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Remote Sensing and Machine Learning as Contemporary Indigenous Knowledge and Cutural Praxis: Mapping Sagittaria lancifolia in South Florida and the Carribbean
No full textMapping dominant vegetative species across coastal wetlands provides useful insights into traditional ecological and cultural assets, as well as the hydrological and biogeochemical responses to weather variability and shifts in local and regional climate regimes. These areas are often inaccessible given the vast remoteness, complexity, and extent of such landscapes. Remote sensing is considered a vital tool for accurately mapping vegetation communities in coastal wetlands, supporting indigenous traditional ecological knowledge and cultural preservation. The primary objective of this research was to use a random forest (RF) classification method on combined multi-source remote sensing and ancillary data to map an important wetland species, Sagittaria lancifolia, or ‘bull-tongue arrowhead’ in the coastal wetlands and Everglades of South Florida and the Northern Karst of Puerto Rico. Data sources include remotely sensed datasets from harmonized Landsat - Sentinel-2(HLS-S30/L30), Sentinel-1 synthetic aperture radar, NASA’s Soil Moisture Active -Passive Level 4 Soil Moisture product, and ancillary datasets such as digital elevation models, soil water content, and soil porosity. Observational data were obtained from the Global Biodiversity Information Facility and field surveys in South Florida and Puerto Rico. The RF classification method was then applied to extracted pixel values of occurrence or non-occurrence of S. lancifolia and yielded an overall accuracy of ~87% accuracy (~65% Kappa statistic) in South Florida and ~95% accuracy (85% Kappa statistic) in Puerto Rico, when compared against test data not used in model training. The RF method produced slightly better accuracy with Landsat 8 data compared to the Sentinel-2 data. The RF-based variable importance analysis showed that elevation and soil water content were the most important factors distinguishing the occurrence of Sagittaria lancifolia from other nearby vegetation species in these regions. The trained model was used to create maps of Sagittaria lancifolia across three study sites in South Florida and two study sites in Puerto Rico. Most classification errors resulted from commission errors, likely due to the variety of ecosystems that S. lancifolia occur in, and the surrounding vegetation that it may be found embedded within. Additionally, sampling data was not an exhaustive list of Sagittaria lancifolia occurrences in the study regions. Nonetheless, this research demonstrated the potential of using satellite data and machine learning techniques in mapping a target vegetation species of high cultural and ecological value, S. lancifolia, within wetlands. Future work will include a larger spatial extent and be enhanced by ground truthing (in-situ) fieldwork to collect observation data in Puerto Rico. Studies can use maps derived from this method to monitor the distribution of S. lancifolia, or other species of interest, which serve a critical role in blue carbon accounting for future economic development and environmental monitoring, as well as serving a role in cultural heritage preservation by better understanding the reproductive habitat distribution of the endangered coqui Eleutherodactylus juanariveroii
The Seven Entrepreneurial Activities Model and Social Entrepreneurship: The Case of Sole Mission
No full textCan a credible prescriptive model for the creation of new social enterprise organizations be developed? This dissertation explored that question by examining the appropriateness of Bisel and Bisel’s (2023) Seven Entrepreneurial Activities (SEA) model for explaining the origination and maintenance of volunteer and donor dependent nonprofit organization (VDDN). While the SEA model was developed to explain the constitution of new for-profit organizations, VDDN are nonprofit entities that realize value propositions via the labor of non-employee volunteers and external financial benefactors, and, therefore, may involve constitutive activities that are distinct from for-profit organizing. A case study of a social entrepreneurial endeavor (i.e., Sole Mission) was conducted, including ethnographic observations and in-depth semi-structured interviews with organizational stakeholders. Sole Mission is a 16-year-old VDDN that provides new, fitted, and appropriate socks and shoes to impoverished American children. Utilizing organizational documents and interview data, an organizational life history was constructed to identify social entrepreneurship (SE) practices, which occurred during the organization’s origin as well as maintenance and reproduction phases. Analysis identified three entrepreneurial practices from the VDDN’s origin phase (0-2 years) and two from the maintenance and reproduction phase (3-16 years). Further analysis of the organizational life history was used to assess whether SEA model activities were apparent. On one hand, analysis revealed the presence of all three SEA model higher order sets in both phases of organizational life. On the other hand, three exigency-based divergences were also observed in relation to the VDDN: (a) affinity opportunities, (b) volunteers not employees, and (c) donors not investors. Taken together, these finding extend organizational communication theory, including communication constitutes organization (CCO) theory, the four flows model, and SEA model. Furthermore, this research contributes to the body of literature on social entrepreneurship. Ultimately, by adding a description of contextually-based divergences, the SEA model is an efficacious prescriptive communication-based model for explaining the constitution of new volunteer and donor dependent nonprofit organization
