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Structuring The Sustainability Of Happiness In Critical Postmodernity: A Metadata, Mixed-Methods Study In Education
No full textThis dissertation study is built upon a pilot study that uses a meta-theoretical analysis on the culture of education. The purpose of it is to examine [un]happiness, a phenomenon influenced by the social, political, and economic systems in Western paradigms within a critical postmodern view, looking at the intricacies of schooling, as no studies have taken a bottom-up approach to investigate the voices of the main stakeholders: students, teachers, and administrators. My study questions whether a holistic, liberating, and socially responsive curriculum could engender a positive learning environment wherein happiness is sustainable. Within happiness studies, hope is essential, so much so that the Declaration of Independence mentions the pursuit of happiness, a philosophical framework also referenced in other countries\u27 constitutions. Neoliberalism today, however, causes many students to lose their sense of hope, which in turn affects their well-being, if not learning itself. Hope is not merely about waiting; it is an active force encapsulated by esperanza - a forward looking aspiration that seeks to bring dreams to fruition. As Paulo Freire suggests, hope is ontological, deeply tied to the very nature of being human and our capacity for transformation. In this study, I set out to examine happiness which, a priori, I thought was a subjective notion. A posteriori, I learned that it has a very objective aspect to it, with many metrics that show this. Worldwide there are countries/societies that are happier than others. I was able to confirm this through a longitudinal lens. I saw structures/ideologies that were conducive to the pursuit of happiness while others were not. These findings are backed up scientifically by the voices of those who experience schooling firsthand: the students, teachers, and administration. Hence, happiness is a byproduct of social justice, including the distribution of \u27dignified\u27 resources as the theories and my theses demonstrate. Education should provide the essential foundations and resources that empower students in a healthy way to reach beyond their dreams. This is what sustainable happiness is all about. The quality of a democracy ultimately depends on the quality of education that people have. Therefore, education and policymaking ought to provide empowerment and structures that sustain happiness.
Key words: postmodernity, sustainability, democracy, epistemology of [un]happiness, holistic education, quality education, the science of well-bein
A Numerical Study Of Self-Assembling Amphiphilic Systems
No full textMixing processes in ternary mixtures involve immiscible fluids such as oil and water, and a surface-active molecule called surfactant. These physical processes find applications in various fields, including enhanced oil recovery, drug delivery design systems, and the formulation of cleaning products. Even though this process has several applications, the mathematical models describing it and the numerical methods solving it are not well understood. The underlying mathematical model is a nonlinear initial-boundary value problem involving sixth-order derivatives and belongs to the class of sixth-order Cahn-Hilliard equations. Authors Sharma and Tierra recently proposed a numerical method to approximate its solutions in two and three dimensions. The proposed numerical method satisfies the key properties of being solvable, satisfying the discrete energy dissipation, being mass conservative, and being second-order accurate in time. Furthermore, numerical results were presented to demonstrate the scheme\u27s effectiveness in capturing the dynamics of phase transitions for a broad parameter range and the self-assembly of the molecules into bilayers. In this work, with the help of a ternary phase diagram, we provide additional numerical studies and demonstrate the numerical scheme\u27s further capability in capturing other self-assembly morphologies, such as micelles. We also demonstrate the effectiveness of the scheme across a broad parameter range in two-dimensional simulations. Our numerical investigations reveal how these parameters govern system morphology across three composition regimes, including: (1) surfactant-dominated (60% surfactant), (2) balanced oil-water (40% each), and (3) oil-dominated (60% oil) mixtures. The results quantitatively correlate parameter variations with interfacial pattern formation, matching established experimental behavior in ternary amphiphilic systems. This work establishes a robust computational framework for studying composition-dependent phase transitions, with natural extensions to three-dimensional systems and more complex interfacial phenomena
Investigating Self-Healing Mechanisms In Host-Pathogen Interactions: A Literature Review Of Macrophage Functions In Tuberculosis
