Chinese tallow tree (Triadica sebifera (L.)) has become a threat to Southern United States forestlands because of the rapid colonization. To explore the growth rate of tallow, numerous growth models were constructed with destructive sampling method from oak-gum-cypress (Quercus/Liquidambar styraciflua/Taxodium distichum) and longleaf/slash pine (Pinus taeda/ Pinus echinata) forests in southern Mississippi. Moreover, stem profile was also fitted with segmented profile models: Max and Burkhart (1976), Cao (2009) modified Max and Burkhart, and Clark et al. (1991). Numerous results showed that: 1) diameter at breast height, volume and biomass of tallow grew faster in oak-gum-cypress forest while height grew faster with DBH in Longleaf/Slash pine forest; 2) the stem of tallow was generally sturdy in oak-gum-cypress forest while it was slender in longleaf/slash pine forest; however, there was no significant difference between them. Growth and taper models provide a tool for managers to estimate future stocking of tallow tree.

The purpose of this work is to generate a shore profile algorithm to be used in estuaries dominated by fine sediments. Numerical models are continually evolving to enhance the overall accuracy of results. However, the typical shore profile is defined as a vertical wall. This work defines the shore as a nonlinear profile which will provide more realistic models. A variety of shore profile equations were examined and tested against a field site, Weeks Bay, Alabama. The most applicable, an equation by S. C. Lee, was modified in order to calculate the entire shore profile length. The distance from the land-water interface to the depth at which sedimentation is negligible can now be modeled with a single equation. Recommendations for the practical aspect of implementation into a numerical model are also considered.

No

The fungus Aspergillus flavus L. is an agricultural threat, particularly for maize (Zea mays L.). Its invasive growth and contamination before and after harvest can lead to the loss of the commodity and cause life threating consequences to humans and livestock that consume a contaminated product. The differential expression profile of two resistant maize inbred lines, Mp313E and Mp719, and two susceptible maize inbred lines, B73 and Va35, were evaluated in the mRNA and protein levels. The experimental designed used allowed to observe the responses of these maize lines to A. flavus inoculation and to the stress caused by wounding on kernels. Candidate genes were selected from a prior published GWAS and pathway analysis for the expression analysis at the mRNA level. Seventeen candidate genes were selected, and gene expression analysis via RT-qPCR was performed for nine of them. Two of characterized candidate genes that showed an upregulation above 2olds in the resistant lines. These genes are involved in oxidation responses and had an associated positive allele effect, e.g. contribute to aflatoxin accumulation. The results indicate that their role is not necessarily to make the plant more susceptible to the accumulation of aflatoxin but rather to alleviate the oxidative stress caused by the fungus. Susceptible lines, in general, did not show any difference in the expression of the selected candidate genes. At the protein level, an in-gel analysis identified a variety of stress-related proteins that were upregulated in response to A. flavus inoculation and to wounding stress in the resistant lines. A list of genes associated to the proteins identified was compiled for further characterizations and possible use as molecular markers in marker-assisted selection of commercial maize lines resistant to A. flavus.

Electric vehicles with autonomous driving are the future of transportation, as they are sustainable, efficient, environmentally friendly, and can provide collision-free congestion-free driving. However, the sensing and control technology adds new accessory loads which increase the vehicle energy use. Thus, it is critical to minimize energy use where possible, and optimal speed planning is a promising way to achieve this goal and is thus the topic of study for this thesis. First, a low-computation framework for the onboard calculation of energy-optimal cruising speed of battery electric vehicles is proposed. The framework is used to investigate the critical parameters for energy-optimal cruising speed determination, and it includes major internal and external vehicle losses, uses accurate motor-inverter efficiency maps as look-up tables, and does not require knowledge of the future route. This framework is validated using three electric vehicle models in MATLAB/SIMULINK. Secondly, a novel two-level model predictive control (MPC) speed control algorithm for battery electric autonomous vehicles as a successive convex optimization problem is proposed. The proposed successive convex approach produces a highly accurate optimal speed profile while also being solvable in real-time with the vehicle's onboard computing resources. This algorithm is used to perform a variety of simulated test cases, which show an energy savings potential of about 1% to 20% for different driving conditions, compared to a non-energy-optimal driving profile. Lastly, the research is expanded to consider fuel cell hybrid electric vehicles (FCHEVs), which have the added need for an optimal energy management strategy inv addition to optimal speed planning. Novel successive and integrated convex speed planning and energy management algorithms are proposed to solve the minimum hydrogen consumption problem for autonomous FCHEVs. The simulation results show that the proposed integrated method, which considers fuel cell system efficiency in the optimization objective function for speed planning, leads to 0.19% to 2.37% less hydrogen consumption compared to the successive method on short drive cycles with varying accessory loads. On the same test cycles, the integrated method uses 10.12% to 21.62% less hydrogen than an arbitrary constant-speed profile. / Thesis / Doctor of Philosophy (PhD) / Autonomous vehicles are expected to be the future of transportation, however, the high continuous electrical accessory power needed for control and perception is a challenge. Fortunately, there is an inherent property of speed planning for autonomous vehicles that can help deal with this problem. This thesis focuses on optimal speed planning to minimize energy use, proposing convex methods considering detailed internal and external losses for battery electric vehicles (BEVs), and optimal speed planning integrated with optimal energy management for fuel cell hybrid electric vehicles (FCHEVs). The proposed framework in this thesis is accurate while maintaining a low computational effort, which are the desired criteria for real-time algorithms.

