Artificial Neural Network-Based Robotic Control

Artificial neural networks (ANNs) are highly-capable alternatives to traditional problem solving schemes due to their ability to solve non-linear systems with a nonalgorithmic approach. The applications of ANNs range from process control to pattern recognition and, with increasing importance, robotics. This paper demonstrates continuous control of a robot using the deep deterministic policy gradients (DDPG) algorithm, an actor-critic reinforcement learning strategy, originally conceived by Google DeepMind. After training, the robot performs controlled locomotion within an enclosed area. The paper also details the robot design process and explores the challenges of implementation in a real-time system

Radial Basis Function Generated Finite Differences for the Nonlinear Schrodinger Equation

Solutions to the one-dimensional and two-dimensional nonlinear Schrodinger (NLS) equation are obtained numerically using methods based on radial basis functions (RBFs). Periodic boundary conditions are enforced with a non-periodic initial condition over varying domain sizes. The spatial structure of the solutions is represented using RBFs while several explicit and implicit iterative methods for solving ordinary differential equations (ODEs) are used in temporal discretization for the approximate solutions to the NLS equation. Splitting schemes, integration factors and hyperviscosity are used to stabilize the time-stepping schemes and are compared with one another in terms of computational efficiency and accuracy. This thesis shows that RBFs can be used to numerically solve the NLS with reasonable accuracy. Integration factors and splitting methods yield improvements in stability at the cost of computation time; both methods produce solutions of similar accuracy while splitting methods are slightly less expensive to implement than integration factors (computation times were of the same order of magnitude). The use of hyperviscosity can lead to an improvement in stability but can also lead to increased errors if the relevant parameters are not chosen carefully

Acoustic Waveform Optimization for Three-dimensional Object Geometries

In recent years, deep learning (DL) has become an increasingly important tool for many different types of classification, identification, and related problems including inverse radar and sonar applications. This thesis studies the degree to which radar and sonar systems may be optimized for DL algorithms in a theoretical setting. Most of the current existing literature found on using DL involve solving a full inverse scattering problem (ISP), that is to determine the properties and/or geometry of the scatter from nearly complete measurements of the scattered field. Methods suitable for use in two-dimensional space have been proposed and demonstrated with varying accuracies. In this work, we simplify the problem to only identifying objects in a previously collected catalog, which allows for accurate geometry classification with far less data. To produce a data set suitable for deep learning, we obtain frequency domain far-field solutions corresponding to known geometries by means of a recently developed fast three-dimensional scattering solver. The scattered fields are then modulated using a multitude of learnable waveforms to identify frequencies for which learning/training is optimal. A major result of this research is its ability to identify an optimal radar/sonar signal that can be transmitted, which maximizes a DL algorithm’s ability to correctly identify any potential scattering obstacles. By assuming that the set of object geometries/scatterers is known (although the orientation of the scatterer to the incident field is still considered unknown), accurate identification is possible under more realistic conditions than is possible with current inverse scattering algorithms. For instance, we show that collecting just the back-scattered far-field data is sufficient (which only assumes the radar source can also receive the scattered signal as opposed to typical ISP algorithms which depend on many different signal receivers surrounding the target in all directions). Normally distributed noise is also introduced during training as a means of regularization, to explore different waveforms that a deep learning optimizer may gravitate towards, and as a way to more accurately simulate real-world conditions where collected data may be imperfect. In military radar applications, this work is applicable in the sense that if a catalog of enemy aircraft exists, i.e. there is no need for a generalized reconstruction of the target geometry, one can simply classifying the enemy aircraft based off the return signal. The outcome of this work could potentially aid in the design and implementation of future advanced DL-based radar and sonar detection systems

Developmental Expression of Monocarboxylate Transporter 1 and 4 in Rat Liver

PURPOSE: Monocarboxylate transporters (MCT) are proton-coupled integral membrane proteins that control the influx and efflux of endogenous monocarboxylates such as lactate, acetate and pyruvate. They also transport and mediate the clearance of drugs such as valproate and gamma-hydroxybutyrate. CD147 functions as ancillary protein that chaperones MCT1 and MCT4 to the cell membrane. There is limited data on the maturation of MCT and CD147 expression in tissues related to drug distribution and clearance. The objective of the present study was to quantify hepatic MCT1, MCT4, and CD147 mRNA, whole cell and membrane protein expression from birth to sexual maturity. METHODS: Liver tissues were collected from male and female Sprague Dawley rats at postnatal days (PND) 1, 3, 5, 7, 10, 14, 18, 21, 28, 35, and 42 (n = 3 - 5). Hepatic mRNA, total and membrane protein expression of MCT1, MCT4, and CD147 was evaluated via qPCR and western blot. RESULTS: MCT1 mRNA and protein demonstrated nonlinear maturation patterns. MCT1 and CD147 membrane protein exhibited low expression at birth, with expression increasing three-fold by PND14, followed by a decline in expression at sexual maturity. MCT4 mRNA had highest expression at PND 1, with decreasing expression towards sexual maturity. In contrast, MCT4 membrane protein exhibited minimal expression from birth through weaning before a 10-fold surge at PND35, whereupon there was a sharp decline in expression at PND42. There was a significant positive correlation between MCT1 and CD147 whole cell and membrane expression, while MCT4 membrane expression demonstrated a weak negative correlation with CD147. CONCLUSION: Our study elucidates the transcriptional and translational maturation patterns of MCT1, MCT4 and CD147 expression, with isoform- dependent differences in the liver. Changes in transporter expression during development may greatly influence drug distribution and clearance in pediatric populations

