Texas A&M University-Kingsville: AKM Digital Repository
Not a member yet
1689 research outputs found
Sort by
Impact of array imperfections on MUSIC algorithm for DOA estimation
No full textWireless communication is one of the most essential parts of current society. 5G systems became the current standard for cellular communications. Older systems lack efficiency as they blindly emit radiation spheres with the aim of encapsulating the signal. This method works, but is an inefficient use of resources, Smart antennas have the potential to vastly improve wireless communications by directing their beam in a specific direction and tracking the user. This saves power and leads to better communication. The issue is aiding the smart antenna system to determine the beam direction initially and to follow the communication device. This can be achieved through different algorithms that will help direct the beam and steer it. Smart antennas consist of two systems: Direction of Arrival Estimation and Beamforming. Direction of Arrival (DOA) estimation aims to estimate the arrival of the signals to antenna arrays. There are many DOA methods available in the literature and each of these methods have their advantages and disadvantages. One of the most widely and efficient methods is known as MUltiple SIgnal Classification (MUSIC). In this work, we will investigate the behavior of MUSIC algorithm under imperfect array conditions for DOA estimation. It will be assumed that the antenna elements in the arrays are spaced by half a wavelength from each other. In practice there might be deviations, or even some of the antenna elements might malfunction under these conditions. Understanding the performance of the overall DOA estimation system employing the MUSIC algorithm is crucial. This thesis focuses on analyzing the behavior and effectiveness of the MUSIC algorithm within this framework
Additive manufacturing : a vision of industrial revolution
No full textThe research investigates how the industry 4.0 paradigm, which incorporates artificial intelligence (AI), internet of things (IoT), and autonomous robotics into manufacturing, and 3D printing are interconnected. It encourages improving operational knowledge and smart material production with additive manufacturing (AM) equipment using an IoT-based control system. The study additionally examines how 3D printing might be used in the automotive, food, healthcare, and construction industries. Prominent 3D printer manufacturer Stratasys has proven the degree to which IoT and enterprise resource planning (ERP) systems function for print-on-advancedemand (PoAD) manufacturing.
The manufacturing industry is experiencing a transformation because of additive manufacturing (AM), which incorporates data mining, predictive modeling, materials science, and sustainability principles. It improves sustainability practices, minimizes time to market, simplifies supply chains, and optimizes resource consumption. With its diverse uses in material production, fast prototyping, and creative design, additive manufacturing (AM) enables previously unattainable levels of manufacturing freedom.
The research explores the methodologies, products, applications, and environmental concerns surrounding 3D printing or additive manufacturing. It also emphasizes the importance of 3D printing innovations to reduce the costs, enhance reliability, and facilitate autonomous operation. The research also highlights the requirement for additional investigation into the exposure to nanoparticles and ultrafine particulate matter; UFPs and PM emission control in accordance to the globe environmental recommendations
Students' sense of belonging and barriers to success within the College of Agriculture and Natural Resources
No full textThis study explores the sense of belonging among students in the College of Agriculture and Natural Resources at Texas A&M University–Kingsville (TAMUK), marking the first investigation of its kind at the institution. Recognizing the vital role of belonging in student retention and success, the research addresses a critical gap in agricultural education literature, particularly concerning the increasingly diverse demographics of modern agricultural programs.
Using a quantitative design, a Sense of Belonging Scale survey was administered to measure classroom comfort, peer support, faculty support, and perceived isolation.
