Dissertations / Theses on the topic '109999 Technology not elsewhere classified'

study, a home use testing (HUT) of instant tuwo and fura porridges in Niamey, Niger indicated that overall consumers’ acceptability was good. In a market test conducted in Niamey over 20 weeks, in collaboration with local cereal processors and distributors, showed repeat purchases with good frequency of sale of instant tuwo and fura porridges that have generated about 1/3 in total sales of 35 products related millet and cereal based foods in the period. In conclusion, the low-cost single screw extruder has the potential to catalyze and increase demand and diversification of uses of millet grains, to develop market-driven nutritious and healthy grain-based foods, to benefit smallholders’ farmers, and favor growth of small- and medium-scale entrepreneur processors in West Africa.

Numerous studies have shown multiple benefits gained through professional development for teachers, which address increasing pedagogical content knowledge, increasing student achievement and increasing teacher job satisfaction (Banks, 2008; Colbert, Brown, Choi &Thomas, 2008; Dash, de Kramer, O’Dwyer, Masters & Russell, 2012). However, barriers to professional development, including cost, time and access, make face-to-face professional development sessions difficult to meet teacher needs (Carr, 2016). Online professional development can help to address these barriers, while meeting the needs of teachers. Employing best practices established through both online learning pedagogy and professional development techniques, teachers can increase their pedagogical content knowledge and opportunities for collaboration using online platforms. This study demonstrated, using a blend of qualitative and quantitative assessments that online professional development was comparable in effectiveness to face-to-face professional development in preparation for teaching an introductory logistics course and the Manufacturing Skills Standards Council Certified Logistics Associate certification exam.

Opioid addiction has become a global epidemic and a national health crisis in recent years. In 2016, approximately 64,000 Americans under 50 years old were killed because of opioid overdoses. The aim of developing an abuse deterrent opioid is to render any form of manipulation that encourages abuse challenging and therefore, non-profitable. With this goal, the Food and Drug Administration (FDA) is extensively supporting research into the development of abuse deterrent technologies and prioritizing their production as a public health necessity. Abuse deterrent approaches include but are not limited to the following: (1) using a physical barrier (e.g., Polyethylene oxide PEO) that basically limit the release of the drugs in the blood or the digestive tract and prevent mechanical alteration of the drugs by crushing, grating, grinding, chewing etc, (2) using chemical barriers that employ gelling agents that prevent the aqueous or organic extraction of the drugs, and (3) combining the drug with an antagonist that blocks the post-abuse euphoria.

PEO is a popular polymer used as a matrix in these complex opioid products. The polymer is responsible for the abuse deterrent properties as well as extended release behavior of opioid drugs. PEO hinders the extraction of Opioid drugs from Abuse Deterrent Formulations (ADF), makes it challenging to be injected, and resists mechanical stress and pulverization when crushed. PEO can be subjected to thermal processing such as thermal curing, compression molding, melt extrusion, and injection molding owing to its thermoplasticity.

Assessment of the impact of using various manufacturing processes to develop ADFs and the effect of using various grades of this polymer is essential to improve upon the next generation of ADFs. There are three main categories of premarket studies: Category 1 – laboratory based (in-vitro manipulations and drug extractions), category 2 – pharmacokinetic and category 3 –clinical. These studies are required by the FDA to demonstrate that a given formulation exhibit abuse deterrent properties before a drug product is released to the market. In vitro laboratory based manipulation and extraction studies which are used to assess AD properties of these products are challenging, but essential for product development and generic abuse deterrent product approvals. It is important to realize that there is a great correlation between the laboratory based in vitro manipulation and extraction studies and the expectations of potential abuse and misuse of opioid drugs. The ability of these studies to mimic the manipulation techniques applied by abusers to defeat the abuse deterrent properties of a given formulation optimizes predictions on post-market abuse and misuse potential of ADFs. These studies should also correlate well with in-vivo studies since there is a direct correlation with the concentration (mg/mL in water) and the “high” obtained by an abuser.

This research aimed to conduct laboratory based in vitro manipulation and extraction studies to investigate failure modes of PEO-based prescription opioids and Promethazine Hydrochloride PMZ HCl tablets. It highlighted the formulation components and the manufacturing parameters that might affect the dose dumping of Active Pharmaceutical Ingredients (APIs). Furthermore, this research identified model compounds that can be used as surrogates for Oxycodone and the best experimental setup that can be used to conduct smoking simulation experiments. Moreover, it provided an overview of the societal impacts of the opioid crisis in the state of Indiana.

