Texas A&M University-Kingsville: AKM Digital Repository
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Interaction of transcription factors and its effect on stem induction
No full textGlioblastoma Multiforme (GBM) is an invasive brain tumor with a poor prognosis. GBM presents high mortality and is known to have a mean survival duration of 12-18 months after diagnosis. The GBM stem-like cancer cells present treatment challenges due to high recurrence and therapeutic resistance. The SOX2, OLIG2, POU3F2, and SALL2 proteins were identified as the transcription factors required to induce GBM into cancer stem cells and maintain the GBM tumor-forming capability. For the first research aim, we investigated the protein levels of SOX2 and SALL2 in differentiated GBM cells and GBM cancer stem cells. The result showed no detection of SALL2 and SOX2 proteins in both GBM differentiated cells and tumor spheroids. We also investigated the interaction of SALL2 and SOX2 in differentiated GBM cells after overexpression of both proteins. The result is insufficient to confirm the SALL2-SOX2 interaction. The outcome of this experiment suggests that our future studies should focus on improvement and troubleshooting of our experimental strategies and procedures, determining the SALL2 and SOX2 interactions and how the potential protein complex induces GBM stem cell-like characteristics
Intercultural conflict styles in post-secondary Agriculture Science students
No full textWith diversity and inclusion being an important topic in the world today it is important to understand how one’s culture can lead them to resolving conflict in ways that may be different than those of other cultural backgrounds. The purpose of this study was to determine students
conflict styles within the College of Agriculture and Natural Resources and to determine if there were any differences based upon students’ demographics which included: biological sex, race or ethnicity, grade level, and major. This study was guided by the Intercultural Conflict Style
Inventory (ICS) created by Dr. Mitchell R. Hammer. After IRB approval the researcher gave the inventory to a sample of 66 students within the College of Agriculture and Natural Resources. The students were first scored based on the Direct vs. Indirect scale and the Emotional Restraint vs. Emotional Expressive Scale. From these scores the researcher found that a majority of
students within this sample could be categorized in the Discussion style of conflict resolution and a lower percentage of students that could be categorized in the Dynamic style of conflict resolution
Vehicle controller analysis and design for the automotive industry for a better vehicle performance
No full textIn our world, technology is adapting to the needs that society strives for in advancements, and control systems play an enormous role in improving technology. Different techniques, ranging from classical to intelligent controllers, have been proposed and applied to enhance and optimize general system performance. For example, in the automotive industry, multiple control systems are implemented into the automotive system to create successful and better-performing vehicles. As part of this research, adaptive cruise control in cars as applied by researchers in the automotive industry is investigated, and re-designed systems for better performance are recommended. As part of the research work, a Proportional-Integral-Derivative controller, Fuzzy Logic controller, Model Predictive Controller, Linear Quadratic Tracking, Neural Network, Neuro-Fuzzy, Neuro-Fuzzy-LQT, and LQT-Neural Network controllers are reviewed and discussed regarding the advantages and disadvantages of each technique. These controllers are applied in various cases where the objectives are to keep a desired speed and avoid a collision. Based on the simulation results, the system performances are compared, analyzed, and discussed. As a result, the Linear Quadratic Tracking combined with a Neuro-Fuzzy controller outperforms the other controllers in the objectives assigned
The in situ bioremediation of an in situ recovery mining site for the reduction/immobilization of uranium utilizing gaseous hydrogen
No full textThe most common method currently used for restoring groundwater at in-situ recovery (ISR) uranium mining sites is reverse osmosis (RO) and reinjection of the permeate. However, this practice does not restore the formation to its original reduced state, and in many cases groundwater uranium concentrations are not restored to pre-mining baseline levels. This study was performed to evaluate the effectiveness of introducing dissolved hydrogen into a post-mined formation at an ISR mining site to stimulate the chemical reduction and immobilization of residual soluble uranium. The main objectives of this research project were: 1) to develop and optimize a system for minimizing air entrainment during water injection when employing a membrane gas-transfer device for down-hole hydrogen infusion; 2) to assess whether injecting dissolved hydrogen using the membrane gas-transfer device can promote immobilization of dissolved uranium in groundwater to near or below pre-mining concentrations; and 3) to model the extent to which the presence of solid-phase ferric iron in the formation would consume reducing equivalents and adversely affect the radial transport of hydrogen into the formation. Approximately 30,000 gallons of groundwater were pumped to the surface and then re-injected into the subsurface while being supplied with dissolved hydrogen using the down-hole membrane gas infusion device. The groundwater was pumped back to the surface after a month to evaluate the extent to which dissolved uranium had been removed. Results indicated an approximately 86% reduction in soluble uranium concentration was achieved and sustained for one year. Microbial analyses indicated a significant increase in iron-reducing bacteria, but less significant increases in sulfate-reducing bacteria. A bromide tracer study was performed concurrently with the hydrogen injection study so that the effective zone of influence of the push-pull test could be estimated, while pump tests were performed before and after the hydrogen injection study so the effect of the injected hydrogen on the formation permeability could also be assessed. An additional hydrogen injection showed limited additional results and a decrease in hydraulic conductivity. Finally, geochemical and hydraulic modeling was conducted to predict the zone of influence surrounding the injection well, the redox potential of the groundwater within the zone of influence, and the potential longevity of this remediation treatment process to give a more comprehensive picture of its effectiveness