These results highlight the importance of fostering mentorship programs, increasing cultural representation, and improving faculty-student engagement to create a more inclusive environment. By addressing these areas, TAMUK’s College of Agriculture can better support student retention and success while setting an example for similar institutions nationwide. This study provides actionable recommendations for educators and administrators seeking to align their programs with the evolving needs of a diverse student population in agricultural education
Evaluation of Field-scale stormwater bioretention cells in nutrient removal in a semi-arid basin of South Texas
No full textBioretention technology has become a viable alternative for stormwater runoff treatment in watersheds that have dominant urban land use. Bioretention Systems are one of the Low Impact Development Strategies that are effective in reducing runoff and enhancing water quality, however, the performance of different bioretention filter media requires further studies. This field-scale study highlights a comparison of the efficiencies of bioretention systems designed with different filtering media (0.5-inch recycled concrete aggregate (RCA) and 1-inch river rock (RR) designed to reduce stormwater runoff and pollutant load in a semi-arid basin of South Texas. The study included thorough assessments of the technology application for rain events of 0.25 inches of precipitation and higher. Water quality analyses from bioretention pollutant load reduction show that there was a significant pollutant load reduction in Total Kjeldahl Nitrogen, Total phosphorus, Biological Oxygen Demand, and indicator bacteria (E. coli) under low to high rainfall intensity events (0.5 to 1.5 inches/day). However, pollutant load reduction shows a slight increase in Total Suspended Solids under high rainfall intensity events (1 to 1.5 inches/day). The treatment efficiency of runoff reduction and water quality enhancement are influenced by antecedent dry periods (ADP), hydraulic conductivity and porosity. However, the stormwater runoff was significantly reduced under both RCA and RR BRC media. Overall, RCA performed better in reducing stormwater runoff. Results of this research will benefit planners and engineers in other regions when applying bioretention technologies to mitigate adverse effects of stormwater runoff, and for the selection of cost-effective media for optimal bioretention system designs
On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design
No full textModeling of energy consumption in buildings is essential for different applications such as building energy management and establishing baselines. This makes building energy consumption estimation as a key tool to reduce energy consumption and emissions. Energy performance of building is complex, since it depends on several parameters related to the building characteristics, equipment and systems, weather, occupants, and sociological influences. This paper presents a new model to predict and quantify energy consumption in commercial buildings in the early stages of building design. Building simulation software including eQUEST and DOE-2 was used to build and simulate individual building configuration that were generated using Monte Carlo simulation techniques. Ten thousands simulations for seven building shapes were performed to create a comprehensive dataset covering the full ranges of design parameters. The present study considered building materials, their thickness, building shape, and occupant schedule as design variables since building energy performance is sensitive to these variables. Then, the results of the energy simulations were implemented into a set of regression equation to predict the energy consumption in each design scenario. A good agreement was seen between the predicted data based on the developed regression model and DOE simulation and the maximum error was less than 5%. It is envisioned that the developed regression models can be used to estimate the total energy consumption in the early stages of the design when different building schemes and design concepts are being considered
Development and uncertainty reduction of monthly to seasonal surface water-groundwater forecasts in multiple basins in different hydroclimatic settings
No full textSkillful surface water (SW) and groundwater (GW) level forecasts are essential for water managers and agricultural producers. However, their use is currently limited in making real-time decisions due to uncertainties caused by several factors, such as model inputs and structure and data processing techniques, such as spatial downscaling and temporal disaggregation of coarsescale climatic data. In this study, such uncertainties in SW and GW forecasts were reduced by combining several techniques, ranging from the least data-intensive (statistical modeling) to more complex physically-based modeling (integrated SW-GW modeling). First, we developed monthly streamflow forecasts with up to 4 months' lead time based on precipitation and temperature forecasts obtained from two different General Circulation Models (GCMs: ECHAM4.5 and CFSv2) for four basins located in Texas under different climatic conditions. The estimates from two GCMS were combined using multi-model combinations. The results showed that the combined streamflow forecasts have better skills than no-forecasts (MSSS > 0) in the Clear Fork Brazos and Llano River basins during winter, spring, and summer months for up to 4 month-lead times. During El Nino Southern Oscillations (ENSO) years, the forecasting skill was enhanced during winter, spring, and fall months for up to 4 months of lead time. Subsequently, integrated SW-GW forecasts were developed for a semi-arid Guadalupe - San Marcos basin using the Penn State Integrated Hydrologic Model (PIHM). The streamflow forecasts had better
