
Artificial Intelligence-Based GPS Spoofing Detection and Implementation with Applications To Unmanned Aerial Vehicles
No full textIn this work, machine learning (ML) modeling is proposed for the detection and classification of global positioning system (GPS) spoofing in unmanned aerial vehicles (UAVs). Three testing scenarios are implemented in an outdoor yet controlled setup to investigate static and dynamic attacks. In these scenarios, authentic sets of GPS signal features are collected, followed by other sets obtained while the UAV is under spoofing attacks launched with a software-defined radio (SDR) transceiver module. All sets are standardized, analyzed for correlation, and reduced according to feature importance prior to their exploitation in training, validating, and testing different multiclass ML classifiers. Two schemes for the dataset are proposed, location-dependent and location-independent datasets. The location-dependent dataset keeps the location specific features which are latitude, longitude, and altitude. On the other hand, the location-independent dataset excludes these features. The resulting performance evaluation of these classifiers shows a detection rate (DR), misdetection rate (MDR), and false alarm rate (FAR) better than 92%, 13%, and 4%, respectively, together with a sub-millisecond detection time. Hence, the proposed modeling facilitates accurate real-time GPS spoofing detection and classification for UAV applications.Then, a three-class ML model is implemented on a UAV with a Raspberry Pi processor for classifying the two GPS spoofing attacks (i.e., static, dynamic) in real-time. First, several models are developed and tested utilizing the prepared dataset. Models evaluation is carried out using the DR, F-score, FAR, and MDR, which all showed an acceptable performance. Then, the optimum model is loaded to the onboard processor and tested for real-time detection and classification. Location-dependent applications, such as fixed-route public transportation, are expected to benefit from the methodology presented herein as the longitude, latitude, and altitude features are characterized in the implemented model
Supporting Content Generation in Virtual Environments Based on Optimization
No full textContent generation in virtual environments is becoming increasingly important with the rise of virtual and augmented reality technologies and growing demand for immersive experiences. This raises a problem of efficient content generation to meet with higher requirements for both the quantity and quality of the contents that could be used inside virtual environments. This dissertation explored the possibilities of formulating design problems as computational problems based on optimization theory in different scenarios, and explored what can be viable application cases, as well as what can be viable cost terms for each application case based on the theory. In total four application scenarios are included. The optimization theory used in this dissertation is the Markov chain Monte Carlo optimization method called “simulated annealing”. By doing this we can transform a design problem to a computation problem and use computational methods to quickly solve the problem and generate content.This dissertation contains the papers published by the author during her Ph.D. Each published article included in this dissertation deals with a specific application case based on optimization theory.The author investigated four distinct application cases. The first case centered on the synthesis of drills for virtual reality racket sports. The second case focused on designing virtual reality game level layouts, based on the layout of a real-world environment. The third case explored collaborative gameplay design, with the aim of synthesizing game levels that require a predetermined degree of collaboration between two players to complete. The aim of the fourth application case was to create virtual reality fire evacuation training drills that could be used for training purposes in simulated environments. Different cost terms are proposed based on different application cases to synthesize contents that align with the design intent
Spray Overlap and Heat Transfer Coefficient Uniformity in Continuous Casting
No full textContinuous casting is an efficient method of producing large volumes of semi-finished steel products. Uniform and efficient heat removal is required to ensure the steel is produced without any cracks while meeting the high steel production target. One of the challenges in the continuous casting process is providing uniform spray cooling rate as needed based on the casting production rate. Improved control of steel cooling rate is critical. The heat removal rate is dependent on the spray nozzle configurations (nozzle spray angle, distance between spray nozzles, nozzle stand-off distance, and water flow rate). The objective of this study is to analyze spray cooling for two nozzles with overlapping sprays. The Lagrangian approach is adopted to track the droplets. In order to predict the slab cooling accurately in the overlap region, droplet breakup and collision are included in the model. The effects of different spray overlap region sizes on the heat transfer coefficient are evaluated by changing the nozzle-to-nozzle distance. The results show that there is an optimum size of spray overlap which provides uniform heat transfer between the two adjacent nozzles. Further increase of the overlap increases heat transfers in the overlap region, which could lead to overcooling of the slab