Teachers’ perceptions of the effects of COVID-19 on culturally responsive pedagogy
No full textThe COVID-19 pandemic caused school closures that emphasized the educational inequities of culturally diverse students. With an already existing achievement gap identified between English Learners (ELs) and their native English-speaking peers, teacher support was necessary during this pandemic. Education needed to focus on equity and culturally responsive-sustaining education as teaching moved online so that students were equipped with the skills, knowledge, and tools to transform society and create a path of liberation. Ultimately, when content is out of reach, the achievement gap is never going to diminish; therefore, culturally responsive-sustaining education alleviated these inequities. The purpose of this qualitative, phenomenological study was to document teachers’ perceptions of the effects of COVID-19 on culturally responsive teaching. The focus was on understanding the COVID phenomenon as experienced by bilingual 1st-5th grade teachers. It was found that culturally responsive teaching in a remote classroom facilitated engagement by forming connections to the curriculum and through the use of technological features. Additionally, culturally responsive teaching in a home environment entailed elements that were out of the teachers' control. The findings in this study will redound the importance of culturally responsive teaching and teachers’ perceptions of the effects of COVID-19 on culturally responsive teaching by painting a picture of how educators navigated the school closures; thus, researchers will have a baseline to move forward
Correction of gain mismatch for time interleaved analog to digital converter system
No full textAnalog to Digital Converter (ADC) converts analog signals to digital signals used in electronic warfare, Radio Frequency (RF) recording systems, radars, and other data acquisition. Such applications deal with super high frequency and require ADCs with high sampling rate. Time
Interleaved ADC (TI-ADC) systems are used to increase the sampling rate. In TI-ADC, several ADCs are interleaved to increase the sampling rate. Ideally, all ADCs in the system will have the same gain. However, mismatch in gains of the ADCs has been found in the system when ADCs are interleaved because of the ADC’s imperfection. The gain mismatch results in spurious peaks in the spectrum and reduces the TI-ADC system's dynamic range. This master's thesis focuses on the correction of gain mismatch for TI-ADC System. The Discrete Time Fourier Transform (DTFT) of a sampled sequence obtained by TI-ADC with the gain mismatch is derived and compared to the DTFT of a sequence without any gain mismatch. Sinusoidal signal at Nyquist frequency is used to detect the gain mismatch. In this thesis three channel TI-ADC is used
Response of commercial cotton varieties to Xanthomonas citri pv. malvacearum at early developmental stages in South Texas
No full textCotton is the most profitable non-food crop in the world. Xanthomonas citri pv. malvacearum (Xcm) is a pathogen which causes bacterial blight in cotton. Bacterial blight has
been controlled for more than 50 years by breeding for host plant resistance. However, recent sporadic outbreaks in the US have raised concerns among growers and scientists about the possible development of resistance in Xcm, resulting in disease outbreaks in commercially
available resistant varieties. The objective of the study was to evaluate seven commercial cotton varieties for resistance towards Xcm when inoculated at three cotton growth stages. The study consisted of one resistant (NG 5711), two susceptible (NG 3406 & DP 1725), and four
moderately susceptible cotton varieties (NG 3729, DP 1646, DP 1845 & DP 1948). All varieties were inoculated with Xcm at match head, candle, and pink flower stages at a concentration of 106 cfu per ml. The inoculation mixture consisted of Xcm, Silwet L-77®(0.25%v/v), and deionized water. Disease incidence and severity data were collected seven and fourteen days post-inoculation. A statistically significant difference was observed among the varieties both for disease incidence and severity. Results of this study indicate that susceptible varieties had significantly greater disease expression than the resistant varieties (P<0.05). Between partially resistant varieties, disease expression was greater in variety NG 3729 and lowest in DP 1948. Continuous evaluation of commercial cotton varieties for resistance towards bacterial blight is
vital to identify and control the spread of epidemics
Human face detection and recognition using advanced image processing and deep learning techniques
No full textIn this work, a proposed framework is suggested with the face detection algorithm using a classifier that is used to detect either a face (1) in an image or not a face (0). To get more accuracy, classifiers were trained to detect faces using hundred images. A commonly used image processing library in this work, OpenCV, includes Deep Neural Network (DNN) Face Detection and Haar Cascades, two types of classifiers. Due to the computational working difficulties faced with object intensities, the proposed methodology in this work uses the Viola Jones method based on Haar-like features and DNN. Use of Adaboost for cascading a variety of distinctive features results in a fast classifier that can efficiently extract features.