skill during late fall and winter. In particular, the PIHM was able to simulate the drought events relatively better than the flood events for up to 2-3-months lead time. The simulated GW variability was driven by precipitation patterns at a 1-month lead time. The forecasting skill for GW was relatively better at 2-3 months lead time. The study also enhanced the usability of the model by developing multiple scripts to generate the PIHM input files as well as reduce the computational time for model setup
Comparison between the American Board of Family Medicine In-training Examination scores of U.S.-based Family Medicine Residency Programs that taught systematic and non-systematic pharmacotherapy curricula
No full textThe importance of systematic pharmacotherapy curriculum (SPC) in family medicine (FM) graduate medical education (GME) has been published. The research problem was that it was unknown if the American Board of Family Medicine (ABFM) In-training Examination (ITE) scores differed between U.S.-based Family Medicine Residency Programs (FMRPs) that taught using systematic pharmacotherapy curriculum (USPC) or not. SPC has the potential to enhance performance on the ABFM ITEs, the ABFM initial certification exam, and ultimately improve our country’s health and healthcare. The purpose of my quantitative comparative nonexperimental study was to examine if there was a difference between the ABFM ITE scores of U.S.-based FMRPs that taught USPC and U.S.-based FMRPs that taught not using systematic pharmacotherapy curriculum (NUSPC). The theoretical framework for my study was Wesley Null’s systematic curriculum theory. The research question asked if there was a statistically significant difference in 2020 ABFM Postgraduate Year 3 (PGY-3) Pharmacotherapy ITE scaled mean scores between U.S.-based FMRPs that taught USPC and U.S.-based FMRPs that taught NUSPC. Data for my study was obtained from requested ABFM ITE information and an emailed survey to FMRPs. A census of 721 U.S.-based FMRPs was surveyed, and the responses represented a volunteer sample of participants. Descriptive and inferential statistics were used to analyze data. A two-tailed independent-samples t-test was employed to reject or retain the null hypothesis of no statistically significant differences between two groups. U.S.-based FMRPs that taught USPC significantly (and with a large effect) outperformed U.S.-based FMRPs that taught NUSPC in the 2020 ABFM PGY-3 Pharmacotherapy ITE scaled mean scores by approximately 50 points. If an FMRP has problems with their residents performing well in the annual ABFM ITEs or FM initial certification exam, they should strongly consider a systematic curriculum to teach pharmacotherapy
Machine learning for autonomous fault detection in wind turbine blades
No full textOver the last decade, wind energy has emerged as one of the major contributors to green energy sources, generating nearly half of the total renewable energy in the USA. Due to the high wind speed, structural loads, extreme temperature, etc., the operating environment of the wind turbine (WT) is harsh and makes the turbine blades susceptible to damage. To ensure the desired level of power demand and reduce unscheduled downtime, it is required to monitor the turbine blades consistently to prevent any structural damage. Machine vision-based techniques have recently gained significant popularity for continuous monitoring and predicting different fault classes in wind turbine blades. A significant number of machine learning algorithms such as CNN, YOLOv5, and Mask R-CNN have already been studied for autonomous fault detection in wind turbines. In this thesis, the application of the YOLOv7 is investigated for autonomous fault detection in wind turbines and compared with the performance of the state-of-the-art Mask R-CNN model. Drone images of four different types of faults such as edge erosion, surface damage, VG panel error, and lighting receptor defect are collected and analyzed to investigate the performance of the models. Different image augmentation methods are implemented to enhance generalization. The result of the experiment reveals that the YOLOv7 outperforms Mask R-CNN fault detection accuracy by 11.00% and fault detection speed by 108.33%
Effects of remolding on fresh concrete and vibrations on hardened concrete
No full textGround vibrations recorded during pile driving operations were replicated with a vibrating mechanism prepared in the lab. Concrete cubes of 6” ×6” ×6” with a vertical rebar embedded in it were prepared and vibrated with a drop table at different ages and later pullout test were performed to test specimen bond strength. There were controlled samples which were not vibrated but tested for their bond strength. Based on the results of this study, vibrated specimens were found have to have lower bond strength when compared with non-vibrated specimens. Then, a normalized curve was plotted to observe the effect of vibration in specimen bond strength at different ages. From this curve, it is estimated that concrete elements need to be cured approximately a minimum of 7 days without exposure to vibrations to prevent premature bond strength reduction.