Recovery of Carbon and Nitrogen Cycling and Microbial Community Functionality in a Post-Lignite Mining Rehabilitation Chronosequence in East Texas

Surface mining for coal alters the original soil profile characteristics and the associated physical, chemical, and biological conditions. Our objectives were to compare soil characteristics and the distribution of nutrients to 1 m depth over a chronosequence of 40 years to determine when a reclaimed mine soil (RMS) returned to premined conditions. We sampled 5 sites aged 0 to 20 years reclaimed by the crosspit spreader technique (CP) and 3 sites aged 20 to 40 years reclaimed by the mixed overburden technique (MO). An unmined site (UM) served as a control. Changes in soil texture (sand to clay loam) after mining corresponded with increased macroaggregation (>2 mm) and enhanced C sequestration up to ~250 Mg C ha-1 at the MO20 site. Soil chemical [pH, electrical conductivity (EC), and sodium adsorption ratio (SAR)] and physical properties [bulk density (BD) and texture] met or exceeded reclamation and revegetation standards. Most soil C was associated with organic matter, but a small amount of lignitic C was detected in some samples. Soil organic C and N reached or exceeded premined concentrations after 0 and 10 years, respectively. Soil NO3--N and P did not reach premined conditions, but soil K, Ca, Mg and S exceeded premined conditions and stratified after 10-15 years. Micronutrients exceeded premined concentrations. Soil microbial biomass and mineralization rates recovered after 16 years of reclamation. Bacteria and fungi recovered to premined levels after 20 years. The CP20 site was most closely related to the UM site, but sites 10 years and older were comparable. Dominant phyla (Actinobacteria, Acidobacteria and Proteobacteria; 70% of all sequences) returned to premined levels after 10 years, which correlated with soil quality indicators, suggesting the importance of these phyla in soil health. Community-level physiological profiles did not differ between sites and metabolic diversity peaked at CP15 and CP20. GeoChip showed separation between the UM sites and reclamation sites. Soil microbial functionality appeared to recover faster than taxonomic composition of the soil microbial community. Further analysis of functional genes will expand upon this research so that we may better quantify soil quality in RMS

Molecular profiling of T-helper immune genes during dengue virus infection

In this study, we provide a comprehensive molecular profiling of the involvement of T- helper (Th) genes during dengue virus infection of different cell types. The Th gene profiles of three human cell types (monocytes, T-cells and hepatocytes) were analyzed simultaneously via array-based RT-PCR upon infection with dengue virus. Differential regulation of 41 Th genes was identified and of which 20 of those genes may contribute to immuno-pathogenesis of dengue virus infection by regulating inflammation, thrombocytopenia and vascular permeability. Among the strongly up-regulated genes were the RANTES, CC-CKR3, IRF4, CLEC2C, IL-6 and TLR6, which are potent inducer of inflammation and vascular permeability. Profiling genes obtained from this study may serve as potential biomarkers and the modulation of Th immune responses during dengue virus infection has important implications in disease outcome

Replication of Alphaviruses: A Review on the Entry Process of Alphaviruses into Cells

Alphaviruses are small, enveloped viruses, ~70 nm in diameter, containing a single-stranded, positive-sense, RNA genome. Viruses belonging to this genus are predominantly arthropod-borne viruses, known to cause disease in humans. Their potential threat to human health was most recently exemplified by the 2005 Chikungunya virus outbreak in La Reunion, highlighting the necessity to understand events in the life-cycle of these medically important human pathogens. The replication and propagation of viruses is dependent on entry into permissive cells. Viral entry is initiated by attachment of virions to cells, leading to internalization, and uncoating to release genetic material for replication and propagation. Studies on alphaviruses have revealed entry via a receptor-mediated, endocytic pathway. In this paper, the different stages of alphavirus entry are examined, with examples from Semliki Forest virus, Sindbis virus, Chikungunya virus, and Venezuelan equine encephalitis virus described

Senior Recital: Jarod Dylan Boles, double bass

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Mr. Boles studies doiuble bass with Joseph McFadden.https://digitalcommons.kennesaw.edu/musicprograms/1524/thumbnail.jp

N 2-(7-Bromo-9-butyl-9H-carbazol-2-yl)-9,9-diethyl-N 2,N 7,N 7-triphenyl-9H-fluorene-2,7-diamine

In the title mol­ecule, C51H46BrN3, the central fluorene residue is planar (r.m.s. deviation = 0.0203 Å), as is the carbazole system (r.m.s. deviation = 0.0154 Å), and these groups are almost orthogonal [dihedral angle = 79.72 (3)°]. The three-dimensional architecture is consolidated by C—H⋯π inter­actions. The butyl substituent is disordered with two sites resolved for the terminal propyl atoms; the major component had a site-occupancy factor of 0.686 (3)