Texas A&M University-Kingsville: AKM Digital Repository
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Rural superintendents' perceptions regarding Texas HB 1842 district of innovation designation to overcome barriers
No full textThe top-down approach of federal and state agencies to laws, mandates, and reform has eroded the local control of school boards. Rural schools often struggle to overcome challenges related to geographic isolation, funding, and staffing. Consequently, rural district superintendents must accomplish more with less to overcome the barriers rural school communities face. In 2015, Texas HB 1842 established Districts of Innovation (DOI), a designation meant to encourage innovation via increased local control and flexibility. The problem for rural districts and their superintendents is one of adapting and overcoming to avoid perishing. While the DOI has increased local control, it is unknown if DOI districts have overcome barriers. The purpose of this study was to explore perceptions of superintendents serving in rural districts in a region located in West Texas, regarding implementation and impact of the DOI designation to overcome barriers. The conceptual framework for this study was based on Fullan’s Change Theory and its four broad phases: initiation, implementation, continuation, and outcome. This basic qualitative study used criterion-based sampling to identify eight superintendents to interview based on the innovative practices that they developed and implemented using the DOI designation, with an emphasis on successes or failures to determine DOI impact. Data analysis consisted of transcribing participant responses to an instrument protocol, reviewed by an expert committee, and then manually coding and analyzing participant responses for patterns. As themes emerged, they were categorized and explored. The results indicated that participants perceive the DOI designation as a successful initiative and that it has been used to improve their rural districts’ ability to overcome challenges and barriers. In addition, evidence that the DOI may have established an alternative pathway to increase the number of certified teachers was discovered and remains an area that warrants further research
Aspects of hunting of Northern bobwhite populations: temporal and spatial analysis
No full textNorthern bobwhites (Colinus virginianus) have been studied intensively now for more than a century. Despite the attention, widespread declines have occurred across their geographic ranges. These declines raise concerns regarding the long-term sustainability of populations exposed to hunting. However, population trends of northern bobwhites in South Texas seem to lack the long-term declines occurring across much of the state and elsewhere. Research has attributed this to favorable range management practices, large property sizes, and economic incentives derived from hunting lease fees in the region. The recommended harvest rate for South Texas is 20% of the fall abundance, including factoring for crippled individuals. This harvest rate is based on simulations of empirical data but still requires thorough evaluations in the field. We assessed the 20% recommendation during the 2018–2019, 2019–2020, and 2020–2021 hunting seasons on East Foundation properties in Jim Hogg County, Texas, using designated hunted (15,030 acres) and non-hunted sites (10,813 acres). We estimated multi-temporal bobwhite densities (e.g., 4 per hunting season × 3 seasons) using line-transect distance sampling from a helicopter platform and recorded bobwhite hunting details using Garmin GPS units (i.e., trucks and pointing dogs) and detailed hunting logs. Our specific objectives were to (1) evaluate the harvest rate recommendation for northern bobwhite populations in South Texas by comparing temporal trends between hunted and non-hunted sites (Chapter 2), (2) analyze the temporal and spatial dynamics of quail hunts in South Texas (Chapter 3), and (3) evaluate the spatial effects of harvest-related hunting pressure on local distributions of northern bobwhites (Chapter 4). According to our bobwhite density estimates, spring densities on both sites (e.g., hunted vs. non-hunted) were similar through the first two years but diverged in 2020–2021, with bobwhite densities 129% higher on the non-hunted site (Chapter 2). Hunting parties effectively covered 23.8 ± 0.3 hectares per hour, with hunts lasting 3.5 ± 0.1 hours in the morning and 1.7 ± 0.1 hours in the evening (Chapter 3). We also found that hunting pressure associated with a 20% harvest (i.e., low hunting pressure; 5.3–8.3-gun hours/100 ha) has a minimal influence on the change in bobwhite density at 16 ha resolution, with the year (i.e., starting or peak density per year) as the primarily influence (Chapter 4). Our results will assist managers in making decisions regarding sustainable harvest practices and aid with the strategical distributions of hunting pressure across properties and hunting seasons
An elaborate analysis of the correlation between air pollution and birth defect in different counties of Texas