This study compared the accuracy of criminal profiles produced by individuals versus profiles produced by teams of two. Participants were 239 college students who were randomly assigned to work alone or in a team. Participants were asked to read a double sexual homicide case, profile the offender, and answer the Profiling Offender Characteristics Questionnaire. The results indicate that although novice profiler teams only slightly outperformed individuals on overall profile accuracy, teamwork significantly improved profiling accuracy for the cognitive characteristics of the offender. In contrast, teams and individuals showed similar profile accuracy for all other facets of the offender. These results may reflect the type of reasoning used by teams and individuals to profile different offender characteristics.

Silicon Carbide devices are proving to be most promising for high power and high-temperature application in recent times. Efficient and accurate characterization of the device characteristics is key to the fabrication of high quality devices and reproduction of the quality of the devices fabricated. Capacitance-Voltage profiling is one of the most commonly used techniques to measure the doping profiles of semiconductors. However, interpretation of C-V profiling in the presence of traps in the material becomes complicated. Various complications arising from compensation between donors and acceptors, partial ionization of dopants and presence of deep level impurities could yield anomalous measured profile. Silicon Carbide being a wide bandgap semiconductor, many impurities commonly found such as Boron and Aluminum are not completely ionized at Room temperature. This leads to complications in calculating doping profiles when the trap levels are deeper. Other complications arising due to series resistance effect and diode edge effect may also affect the measured profile. Accounting for these complications may be difficult by mere observation of the measured profile. Simulation can be an excellent tool to extract parameters of interest from experimental results that are influenced by non-ideal effects. Fitting of the experimentally obtained data with simulated profile using specific models may be a useful technique to quantitatively account for the deviations from the actual profiles.

Laser induced breakdown spectroscopy (LIBS) was tested to examine its applicability to remote and in suit analysis in inaccessible situation. Two types of liquid sample (slurry) prepared for simulating vitrification of liquid hazardous wastes was tested. In situ analysis ability makes the LIBS technique practical for analysis of the slurry samples during vitrification, which is in inaccessible situation. For the first slurry sample, two slurry circulation systems were devised to overcome major technical problems associated with LIBS measurements of slurry samples - namely sedimentation and change in the lens-to-sample distance (L.T.S.D) during measurement. The second slurry sample contained less water and is able to be managed in a small glass container during test. We applied direct analysis of slurry sample filled in glass container. Spectroscopic analysis was performed using two different detection systems: Czerny-Turner and Echelle spectrometer systems. In particular, spectroscopic analysis of data from an echelle spectrometer shows the high efficiency for simultaneously determining physical quantities of all elements of interest. We also evaluate LIBS technique to tin alloy samples for the purpose of quantitative analysis by using Echelle spectrometer system. Unknown samples without information of elemental composition were tested to estimate several sample compositions simultaneously. An artificial neural network, calibration method, and chemical analysis were applied to estimate the elemental concentrations of impurities in tin (Sn) alloy.

There has been much concern about how teachers teach math in the elementary classroom. With increasing accountability standards from the No Child Left Behind Act (NCLB; United States Department of Education 2002), students in the elementary grades are tested for mathematical ability. Elementary teachers have felt pressured to teach certain subjects in the classroom as a result of statewide and nationally mandated student testing. This research should add to the data available to those higher institutions that are seeking ways to design programs to help improve the teaching of mathematics and the understanding mathematics students possess. Faculty members in the elementary education program at Mississippi State University were interested in determining ways to assist students in becoming better teachers. The Conference Board of the Mathematical Sciences (2001) has made many statements about the need for teachers to have deep understanding of school mathematics concepts and procedures. This study was designed as a first step in determining if the mathematics courses and materials are helping pre-service teachers comply with NCTM, Principles and Standards for Schools Mathematics (2000), and the No Child Left Behind Act (2001). Also it was designed to determine the needs of students and to help university faculty find ways to help math educators make sense of mathematics. The overall goal of the study was to assist elementary education faculty in establishing a descriptive profile in mathematics for pre-service elementary education teacher majors. Descriptive statistics were generated for the variables in this study. Data analysis of the descriptive profile revealed that most teacher made tests such as grades in university required mathematics courses; scores on grades in Structure of the Real Number System, Problem Solving, and Informal Geometry and Measurement, 45 GPA core courses; Student Teaching; and Overall GPA categories were consistent across cohorts of students. A recommendation was made to survey the original sample two to three years later with survey questions and open-ended questions to see if what they learn as preservice elementary majors helped them to teach mathematics as novice teachers.

No description available.