Development of a college level freshman biology course was undertaken in response to government reports that American students have fallen behind students of other countries in the area of the sciences. Teaching strategies were investigated to accomplish two objectives, to define essential academic material to include in the course and to investigate teaching techniques that would increase deep processing of the information. An active process that consisted of applying the cognitive information to solving problems or developing answers to questions was defined as critical thinking. Critical thinking was incorporated into the course by the use of case studies.

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Frying oil is a heat and mass transfer medium, which affects the quality of food. The reaction mechanisms in deep-frying oils are mainly thermal oxidation, hydrolysis, and polymerization, which result in lipid deterioration. Addition of synthetic or natural antioxidants can effectively slow down lipid deterioration during deep-frying. Total polar components, polymerized triglycerides, p-anisidine value, acid value and iodine value are reliable indicators for assessing oil degradation during frying. Color darkening of deep-frying oils is one of apparent changes during deep-frying and is closely associated with the levels of decomposition compounds in the frying oils. However, the evidence of the relationship between color and deep-frying oil quality indicators are scanty. The main objective of this thesis is to develop a model for rapid assessment of oil quality during 30-hour deep-frying processes using oil color and quality as indicators. Significant color changes (p < 0.05) were observed in soybean oil as compared to canola and sunflower oil during 30-hour deep-frying trials. Canolol-enriched frying oils showed the highest color values before deep-frying, but the final results showed the least color changes (p < 0.05) during the 30-hour deep-frying trials. The highest percentage of total polar components (15.55 %), polymeric triglycerides (9.3 %), and p-Anisidine value (62.34) were found in TBHQ-enriched deep-frying oil samples in soybean oil. The highest acid value (3.06 mg KOH/100g) was found in canolol-enriched frying oil samples in canola oil. Rosemary and canolol-enriched deep-frying oil samples showed significant effect (p < 0.05) on color changes while reducing formation of total polar components, polymeric triglycerides, and aldehydes during the 30-hour deep-frying study. Significant correlations (p < 0.05) were found between color and oil quality indicators in all of the deep-frying oil samples; significant regression (p < 0.05) models are expressing the level of oil deterioration from color (light-dark, red-green, yellow-blue) in deep-frying oils. Overall, this study established several models using color as an indicator aiming to rapidly assess deep-frying oil quality. / October 2016

This work focuses on diffusion and advection through cementitious media, the work arises from two research contracts undertaken at Loughborough University: Experiments to Demonstrate Chemical Containment funded by UK NDA and the SKIN project, funded by the European Atomic Energy Community's Seventh Framework Programme. Diffusion will be one of the most significant mechanisms controlling any radionuclide migration from a nuclear waste, deep geological disposal facility. Advection may also occur, particularly as the immediate post closure groundwater rebound and equilibration proceeds but is expected to be limited by effective siting and management during the operational phase of the facility. In this work advection is investigated at laboratory scale as a possible shorter timescale technique for providing insight into the much slower process of diffusion. Radial techniques for diffusion and advection have been developed and the developmental process is presented in some detail. Both techniques use a cylindrical sample geometry that allows the radionuclide of interest to be introduced into a core drilled through the centre of the test material. For diffusion the core is sealed and submerged in a container of receiving solution which is sampled and analysed as the radionuclide diffuses into it. For advection, a cell has been designed that allows inflow via the central core to pass through the sample in a radial manner and be collected as it exits from the outer surface. The radionuclide of interest can be injected directly into the central core without significant disturbance to the advective flow. Minor improvements continue to be made but both techniques have provided good quality, reproducible results. The majority of the work is concentrated on a potential cemetitious backfill known as NRVB (Nirex Reference Vault Backfill) this is a high porosity, high calcium carbonate content cementitious material. The radioisotopes used in this work are 3H (in tritiated water), 137Cs, 125I, 90Sr, 45Ca, 63Ni, 152Eu, 241Am along with U and Th salts. In addition the effect of cellulose degradation products (CDP) on radioisotope mobility was investigated by manufacturing solutions where paper tissues were degraded in water, at 80°C, in the absence of air and at high pH due to the presence of the components of NRVB. All diffusion experiments were carried out under a nitrogen atmosphere. All advection experiments were undertaken using an eluent reservoir pressurised with nitrogen where the system remained closed up to the point of final sample collection. Results for tritiated water and the monovalent ions of Cs and I were produced on a timescale of weeks to months for both diffusion and advection. The divalent ions of Sr, Ca and Ni produced results on a timescale of months to years. Variations of the experiments were undertaken using the CDP solutions. The effects of CDP were much more apparent at radiotracer concentration than the much higher radiotracer with non-active carrier, concentration. In the presence of CDP Cs, I and Ni were found to migrate more quickly; Sr and Ca were found to migrate more slowly. Additional Sr experiments were undertaken at elevated ionic strength to evaluate the effect of the higher dissolved solids content of the CDP solutions. Some of the results for HTO, Cs, I and Sr have been modelled using a simple numerical representation of the system in GoldSim to estimate effective diffusivity and partition coefficient. The diffusion model successfully produced outputs that were comparable to literature values. The advection model is not yet producing good matches with the observed data but it continues to be developed and more processes will be added as new results become available. Autoradiography has been used to visualise the radionuclide migration and several images are reproduced that show the fate of the radiotracers retained on the NRVB at the end of the experiments. As the experimental programme progressed it was clear that results could not be produced in a suitable timescale for Eu, Am U and Th. These experiments have been retained and will be monitored every six months until either diffusion is detected or the volume of receiving liquid is inadequate to ensure the NRVB is saturated.