Mathematical Optimization Models for Treatment Planning of Spatially Fractionated Radiation Therapy
No full textSpatially Fractionated Radiation Therapy (SFRT) is an emerging approach in radiationoncology that has gained interest in recent years as an effective method for the treatment of large and bulky tumors. Rather than prescribing a consistent, high dose of radiation to the entire tumor volume, SFRT prescribes delivery of high doses of radiation to discrete spheres within the tumor. While clinical guidelines exist regarding sphere size and separation, currently there is no rigorously defined method that places these spheres within the tumor. Instead, treatment planners rely on their own experience to determine sphere locations, aiming to maximize the number of spheres while satisfying clinical constraints. This step is typically followed by fluence map optimization, the primary task in radiation therapy planning, where the intensity of the radiation “beamlets” is computed. This manual sphere placement process introduces variation in both the number of spheres placed and the locations of these spheres, which can result in inconsistent and suboptimal treatment plans with potentially unfavorable patient outcomes. In this thesis, we propose a novel mathematical optimization model that computes the optimal SFRT treatment plan that places the maximum number of spheres within the tumor by integrating an instance of the maximum independent set (MIS) problem within fluence map optimization. Our model places spheres within the tumor in the configuration that maximizes radiation dose to the tumor and minimizes radiation dose to healthy tissues. We conduct an experimental evaluation of our model using real imaging data provided by the National Cancer Institute through The Cancer Imaging Archive (TCIA). Our results demonstrate the effectiveness and flexibility of the proposed framework and its potential to assist clinicians in making more informed decisions that improve patient outcomes
PREDICTING STELLAR AGES FROM CHEMICAL ABUNDANCES WITH XGBOOST: A MACHINE LEARNING APPROACH TO GALACTIC ARCHAEOLOGY
No full textEstimating stellar ages remains a central challenge in astrophysics, particularly for stars outside the narrow evolutionary phases where traditional methods like isochrone fitting are reliable. This study explores the use of supervised machine learning, specifically, the Extreme Gradient Boosting (XGBoost) algorithm, to infer stellar ages from chemical abundance ratios derived from the GALAH DR4 spectroscopic survey. A training set of 20,303 Main Sequence Turn-Off stars was constructed using strict quality cuts and age labels obtained via Bayesian isochrone fitting. The XGBoost model was trained on 15 elemental abundance ratios and tuned through exhaustive hyperparameter optimization with five-fold cross-validation. The final model achieves a root mean squared error (RMSE) of 1.38 Gyr on a held-out test set, generalizes well across stellar populations not seen during training, and reproduces known chemical evolution trends such as the anti-correlation between [Fe/H] and age and the positive correlation between [/Fe] and age. Feature importance analysis reveals that [Mg/Fe], [Ca/Fe], and [Y/Fe] are the most informative predictors of age, consistent with theoretical nucleosynthetic expectations. Residual analyses expose a tendency for the model to regress predictions toward the mean age in underrepresented regions, particularly for very young and very old stars. These results demonstrate that machine learning can extract robust age information from chemical abundance patterns and extend age-dating to stellar populations for which traditional techniques break down. This work highlights the power of data-driven approaches in galactic archaeology and paves the way for scalable age estimation in next-generation spectroscopic surveys
Deep Points of Cluster Varieties
No full textA cluster algebra determines an associated cluster variety, and each cluster determines a cluster torus in that variety. The cluster tori sometimes cover the entire cluster variety, but not always; we refer to any points not contained in any cluster torus as deep points. Any geometrically interesting points in the variety (e.g. singularities) must be deep, and many pathologies of the cluster algebra can be localized to those deep points. We describe the deep points of cluster algebras of unpunctured polygons, unpunctured marked surfaces, punctured polygons, and punctured surfaces with at least two boundary marked points. As a consequence, we classify the deep points of cluster algebras of types A_n and D_n. We also classify the deep points of the Markov cluster algebra — the cluster algebra of the once-punctured torus — and its upper cluster algebra. We then examine the deep points of skew-symmetrizable cluster algebras that arise from a folding of an acyclic quiver by a finite group of automorphisms. In particular, we study the deep points of cluster algebras of types B_n, C_n, and F_4 via foldings of quivers of types A_{2n-1}, D_{n+1} and E_6, respectively