No full textMany infants in United States at large and Texas in particular, continue to be born with various birth defects. Air pollution significantly contributes adversely to other pregnancy outcomes including fetal deaths, preterm birth, and low birth weight. To determine a cause for this anomaly, the correlations between air pollutants and birth defects in Texas were studied. Eleven regions and twenty-nine counties in Texas were chosen for data collection and analysis. Birth defect and specific air pollutant data were collected between 1999 and 2014. The criteria pollutants, which are ozone, carbon monoxide, nitrogen dioxide, particulate matter, sulfur dioxide and lead, were used in this study because they are considered highly hazardous to human health. This project conducted correlation analysis and regression analysis on the air pollutant data and birth effect data. Several air pollutants show significant correlation with birth defects. However, the underlying reasons of these significant correlations should be further explored in the future research
Application of blockchain technology in project management
No full textBlockchain applications seek to establish an ecosystem that is worthy of credit among its users in high-security requirements. The Project Management field has not seen comprehensive research on blockchain application systems other enterprises such as finance, insurance, the internet of things, and supply chain. This research surveyed current and prospective blockchain applications to fill this gap and identified vital characteristics applied to the project management field. This research aimed to build a prototype of a blockchain-based project management application. A review of current blockchain applications determined an outline of the operation model and advantages of blockchain technology. The resulting operation model served as a framework to design blockchain solutions to areas of the project management field. The model deployed a smart contract on a permissionless blockchain testing environment – EOSIO, to track project activities and manage project resources. The resulting model verified and recorded each project stakeholder transaction based on the smart contract across the blockchain nodes. This research demonstrates the feasibility of developing and highly secured decentralized project management tools that do not require a trusted third party. Future studies can assess the impact of using permissionless versus permissioned-based project management tools
FPGA implementation of advanced encryption standard algorithm using a hybrid approach
No full textWith the rapid development in information technology, securing data against cryptanalytic attacks is a very important issue. Different types of cryptographic algorithms have been developed and utilized to secure data. In this report, some of these popular algorithms have been explained and more detail has been provided on the Advanced Encryption Standard (AES) algorithm. In this report, the RTL (Register Transfer Level) implementation of a hybrid AES algorithm for a 128-bit
key size is described. In the implemented design, the S-box module of the algorithm is designed using Recurrent Neural Network (RNN) and Genetic Algorithm (GA) to enhance the performance of conventional AES in terms of security and speed. The implemented Jordan RNN uses the sigmoid function as an activation function to add non-linearity. RNN has trained in MATLAB and Weight matrices generated in the MATLAB have been used in the RTL implementation of the RNN. The implemented GA uses a cross-over and mutation process of the actual GA to improve the randomness of the S-box. This report embodies a detailed explanation of all AES modules'
implementation at all abstract levels. This report also includes simulation results that verify the functionality of all the AES modules at all the abstract levels
Analysis and synthesis of conical coil springs
No full textSprings are mechanical devices that are employed to resist forces, store energy, absorb shocks, mitigate vibrations, or maintain parts contacting each other. Spring strips are commonly coiled in the forms of helixes for either extension or compression. Helical springs usually have cylindrical shapes that have constant coil diameter, constant pitch, and constant spring rate. Unlike
conventional cylindrical coil springs, the coil diameter of conically coiled springs is variable. They have conical or tapered shapes with a large coil diameter at the base and a small coil diameter at the top. The variable coil diameter enables conical coil springs to generate desired load-deflection relationships, have high lateral stability, and low buckling liability. In addition, conical compression springs can have significantly larger compression or shorter compressed height than conventional helical compression springs. The compressed height of a conical compression spring can reach its limit that is, the diameter of the spring wire if it is appropriately synthesized. The
