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Description
Obesity has consistently presented a significant challenge, with excess body fat contributing to the development of numerous severe conditions such as diabetes, cardiovascular disease, cancer, and various musculoskeletal disorders. In this study, different methods are proposed to study substrate utilization (carbohydrates, proteins, and fats) in the human body and validate the biomarkers enabling to investigation of weight management and monitor metabolic health. The first technique to study was Indirect calorimetry, which assessed Resting Energy Expenditure (REE) and measured parameters like oxygen consumption (VO2) and carbon dioxide production (VCO2). A validation study was conducted to study the effectiveness of the medical device Breezing Med determining REE, VO2, and VCO2. The results were compared with correlation slopes and regression coefficients close to 1. Indirect Calorimetry can be used to determine carbohydrate and fat utilization but it requires additional correction for protein utilization. Protein utilization can be studied by analyzing urinary nitrogen. Therefore, a secondary technique was studied for identifying urea and ammonia concentration in human urine samples. Along this line two methods for detecting urea were explored, a colorimetric technique and it was validated against the Ion-Selective method. The results were then compared by correlation analysis of urine samples measured with both methods simultaneously curves. The equations for fat, carb, and protein oxidation, involving VO2, VCO2 consumption, and urinary nitrogen were implemented and validated, using the above-described methods in a human subject study with 16 subjects. The measurements included diverse diets (normal vs. high fat/protein) in normal energy balance and pre-/post interventions of exercise, fasting, and a high-fat meal. It can be concluded that the indirect calorimetry portable method in conjunction with urine urea methods are important to help the understanding of substrate utilization in human subjects, and therefore, excellent tools to contribute to the treatments and interventions of obesity and overweighted populations.
ContributorsPradhan, Ayushi (Author) / Forzani, Erica (Thesis advisor) / Lind, Mary Laura (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2023
Description
A key contribution of human factors engineering is the concept of workload: a construct that represents the relationship between an operator’s cognitive resources, the demands of their task, and performance. Understanding workload can lead to improvements in safety and performance for people working in critical environments, particularly within action teams. Recently, there has been interest in considering how the workload of a team as a whole may differ from that of an individual, prompting investigation into team workload as a distinct team-level construct. In empirical research, team-level workload is often considered as the sum or average of individual team members' workloads. However, the intrinsic characteristics of action teams—such as interdependence and heterogeneity—challenge this assumption, and traditional methods of measuring team workload might be unsuitable. This dissertation delves into this issue with a review of empirical work in action teams, pinpointing several gaps. Next, the development of a testbed is described and used to address two pressing gaps regarding the impact of interdependence and how team communications relate to team workload states and performance. An experiment was conducted with forty 3-person teams collaborating in an action team task. Results of this experiment suggest that the traditional way of measuring workload in action teams via subjective questionnaires averaged at the team level has some major shortcomings, particularly when demands are elevated, and action teams are highly interdependent. The results also suggested that several communication measures are associated with increases in demands, laying the groundwork for team-level communication-based measures of team workload. The results are synthesized with findings from the literature to provide a way forward for conceptualizing and measuring team workload in action teams.
ContributorsJohnson, Craig Jonathon (Author) / Cooke, Nancy J (Thesis advisor) / Gutzwiller, Robert S (Committee member) / Holder, Eric (Committee member) / Amazeen, Polemnia G (Committee member) / Arizona State University (Publisher)
Created2023
Description
The interpersonal, subjective, and communication skills we carry with us are crucial to our professional successes, sometimes even more crucial than the technical skills we use to execute tasks. The engineering industry is wildly technical and competitive in order to define a better tomorrow for the human population. However, such a technical field often neglects the use of these soft skills, both originating from students, employees, and companies. In this thesis, I delve into the importance and various applications of soft skills within the engineering industry, the presence of a gap among engineers' expected versus actual soft skill usage, and if anything can be done to mend that gap.
ContributorsHove, Colton (Author) / Montoya, Detra (Thesis director) / Schlacter, John (Committee member) / Barrett, The Honors College (Contributor) / Department of Marketing (Contributor)
Created2023-12
Description
This project is centered around a decade-old video game called League of Legends, which is one of the most popular video games in esports. Due to its nature of being a complex team-based strategy game, intuitive human predictions of the game’s outcome are relatively unreliable. Many approaches have been adopted to assist intuitive human predictions in traditional team-based sports, such as the Least Squares Method and various supervised machine learning algorithms. These methods have been significantly outperforming human predictions. The objective of this research is, hence, to test whether the predictive models generated using these methods can achieve a similar level of reliability in a more complex game like League of Legends.
