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Synthesis of Materials for Additive Manufacturing of Enhanced Polymers and Textured Ceramics
No full textThis research will be divided into two sections. The first section investigates the development of textured Barium Titanate (BaTiO3) ceramics fabricated through Direct Ink Write Printing (DIW) integrated with Templated Grain Growth (TGG). This approach aims to develop lead-free piezoelectric materials with grain alignment during printing process to promote high piezoelectric performance comparable to those in lead-based ceramics. This research evaluates the impact of the DIW-TGG process on grain orientation, material densification, and piezoelectric response, illustrating its potential for developing high-performance ceramics for advanced applications such as sensors, actuators, and other piezoelectric devices. The second project explores the chemical stability and degradation characteristics of stereolithography (SLA)-printed polymers. This research evaluates the impact of varying printing parameters and post-curing procedures impact on the long-term resilience when subjected to UV exposure, humidity, and mechanical stress. The researchâ??s objective is to identify processing conditions that diminish material degradation and enhance the reliability and performance of SLA-printed components for use in high-demand industrial applications. Overall, this thesis underscores the synthesis and engineering of high-performance smart materials through additive manufacturing. The research findings support the advancement of next-generation ceramics and polymers that are sustainable and suitable for the rigorous standards of modern industries
A Machine Learning Approach For Estimating Evapotranspiration For Urban Landscaping Vegetation In Semi-Arid Regions
No full textWater management is important for residents in semi-arid urban areas due to increasing demand, water scarcity, and rising costs. It is estimated that in semi-arid regions, 40-70% of the household water consumption is used in landscaping. Therefore, urban landscaping water use can substantially contribute to water conservation. This work aims to estimate the water needs of urban landscaping vegetation to inform residents in semi-arid regions.Evapotranspiration indicates water and energy exchange between the atmosphere, soil, and vegetation. This interaction depends on solar radiation, evaporation, transpiration, and other biophysical parameters. Evapotranspiration has become a reference for water management in agriculture (e.g., crop irrigation). However, the evapotranspiration standards do not include urban landscaping vegetation. This project aims to create an evapotranspiration estimation model for urban landscaping vegetation in semi-arid regions using Machine Learning (ML).
To achieve this goal, four Deep Learning (DL) models were implemented: Multilayer Perceptron (MLP), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional-LSTM (Conv-LSTM). These models were trained using meteorological data from thirty-tree stations, including evapotranspiration. The DL models were compared against four benchmark Machine Learning (ML) regression models: linear regression (LR), XGBRegressor (XGBR), support vector regressor (SVR), and random forest regressor (RFR). The performance of the eight ML models to estimate evapotranspiration was evaluated using regression metrics, including R-squared (R2), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). From the DL models, the MLP network produced the best data fit (R2 = 0.9694). The ML regression models show a slightly better performance than MLP, with a difference of R2 less than 0.01. Results also show that the RFR model performed better during the summer months, and the MLP model performed better the rest of the year. These results suggest that more than one model can be used to optimize the evapotranspiration estimation depending on the season. In addition, a proof-of-concept is presented to illustrate how the proposed evapotranspiration models can be used to calculate the water-loss amount of an urban landscaping plant (i.e., sunflower) due to transpiration and evaporation. This process required identifying a plantâ??s species coefficient from the literature and evapotranspiration data from an additional weather station in the region. The evaluation metrics using the additional weather station data showed a performance of R2 = 0.9156 for the MLP model and an R2 = 0.9320 using the RFR model. The plant\u27s water-loss amount can be used to identify the plant\u27s daily water needs and inform irrigation schedules. This work is a first step towards supporting water conservation strategies in household urban landscaping, a critical need in semi-arid regions
Examining Key Predictors Of Enrollment At A Regional, Open-Access Research University On The U.S. - Mexico Border
