Digital Commons Network^™

Understanding The Views Of Beef Show Cattle Parents And The Reasons Why Their Children Participate In Beef Cattle Shows, Matt Schiel

LSU Master's Theses

The population for this qualitative study were 10 families who participated in showing a heifer at the 2022 Fort Worth Stock Show and Rodeo during the junior heifer show weekend. The families were all from Texas. This study examined why parents allow their children to participate in showing cattle using a case study design. The study was grounded in human capital theory. Interviews and observations were used for the study’s findings. Results showed that the children who showed beef cattle learned responsibility, interpersonal skills, character development, and hard work.

Future research topics include further case studies using the same methodology …

Understanding Community Assembly Processes Of Foundation Species Within Hyper-Diverse Ecosystems, Carrie Barker

LSU Doctoral Dissertations

Foundation species provide the structure for other species and can significantly affect the composition and overall diversity within a community. Multiple foundation species can coexist within a community, and each can have differential impacts. My dissertation aims to understand the effects different foundation species have on diversity and the community assembly processes responsible for structuring diversity within their respective communities.

First, I quantified the diversity of groundcover plants within the prominent foundation species, bunchgrasses, (dominated by Shizachyrium scoparium) compared to groundcover plant diversity within patches of the alternative foundation species bracken fern (Pteridium aquilinum) and gallberry ( …

The Influence Of Selected Personal And Academic Demographic Characteristics On Persistence To Degree Completion Among Traditional-Age Undergraduate Students At A Research University In The Southeastern United States, Michael Eugene Mckey Jr.

LSU Doctoral Dissertations

A college education has never been more important than it is today. With nearly two-thirds of jobs in the future requiring some form of advanced education, and approximately one-third of those requiring a bachelor’s degree, it is imperative that research be undertaken to address the factors that contribute to the success of students in college, more so regarding the persistence and retention of those students. Therefore, the purpose of this study was to determine the influence of selected personal and academic demographic characteristics on the persistence to graduation, specifically, within six years. A causal-comparative research design was used as the …

Human Total, Basal And Activity Energy Expenditures Are Independent Of Ambient Environmental Temperature, Xueying Zhang, Yosuke Yamada, Hiroyuki Sagayama, Philip N. Ainslie, Ellen E. Blaak, Maciej S. Buchowski, Graeme L. Close, Jamie A. Cooper, Sai Krupa Das, Lara R. Dugas, Michael Gurven, Asmaa El Hamdouchi, Sumei Hu, Noorjehan Joonas, Peter Katzmarzyk, William E. Kraus, Robert F. Kushner, William R. Leonard, Corby K. Martin, Erwin P. Meijer, Marian L. Neuhouser, Robert M. Ojiambo, Yannis P. Pitsiladis, Guy Plasqui, Ross L. Prentice, Susan B. Racette, Eric Ravussin, Leanne M. Redman, Rebecca M. Reynolds, Susan B. Roberts, Luis B. Sardinha, Analiza M. Silva, Eric Stice, Samuel S. Urlacher

Clinical Research Faculty Publications

Lower ambient temperature (Ta) requires greater energy expenditure to sustain body temperature. However, effects of Ta on human energetics may be buffered by environmental modification and behavioral compensation. We used the IAEA DLW database for adults in the USA (n = 3213) to determine the effect of Ta (−10 to +30°C) on TEE, basal (BEE) and activity energy expenditure (AEE) and physical activity level (PAL). There were no significant relationships (p > 0.05) between maximum, minimum and average Ta and TEE, BEE, AEE and PAL. After adjustment for fat-free mass, fat mass and age, statistically significant (p < 0.01) relationships between TEE, BEE and Ta emerged in females but the effect sizes were not biologically meaningful. Temperatures inside buildings are regulated at 18–25°C independent of latitude. Hence, adults in the US modify their environments to keep TEE constant across a wide range of external ambient temperatures.

Using 2d Hec-Ras Modeling With Vertical Feature Extraction To Inform Ecological Design In The Lower Atchafalaya River Basin, Louisiana, Colin Anderson

LSU Master's Theses

A 2D HEC-RAS model was created to demonstrate applications of hydraulic modeling for informing ecological design. This was done by comparing the hydrologic conditions of a baseline year (2010) to that of the environmental flow prescription from Kozak et al. (2016). The inundation results are relevant to hydraulic connectivity and cypress regeneration.