ContributorsWang, Jiahao (Author) / Zandieh, Michelle (Thesis director) / Lee, Inyoung (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / College of Integrative Sciences and Arts (Contributor)
Created2023-12
Description
For this study, my overarching goal was to understand the possibilities of humanity’s future in space exploration. Addressing the future of space exploration not only opens doors for a multitude of discoveries but may answer questions that can be essential to our survival on Earth. This study, more specifically, aimed to determine how college students at Arizona State University, engineering and astronomy students in particular, visualize the future of space exploration, as in the future, they will become the leading experts at the forefront of all space-related developments. The method through which I have conducted this study is a short survey, consisting of a variety of questions, designed to encourage students to develop their own unique interpretations of space exploration and ultimately, its imminent future. The results ultimately demonstrated that most participants in the study believed that political obstacles were the most prevalent concern in the further development of space exploration. There also appeared to be a moderate outlook on the future success and vitality of space exploration among student scientists and engineers. From a statistical standpoint, there appeared to be no alarming difference of opinion between these two ASU student groups.
ContributorsMontano, Sebastian (Author) / Voorhees, Matthew (Thesis director) / Aganaba, Timiebi (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / School of Earth and Space Exploration (Contributor)
Created2023-12
Description
The goal of this project is to measure the effects of the use of dynamic circuit technology within quantum neural networks. Quantum neural networks are a type of neural network that utilizes quantum encoding and manipulation techniques to learn to solve a problem using quantum or classical data. In their current form these neural networks are linear in nature, not allowing for alternative execution paths, but using dynamic circuits they can be made nonlinear and can execute different paths. We measured the effects of these dynamic circuits on the training time, accuracy, and effective dimension of the quantum neural network across multiple trials to see the impacts of the nonlinear behavior.
ContributorsLynch, Brian (Author) / De Luca, Gennaro (Thesis director) / Chen, Yinong (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2023-12
Description
This creative project details 5 engineers who made contributions to the ways that we live life today, yet have received little to no recognition for their efforts. The 5 engineers presented are Gottfried Wilhelm Leibniz, George Stephenson, Charles Babbage, David Alter, and Nikola Tesla. Each engineer is detailed via a portrait and a biography that covers a little bit of their life and the contributions that they made.
ContributorsNieves, Timothy (Author) / Davis, Turner (Thesis director) / Green, Heather (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2023-12
Description
Scientific research encompasses a variety of objectives, including measurement, making predictions, identifying laws, and more. The advent of advanced measurement technologies and computational methods has largely automated the processes of big data collection and prediction. However, the discovery of laws, particularly universal ones, still heavily relies on human intellect. Even with human intelligence, complex systems present a unique challenge in discerning the laws that govern them. Even the preliminary step, system description, poses a substantial challenge. Numerous metrics have been developed, but universally applicable laws remain elusive. Due to the cognitive limitations of human comprehension, a direct understanding of big data derived from complex systems is impractical. Therefore, simplification becomes essential for identifying hidden regularities, enabling scientists to abstract observations or draw connections with existing knowledge. As a result, the concept of macrostates -- simplified, lower-dimensional representations of high-dimensional systems -- proves to be indispensable. Macrostates serve a role beyond simplification. They are integral in deciphering reusable laws for complex systems. In physics, macrostates form the foundation for constructing laws and provide building blocks for studying relationships between quantities, rather than pursuing case-by-case analysis. Therefore, the concept of macrostates facilitates the discovery of regularities across various systems. Recognizing the importance of macrostates, I propose the relational macrostate theory and a machine learning framework, MacroNet, to identify macrostates and design microstates. The relational macrostate theory defines a macrostate based on the relationships between observations, enabling the abstraction from microscopic details. In MacroNet, I propose an architecture to encode microstates into macrostates, allowing for the sampling of microstates associated with a specific macrostate. My experiments on simulated systems demonstrate the effectiveness of this theory and method in identifying macrostates such as energy. Furthermore, I apply this theory and method to a complex chemical system, analyzing oil droplets with intricate movement patterns in a Petri dish, to answer the question, ``which combinations of parameters control which behavior?'' The macrostate theory allows me to identify a two-dimensional macrostate, establish a mapping between the chemical compound and the macrostate, and decipher the relationship between oil droplet patterns and the macrostate.