No full textTuberculosis (TB) remains a leading cause of mortality, particularly in underdeveloped regions. Mycobacterium tuberculosis (Mtb) is regarded as one of the most successful pathogens due to its ability to evade host immunity and persist within granulomas. Macrophages are the first-line host defenders that engulf Mtb into phagosomes, which initiates lysosomal activity for degradation. However, the virulence factor of Mtb is able to inhibit phagolysosome fusion. The host immune system forms granulomas to contain the mycobacterium at the site of inflammation. This, in turn, causes a necrotic state and causes tissue damage from the prolonged inflammation. Studies suggest that the host\u27s inflammatory response contributes more to tissue damage than the pathogen itself. This gives a different point of view in infectious disease treatment, where therapeutic strategies should balance tissue repair and host-immune-induced damage. To address this, we have developed a mathematical Host-Pathogen Interaction (HPI) model that highlights the importance of the host\u27s ability to repair (termed self-healing power) in infection recovery. This model suggests that host survival relies on an optimal balance between immune-mediated pathogen control and healing-mediated tissue repair. However, current literature often conflates the host\u27s healing mechanisms with immune function, and there is no quantitative framework to determine how much immune activation is detrimental to tissue repair. To clarify this distinction, we use alveolar macrophages as a model system due to their diverse roles in TB pathogenesis, including pro-inflammatory responses, anti-inflammatory activities, and tissue repair functions. This review aims to characterize macrophage heterogeneity, polarization, and their roles in both immune defense and tissue repair, leading to the definition of the host\u27s healing function as a distinct entity from immune function within the framework of the HPI model. By distinguishing between tissue repair and immune defense, this work provides a theoretical foundation for developing therapeutic strategies that enhance self-healing while maintaining immune balance, ultimately contributing to improved TB treatment approaches
Machine Learning And Time Series Forecasting For Hydropower Predictions
No full textRecent advancements in machine learning have led to the design of many neural network architectures aimed at solving real-world problems. Each network works to make predictions by finding patterns in the provided data. One common application is time series forecasting, where a model predicts future events based on historical time series data. Time series forecasting is used in a variety of fields, one example being in predicting water releases of Hybrid Floating Photovoltaic-Hydropower (HFPVH) systems. As global population growth drives an increase in energy demand, the need for resilient and sustainable energy generation has become urgent. HFPVH systems have emerged as a promising renewable energy source to help with the increase in energy demand. This master\u27s thesis explores modifying a time series forecasting technique known as Long Short-Term Memory (LSTM). As machine learning has evolved, networks have continuously grown in size, resulting in better predictions. However, this commonly comes with trade-offs in longer training time, energy consumption, and hardware constraints. These trade-offs highlight the importance of a lightweight LSTM architecture that maintains or enhances the predictive accuracy of the standard LSTM model while maintaining a smaller network size. Furthermore, the proposed LSTM architecture integrates a dynamic outlier filter that aims to maintain the same data flow through the LSTM but enhance the convergence of the model\u27s predictions, reducing the need for deeper architectures. The improved architecture is tested across multiple forecasting scenarios, including energy consumption rates, pollution levels, Apple stock prices, and HFPVH systems, showcasing improved accuracy and efficiency compared to the standard LSTM model. In addition to forecasting, this thesis develops a modular constraint-based system that ensures that policies for HFPVH are maintained. Its modularity offers the flexibility of interchanging different functions or models in the simulation, enabling reliable performance in a wide range of climates
Multi-material Cell Clipping on the GPU
No full textThe process of clipping a multi-material cell finds diverse applications across fields such as numerical simulations and computer graphics visualization. Computational fluid dynamics problem often combines multiple materials with different physical properties. The interfaces between those materials may be a part of the solution and evolve in time and can be non-aligned with the mesh. When volume conservation is crucial, interface reconstruction methods are used to approximate such material interfaces. They involve multiple steps, one of which is the process known as clipping. Clipping consists of intersecting and cutting a given cell with a material interface (represented by a line or a plane), in a way that the resulting material polytopes are topologically valid (no inverted or degenerate shapes) and preserves the material volumes. It involves a few steps and relies on a correct and adequate representation of the polytope to be clipped. The process of efficient polytope clipping is an active topic of research, particularly for multicore central processing units (CPUs). This research aims to develop a 2-dimensional (2D) clipping algorithm from the ground up by building on and refining on our previous methods for the graphics processing unit (GPU). The work includes adapting the algorithm to handle unstructured meshes, arbitrary line configurations, and specific corner cases. In addition to updating our previous clipping algorithm, the research also explores a range of optimization techniques aimed at improving performance in areas of the clipping algorithm that take them most time to execute
