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The use of interference microscopy has enabled the direct observation of transient concentration profiles generated by intracrystalline transport diffusion in nanoporous materials. The thus accessible intracrystalline concentration profiles contain a wealth of information which cannot be deduced by any macroscopic method. In this paper, we illustrate five different ways for determining the concentration-dependent diffusivity in one-dimensional systems and two for the surface permeability. These methods are discussed by application to concentration profiles evolving during the uptake of methanol by the zeolite ferrierite and of methanol by the metal organic framework (MOF) manganese(II) formate. We show that the diffusivity can be calculated most precisely by means of Fick’s 1st law. As the circumstances permit, Boltzmann’s integration method also yields very precise results. Furthermore, we present a simple procedure that enables the estimation of the influence of the surface barrier on the overall

Within the field of developmental psychopathology, research has repeatedly demonstrated that there are multiple complex and dynamic pathways originating in childhood that may lead to the development of internalizing and externalizing problems among adolescents. However, additional research is needed that examines the unique and concurrent contributions among child-, parent-, and family-level risk and resilience factors during childhood that may be associated with internalizing and externalizing problems in adolescence. To address this gap, the current study utilized a person-centered approach to identify profiles of risk and resilience factors among youth in middle childhood (ages 10-12) characterized by the quality and quantity of (a) child-level factors (i.e., temperamental features, executive functioning abilities); (b) parent-level factors (i.e., parental acceptance, control, disciplinary style); and (c) family-level factors (i.e., family cohesion, conflict, organization) among a sample of 775 participants (Aim 1). The study also examined internalizing and externalizing symptom profiles in adolescence (age 16) by identifying subgroups of youth characterized by the quality and quantity of internalizing and externalizing problems within each of the identified childhood risk profiles (Aim 2). Lastly, the study investigated transitions from childhood risk profiles to adolescent symptom profiles (Aim 3). Results demonstrated that a four-class model best fit the data in regard to childhood risk profiles, with classes of youth most saliently characterized by (a) accepting parents, (b) controlling parents, (c) disengaged parents, and (d) chaotic homes. With regard to adolescent internalizing and externalizing symptom profiles, results indicated a three-class model best fit the data and included classes distinguished by the presence of (a) low symptoms, (b) moderate symptoms, and (c) high internalizing and moderate externalizing symptoms. Most youth from the four childhood risk profiles transitioned to the low symptom profile at age 16; however, youth from the chaotic home profile were more likely to transition into one of the two higher-level symptom profiles. Findings enhance our understanding of risk and resilience by identifying distinct childhood risk profiles and corresponding adolescent symptom profiles. These findings will have implications for both prevention and treatment efforts that target specific risk factors within each risk profile. / Psychology

Discrete choice experiments are used when choice alternatives can be described in terms of attributes. The objective is to infer the value that respondents attach to attribute levels. Respondents are presented sets of profiles based on attributes specified at certain levels and asked to select the profile they consider best. When the number of attributes or attribute levels becomes large, the profiles in a single choice set may be too numerous for respondents to make precise decisions. One strategy for reducing the size of choice sets is the sub-setting of attributes. However, the optimality of these reduced size choice sets has not been examined in the literature. We examine the optimality of reduced size choice sets for 2^n experiments using information per profile (IPP) as the optimality criteria. We propose a new approach for calculating the IPP of designs obtained by dividing attributes into two or more subsets with one, two, and in general, r overlapping attributes, and compare the IPP of the reduced size designs with the original full designs. Next we examine the IPP of choice designs based on 3^n factorial experiments. We calculate the IPP of reduced size designs obtained by sub-setting attributes in 3^n plans and compare them to the original full designs. / Statistics