height of an undeformed conical extension spring can have the height of its spring wire if the spring pitch is chosen to be zero. The shape of an undeformed conical extension spring can be flat if it is needed. The variable coil diameter of conical coil springs provides them with unique features but also raises their synthesis difficulties. Synthesizing conical coil springs that require large spring
compression or short, deformed spring height or constant spring rate is challenging. This research is motivated by surmounting the current challenges facing conical coil springs. In this research, different conical compression and extension springs will be modeled. Their performances will be simulated using the created models. The force-deflection relationships of conical coil springs will
be analyzed. The results from this research will provide useful guidelines for developing conical compression and extension springs
Face detection and recogition for online authentication systems
No full textFace identification has been one of the most intriguing and significant fields in the past twenty years. The reasons motivating face identification come from the need for programmed recognition frameworks, the interest in development of machine vision frameworks for face discovery, and the arrangement of human-machine interfaces, etc. Research in developing face identification frameworks has been undergoing advancement for the past several decades. Face identification is one of the advanced explorations in image processing and machine vision. This research has different functional applications within computer or cyber security frameworks. Identifying the face region location is an important image processing task in this research of machine vision. Face area or region is a biometric image object or structure that is to be used in later stages of face recognition to perceive or affirm a person’s identity from an electronic picture, which is needed in security applications. This research will explore face detection and recognition approaches and methods that can be extended to build a framework for secure biometric authentication systems. The proposed methodology of this work uses an enhanced Dlib-ML algorithm for face recognition with Haar features for face detection. This method has a faster computation speed (0.2 seconds per image) and higher accuracy (99.38%) as compared to several previous state-of-the-art methods
Warning system in smart vehicles for detection of traffic signs, lights, and obstacles
No full textVehicle accidents can occur due to driver negligence and failure to follow traffic rules. For safe driving, the driver needs to be aware of different situations occurring on the road while driving. To improve a driver’s ability to maintain their focus and reduce accidents, this research proposes to enhance an automated driver early warning system. Such a system would alert the driver whenever a traffic object is detected. This research specifically focuses on improving detection of traffic signs, signals, pedestrians, and obstacles. In this work, a pre-processing enhancement with ESRGAN (Enhanced Super Resolution Generative Adversarial Network) is employed for enhancing the contrast and resolution of the extracted traffic objects. Many advancements have been made in object detection and image processing, but vehicle systems identifying the traffic signs, lights, pedestrians, and obstacles still need improvement. This research also compares the accuracy of two algorithms, You Only Look Once version 3 (YOLOv3) and You Only Look Once version 4 (YOLOv4) with and without the preprocessing ESRGAN enhancement. This research would facilitate advancing automatic systems in autonomous vehicles for sensing traffic objects in real-time. The main objective of this research is to increase the performance of traffic object detection in terms of accuracy, and compare deep learning techniques to see which would result in better performance. The proposed enhancement in this research is applying preprocessing using ESRGAN to the traffic objects, which increases the accuracy of the traffic object detection in different conditions such as nighttime, rain, or extreme weather conditions. The enhanced YOLOv3 model was found to have an average accuracy of 92.83% for traffic object detection, compared to only 87.11% for the unenhanced YOLOv3 model, which is a 5.72% improvement. The enhanced YOLOv4 model was found to have an average accuracy of 94.16% for traffic object detection, compared to only 87.32% for the unenhanced YOLOv4 model, which is a 6.84% improvement
Response of tanglehead (Heteropogon contortus) expansion to prescribed fire and cattle grazing and estimation of crude protein in tanglehead using unmanned aerial vehicles
No full textTanglehead (Heteropogon contortus) is a native invasive grass that is spreading throughout the sandy soils on South Texas rangelands. Mature tanglehead plants are coarse, unpalatable and low in percent crude protein. Prescribed fire is a common management strategy