Screening of possibilities of deep subsurface microflora to decompose selected organic compounds Abstract The aim of the study is to test the deep microflora bacterial strains for their ability to grow in oily environment, aliphatic hydrocarbons and toluene were taken as examples of aromatic hydrocarbons and where the cultivation of selected strain, were produced for testing its growth and microbial activity of selected strains in conditions simulating soil conditions; assess the usability these strains in practical remediation of contamination by oil. This thesis deals with the evaluation of possible use of selected strains of deep microflora for oil decomposition. It is evident, that some groups of microorganisms living in the Tertiary claystones at depths of 30-450 m below the surface are the biodegradable fossil organic matter type of kerogen. Chemical findings indicate that, this organic matter consists of various lengths of aliphatic chains, and thus the assumption that microorganisms decomposing kerogen might be able to disassemble oil and petroleum products. The findings of our experiment indicate that benzene and toluene, as well as kerogen are highly resistant to organic compounds and evidence of microbial degradation are rare. Utilization of oil as representative aliphatic compounds is better...

This thesis presents measurements of charged current deep inelastic scattering cross sections in e+p collisions with longitudinally polarised positron beams. The measurements are based on data taken by the ZEUS detector at the HERA collider during the 2006-2007 run- ning period. The data sample has an integrated luminosity of 132 pb-1 and was taken at a centre-of-mass energy of 318 GeY. The total cross section has been measured at positive and negative values of the longitu- dinal polarisation of the positron beam (Pe). In addition, the single differential cross sections dδ / dQ2, dδ / dx and dδ / dy have been measured for Q2 > 200 Ge y2, also using both positively and negatively polarised positron beams. The reduced cross section has been measured in nine bins of Q2 in the kinematic range 280 < Q2 < 30000 Gey2 and 0.0078 < x < 0.42. The results are compared against the descriptions provided by the CTEQ6.6, MSTW 2008, HEARPDF1.0 and ZEUS-JETS PDFs. In general, the measured cross sections are well described by these predictions. Based on the measurement of the total cross section as a function of the polarisation of the positron beam, a lower limit on the mass of a hypothetical right-handed W boson has been extracted from the upper limit of the cross-section at Pe = -1. This limit is complementary to the limits obtained from direct searches (for example at CDF and D0) because the limit presented herein is for a space-like vV, whereas for direct searches, the limit on the mass of a time-like W boson is obtained. The results of this analysis have been published and have been included ill the determination of the HERAPDF theoretical prediction and also in HI and ZEUS combined results.