Investigations of the failure modes of the PEO-based prescription opioids and PMZ HCL tablets showed that physical manipulation techniques via chopping or grinding are much more effective in the destruction of the PEO matrix than thermal manipulation via the application of heat thus promoting the fast release. The factor with the most significant effect on the failure modes of PMZ HCL tablets was the application of physical manipulation, while the one with the lowest impact was the polymer grade. Moreover, producing PEO-based matrix tablets via Direct Compression DC significantly affected dose dumping behavior of the API from the drug products. The production of the PEO-based matrix tablets via DC was found to be favored over the usage of the melt extrusion method and molding techniques. It was clear that DC kept the integrity of the polymer, allowed for slow and controlled release fashion of the API, and rendered the extraction process relatively hard compared to the Hot Melt Extrusion HME and Molding techniques.

Furthermore, the release profile of the investigated PMZ HCL products consisted of various phases of polymer swelling and API release. Thermal manipulations via the application of heat were found to accelerate the dose dumping behavior (90% release) of the APIs from the compressed, extruded, and molded PEO-based matrix formulations similarly. On the other hand, heating was much more effective in the extraction of APIs than chopping or grinding thus promoting the ability to draw a solution containing the API into a syringe for injection relatively easy and facilitate higher % API recovery.

Among the formulation components that might have an impact on the AD properties of the PEO-based drug products are; the choice of the antioxidant, the use of complexing agents, chelating agents, and plasticizers. On the other hand, manufacturing process variables that might have a critical impact on AD properties of the PEO-based drug products include but are not limited to; processing temperature compared to the melting point of the polymer and time of exposure

PMZ HCl was used as a model drug for Oxycodone in dissolution and extractability studies, while Caffeine and L-Nicotine were used as model drugs in smoking simulation experiments. The combination of the propane torch and Kugelrohr apparatus mimic the real-world scenario for smoking Opioids; however, this experimental setup caused thermal degradation rather than vaporization of some model drugs.

According to the National Center for Health Statistics; a statistically significant increase in drug overdose death rates was reported in 2016 in the state of Indiana among other states. The number of deaths related to opioid pain relievers increased by 3732 folds in 2017 compared to the number of deaths in 2014. Moreover, Males were more affected by the opioid crisis than females. On the other hand, the age group 25-44 years, and white people were the most affected by the opioid crisis in Indiana.

In animated film production, a great bulk of work goes into animating and scene layout. Currently, the industry is centered around flat screens and keyboard and mouse interfaces, which require a substantial amount of time to lay out scene sets and animate shots, resulting in high production costs and lengthy production schedules. This study was designed to determine whether immersive VR interfaces could reduce the time taken to lay out 3D scene sets and animate those scenes compared to traditional interfaces. More specifically, this study compared the time 38 participants took to set up and animate a 3D scene by using two different interfaces, e.g. a VR immersive interface and a traditional mouse and keyboard interface. Further, the study investigated subjects’ interface preference. Findings show that the virtual reality interface was about twice as fast for laying out the scene as the traditional interface. When surveyed, participants preferred the virtual reality interface by a large margin over the provided traditional interface.

Hydrological modeling has been a valuable tool to understand the processes governing water distribution, quantity, and quality of the planet Earth. Through models, one has been able to grasp processes such as runoff, soil moisture, soil erosion, subsurface drainage, plant growth, evapotranspiration, and effects of land use changes on hydrology at field and watershed scales. The number and diversity of water-related challenges are vast and expected to increase. As a result, current models need to be under continuous modifications to extend their application to more complex processes. Several models have been extensively developed in recent years. These models include the Soil and Water Assessment Tool (SWAT), Variable Infiltration Capacity (VIC) model, MIKE-SHE, and the Water Erosion Prediction Project (WEPP) model. The latter, although it is a well-validated model at field scales, the WEPP watershed model has been limited to small catchments, and almost no research has been introduced regarding water quality issues (only one study).

In this research, three objectives were proposed to improve the WEPP model in three areas where either the model has not been applied, or modifications can be performed to improve algorithms of the processes within the model (e.g. erosion, runoff, drainage). The enhancements impact the WEPP model by improving the current stochastic weather generation, extending its applicability to subsurface drainage estimation, and formulating a new routing model that allows future incorporation of transport of reactive solutes.

