22505 research outputs found
Sort by
HEUTAGOGY IN EDUCATION: TRANSFORMING THE LEARNER EXPERIENCE
No full textHeutagogy (pronounced hyoo-tuh-goh-jee), derived from the Greek word heureske, meaning to discover, is closely related to heuristic learning. Coined by Hase and Kenyon in 2000, heutagogy refers to self-determined learning that operates independently of traditional teaching structures. Unlike pedagogy and andragogy, which emphasize teacher-directed instruction, heutagogy shifts the focus to learner autonomy, allowing students to choose what and how they learn.
Heutagogy represents a shift from teacher-centered to learner-centered education, grounded in two key philosophical approaches: humanism and constructivism. Humanism places the learner at the center of the educational process, while constructivism emphasizes active, self-determined learning. Although andragogy, which focuses on adult learning, was seen as a precursor to heutagogy, research suggests that heutagogy is particularly suited to the digital age, with web-based learning, new information technologies, and distance education methods providing ideal contexts for its implementation.
The research gap of this paper examines the development of heutagogy as an educational model, comparing it with pedagogy and andragogy and explores its applications in modern education, particularly in web-based learning environments. As digital technologies continue to reshape education, heutagogy offers a flexible approach that fosters lifelong learning, critical thinking and adaptability as a main objective of the paper
Using Probabilistic Component Separation to Measure the Sunyaev-Zel’dovich Effect in Herschel-SPIRE Galaxy Cluster Observations
No full textGalaxy clusters evolve over cosmic history from small gravitational over-densities at redshift z \u3e 7 to become the most massive gravitationally bound objects in the universe today. The demographics of galaxies populating clusters have shifted significantly over this time, from sites of intense star formation at z ~ 2-4, to largely quiescent objects in the current epoch. An important phase of this evolution involves the development of a hot, ionized intracluster medium (ICM). Its formation around cosmic noon overlaps with the peak in star formation, which requires a multi-wavelength observational approach to fully characterize. One way to study the properties of the ICM is the Sunyaev-Zel’dovich (SZ) effect, which is a distortion in the cosmic microwave background (CMB) radiation caused by scattering from the electrons in the hot ICM. The research presented in this thesis combines data from the Herschel Space Observatory, Hubble Space Telescope, Chandra X-ray Observatory, and ground based sub-mm telescopes to measure the amplitude of the SZ spectrum in the 350 and 500 micron bands of the Herschel-SPIRE instrument. I present updates to a probabilistic component separation tool used to disentangle the signals in SPIRE maps, including galaxies in and lensed by the cluster, the SZ effect, local galactic cirrus emission, and any dust components present in the cluster environment. I discuss results from SPIRE observations of the GOODS-N map to establish the pipeline’s ability to resolve sources without the presence of any SZ signal, and finally present updated results for measurements of the SZ effect in a sample of 8 galaxy clusters
High-Energy and On the Move: Exploring the Kinematic and X-ray Properties of Young Stars near Earth with Gaia, Chandra, and eROSITA
No full textYoung stars in the solar neighborhood provide a unique and insightful window into stellar evolution during the first few hundred million years after formation. Leveraging archival photometric and astrometric data from ESA’s Gaia Space Astrometry mission, as well as photometric data from NASA (2MASS and WISE) archives, I have identified over 50 new members of the nearby (D ∼ 100 pc), 3–8 Myr-old Epsilon Cha association (ECA), including six new proto-brown dwarf candidates. By modeling the kinematics and photospheric properties of both newly identified and previously known members of the ECA and Lower Centaurus Crux (LCC), I further established the connection between the two associations. During my ECA membership study, I discovered one particularly interesting candidate: TOI 1227, a very low-mass (red dwarf) pre-main sequence star with a transiting exoplanet detected by NASA’s TESS exoplanet-hunting mission. I have re-assessed the age of TOI 1227 from ∼ 11 Myr to ∼ 8 Myr, and used new Chandra X-ray observations to estimate the planet’s atmospheric mass loss and its likely long-term evolution. Drawing on newly released data from ESA’s eROSITA all-sky survey (eRASS) in combination with Gaia and TESS archival data, I conducted a large-scale statistical analysis of X-ray activity in more than 10,000 stars younger than 1 Gyr and within 200 pc of the Sun. Finally, I used Chandra X-ray imaging and NASA (GALEX/Gaia/2MASS) archival data to investigate a population of candidate nearby young stars with strong ultraviolet emission but unexpectedly weak X-ray output. I link the unusually high-energy radiation characteristics of these candidates to their binary star nature; several systems are newly resolved in X-rays by Chandra. These research efforts have yielded various new insights into the early evolution of young stars and their planetary systems. I have demonstrated that the ECA/LCC region serves as a nearby, exemplary case of sequential star formation; identified new low-mass stellar objects that are strong candidates for future investigations into planet formation around brown dwarfs; and refined the age estimate and projected the future evolution of TOI 1227, a very young and key exoplanetary system located within the ECA and LCC. I have developed techniques to differentiate genuine pre-main-sequence stars from close binary main sequence stars that mimic their characteristics. By leveraging the combined strengths of eRASS, TESS, and Gaia data, synthesizing X-ray emission, stellar rotation, and age, I produced detailed empirical rotation-activity-age relations. These relations will provide valuable benchmarks for modern models of stellar magnetic dynamos and their observable surface activity