ContributorsZhang, Yanbo (Author) / Walker, Sara I (Thesis advisor) / Anbar, Ariel (Committee member) / Daniels, Bryan (Committee member) / Das, Jnaneshwar (Committee member) / Davies, Paul (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this dissertation, the nanofabrication process is characterized for fabrication of nanostructure on surface of silicon and gallium phosphide using silica nanosphere lithography (SNL) and metal assisted chemical etching (MACE) process. The SNL process allows fast process time and well defined silica nanosphere monolayer by spin-coating process after mixing N,N-dimethyl-formamide (DMF) solvent. The MACE process achieves the high aspect ratio structure fabrication using the reaction between metal and wet chemical. The nanostructures are fabricated on Si surface for enhanced light management, but, without proper surface passivation those gains hardly impact the performance of the solar cell. The surface passivation of nanostructures is challenging, not only due to larger surface areas and aspect ratios, but also has a direct result of the nanofabrication processes. In this research, the surface passivation of silicon nanostructures is improved by modifying the silica nanosphere lithography (SNL) and the metal assisted chemical etching (MACE) processes, frequently used to fabricate nanostructures. The implementation of a protective silicon oxide layer is proposed prior to the lithography process to mitigate the impact of the plasma etching during the SNL. Additionally, several adhesion layers are studied, chromium (Cr), nickel (Ni) and titanium (Ti) with gold (Au), used in the MACE process. The metal contamination is one of main damage and Ti makes the mitigation of metal contamination. Finally, a new chemical etching step is introduced, using potassium hydroxide at room temperature, to smooth the surface of the nanostructures after the MACE process. This chemical treatment allows to improve passivation by surface area control and removing surface defects. In this research, I demonstrate the Aluminum Oxide (Al2O3) passivation on nanostructure using atomic layer deposition (ALD) process. 10nm of Al2O3 layer makes effective passivation on nanostructure with optimized post annealing in forming gas (N2/H2) environment. However, 10nm thickness is not suitable for hetero structure because of carrier transportation. For carrier transportation, ultrathin Al2O3 (≤ 1nm) layer is used for passivation, but effective passivation is not achieved because of insufficient hydrogen contents. This issue is solved to use additional ultrathin SiO2 (1nm) below Al2O3 layer and hydrogenation from doped a-Si:H.
Moreover, the nanostructure is creased on gallium phosphide (GaP) by SNL and MACE process. The fabrication process is modified by control of metal layer and MACE solution.
ContributorsKim, Sangpyeong (Author) / Honsberg, Christiana (Thesis advisor) / Bowden, Stuart (Committee member) / Goryll, Michael (Committee member) / Augusto, Andre (Committee member) / Arizona State University (Publisher)
Created2021
Description
The world faces significant environmental and social challenges due to high economic development, population growth, industrialization, rapid urbanization, and unsustainable consumption. Global communities are taking the necessary measures to confront these international challenges and applying sustainable development principles across all sectors. Construction is a critical driving instrument of economic activity, and to achieve sustainable development, it is vital to transform conventional construction into a more sustainable model. The research investigated sustainable construction perceptions in Kuwait, a rapidly growing country with a high volume of construction activities. Kuwait has ambitious plans to transition into a more sustainable economic development model, and the construction industry needs to align with these plans. This research aims to identify the characteristics of sustainable construction applications in the Kuwaiti construction market, such as awareness, current perceptions, drivers and barriers, and the construction regulations' impact. The research utilized a qualitative approach to answer research questions and deliver research objectives by conducting eleven Semi-structured interviews with experienced professionals in the Kuwaiti construction market to collect rich data that reflects insights and understandings of the Kuwaiti construction industry. The Thematic analysis of the data resulted in six themes and one sub-theme that presented reflections, insights, and perspectives on sustainable construction perceptions in the Kuwaiti construction market. The research findings reflected poor sustainable construction awareness and poor environmental and social application in the construction industry, the determinant role of construction regulations in promoting sustainable construction. and barriers and drivers to sustainable construction applications. The research concluded with answers to research questions, delivery of research objectives, and an explanation of sustainable construction perceptions in the Kuwaiti construction market.
Contributorsalsalem, mohammad salem (Author) / Duran, Melanie (Thesis advisor) / Chong, Oswald (Committee member) / Sullivan, Kenneth (Committee member) / Grau, David (Committee member) / Arizona State University (Publisher)
Created2023