No full textEach year, approximately 64% of admitted students do not enroll at the University of Texas at El Paso (UTEP). This is particularly concerning given the projected decline in regional high school graduates beginning in the 2025-2026 academic year. The El Paso region is also predominantly composed of groups who have been historically underrepresented in higher education, including Hispanic or Latino students, individuals from low socio-economic backgrounds, and first-generation college students. Using logistic regression analysis, this quantitative study investigates how seven key factors impact the likelihood of enrollment among admitted UTEP applicants. The study utilized admissions data provided by UTEP’s Texas Public Information Act office. The predictive model developed by two different statistical software packages (SPSS and R) demonstrates improvements in accuracy of 9.7% and 8.4% over the null model. Results revealed that college readiness, as defined by the Texas Administrative Code, had the strongest impact on the likelihood of enrollment. Increases in High school quartile and prior college credits both positively influence the likelihood of enrollment while first-generation status and increases in unmet financial need and applicant distance from UTEP negatively impact the likelihood of enrollment. Notably, students selected for financial aid verification are more likely to enroll, challenging initial assumptions. A potential scale of measurement error in the unmet financial need variable was also identified. The model explains a modest proportion of variance in enrollment and has moderate predictive power (25.4% Nagelkerke R2 and .765 AUC ROC score). However, the results of the study provide actionable insights to support UTEP’s enrollment strategies. The study recommends utilizing machine learning for any future predictive modeling. The study illustrates the potential of predictive modeling to inform campus leaders and advance UTEP’s mission of expanding educational opportunities
A Comprehensive Study of Cyber Security Attacks, Threats, and Risk Mitigation, and How to Reduce Impact on Small and Medium Enterprise by Creating and Using an Incident Response Plan
No full textCybersecurity is an ever-growing issue with more implications for world-wide impact than dedicated resources or tactics to resolve the threat. In a constantly changing and evolving world, where technology outpaces the ability to rationalize and understand its benefits, the disadvantages grow at an accelerated pace. For instance, the technology that exists to protect against computer attacks is obsolete as soon as developed and deployed. This starts a perpetual cycle for defense versus attack, and with the world becoming more interconnected and reliant on the internet of things, the issue will continue to get worse. Within this problem exists a way to help isolate, mitigate, and deter risk to individuals and corporations as they grasp a growing, connected world. This issue is intensified as small and medium businesses struggle to find themselves similarly equipped to manage modern challenges in cybersecurity, as many lack the technical expertise and staff necessary to combat cyber threats. To further understand the challenges of cybersecurity attacks and potential consistent ways of mediation, a review and analysis of literature about cybersecurity threats, risks and recovery methods was conducted. The articles reviewed help build the case that no industry is safe from attack, and that no consistent way to deter attacks and rebuild from them exists. By using the literature findings, it can lay the framework of finding ways to resolve this growing problem and the results of this study can support a way to offer a unified front to deter cyber-attacks and assist small and medium size businesses with protecting themselves from the growing cybersecurity threats
Navigating Social And Academic Spaces As A Non-Traditional Student In A Doctoral Program: An Autoethnography
No full textThis autoethnography examines my personal, professional, and academic experiences throughout my educational journey, aiming to address gaps in scholarly literature relevant to non-traditional students at all educational levels, particularly at the doctoral level. As a non-traditional student, I have encountered significant challenges during my academic career. I have often felt like an outsider due to my age, generational gap, and educational background. The impostor phenomenon has intensified my feelings of being an outcast, even though I have had a prosperous professional career. I have drawn upon my memories, reflections, narratives, scholarly literature, documents, and personal artifacts to provide a comprehensive account of my journey. By revisiting moments from my distant past, immediate past, and present, I hope readers will vividly experience the trauma, insecurities, anxiety, joy, personal satisfaction, and moments of happiness I have encountered in my social and academic spaces. I have examined how the impostor phenomenon can exacerbate anxiety and other mental health challenges (Thompson et al., 2000). Additionally, I explored how validation, resilience, spiritual resilience, and self-determination can influence the success of non-traditional students in earning their doctoral degrees. This research has been guided by Constructivist epistemology and framed by the Theory of Reflexivity. Through analyzing and interpreting my behaviors, thoughts, and experiences concerning others in society, I have gained insights that can contribute to systemic changes and enhance professional doctoral degree programs, especially for non-traditional students.