Enhancing Remote Operator Situational Awareness Leveraging technology for Improved Decision-Making in Urban Emergency Response
No full textThe aim of this research is to develop a technology enabled Unified Emergency Response Platform (UERP) that enhances situational awareness, decision-making, and response efficiency during urban fire emergencies. Traditional systems, such as Hassantuk, are primarily reactive and focus on alarm verification, often leading to delays and limited coordination among stakeholders. To address these challenges, this study proposes an integrated framework that leverages the Internet of Things (IoT), Artificial Intelligence (AI), digital twins, and predictive analytics to enable early fire detection, rapid alarm validation, and optimized incident response. The platform is structured around three key phases: (1) Early detection and prediction through IoT-enabled sensors and predictive models; (2) AI-driven alarm validation, reducing false positives and verification time; and (3) Coordinated incident response supported by real-time dashboards, digital twins, and automated workflows. A comparative analysis of Omniconn and Hassantuk highlights the need for interoperability and proactive intelligence to complement national emergency infrastructure. Data analysis from Omniconn reports and Kaggle fire incident datasets demonstrates that the proposed system can significantly reduce response latency and minimize property damage. The findings contribute to both academic and practical domains by outlining a scalable, data-driven framework that can transform urban fire emergency management into a proactive and resilient ecosystem. Keywords: Smart Cities, Fire Emergency Response, Situational Awareness, IoT, AI, Digital Twin, Knowledge Graph, Predictive Analytics, Wearable Health Monitoring, Adaptive Dashboards, Command and Control Systems, Interoperability, Emergency Dispatch System, Augmented Reality (AR), Sensor Networks, Urban Fire Safety, Urban Emergency Response Platform (UERP
basil_dock: Development of Accessible and Customizable Molecular Docking Procedures
No full textMolecular docking is a computational technique often used in drug design due to its ability to predict ligand binding potential, conformation, and location for a given drug target. Docking as a screening tool also comes at a greatly reduced cost compared to wet bench work. Despite its usefulness, molecular docking is often inaccessible to novice users, as to perform molecular docking, most programs require at least a basic knowledge of command line and computer programming for both installation and utilization. Additionally, tutorials for the most popular programs tend to be inflexible, instructing how to bind predetermined molecules to a specified receptor and requiring the use of other programs and tools to download and prepare structures outside of the docking software. To increase access to molecular docking, basil_dock utilizes a series of easy-to-use Jupyter notebooks that do not assume user familiarity with molecular docking procedures, scripting, or command line usage. The series includes three notebooks that were created to reflect different steps in the molecular docking process: (1) the preparation of ligand and protein files prior to docking, (2) the docking of ligands to a protein receptor, and (3) analysis of docking scores to determine how differences in ligands can affect protein-ligand binding. The notebooks encourage flexibility and customization in exploring docking procedures and systems, as well as teaching users the basis behind molecular docking without having to leave the environment to obtain information and materials from other applications. In addition to the series of Jupyter notebooks, a graphical user interface for basil_dock has been created using Streamlit for ease of access. Separated into eight distinct pages, the Streamlit application translates the Jupyter notebooks into a webpage that can be installed and accessed locally or through the Streamlit Community Cloud. For all versions of basil_dock, accessibility and customizability remain at the forefront of program, aiming to provide the means to understand and perform molecular docking to all who wish to do so
Exploring Deaf And Hard of Hearing Peoples\u27 Perspectives On Tasks In Augmented Reality: Interacting With 3D Objects And Instructional Comprehension
No full textTasks in augmented reality (AR), such as 3D interaction and instructional comprehension, are often designed for users with uniform sensory abilities. Such an approach, however, can overlook the more nuanced needs of Deaf and Hard of Hearing (DHH) users who might have reduced auditory perception. To better understand these challenges, our study utilized the single-player AR game Angry Birds AR as a probe to explore how 11 DHH participants and 15 hearing participants experienced AR interactions. Our findings highlight that DHH users prefer interaction based on context, effective haptic cues, audio cue substitutes, and clear instructional design. We, therefore, propose the following design recommendations to enhance the accessibility of AR for DHH users. This includes customizable UI options, modular feedback systems, and virtual avatars for sign language instructions
A Decision Support System for Global Vaccine Funding: Data-Driven Proposal Scoring, Epidemiological Risk Modeling, and Portfolio Optimization
