SURFACE REACTIVITY OF IRON, MANGANESE MINERALS AND THEIR ENVIRONMENTAL IMPLICATIONS

The focus of the thesis research was to investigate the surface reactivity of three different minerals, pyrite (FeS2), an ordered form of ferrihydrite (an iron oxyhydroxide phase), and birnessite (MnO2), toward environmentally relevant aqueous reactants. In particular, research was carried out with the goals of 1) understanding the redox chemistry of nitrite (NO2-) and nitrate (NO3-) on pyrite and 2) understanding the redox (photo) chemistry of arsenite (AsO2-, As(III)) on ordered ferrihydrite and birnessite. A motivation for all these studies stemmed in part from the recognition that NO2-, NO3-, and As(III) are all environmental pollutants when they are present at sufficiently high concentration in the environment. The removal of these species or conversion of each of them on mineral surfaces to more benign chemical species is of importance in the realm of environmental chemistry. In the case of NO2- and NO3- on pyrite, an additional and primary motivation for the research was that it has been hypothesized in the "origin-of-life" community that the reaction of NO2- and NO3- with iron sulfide (e.g., pyrite) may have played a role in the production of ammonia (NH3) on early Earth. Such prebiotic chemistry had been hypothesized to an essential step in the production of biomolecules that included proteins. With regard to the NO2- reaction with pyrite, results detailed in this thesis showed that ammonia in µmol/kg quantities could be produced by reacting NO2- in the presence of pyrite under anaerobic conditions. The concentration of NH3 (detected as ammonium, NH4+, in solution) was a strong function of the reaction temperature. At the lower temperatures studied (22oC and 70oC), a small amount of NH4+ was formed, but µmol.kg-1 amounts of NH4+ were formed at a reaction temperature of 120oC. Only about 5% of the initial NO2- concentration was converted to NH4+. In the NO3-/pyrite system, the NO3- reactant concentration remained unchanged at all the three reaction temperatures studied, consistent with the low amounts of NH4+ formed in these experiments. Finally, it was shown using in situ infrared spectroscopy that surface-bound NO formed on pyrite during the conversion of the nitrogen oxides to ammonia. Overall, it was shown that the kinetics of NH4+ formation was slower for NO3- than that observed for NO2-. Studies presented in this thesis that focused on the surface reactivity of As(III) on ordered ferrihydrite and birnessite nano particles showed that As(III) could be oxidized to arsenate (referred to as As(V)) in the presence of simulated solar radiation. In the ordered ferrihydrite circumstance the adsorption of As(III) and photo-induced oxidation to As(V) was compared to the same reaction on the more disordered and smaller ferrihydrite particles (known as "2-line" ferrihydrite). A comparison of the adsorption rate of As(III) on the two surfaces in the presence of light after normalizing for differences in surface area showed that the ordered ferrihydrite exhibited a higher arsenic adsorption rate. Also, the oxidation rate of As(III) to As(V) in the presence of light on the ordered ferrihydrite showed a strong dependence on the amount of dissolved oxygen in solution while the oxidation rate on the more disordered form showed no such dependence. It was proposed that differences in the rates of the heterogeneous oxidation rate of ferrous iron with dissolved oxygen on the two surfaces were the reason for this behavior. Finally, the photo-induced oxidation of As(III) to As(V) on Na- and K-birnessite at solution pHs of 5.0 and 7.4 was investigated. It was shown that the oxidation rate of As(III) to As(V) occurred at a faster rate on birnessite in the presence of light when compared to the same system in the dark. Mn(II) formed during the reductive dissolution of birnessite during the oxidation of As(III) was experimentally observed at pH 5.0, but not at pH 7.4. Experiments were also conducted that investigated the reductive dissolution of Na- and K-birnessite (having different sizes and average oxidation states) by As(III) under more alkaline conditions. These experiments were conducted at pH 8.5 and the post-reaction samples were analyzed with X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). It was shown under these alkaline conditions using X-ray diffraction that structural changes occurred in/on both the Na- and K-birnessite during the As(III) oxidation reaction.Chemistr

Dangerous Research: Threat of bioterrorism creates need for new precautions

pp. 7-

Symbolic Regression for Data-Driven Equation Discovery: A Physics-Informed Approach

Symbolic Regression (SR) is a cutting-edge machine learning technique that discovers mathematical expressions representing the underlying patterns in data. Unlike traditional regression, SR explores a wide range of mathematical models, allowing for flexible and interpretable solutions. We utilize PySR, a highly customizable symbolic regression package, which combines genetic programming and modern optimization methods to efficiently search for interpretable equations. PySR balances model complexity with performance by penalizing overly complex expressions while optimizing accuracy. In this thesis, we apply Physics-informed Symbolic Regression through PySR to model the x and t dependence of the flavor isovector combination Hu − d(x,t,ζ ,Q2 ) at ζ = 0 and Q2 = 4GeV2 . Our PySR models are trained on Generalized Parton Distribution (GPD) pseudo data, sourced from both Lattice QCD and contemporary models such as GGL. By incorporating physics constraints into the symbolic regression process, our models satisfy physical principles relevant to GPDs while achieving low mean-squared error. A custom loss function was employed, encouraging models that both fit the data well and adhered to physical constraints. We compare the performance of PySR-derived GPDs against traditional Regge parameterizations and Neural Network-based GPDs.This research demonstrates the power of PySR for extracting interpretable expressions from complex physics data, offering a novel tool for scientific modeling that merges data-driven techniques with domain-specific knowledge