used to increase the palatability and utilization of tanglehead by cattle (Bos taurus). The regrowth following the removal of the old forage, is more palatable and higher in crude protein. The goal of my first chapter was to evaluate the effects of four different treatment combinations of prescribed fire and grazing on the expanse of tanglehead. The specific objective of this chapter was to determine the rate of individual tanglehead plants’ expansion under four treatment combinations. In the burning and grazing treatment the basal circumference, percent bare ground, and tanglehead foliar cover for 2 years following the prescribed fire did not increase. The use of unmanned aerial vehicles (UAVs) has greatly improved opportunities to assess and
monitor the health of the vegetation on rangelands. The objective of my second chapter was to estimate percent crude protein in tanglehead using plant spectral signatures. This was achieved by testing five spectral bands and four vegetation indices derived from UAV multispectral imagery. Results in my study showed the Normalized Green-Red-Difference Index (NGRDI) vegetation index, and the near infrared (NIR) reflectance band to have significant relationships with r2 values of 0.60 and 0.58 respectively. The combination of the red, blue, and NIR reflectance bands in a multiple regression model produced the best explanatory performance
(R2adj = 0.73) with combustion-estimated percent crude protein. The burning and grazing treatment was successful to control the spread of tanglehead, and percent crude protein percentage can be estimated using drones, which can help with critical management decisions to
maintain cattle body condition scores and achieve optimum cattle productivity
Effects of habitat restoration on Texas horned lizards and their prey
No full textTexas horned lizards (Phrynosoma cornutum) are a western iconic species that were once numerous and widely distributed across the south-central United States. Their numbers have drastically declined, and their distribution has become patchy, thus they are listed as threatened by Texas and have protected status in Oklahoma, Colorado, and Arizona. It has been speculated
that habitat restoration practices, which return formally native grasslands back to their original form, could help rebound the abundance of Texas horned lizards. However, this concept has yet to be tested. In 2013, a 118 ha pasture in La Salle County, Texas, was converted from a
monoculture of buffelgrass (Cenchrus ciliaris) and old-world bluestems (Bothriochloa sp.) back to native forbs and grasses. Five years after restoration, harvester ants (Pogonomyrmex sp.) and Texas horned lizards appear to have recolonized the area; however, this was not substantiated by quantitative data. From 2018-2019, I monitored the abundance of Texas horned lizards, their
prey, the harvester ant, and competitor ant species, the red-imported fire ant (Solenopsis invicta) on the 118 ha restored native grassland pasture (i.e., experimental site; hereafter restoration/ restored pasture) and on a similar-sized non-native grassland pasture as a control (hereafter;
control pasture). As of 2018 the restoration pasture had reached 98% cover of native grasses compared to a previous 2% cover. Texas horned lizards were well-established on both the restored and control pastures; however, more (χ2 = 7.3, df = 1, P < 0.01) Texas horned lizards were collected from the control pasture during 2018 (24% of χ2-value) than the restoration pasture. The trend reversed in 2019 with fewer Texas horned lizards (41% of χ2-value) collected on the control pasture than the restoration pasture. An interaction between treatment and time period occurred in the effect of restoration on harvester ant density (F12,1176 = 1.78, P < 0.044):
ant mound densities between the restoration and control pastures initially were not different (t = 0.46. df = 1, P > 0.51) during June 2018, but harvester ant mounds quickly increased on the restoration pasture, while ant mound density slowly increased on the control pasture. This
resulted in a trend (t = 1.9, df = 1, P < 0.10) toward a greater density of mounds on the restoration pasture during January 2019. Fire ant mound density was greater on the restoration pasture (7.2 ± 1.5, range = 4-12 mounds/ha) than on the control pasture (2.0 ± 1.5, range = 0-4 mounds/ha). Activities of both harvester and red-imported fire ant mounds as affected by air and
soil temperatures varied between pastures. Although there was little difference in abundance for Texas horned lizards between pastures, harvester ant mounds were abundant on both, which is beneficial for Texas horned lizards. With restoration disturbance and favorable conditions, redimported fire ant mounds progressively established; however, establishment did not appear to hinder establishment of harvester ant mounds as speculated to occur. Large scale restoration did not appear to have a direct positive effect on Texas horned lizard numbers; however, indirect effects on their prey and competitor ant species