The hydraulic model was created by assimilating numerous data sources, refining the model with vertical feature (VF) extraction, and calibrating/validating the model. Several topo-bathymetric sources were combined to create the digital elevation model (DEM). Most importantly, this included bathymetry of the backswamp. Polylines of significant VFs in the …

Hexavalent Chromium Increases The Metabolism And Genotoxicity Of Aromatic Amine Carcinogens 4-Aminobiphenyl And Β-Naphthylamine In Immortalized Human Lung Epithelial Cells, James T.F. Wise, Raúl A. Salazar-González, Kennedy M. Walls, Mark A. Doll, Mariam R. Habil, David W. Hein

Faculty Publications

Humans are exposed to carcinogenic chemicals via occupational and environmental exposures. Common chemicals of concern that can occur in exposures together are aromatic amines (e.g., 4-aminobiphenyl [4-ABP] and β-naphthylamine [BNA]) and hexavalent chromium (Cr[VI]). Arylamine N-acetyltransferases 1 and 2 (NAT1 and NAT2) are key to the metabolism of aromatic amines and their genotoxicity. The effects of Cr(VI) on the metabolism of aromatic amines remains unknown as well as how it may affect their ensuing toxicity. The objective of the research presented here is to investigate the effects of Cr(VI) on the metabolism and genotoxicity of 4-ABP and BNA in immortalized …

Longitudinal Associations Between Housing Instability, Primary Caregiver's Mental Health, Parenting Skills, And Child Behavior Problems: A Latent Growth Modeling Approach, Xi Du

LSU Doctoral Dissertations

Children who grow up in disruptive environments have heightened vulnerability to psychological and behavioral difficulties, which may influence overall well-being through the course of their lives. This study combined a life course perspective with Conger’s family stress model to investigate the longitudinal associations between housing instability, primary caregiver’s mental health, parenting skills, and child internalizing/externalizing behavior problems by unpacking dynamic change from a focal child’s early childhood to adolescence in each factor.

Data came from the Panel Study of Income Dynamics and its Child Development Supplement survey. The analytic sample comprises 783 children who aged 3-7 at enrollment with consistent …

Incorporating Geopolymer Binders To Fully Utilize Recycled Concrete Aggregates, Matthew Upshaw

LSU Doctoral Dissertations

This study aims to develop a geopolymer concrete binder that will yield desirable strength and durability characteristics when applied to recycled aggregate concrete. In order to achieve this, a geopolymer binder consisting of a metakaolin-silica fume aluminosilicate source material blend activated by potassium-based alkaline solutions is developed and used in place of ordinary Portland cement (OPC) with four different levels of recycled aggregate replacement ratios. The properties of these concretes were analyzed and compared with the properties of OPC concretes produced with the same replacement ratio levels. The results showed that the geopolymer recycled aggregate concrete (GRAC) achieved greater than …

Computational Imaging For Shape Understanding, Yuqi Ding

LSU Doctoral Dissertations

Geometry is the essential property of real-world scenes. Understanding the shape of the object is critical to many computer vision applications. In this dissertation, we explore using computational imaging approaches to recover the geometry of real-world scenes. Computational imaging is an emerging technique that uses the co-designs of image hardware and computational software to expand the capacity of traditional cameras. To tackle face recognition in the uncontrolled environment, we study 2D color image and 3D shape to deal with body movement and self-occlusion. Especially, we use multiple RGB-D cameras to fuse the varying pose and register the front face in …

Improved Building-Specific Flood Risk Assessment And Implications Of Depth-Damage Function Selection, Ehab Gnan, Carol J. Friedland, Md Adilur Rahim, Rubayet Bin Mostafiz, Robert V. Rohli, Fatemeh Orooji, Arash Taghinezhad, Jasmin Mcelwee

Faculty Publications

Average annual loss (AAL) is traditionally used as the basis of assessing flood risk and evaluating risk mitigation measures. This research presents an improved implementation to estimate building-specific AAL, with the flood hazard of a building represented by the Gumbel extreme value distribution. AAL is then calculated by integrating the area under the overall loss-exceedance probability curve using trapezoidal Riemann sums. This implementation is compared with existing AAL estimations from flood risk assessment. A sensitivity analysis is conducted to examine the variability in AAL results based on depth-damage function (DDF) choice. To demonstrate the methodology, a one-story single-family residence is …

Insight In Thermomechanical Constitutive Modeling Of Shape Memory Polymers, Amir Shojaei, Wei Xu, Cheng Yan, Qianxi Yang, Guoqiang Li