The DNN used in the proposed methodology uses the Single-Shot-Multibox Detector with a neural network architecture as the backbone for extraction of the features from the web camera. Eventually, this leads to correct facial detection. The webcam can be used to detect different parts of the trained faces dataset stored in the database in the proposed frameworks.
In this work, the Principal Component Analysis (PCA), the region-based convolutional neural network (RCNN), and the Fast-RCNN methods showed lower accuracies, sensitivity, and higher miss rate when compared to the proposed algorithm. The proposed method uses the DNN OpenFace face recognizer with a Haar Cascade face detection classifier and Holistically Nested Edge Detection (HED) preprocessing. Proposed method resulted in an accuracy of 99.010%, F-1 score 99.003%, a precision 99.134%, and a recall 97.200% when preprocessed with the edge-aware filter, edge-detecting filter, and HED. The proposed method resulted in an improvement in accuracy 3.91% over the PCA, 0.31% over RCNN, and 1.41% over the Fast RCNN methods
Darrieus wind turbines with twisted blades
No full textWind energy is one of the highly available renewable energy resources that are environmentally friendly. Wind energy can be converted from mechanical energy into electricity by using wind turbines. Darrieus wind turbines form one of the most common types of vertical axis wind turbines. The merits of Darrieus wind turbines include simple structure and construction, easy installation and maintenance, and no need for yaw motion mechanism. Darrieus wind turbines are life-type vertical axis wind turbines that have relatively higher power conversion efficiency than that from the drag-type vertical axis wind turbines such as Savonius wind turbines.
Although the merits of Darrieus wind turbines are remarkable. They also have their demerits. Compared with other types of vertical axis wind turbines, Darrieus wind turbines have relatively low static torque that weakens their self-starting capability. The research goal is to improve the performance of Darrieus wind turbines. In this research, Darrieus wind turbines with different blade shapes and numbers are simulated. The effects of the design parameters on the performance of Darrieus wind turbines are analyzed. Darrieus wind turbines are designed based on their geometric parameters to improve their performance
An effective method for multi criteria decision making in post disaster resource management
No full textDisasters, such as hurricanes, earthquakes, and floods have caused significant loss of life and destroyed social infrastructure worldwide. From mid-2019 to mid-2020, 15 storms and tropical cyclones killed at least 188 people and generated more than $15 billion in damages in the United States. After a disaster, recovery of social infrastructure is essential for its residents and the community's economy. Many resources, such as human labor and building materials, are needed quickly in the recovery process. However, they are limited, and may delay the recovery. This research aims to develop an efficient yet effective method to distribute limited resources seamlessly and in a timely manner while minimizing the recovery time following a disaster. To achieve this objective, first, various public infrastructure damages were categorized according to the nature of the damage. The categorized injuries were compared using different factors to prioritize the resilience-related tasks. A modified Analytical Hierarchy Process technique was developed and tested using MATLAB for prioritization. The available resources were assigned by order of priority. After the resource distribution, a resource leveling technique was used to balance the demand and availability aspects. For the what-if situation analysis, various scenarios were developed and discussed. The test results showed that the proposed method efficiently provided resource allocation in post-disaster resource management