A Study Of End-Cut Preference In Tree-Based Modeling
No full textDecision trees, particularly those built using the Classification and Regression Trees (CART) algorithm, are widely used for their interpretability and flexibility. However, the greedy nature of the CART splitting procedure gives rise to the end-cut preference (ECP) phenomenon, wherein split points near the extremes of predictor ranges are favored. This study offers a comprehensive investigation of ECP, exploring its theoretical underpinnings, practical manifestations, and implications for both single decision trees and ensemble methods such as Random Forests. Through theoretical analysis and simulation studies, we examine how ECP affects tree structure, variable selection, and predictive accuracy across tree-structured, linear, and nonlinear settings. Our findings reveal that while ECP may have negligible impact on individual tree accuracy, it can negatively influence Random Forests, possibly due to reduced model diversity. To address this, we evaluate the Smooth Sigmoid Surrogate (SSS) method as a regularized alternative to the traditional greedy search, demonstrating its potential to mitigate ECP and enhance model robustness. These insights contribute to a deeper understanding of recursive partitioning methods and inform the design of more reliable tree-based learning algorithms
Pathways of Nitrogen Loss After Fertilization in Dryland Ecosystems
No full textDryland ecosystems cover approximately 41% of the Earth’s terrestrial surface, and are characterized by limited moisture, nutrient-poor soils, and pulsed precipitation regimes that collectively constrain nutrient cycling. Although nitrogen (N) is often assumed to be a primary limiting nutrient in these environments, fertilization studies frequently reveal minimal biotic response to added N—a pattern known as the dryland nitrogen paradox. This paradox has been attributed to rapid nitrogen loss via gaseous emissions and leaching, exacerbated by interactions with water and phosphorus (P) availability. To investigate these loss pathways, we conducted a field experiment at the Jornada Experimental Range in the northern Chihuahuan Desert using a factorial design of N, P, and water (W) additions. We quantified nitrogen loss during the 2024 monsoon season through both short-term gas fluxes (using a T200UP NO/NO2 Analyzer) and cumulative fluxes (using Ogawa passive samplers), as well as nutrient leaching via ion exchange resins. Our results show that nitrogen additions significantly increased gaseous N emissions (NOₓ, NH₃) and nitrate leaching, with prolonged fluxes observed weeks after fertilization. While phosphorus alone had limited effects, N×P interactions notably influenced NH₃ emissions and phosphate retention. Water additions amplified nitrate leaching under nitrogen treatments but did not significantly alter cumulative gaseous fluxes. These findings demonstrate that nutrient loss in drylands is temporally dynamic, context-dependent, and driven by co-limiting factors. Our work reinforces the need for integrated nutrient-water management strategies in drylands and contributes to a broader understanding of nitrogen cycling under global change
Best Strategies for Bilingual Education: How Can We Explain Their Success?
No full textWhen designing AI-based tools for education, it is important to take into account the experience of human teachers. In this, it is necessary to distinguish between the education features that are justified by the general features of the corresponding education task -- these features should be taken into account in AI-based learning as well -- and features which are specific for traditional non-AI teaching. In this paper, on the important example of bilingual education, we show that several empirically successful teaching strategies can be explained in the general context -- and thus, should be implemented in AI-based teaching as well
Egyptian Triangle and Geometry of Airplane Wings: A Simplified Explanation
No full textIn historically first planes, wings were orthogonal to the fuselage. However, later it turned out that from the aerodynamic viewpoint, it is most efficient to place the wings at about 37 degrees from this orthogonal direction -- and this is where wings are placed in most modern planes. There exist theoretical explanations for this optimality -- explanations based on solving the equations of aerodynamics. In such situations when only a complex not-very-intuitive explanation exists, it is desirable to come up with a simpler more intuitive explanation. For the wing angles, such an explanation is provided in this paper. Namely, we show that, somewhat surprisingly, this is all related to the so-called Egyptian triangle -- a right triangle with sides 3, 4, and 5. The name for this triangle comes from the fact that already the ancient Egyptians were very familiar with this triangle -- they used it to accurately reproduce the right angle