Faculty Publications

Shape memory polymers (SMPs) are a new member of the smart materials family. SMPs have found wide applications or potential applications in almost all manmade structures and devices. In order to better design smart structures and devices using SMPs, thermomechanical constitutive modeling is essential. In this insight paper, we will focus on presenting several multi-length-scale and multi-physics modeling frameworks, including the thermodynamics consistent model, elasto-viscoplastic model, statistical mechanics model, and phase evaluation law model. The SMPs modeled will include amorphous one-way shape memory polymers, semicrystalline one-way shape memory polymers, semicrystalline two-way shape memory polymers, and functional and mechanical damage effects …

Evaluating Serverless Computing, Charitra Maharjan

LSU Master's Theses

Function as a Service (FaaS) is gaining admiration because of its way of deploying the computations to serverless backends in the different clouds. It transfers the complexity of provisioning and allocating the necessary resources for an application to the cloud providers. The cloud providers also give an illusion of always availability of resources to the users. Among the cloud providers, AWS serverless platform offers a new paradigm for developing cloud applications without worrying about the underlying hardware infrastructure. It manages not only the resource provisioning and scaling of an application but also provides an opportunity to reimagine the cloud infrastructure …

Ultraviolet Chiral Plasmonics, Tiago Ramos Leite Da Silva

LSU Doctoral Dissertations

The far- (λ < 250 nm) ultraviolet (UV) chiroptical response from protein secondary structures can aid in understanding protein interactions for drug discovery. Chiral metasurfaces have attracted attention to enhance the intrinsically weak biomolecular optical activity for improved structural biology characterization. However, due to materials and fabrication challenges, current metasurfaces mostly operate in the visible and near-infrared regimes with limited, if any, response in far-UV. The large spectral mismatch between metasurface and biomolecular resonances limits the chiral enhancement factor. In this work, we study aluminum planar metamaterials which display optical activity in the UV region. In the first part of this work, we experimentally and computationally investigate the resonant plasmonic-biomolecular interactions in the far-UV. To the best of our knowledge, our work is the first to experimentally explore these resonant interactions in the biologically relevant far-UV regime. We demonstrate experimentally and computationally enhanced chiral sensing of sub-10 nm amino acid films. We develop a full-wave computational model of a biomolecular film on an aluminum gammadion array using experimentally derived chiral parameters (κ) for L- and DTyrosine films. Our model shows that detectable enhancements in the circular dichroism (CD) and birefringence (CB) signals from small amounts of biomolecules are only possible when tight spectral overlap exists between the plasmonic and biomolecular chiroptical signals. We support this conclusion experimentally by fabricating aluminum gammadion arrays on fused silica substrates and sublimating ultrathin racemic, L- and D- Tyrosine films on the arrays. Complete Mueller matrix analysis of the resonant plasmonic-biomolecular system agrees with our computational results and demonstrates the importance of using far-UV active metasurfaces for enhancing natural optical activity. In the second part of this work, we study hybrid aluminum reflective metasurfaces, which leverage both local and propagating surface plasmons (PSP). These materials display strong bi-anisotropy with rich polarization conversion properties. We explore several geometries and verify how their symmetry properties affect their far-field response. Our observations provide an opportunity for researchers to improve the chiral response of plasmonic metasurfaces, especially in the challenging ultraviolet regime.

Organic Matter And Nutrient Cycling In Coastal Wetlands And Submerged Aquatic Ecosystems In An Age Of Rapid Environmental Change—The Anthropocene, Tracy Elsey-Quirk, Jeffrey C. Cornwell

Faculty Publications

No abstract provided.

A New Ocean State After Nuclear War, Cheryl S. Harrison, Tyler Rohr, Alice Duvivier, Elizabeth A. Maroon, Scott Bachman, Charles G. Bardeen, Joshua Coupe, Victoria Garza, Ryan Heneghan, Nicole S. Lovenduski, Philipp Neubauer, Victor Rangel, Alan Robock, Kim Scherrer, Samantha Stevenson, Owen B. Toon

Faculty Publications

Nuclear war would produce dire global consequences for humans and our environment. We simulated climate impacts of US-Russia and India-Pakistan nuclear wars in an Earth System Model, here, we report on the ocean impacts. Like volcanic eruptions and large forest fires, firestorms from nuclear war would transport light-blocking aerosols to the stratosphere, resulting in global cooling. The ocean responds over two timescales: a rapid cooling event and a long recovery, indicating a hysteresis response of the ocean to global cooling. Surface cooling drives sea ice expansion, enhanced meridional overturning, and intensified ocean vertical mixing that is expanded, deeper, and longer …

Numerical Experiments Of Temperature Mixing And Post-Storm Re-Stratification Over The Louisiana Shelf During Hurricane Katrina (2005), Mohammad Nabi Allahdadi, Chunyan Li, Nazanin Chaichitehrani