No full textGlobal health financing must continually stretch limited immunization resources across competing priorities, and Gavi, the Vaccine Alliance, plays a central role in this landscape. This work reframes Gavi’s funding decisions as a multi-objective portfolio optimization problem that jointly considers health impact, equity, cost-effectiveness, sustainability, and epidemiological interdependence. To operationalize this approach, this study develops a decision support system grounded in Modern Portfolio Theory, treating each proposal as an asset characterized by expected return and risk. Proposal returns are estimated through a heterogeneous data-fusion pipeline that generates quantitative, multi-objective scores aligned with Gavi’s strategic priorities, while epidemiological risk is quantified using a novel agent-based network model that infers cross-country outbreak spillover via an epidemiological gravity mechanism. The integrated system, combining scoring, risk modeling, and portfolio optimization, was evaluated using a full factorial experimental design spanning 9 combinations of strategic priorities, initial disease burden, and budget. The risk-aware allocation approach achieved a median 1.31% (~242,000 fewer cases in the nominal 18.5M population) reduction in new infections, with improvements up to 15.15% under severe outbreaks, while maintaining near-complete budget utilization. These findings demonstrate that explicitly incorporating epidemiological interdependence into funding allocation offers more consistent, transparent, and epidemiologically foresighted recommendations, providing a scalable quantitative baseline to complement and strengthen expert review within Gavi’s decision-making process
Symbiotic Furniture Design: A Single Chair Integrating Epiphytes with Taiwanese Window Grille Structures
No full textPlants have been integrated into residential and interior environments for centuries, yet research on the psychological effects and interactive potential of plants in furniture design is still relatively limited. Traditional planting methods, such as individual potted plants or large green walls, often require additional floor or wall space, which is not ideal for environments with limited living space. Therefore, this study investigates how plants can be integrated into furniture design to enhance the effect of interior greenery and user engagement, and to redefine the relationship between furniture and the natural environment. This research explores a furniture design that incorporates epiphytic plants and references the cultural and structural aesthetics of traditional Taiwanese window grilles. The project explores how furniture can accommodate indoor greenery without relying on soil-based systems or high-maintenance plant care. The chair structure consists of transparent acrylic panels bordered by decorative plastic grilles inspired by historic architectural patterns. These grilles function not only as visual elements but also as supportive surfaces for placing moss, which serves as a base for epiphytes such as Tiransia. This design integrates greenness into living spaces with functionality and spatial efficiency. By proposing design strategies centered on maintaining convenience, cultural relevance, and spatial adaptability, this project contributes to the field of biophilic furniture. It addresses the current gap in structurally integrated green furniture and offers a viable approach to enhancing compact indoor environments through product design
Disposal
No full textDisposal is a mixed-media animated short film created as a graduate thesis project. The story explores the protagonist’s search for meaning in life and his defiance of a predetermined fate. At the beginning, he is an ordinary, guileless figure without remarkable qualities. In his world, life revolves around constant competition, and one’s worth is measured by their position on a ranking board. Only the top three may pass through the golden gate, a symbol of transcendence. Others must continue the endless cycle of contests, while the lowest seven face incineration in the Disposal. Despite his efforts, the protagonist falls into the bottom (seventh) position. Unlike others, he fears death and resists annihilation. Driven by desperation, he breaks the rules and sneaks into the golden gate. Yet instead of paradise, he witnesses a horrifying truth: cold mechanical arms drain the blood of the top three elites, leaving behind only empty husks. Terrified by the scene, he faints. When he awakens, he finds himself in a strange dimension where his emotional rebellion becomes a delicacy for higher beings. His defiance, once an act of survival, is consumed as entertainment. In the end, he dies tragically, his struggle reduced to nothing more than a spectacle
The Role of AI and Predictive Policing in Crime Prevention
No full textThe current thesis examines the application of Artificial Intelligence (AI) in the arena of predictive policing and crime forecasting using an integrated case study based on the empirical approach supported by a narrative review of literature. Due to the growing use of digital data by the law-enforcement agencies, AI techniques, including machine learning and spatio-temporal modelling, are implemented to detect patterns of crimes, predict high-risk areas, and assist law-enforcement decision-making. Though these technologies have the potential to make the processes of accuracy and resource allocation better, they also bring up the issue of the fairness, transparency, and disproportionate effects on the marginalised groups. In order to investigate these dynamics, this paper uses crime statistics of the City of Chicago and builds machine learning models to predict instances of assault using spatial and time characteristics. The results of the modelling show that advanced AI methods can have a high predictive performance, as compared to traditional baselines, especially in place-based forecasting. The Random Forest model was selected as the best performer, achieving a balanced F1-Score of 0.402 and a ROC AUC of 0.586, which indicates a moderate, non-random ability to identify crime hotspots. In addition to the empirical study, a narrative review of the current studies in criminology, data science, and public policy serves as an indicator of the existing controversies in the field of AI-enabled policing more specifically with regard to algorithmic bias and governance as well as accountability. In general, the results indicate that AI may have substantial analytical utility in the form of careful and context-specific application, but only when there are proper mechanisms of responsible implementation and efficient oversight. Instead of a solely technical problem, predictive policing turns out to be a socio-technical issue of governance, which requires the development of balanced systems to balance efficiency, fairness, and trust towards the government