Another test called delayed vibration was performed by remolding and re-rodding concrete cylinders measuring 4” ×8”. The remolding and re-rodding was done for up to 4.5 hours after casting the cylinders. These samples were then tested for their compressive strength on the 28th day. It was observed that there is no harm on compressive strength by delayed vibrations. Compressive strength increased up to 17% on specimens re-vibrated at 2.5 hours after the casting.
The overall outcome of these two tests delineates that bond strength between concrete and reinforcing bar decreases when vibrations are applied in its early days once concrete has hardened, whereas the compressive strength of concrete can increase if vibrations are applied to it before it hardens
Forecasting spatial abundance of northern bobwhite in South Texas using roadside surveys
No full textPopulations of northern bobwhite (Colinus virginianus) in the U.S. have been declining at least for the past 50 years, resulting in concern for the current and future status of the species from wildlife biologists, managers, and general public. Texas is one of the last remaining strongholds for bobwhites, and bobwhite populations in certain Texas ecoregions have declined in recent decades. Given that bobwhites are an important game species subject to harvest, developing effective and reliable measures of population abundance is important for proper management and conservation of the species. Currently, harvest prescriptions by landowners are based on general population trends observed in abundance indices (e.g., bobwhite counts on roadside surveys or aerial surveys) or regional level bobwhite indices (i.e., Texas Parks and Wildlife’s Quail Forecast). Abundance indices are often used to assess the abundance of a species in one area compared to another. However, current roadside survey methodology used to assess bobwhite abundance has several limitations and requires further refinement below being utilized in harvest management. The purpose of my research is to develop a more precise and finer-scale estimate of bobwhite relative abundance that may be used to forecast the quail season at a regional extent (southern Texas). Specifically, my objectives are to 1) quantify the relationship at a region scale (50-km focal window and 16-km focal window) between bobwhite relative abundance and landscape characteristics (percent bobwhite habitat and surrounding energy infrastructure density) and weather characteristics (annual and seasonal precipitation, summer temperature, and annual and seasonal drought indices) to develop an annual, spatial map of bobwhite relative abundance in southern Texas; 2) quantify the relationship at a site scale (400-m buffer) between bobwhite relative abundance and site characteristics (percent coverage from different plant functional groups) and weather characteristics (annual and seasonal precipitation, summer temperature, and annual and seasonal drought indices) to develop habitatmanagement recommendations, and 3) estimate probability of detection of quail roadside surveys using N-mixture models. The region-scale analysis (16-km) revealed a positive relationship (R2 =0.41, P = 0.0002) between weather characteristics (seasonal PMDI and summer temperature). At a coarser scale of analysis (50-km), I found no evidence of a relationship between weather characteristics (annual and seasonal precipitation, summer temperature, and annual and seasonal drought indices) and bobwhite relative abundance; year was the only influential variable (R2 = 0.34; P = 0.0002). At the site-scale analysis (400-m), I documented habitat characteristics (perennial forb and grass cover and shrub cover) and annual precipitation having a positive relationship with bobwhite relative abundance. Perennial forb and grass cover had a positive
effect (β = 1.60 ± 0.53, P = 0.006) when compared to other covariates in the model at the sitescale (400-m). Overall, the model at the site-scale (400-m) was significant and had a moderate correlation with bobwhite relative abundance (R2 = 0.34, P < 0.0001). Using N-mixture model analysis based on my best models, I documented that probability of detection decreased from about 0.50 to 0.25 with increasing date within my survey period (Aug‒Sep). My findings of the influence of weather and habitat correspond with previous literature and suggest that remotely sensed datasets may be a viable method to quantify bobwhite abundance. Wildlife managers should implement rangeland practices (e.g., rotation grazing or prescribed burning) that promote the growth of perennial herbaceous vegetation based on my findings