The first contribution was development of a stochastic storm generator based on 5-min time resolution and correlated non-normal Monte Carlo-based numerical simulation. The model considered the correlated and non-normal rainstorm characteristics such as time between storms, duration, and amount of precipitation, as well as the storm intensity structure. The model was tested using precipitation data from a randomly selected 5-min weather station in North Carolina. Results showed that the proposed storm generator captured the essential statistical features of rainstorms and their intensity patterns, preserving the first four moments of monthly storm events, good annual extreme event correspondence, and the correlation structure within each storm. Since the proposed model depends on statistical properties at a site, this may allow the use of synthetic storms in ungauged locations provided relevant information from a regional analysis is available.

A second development included the testing, improvement, and validation of the WEPP model to simulate subsurface flow discharges. The proposed model included the modification of the current subsurface drainage algorithm (Hooghoudt-based expression) and the WEPP model percolation routine. The modified WEPP model was tested and validated on an extensive dataset collected at four experimental sites managed by USDA-ARS within the Lake Erie Watershed. Predicted subsurface discharges show Nash-Sutcliffe Efficiency (NSE) values ranging from 0.50 to 0.70, and percent bias ranging from -30% to +15% at daily and monthly resolutions. Evidence suggests the WEPP model can be used to produce reliable estimates of subsurface flow with minimum calibration.

The last objective presented the theoretical framework for a new hillslope and channel-routing model for the Water Erosion Prediction Project (WEPP) model. The routing model (WEPP-CMT) is based on catchment geomorphology and mass transport theory for flow and transport of reactive solutes. The WEPP-CMT uses the unique functionality of WEPP to simulate hillslope responses under diverse land use and management conditions and a Lagrangian description of the carrier hydrologic runoff at hillslope and channel domains. An example of the model functionality was tested in a sub-catchment of the Upper Cedar River Watershed in the U.S. Pacific Northwest. Results showed that the proposed model provides an acceptable representation of flow at the outlet of the study catchment. Model efficiencies and percent bias for the calibration period and the validation period were NSE = 0.55 and 0.65, and PBIAS = -2.8% and 2.1%, respectively. The WEPP-CMT provides a suitable foundation for the transport of reactive solutes (e.g. nitrates) at basin scales.

Alzheimer’s disease (AD), is a devastating neurodegenerative disorder that destroys the patient’s ability to perform daily living task and eventually, takes their lives. Currently, there are 5.8 million people in North America that suffer from AD. This number is projected to by 13.8 million by the year of 2050. For many years, researchers have been dedicated on performing automated diagnosis based on neuroimaging. There are critical needs in two aspects of AD: 1) computer-based AD classification with MRI images; 2) computer-based tools/system to enhance the AD patient’s quality of life. We are addressing these two gaps via two specific objectives in this study.

For objective 1, the task is to develop a machine-learning based intelligent model for classification of AD conditions (Normal Control [NC], Mild Cognitive Impairment [MCI], Alzheimer’s disease [AD]) based on MRI images. Specifically, four different deep learning models were developed and assessed. The overall average accuracy for AD classification is 81.5%, provided by Multi-Layer-Output model.

For objective 2, a deep learning model was developed and evaluated to recognitze three specific type of indoor scenes (bedroom, living room and dining room). An accuracy of 97% was obtained.

This study showed the potential of application in deep learning models for two different aspects of AD - disease classification and intelligent model-based assistive device for AD patients. Further research and development activities are recommended to further validate these findings on larger and different datasets.

In the current manufacturing industry, static work instructions (WI) are still widely used for manufacturing assembly training and they lack the dynamic information that interactive work instructions can offer. Augmented Reality (AR) training systems are receiving increasing interest in the scientific community, but there is a limited amount of research done on the long-term effect of the AR training systems compared with static training systems. This thesis study was done to investigate if interactive WIs such as AR WIs and 3D PDF WIs have an advantage on training efficiency and knowledge sustainability compared to static paper WIs. Within an experiment, it was observed that there are no differences between the three training methods when it comes to training efficiency, but AR WI proved to be more effective in sustaining the user’s long-term recall precision than paper WI.

Lean Six Sigma (LSS) has been implemented worldwide for many years and has been successful in many organizations. Eloot, Huang, and Lehnich (2013) noted that achieving manufacturing excellence by using LSS was an opportunity for many companies. Liker and Rother (2011) pointed out that only 2% of companies successfully achieved the desired results with Lean plans.