Chemical engineers have routinely used computational tools for modeling, optimizing, and debottlenecking chemical processes. Because of the advances in computational science over the past decade, multivariate statistics and machine learning have become an integral part of the computerization of chemical processes. In this research, we look into using multivariate statistics, machine learning tools, and their combinations through a series of case studies including a case with a successful industrial deployment of machine learning models for fermentation. We use both commercially-available software tools, Aspen ProMV and Python, to demonstrate the feasibility of the computational tools. This work demonstrates a novel application of ensemble-based machine learning methods in bioprocessing, particularly for the prediction of different fermenter types in a fermentation process (to allow for successful data integration) and the prediction of the onset of foaming. We apply two ensemble frameworks, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to build classification and regression models. Excessive foaming can interfere with the mixing of reactants and lead to problems, such as decreasing effective reactor volume, microbial contamination, product loss, and increased reaction time. Physical modeling of foaming is an arduous process as it requires estimation of foam height, which is dynamic in nature and varies for different processes. In addition to foaming prediction, we extend our work to control and prevent foaming by allowing data-driven ad hoc addition of antifoam using exhaust differential pressure as an indicator of foaming. We use large-scale real fermentation data for six different types of sporulating microorganisms to predict foaming over multiple strains of microorganisms and build exploratory time-series driven antifoam profiles for four different fermenter types. In order to successfully predict the antifoam addition from the large-scale multivariate dataset (about half a million instances for 163 batches), we use TPOT (Tree-based Pipeline Optimization Tool), an automated genetic programming algorithm, to find the best pipeline from 600 other pipelines. Our antifoam profiles are able to decrease hourly volume retention by over 53% for a specific fermenter. A decrease in hourly volume retention leads to an increase in fermentation product yield. We also study two different cases associated with the manufacturing of polyolefins, particularly LDPE (low-density polyethylene) and HDPE (high-density polyethylene). Through these cases, we showcase the usage of machine learning and multivariate statistical tools to improve process understanding and enhance the predictive capability for process optimization. By using indirect measurements such as temperature profiles, we demonstrate the viability of such measures in the prediction of polyolefin quality parameters, anomaly detection, and statistical monitoring and control of the chemical processes associated with a LDPE plant. We use dimensionality reduction, visualization tools, and regression analysis to achieve our goals. Using advanced analytical tools and a combination of algorithms such as PCA (Principal Component Analysis), PLS (Partial Least Squares), Random Forest, etc., we identify predictive models that can be used to create inferential schemes. Soft-sensors are widely used for on-line monitoring and real-time prediction of process variables. In one of our cases, we use advanced machine learning algorithms to predict the polymer melt index, which is crucial in determining the product quality of polymers. We use real industrial data from one of the leading chemical engineering companies in the Asia-Pacific region to build a predictive model for a HDPE plant. Lastly, we show an end-to-end workflow for deep learning on both industrial and simulated polyolefin datasets. Thus, using these five cases, we explore the usage of advanced machine learning and multivariate statistical techniques in the optimization of chemical and biochemical processes. The recent advances in computational hardware allow engineers to design such data-driven models, which enhances their capacity to effectively and efficiently monitor and control a process. We showcase that even non-expert chemical engineers can implement such machine learning algorithms with ease using open-source or commercially available software tools. / Doctor of Philosophy / Most chemical and biochemical processes are equipped with advanced probes and connectivity sensors that collect large amounts of data on a daily basis. It is critical to manage and utilize the significant amount of data collected from the start and throughout the development and manufacturing cycle. Chemical engineers have routinely used computational tools for modeling, designing, optimizing, debottlenecking, and troubleshooting chemical processes. Herein, we present different applications of machine learning and multivariate statistics using industrial datasets. This dissertation also includes a deployed industrial solution to mitigate foaming in commercial fermentation reactors as a proof-of-concept (PoC). Our antifoam profiles are able to decrease volume loss by over 53% for a specific fermenter. Throughout this dissertation, we demonstrate applications of several techniques like ensemble methods, automated machine learning, exploratory time series, and deep learning for solving industrial problems. Our aim is to bridge the gap from industrial data acquisition to finding meaningful insights for process optimization.

Irregularities in pavement profiles that exceed standard thresholds are usually rectified using a Diamond Grinding Process. Diamond Grinding is a method of Concrete Pavement Rehabilitation that involves the use of grinding wheels mounted on a machine that scraps off the top surface of the pavement to smooth irregularities. Profile Analysis Software like ProVAL© offers simulation modules that allow users to investigate various grinding strategies and prepare a corrective action plan for the pavement. The major drawback with the current Smoothness Assurance Module© (SAM) in ProVAL© is that it provides numerous grind locations which are both redundant and not feasible in the field. This problem can be overcome by providing a constrained grinding model in which a cost function is minimized; the resulting grinding strategy satisfies requirements at the least possible cost. Another drawback with SAM exists in the built-in grinder models that do not factor in the effect of speed and depth of cut on the grinding head. High speeds or deep cuts will result in the grinding head riding out the cut and likely worsening the roughness. A constrained grinding strategy algorithm with grinder models that factor in speed and depth of cut that results in cost effective grinding with better prediction of post grind surfaces through simulation is developed in this work. The outcome of the developed algorithm is compared to ProVAL's© SAM results. / Master of Science

Manufacturers of passenger vehicles are experiencing increased pressure from consumers and legislators due to the impact of transportation on the environment. Automotive manufacturers are responding by designing more sustainable forms of transportation through a variety of efforts, including increased vehicle efficiency and the electrification of vehicle powertrains (plug in hybrid electric vehicles (PHEV) and battery electric vehicles (BEV)). An additional method for reducing the environmental impact of personal transport is eco-routing, a methodology which selects routes on the basis of energy consumption. Standard navigation systems offer route alternatives between a user clarified origin and destination when there are multiple paths available. These alternatives are commonly weighted on the basis of minimizing either total travel time (TTT) or trip distance. Eco-routing offers an alternative criterion – minimizing route energy consumption. Calculation of the energy consumption of a route necessitates the creation of a velocity profile which models how the route will be driven and a powertrain model which relates energy consumption to the constructed velocity profile. Existing research efforts related to both of these aspects typically require complex analysis and proprietary vehicle properties. A new approach to weighting the energy consumption of different routes is presented within this paper. The process of synthesizing velocity profiles is an improvement upon simpler models while requiring fewer variables as compared to more complex models. A single input, the maximum acceleration, is required to tune driver aggressiveness throughout an entire route. Additionally, powertrain results are simplified through the application of a new parameter, predictive terminal energy. The parameter uses only glider properties as inputs, as compared to dedicated powertrain models which use proprietary vehicle information as inputs which are not readily available from manufacturers. Application of this research reduces computation time and increases the number of vehicles for which this analysis can be applied. An example routing scenario is presented, demonstrating the capability of the velocity synthesis and predictive terminal energy methodologies. / Master of Science