The Gulf of Mexico (GOM) represents a challenge for exploration and production. Most of the sediments coming from North America has bypassed the shelf margin into Deep Water. In an Attempt to attack this challenge this thesis pretends to break the GOM's false bottom, mainly comprised by diverse salt structures and growth fault families. In this attempt, geological and geophysical data are integrated to find clues to potential hydrocarbons indicator (PHI) that could be of Reservoir Quality (RQ). 3D Pre stack depth migrated data comprised of Mississippi Canyon blocks, were interpreted: Top and base of salt, leading to the identification of a PHI represented by a consistent Amplitude Anomaly (AA) below and towards a salt structure. This AA may be of RQ and feasibility evaluation for further decisions may be taken. Following the structural sequences that Govern central GOM during Oligocene through out Miocene was important to support the results.

Background: Logistics have a vital role in supply chain management and those logistics operations are dependent on the availability of packaging material for packing goods and material to be shipped. Forecasting packaging material demand for a long period of time will help organization planning to meet the demand. Using time-series data with Deep Neural Networks for long term forecasting is proposed for research. Objectives: This study is to identify the DNN used in forecasting packaging demand and in similar problems in terms of data, data similar to the available data with the organization (Volvo). Identifying the best-practiced approach for long-term forecasting and then combining the approach with identified and selected DNN for forecasting. The end objective of the thesis is to suggest the best DNN model for packaging demand forecasting. Methods: An experiment is conducted to evaluate the DNN models selected for demand forecasting. Three models are selected by a preliminary systematic literature review. Another Systematic literature review is performed in parallel for identifying metrics to evaluate the models to measure performance. Results from the preliminary literature review were instrumental in performing the experiment. Results: Three models observed in this study are performing well with considerable forecasting values. But based on the type and amount of historical data that models were given to learn, three models have a very slight difference in performance measures in terms of forecasting performance. Comparisons are made with different measures that are selected by the literature review. For a better understanding of the batch size impact on model performance, experimented three models were developed with two different batch sizes. Conclusions: Proposed models are performing considerable forecasting of packaging demand for planning the next 52 weeks (∼ 1 Year). Results show that by adopting DNN in forecasting, reliable packaging demand can be forecasted on time series data for packaging material. The combination of CNN-LSTM is better performing than the respective individual models by a small margin. By extending the forecasting at the granule level of the supply chain (Individual suppliers and plants) will benefit the organization by controlling the inventory and avoiding excess inventory.

Watermarking is a technique used to used to mark the ownership in media such as audio or images by embedding a watermark, e.g. copyrights information, into the media. A good watermarking method should perform this embedding without affecting the quality of the media. Recent methods for watermarking in images uses deep learning to embed and extract the watermark in the images. In this thesis, we investigate watermarking in the hearable frequencies of audio using deep learning. More specifically, we try to create a watermarking method for audio that is robust to noise in the carrier, and that allows for the extraction of the embedded watermark from the audio after being played over-the-air. The proposed method consists of two deep convolutional neural network trained end-to-end on music with simulated noise. Experiments show that the proposed method successfully creates watermarks robust to simulated noise with moderate quality reductions, but it is not robust to the real world noise introduced after playing and recording the audio over-the-air.

To combat fake news, researchers mostly focused on detecting fake news and journalists built and maintained fact-checking sites (e.g., Snopes.com and Politifact.com). However, fake news dissemination has been greatly promoted by social media sites, and these fact-checking sites have not been fully utilized. To overcome these problems and complement existing methods against fake news, in this thesis, we propose a deep-learning based fact-checking URL recommender system to mitigate impact of fake news in social media sites such as Twitter and Facebook. In particular, our proposed framework consists of a multi-relational attentive module and a heterogeneous graph attention network to learn complex/semantic relationship between user-URL pairs, user-user pairs, and URL-URL pairs. Extensive experiments on a real-world dataset show that our proposed framework outperforms seven state-of-the-art recommendation models, achieving at least 3~5.3% improvement.