The presented dissertation identified the critical success factors of LSS implementation for Chinese manufacturing companies and explored the challenges occurring during the LSS transformations. The objectives of this dissertation were:

i. to understand how the employee training process for LSS can be designed using total quality management (TQM) adoption in private manufacturing organizations in China;

ii. to understand how LSS practices can be adopted successfully in SMEs in China;

iii. to examine and explore the critical success factors (CSF) of LSS implementation;

iv. to discuss the challenges occurring during LSS transformation.

Objective (i) was achieved through a descriptive single case study. This case study showed how to apply a design for Six Sigma methodology (DMADV) for staff training in quality management tools in a private organization in China. The author also discussed the problems occurring during the Six Sigma project and explored how organizational culture impacted Six Sigma implementation. Objective (ii) was achieved through a detailed descriptive single case study which recorded how LSS practices were adopted successfully in a SME-VTCL in China using DMAIC methodology. Survey data was collected to identify and explore the critical success factors of LSS implementation in SMEs, by querying the voice of top, middle, and frontline management, as well as frontline workers of these companies. Objectives (iii) and (iv) were realized utilizing descriptive, exploratory, and multi-case studies designed to gather and analyze observational and interview data. The resulting interview data, and the key factors for successful LSS transformation of these three companies were discussed from the perspective of senior management and LSS promoters within the companies. Based on interview data and the Lean iceberg model, a new LSS transformation model was proposed. The author also developed 6 propositions based on the findings from the interviews.

In summary, the results of this study provided value and references for LSS practitioners to expand the body of knowledge on the strategies used to implement LSS successfully inside organizations. The findings of this research may potentially lead more Chinese organizations to successfully adopt LSS to provide customers with high-quality products. The three LSS implementation cases described critical success factors (CSFs) and challenges that occurred during the transformation, may improve the success rate of implementation, help enterprises achieve the desired results through LSS, and enhance the sustainability of LSS implementations.

This study examined how three different manipulations of a single policing stance affected the quality scores and matching performance in a biometric facial recognition system; it was conducted in three phases. In the first phase, the researcher collected qualitative survey data from active, sworn law enforcement officers in 15 states. In the second phase, the researcher collected quantitative data using a single facial recognition subject and a static body-worn camera mounted to an adjustable tripod. In the third phase, the researcher collected quantitative data from body-worn camera-equipped law enforcement officers who filmed a stationary target as they executed a series of specified movements from the interview stance. The second phase tested two different body-worn cameras: one that is popular among law enforcement agencies in the United States, the Axon Body 2; and one that is inexpensive and available to the general public via a popular internet commerce website. The third phase tested only the Axon Body 2. Results of the study showed that matching results are poor in a biometric system where the test body-worn camera was the sensor, with error rates as high as 100% when the body-worn camera wearer was in motion. The general conclusion of this study is that a body-worn camera is not a suitable sensor for a biometric facial recognition system at this time, though advances in camera technology and biometric systems may close the gap in the future.

Copper (Cu) has been used as the main conductor in interconnects due to its low resistivity. However, because of its high diffusivity, diffusion barriers/liners (tantalum nitride/tantalum; TaN/Ta) must be incorporated to surround Cu wires. Otherwise, Cu ions/atoms will drift/diffuse through the inter-metal dielectric (IMD) that separates two distinct interconnects, resulting in circuit shorting and chip failures. The scaling limit of conventional Cu diffusion barriers/liners has become the bottleneck for interconnect technology, which in turn limits the IC performance. The interconnect half-pitch size will reach ~20 nm in the coming sub-5 nm technology nodes. Meanwhile, the TaN/Ta (barrier/liner) bilayer stack has to be > 4 nm to ensure acceptable liner and diffusion barrier properties. Since TaN/Ta occupy a significant portion of the interconnect cross-section and they are much more resistive than Cu, the effective conductance of an ultra-scaled interconnect will be compromised by the thick bilayer. Therefore, two dimensional (2D) layered materials have been explored as diffusion barrier alternatives owing to their atomically thin body thicknesses. However, many of the proposed 2D barriers are prepared at too high temperatures to be compatible with the back-end-of-line (BEOL) technology. In addition, as important as the diffusion barrier properties, the liner properties of 2D materials must be evaluated, which has not yet been pursued.