Faculty Publications

Studying mixing and re-stratification during and after hurricanes have important implications for the simulation of circulation and bio-geochemical processes in oceanic and shelf waters. Numerical experiments using FVCOM on an unstructured computational mesh were implemented to study the direct effect of hurricane winds on the mixing and temperature redistribution of the stratified Louisiana shelf during Hurricane Katrina (2005), as well as the post-storm re-stratification timescale. The model was forced by Katrina’s wind stress obtained from a combination of H-Wind database and NCEP model. The climatological profiles of temperature and salinity for August (the month in which Katrina occurred) from the …

Evaluation Of Fresh And Hardened Properties Of 3d-Printed Engineered Cementitious Composites (Ecc) Designed For Sustainable And Resilient Infrastructure Systems, Maryam Hojati, Muhammad Saeed Zafar, Amir Bakhshi

Data

3D concrete printing is a cutting-edge construction technique that has the potential to revolutionize the construction industry due to cost-saving in terms of labor and formwork costs, efficiency in construction, lower safety-related risks, and a higher degree of automation. However, several issues still make its adoption relatively slower on a large scale. Engineered cementitious composites (ECC), a class of ultra-high-strength concrete, can be a potential solution to some problems, such as reinforcement and durability. The preliminary phase of a Tran SET project focused on the design of 3D printable ECC, considering the concrete mix design proportions and their potential effects …

Evaluation Of Fresh And Hardened Properties Of 3d-Printed Engineered Cementitious Composites (Ecc) Designed For Sustainable And Resilient Infrastructure Systems, Maryam Hojati, Muhammad Saeed Zafar, Amir Bakhshi

Publications

3D concrete printing is a cutting-edge construction technique that has the potential to revolutionize the construction industry due to cost-saving in terms of labor and formwork costs, efficiency in construction, lower safety-related risks, and a higher degree of automation. However, several issues still make its adoption relatively slower on a large scale. Engineered cementitious composites (ECC), a class of ultra-high-strength concrete, can be a potential solution to some problems, such as reinforcement and durability. The preliminary phase of a Tran SET project focused on the design of 3D printable ECC, considering the concrete mix design proportions and their potential effects …

Alternative Supplementary Cementitious Materials In Ultra-High Performance Concrete, Craig Newtson, Seyedsaleh Mousavinezhad, Gregory J, Gonzales, William K. Toledo, Judit M. Garcia

Data

Ultra-high performance concrete (UHPC) is an emerging material with remarkable mechanical and durability properties that contains large amounts of cementitious materials. Silica fume is a main supplementary cementitious material (SCM) in UHPC, however, it is more expensive than cement and other SCMs, so it is often substituted with inexpensive class F fly ash. Unfortunately, future availability of fly ash is uncertain as the energy industry moves toward renewable energy. Fly ash shortages create an urgent need to find cost-effective and environmentally-friendly alternatives for fly ash. This study investigated replacing cement, fly ash, and silica fume in UHPC mixtures with ground …

Effectiveness Assessment Of E-Ticketing Technology In Construction Of Transportation Projects, Sharareh Kermanshachi, Karthik Subramanya

Data

The construction of highway infrastructure has devoted significant resources towards e-Construction to reduce the paperwork and automate the tasks in daily operations. Electronic Ticketing (e-Ticketing) is one such component of e-Construction that aids in the digital transfer of material tickets such as asphalt and concrete which accounts for more than fifty per cent of construction costs. Despite the benefits of e-Ticketing, many state departments and agencies are unwilling to transition into this technology. No studies have identified the cause of the delay in the implementation process, developed a framework to comprehend the platform's full potential, quantified savings, and suggested strategies …

Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan

Publications

Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R² (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …

Development Of A Machine Learning-Based Model To Determine The Optimum And Safe Restriping Timing Of Thermoplastic Pavement Markings In Hot And Humid Climates, Momen R. Mousa, Marwa Hassan

Data

Due to limited budget, most transportation agencies restripe their thermoplastic pavement markings based on a fixed schedule or based on visual inspection instead of monitoring the retroreflectivity and restriping when the retroreflectivity drops below a pre-determined threshold. These strategies are questionable in terms of efficiency and economy. Therefore, previous studies proposed degradation models to predict the retroreflectivity of thermoplastic markings based on key variables. Yet, most of these studies reported low R² (as low as 0.1), which placed little confidence in these models. Therefore, the objective of this study was to evaluate and predict the field performance of thermoplastics …

Alternative Supplementary Cementitious Materials In Ultra-High Performance Concrete, Craig Newtson, Seyedsaleh Mousavinezhad, Gregory J. Gonzales, William K. Toledo, Judit M. Garcia