Low-Power Analog Circuits for Sub-Band Speech Processing

The need for efficient electronics has been increasing by the day, as have the constraints on power and size of the devices. Also the increase in use of mobile and wearable electronics has been leading to innovative methods to conserve power and increase functionality. The traditional approach of signal processing heavily relies on the Digital Signal Processing (DSP) hardware to perform most of the tasks, which has lead to power-hungry circuits. Use of analog front-end devices could prove to be efficient, since most of the real-world data is analog and since the DSP could be spared for more application-specific tasks within the system, thereby resulting in more efficient mixed-signal systems.;The focus in this work is to develop an analog front-end for speech-processing applications with inspiration from biology, and trying to mimic human auditory perception techniques. The circuits are designed in 600nm, 350nm and 180nm CMOS processes and are biased in the sub-threshold region to consume low-power. Also, various modules of the system are connected using multiplexing circuits to allow post-fabrication reconfigurability to suit various applications. These circuits are biased using a network of floating-gate transistors which allow reconfigurability and increased bias accuracy. This thesis mainly describes two modules of the analog front-end used for speech processing: derivative circuit and voltage-mode subtractor circuit, which are used for processing spectrally decomposed signals. These circuits could be used for applications like audio analysis or event detection

Edge-Vertex Dominating Set in Unit Disk Graphs

Given an undirected graph $G=(V,E)$, a vertex $v\in V$ is edge-vertex (ev) dominated by an edge $e\in E$ if $v$ is either incident to $e$ or incident to an adjacent edge of $e$. A set $S^{ev}\subseteq E$ is an edge-vertex dominating set (referred to as ev-dominating set) of $G$ if every vertex of $G$ is ev-dominated by at least one edge of $S^{ev}$. The minimum cardinality of an ev-dominating set is the ev-domination number. The edge-vertex dominating set problem is to find a minimum ev-domination number. In this paper we prove that the ev-dominating set problem is {\tt NP-hard} on unit disk graphs. We also prove that this problem admits a polynomial-time approximation scheme on unit disk graphs. Finally, we give a simple 5-factor linear-time approximation algorithm

Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning

Reinforcement learning (RL) is a powerful tool for finding optimal policies in sequential decision processes. However, deep RL methods suffer from two weaknesses: collecting the amount of agent experience required for practical RL problems is prohibitively expensive, and the learned policies exhibit poor generalization on tasks outside of the training distribution. To mitigate these issues, we introduce automaton distillation, a form of neuro-symbolic transfer learning in which Q-value estimates from a teacher are distilled into a low-dimensional representation in the form of an automaton. We then propose two methods for generating Q-value estimates: static transfer, which reasons over an abstract Markov Decision Process constructed based on prior knowledge, and dynamic transfer, where symbolic information is extracted from a teacher Deep Q-Network (DQN). The resulting Q-value estimates from either method are used to bootstrap learning in the target environment via a modified DQN loss function. We list several failure modes of existing automaton-based transfer methods and demonstrate that both static and dynamic automaton distillation decrease the time required to find optimal policies for various decision tasks

GSU Event Portal

This application system for Hotel Booking is an intranet application, which provides various hotels that are available for the users in the city. The application helps users to select their favorite dishes among the continental dishes that are available. Users also have the choice of choosing their flavors, ingredients, and can add many other choices to their recipe. This application provides an option through which users who have diabetes and blood pressure can place their order according to their choice. After placing the orders, users can add their item to the cart along with the previous orders they made and they can pay through credit card online system. After completing the payment process, application directs the users to the map to locate their address and their order is delivered to their doorstep. Besides the online order facility, the application has an alluring facility, through which users can book for dining and they can also book a banquet or can arrange a hall for parties. Through the responses given by the users the applications provide list of hotels or restaurants that have the required facilities

Transcriptomics Signature from Next-Generation Sequencing Data Reveals New Transcriptomic Biomarkers Related to Prostate Cancer

Prostate cancer is one of the most common types of cancer among Canadian men. Next-generation sequencing using RNA-Seq provides large amounts of data that may reveal novel and informative biomarkers. We introduce a method that uses machine learning techniques to identify transcripts that correlate with prostate cancer development and progression. We have isolated transcripts that have the potential to serve as prognostic indicators and may have tremendous value in guiding treatment decisions. Analysis of normal versus malignant prostate cancer data sets indicates differential expression of the genes HEATR5B, DDC, and GABPB1-AS1 as potential prostate cancer biomarkers. Our study also supports PTGFR, NREP, SCARNA22, DOCK9, FLVCR2, IK2F3, USP13, and CLASP1 as potential biomarkers to predict prostate cancer progression, especially between stage II and subsequent stages of the disease