Keywords: autoethnography, non-traditional student, impostor phenomenon, validation, resilience, resilience in spirituality, social and academic space
Investigation Of Quasi-Static And Low-Velocity Impact Responses Of 12k Im7/5320-1 Laminates Toughened With Vertically Aligned Carbon Nanotubes
No full textComposite structures have become an attractive material selection for researchers and industries such as in aerospace, automotive, naval, and green energy due to their high strength to weight ratios and custom tailor ability through material selection. With the increasing selection of composites as the material of choice for most of these industries understanding their failure mechanisms is imperative to the continued success and implementation of these structures. This failure mostly originating from the resin-rich regions which bond the adjacent ply-to-ply surfaces together which have much weaker mechanical properties than those of the high modulus fibers. In this report commercially available Vertically Aligned Carbon Nanotubes (VACNTs) were purchased and implemented within those resin-rich regions to improve the strength and damage tolerance of composite materials. Their morphological parameters were chosen after a thorough investigation of their length and diameters yielded adverse strengthening effects on composites reinforced by them. Roll-to-roll manufacturing was used to transfer to VACNTs from the large wafers of which they were grown on to the fibers themselves using a combination of heat and pressure. This process has large commercial viability which is a rare commodity in interlaminar reinforcements with the possibility of a high efficiency rate into large batch industrial processing. An in-depth analysis of the microstructure of both pre- and post-tested samples through Scanning Electron Microscopy (SEM), Ultrasonic C-scans, and Micro CT scans were conducted to characterize the structural integrity and the strengthening effects of the VACNTs on unidirectional Carbon Fiber Reinforced Polymer Matrix Composites (CFRPs). The characterized microstructures along with the material performance of the composites yielded valuable insights into how effective the VACNTs were in improving the performance of composites reinforced with them. To the authorâ??s knowledge this is the first study of its kind on this fiber/matrix combination
Optimal Route Planning Considering Microlevel Sequence Of Delivery Task Using Computer Simulation
No full textAs globalization reshapes our industries worldwide, supply chain management has evolved into a highly interconnected world that oversees traditional management, making countries more interdependent. Driven by rapid technological advancements, this shift allows companies to share information in a matter of seconds across continents, aligning operations and building adaptability to quick changes. Therefore, industries have greatly needed to adjust to and use the new technology to their advantage. However, even when this impact can be observed globally, the general impact can be analyzed and eradicated by performing micro-level analysis within the supply chain using technologies to their advantage. AnyLogic is a great tool that helps simulate real-world scenarios to optimize current processes and simulate different potential outcomes for companies. This study aims to showcase the importance of micro-level analysis, simulating how different deliveries would behave for houses, departments, and apartments within the El Paso, TX, location. Key components such as delivery delays and overall processes were thoroughly integrated into the simulation framework. The MOST (Maynard Operation Sequence Technique ) analysis technique and a Monte Carlo simulation analysis were employed to ensure more accurate data in the simulated scenarios and improve the accuracy of delay times. Furthermore, the results obtained within this analysis were translated into another simulation using the GIS mapping features of Anylogic, Javascript coding, state charts, and events. This study showcased the importance of performing a detailed-oriented analysis as it showed scenarios where companies could have hired more employees and had low utilization rates and incomes. In contrast, in some other scenarios, if the correct analysis had not been performed, companies could have hired fewer employees, not performed all the deliveries committed to, and overworked employees
Logarithmic Number System Is Optimal for AI Computations: Theoretical Explanation of Empirical Success
No full textEveryone knows the success story of machine-learning AI. However, the current AI tools are not perfect. We know how to make them better: every time we increase the amount of computations by the order of magnitude, we get a drastic improvement in the performance of the resulting machine learning tools. Training modern AI system requires a tremendous amount of computations -- that already take a lot of time. So, to increase the number of computations, we need to make each computation step faster. One way to do that is to use low-precision arithmetic operations, e.g., with 1 byte per real number instead of the usual 8. It was shown that we can speed up computations even further if we apply an appropriate nonlinear transformation to all the values. Empirically, out of all transformations that were tried, logarithmic (log) transformation works the best. In this paper, we prove that under some reasonable condition, log transformation is indeed optimal. This way, not only we provide a theoretical explanation for the above empirical fact, but we are also proving that log transformation is better than all possible transformations, including the ones that have not been experimentally tried
Migration Shocks and Economic Redistribution: The Case of Return Migration to Mexico
No full textThis paper examines the effects of return migration on inequality, poverty, and income in Mexico, using a reversal in the migration flow in the late 2000s. For the first time since the Great Depression, more migrants returned to Mexico than those who migrated to the United States. We use this return migration shock to construct a two-period panel for municipalities in Mexico and employ an instrumental variable approach using the exogenous variation of U.S. immigration enforcement policies. Our results show that return migration significantly reduced inequality and poverty while boosting income per capita. These effects appear most pronounced municipalities with higher baseline levels of poverty and lower inequality. Furthermore, individual-level analysis suggests that the observed reduction in inequality was driven by wage premiums among returnees and positive spillover effects on non-migrants with a low educational attainment. The findings highlight the potential of return migration as a tool for economic development in migrant-sending countries
Fuzzy Ideas Explain Fechner Law and Help Detect Relation Between Objects in Video
No full textHow to find relation between objects in a video? If two objects are closely related -- e.g., a computer and it mouse -- then they almost always appear together, and thus, their numbers of occurrences are close. However, simply computing the differences between numbers of occurrences is not a good idea: objects with 100 and 110 occurrences are most probably related, but objects with 1 and 5 occurrences probably not, although 5 − 1 is smaller than 110 − 100. A natural idea is, instead, to compute the difference between re-scaled numbers of occurrences, for an appropriate nonlinear re-scaling. In this paper, we show that fuzzy ideas lead to the selection of logarithmic re-scaling, which indeed works very well in video analysis -- and which also explains Fechner Law in psychology, that our perception of difference between two stimuli is determined by the difference between the logarithms of their intensities