Publications

Ultra-high performance concrete (UHPC) is an emerging material with remarkable mechanical and durability properties that contains large amounts of cementitious materials. Silica fume is a main supplementary cementitious material (SCM) in UHPC, however, it is more expensive than cement and other SCMs, so it is often substituted with inexpensive class F fly ash. Unfortunately, future availability of fly ash is uncertain as the energy industry moves toward renewable energy. Fly ash shortages create an urgent need to find cost-effective and environmentally-friendly alternatives for fly ash. This study investigated replacing cement, fly ash, and silica fume in UHPC mixtures with ground …

A Deep Learning Tool For The Assessment Of Pavement Smoothness And Aggregate Segregation During Construction, Mostafa Elseifi, Ramchandra Paudel, Md Tanvir Ahmed Sarkar, Hossam Abohamer, Nirmal Dhakal

Data

Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface …

Using Rice Husk Ash (Rha) As Stabilizing Agent For Problematic Subgrade Soils And Embankments, Zahid Hossain, Rifat Bulut, Fares Tarhuni, Hussein Al-Dakheeli

Data

Arkansas produces the most of the rice in the United States. About 20% of poddy is rice husk (RH), which is burnt under controlled conditions to produce rice rusk ash (RHA). The RHA is considered an environmental hazard and a significant challenge for rice millers. However, RHA is rich in pozzolanic material, which is mainly silica. In this study, RHA is used to stabilize poor soils. Another commonly used stabilizer, hydrated lime (HL), has also been evaluated for comparison purposes. Thus, this study aimed to determine the optimum percentages of RHA, HL, or a combination of these two agents by …

Performance Monitoring Leveraging Advanced Ai Technique With Cnn, Suyun Ham Ph.D, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David, Sanggoo Kang

Publications

The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting …

Increasing Bridge Durability And Service Life With Lidar Enhanced Unmanned Aerial Systems (Uas), Fernando Moreu, Mahsa Sanei, Chris Lippitt

Data

Bridge construction inspections require quantitative measurements and location information. The conventional approach is visual inspection, which in general, is rather time-consuming, expensive due to traffic closure, subjective, and needs special access. Therefore an automated rebar layout detection algorithm was developed to quickly extract quantitative rebar layout information from the LiDAR data. This systematic method can automatically cluster the bridge elements from a 3D point cloud by using LiDAR-equipped UAS data collection and unsupervised machine learning techniques. A new automated inspection system using a LIDAR-equipped UAS can eventually if developed and tested be more reliable as well as less expensive. In …

Performance Monitoring Leveraging Advanced Ai Technique With Cnn, Suyun Ham Ph.D, Stefan Romanoschi, Yin Chao Wu, Dafnik Saril Kumar David, Sanggoo Kang

Data

The main goal of this project is to study and develop a reliable nondestructive testing (NDT)-based structural performance prediction model framework leveraging the advanced machine learning convolutional neural network (CNN) technique and rapid crack evaluation system. There are two steps of application CNN technique in this project: 1) the first step is to identify delamination, noise, and the unexpected signal produced by the existing damage identification algorithm to improve the accuracy of NDT results. The input image or training data of NDT data for CNN is comprehensively studied with several features, such as the duration of the signal, the starting …

Development Of Distress Index Prediction Models For Rehabilitation Treatments In Louisiana Using Advanced Machine Learning Techniques, Momen R. Mousa, Marwa Hassan

Data

Performance prediction models are used by state agencies to predict future trends in distress indices, hence, determining the required maintenance and/or rehabilitation treatment as well as the deterioration rate and remaining pavement service life. However, most of these models are based on a limited number of parameters and cannot predict the performance distress indices reliably. Such limitation resulted in having, most of the time, a maximum prediction period of five years. As a solution and coping with the ever-increasing size of pavement data, machine learning techniques have become a promising alternative. The objective of this study was to develop a …

Covid-19 And Traffic Safety: Exploring Exposure, Crash Frequency And Severity, And Roadway And Network Design, Nicholas N. Ferenchak Ph.D

Publications

Early COVID-19 lockdowns in the first half of 2020 largely kept people at home, thereby reducing motor vehicle traffic levels. Theoretically, reduced traffic exposure should have resulted in reduced motor vehicle crashes. However, a variety of factors may have complicated this relationship. In order to better understand the impact of COVID-19 lockdowns on traffic safety outcomes, we explore fatalities, injuries, and total crashes before and during the lockdowns on both the national and state levels. We provide descriptive statistics and create negative binomial regressions exploring the role of vehicle, user, and built environment factors on traffic safety outcomes. Findings suggest …