Soil columns of predominantly Peawick and Slagle soils had various amounts of alum residuals, lime and plant nutrients incorporated into the topsoil. A weekly dose of rainwater was applied to each column over a six month period. Leachate and soil constituents were analyzed to evaluate migration of specific constituents from the alum residuals through the soil profile and into the groundwater. Soil analysis indicated little, if any, migration of metals from the alum residuals occurred. Metal constituents found within the leachate appear to have originated from the soil rather than the alum residuals. Of the measured anions, nitrate - nitrogen was the only component which had increasing concentrations within the leachate. Much, if not all, of the nitrate can be attributed to the plant nutrients incorporated into the topsoil and disturbance of the topsoil. Nitrate and zinc were the only components that consistently degraded the leachate quality beyond Virginia Water Control Boards' Groundwater Standards, but were within observed ranges of non-sludge amended soil columns. Groundwater contamination is not likely as a result of land application of alum residuals up to a loading of four percent. / Master of Science

Oxytetracycline (OTC) is one of two antibiotics currently available and approved by the U.S. Food and Drug Administration for use as a chemotherapeutic agent in food fish and is widely used in the aquaculture industry. Previous pharmacokinetic studies of OTC have been conducted in cold water and warm water species of fish. However, no pharmacokinetic studies have been conducted on a cool water species such as yellow perch (Perca flavescens). The yellow perch is a cool water game and commercial species with high aquaculture potential. The pharmacokinetic profiles of oxytetracycline (OTC) was determined by measuring plasma concentrations in yellow perch following intraperitoneal (i.p.), intramuscular (i.m.), per os (p.o.), and intracardiac (i.c.) administration at a single dose of 50 mg/kg body weight. Using a modification of a high-performance-liquid-chromatographic (HPLC) technique, the plasma OTC concentrations were determined for each of the four routes of administration. Plasma concentrations were also evaluated in yellow perch exposed to a static 48-hour OTC water bath (100 mg/l). The terminal half-lives (t1/2) of OTC in yellow perch for i.p., i.m., p.o., and i.c. administrations were 112, 124, 50, and 28 h, respectively. The t1/2 for the i.m. route of administration was significantly longer than in any of the published i.m. OTC fish studies to date. However, the times of maximum OTC concentration (tmax) for the i.p., i.m. and p.o. administrations (2, 4, and 15 h, respectively) occurred relatively early in the plasma concentration-time curves. This suggests, that in yellow perch, OTC is initially absorbed very rapidly. The area under the plasma concentration-time curves (AUC) for the i.p., i.m., p.o., and i.c. routes of administration were 1718, 2659, 383, and 134 mcg·h/ml, respectively. No OTC was detected in the plasma of yellow perch following the water bath route of exposure. Finally, in yellow perch, effective therapy (plasma OTC concentrations above MIC values for most bacteria pathogenic to fish — 4 mcg/ml) would be achieved for up to 168 hours following a single i.p. or i.m. injection of 50 mg/kg and for up to 15 hours following a single p.o dose of 50 mg/kg. / Master of Science

The purpose of this study was to gain understanding of how children learn when they are engaged in child initiated, teacher guided activities. Specifically, children's learning processes were documented and interpreted based on how they use their multiple intelligences. Multiple Intelligences refers to Howard Gardner's model of multiple intelligences and his view of how children have many cognitive strengths. Ethnographic methodologies were used to observe, document, and interpret children's behaviors and interactions in the classroom. Seven children were chosen to be focused on for this study out of 15 participants in one preschool classroom at a university Child Development Laboratory setting. The researcher has been the head teacher for these 7 children for two years, which allowed the researcher to gain a better understanding of children's different intelligences and different ways of learning. After collecting and analyzing the data, the researcher found that the children's propensities for learning remained fairly consistent over the course of two years. It became evident that the role of the teacher is very important, as the teacher must be an intimate observer and listener of the children. Teachers and educators should be in constant communication with parents and each other about the child's growth and development and tendencies for learning. By providing children with learning opportunities for the child to use their cognitive strengths, teachers are motivating children and encouraging them to learn. If children see that they can succeed, they may continuously have the motivation to learn. / Master of Science