Monazite Fission-Track Dating of Erosion and Tectonics in the Nepalese Himalaya
No full textA modern orogenic system represents the culmination of tectonic and surface processes over geological time, posing challenges in isolating direct mechanisms and associated timing of relief formation. Modern relief-forming processes are typically dominated by more recent, low-magnitude erosion/exhumation events (<1 – 2 km), which are difficult to resolve with established low-temperature thermochronology and cosmochemistry methods. Monazite fission-track (MzFT) is a relatively new low-temperature thermochronometer with a laboratory assessed partial annealing zone of 140 – -70 °C. Field based estimates, however, have found MzFT to be a useful proxy for cooling through <50 – 25 °C and related to exhumation in the upper 1 – 2 km of the crust. Given the uncertainties between laboratory- and field-based estimates, interpreting a MzFT age can be challenging due to the relative contributions of fault-driven denudation, surface processes and surface temperatures. To assess the applicability of MzFT to resolving upper crustal exhumation, we apply MzFT to the Nepalese Himalaya, an actively uplifting and highly erosive orogenic system associated with a cool subtropical to temperate climate. We focus on the well-studied Kali Gandaki River in central Nepal, which has been previously targeted by a range of established low-temperature thermochronometers. We collected four MzFT samples, with three samples being part of an elevation profile (2498 – 2966 m) and two apatite fission-track (AFT) samples from within the Greater Himalayan Sequence across the Ramgarh Thrust, Main Central Thrust and South Tibetan Detachment. We integrate MzFT data with existing low-temperature thermochronology data and single-grain monazite and apatite geochemistry to isolate incision and/or faulting processes as the driver of rock cooling. MzFT data yield ages of ~0.4 – 0.09 Ma, which we interpret to correlate with recent movement of the Main Frontal Thrust since at least ~280 k.y., and concurrent river incision of the Kali Gandaki drainage
Triadic Dual Language Storybook Reading for Adjustment of Afghan Refugee Children
No full textWith over 100 million individuals forcibly displaced worldwide, refugee children and theirfamilies face numerous challenges, including disrupted access to quality education and developmental support. Despite the unique needs of children in the early years, interventions targeting younger refugee children remain scarce. This pilot study addressed that gap by developing, implementing, and evaluating a short-term dual language storybook reading intervention for young Afghan refugee children (aged 3-5) and their mothers. The intervention aimed to support children’s social-emotional competence, second language acquisition, and parent-child relationship, while also exploring potential improvements in overall family well- being. A cluster randomized controlled trial (CRCT) with a pre-post survey design and focus group interviews was conducted. Although quantitative results were not statistically significant after controlling for covariates, children in the intervention group showed higher gains across key variables. Despite the limited statistical findings, qualitative insights revealed improvements in children’s emotional expression, enthusiasm for learning, use of English at home, and mother- child bonding. Mothers also reported positive changes to their parenting skills, which led to a closer relationship with their children. The study offers preliminary evidence of how family- inclusive, culturally and linguistically responsive practices can support refugee families adjusting to life in a new host country
Gruesome evidence: is verbal or visual evidence more effective in eliciting guilty verdicts?
Get PDFPrevious research shows both visual and verbal gruesome evidence have a positive correlation with guilty verdicts. Although Bright and Goodman-Delahunty (2006) came closest, no previous research, to our knowledge, has directly compared the effectiveness of visual and verbal gruesome evidence on guilty verdicts. This study aimed to bridge that gap within literature. I hypothesized that participants exposed to either type of gruesome evidence will give higher guilt ratings than those exposed to nongruesome evidence. Additionally, I hypothesized that visual gruesome evidence would be more effective at increasing guilt ratings than verbal gruesome evidence and that this difference would be mediated by negative emotion. The study used a 2 x 2 experimental design manipulating photographic and written gruesomeness within a trial summary about a murder and used the modified PANAS to measure negative emotion. Results supported the second hypothesis, but not the first. Despite a successful manipulation based on our manipulation check, gruesome verbal descriptions did not increase guilt ratings or negative emotion. However, gruesome photographic evidence did, regardless of whether it was paired with gruesome or nongruesome verbal descriptions. This effect was mediated by disgust and sadness, but not by anger and anxiety. Implications are discussed. These results provide direction not only for academia, but for those acting as attorneys’ and judges in trial when determining what evidence to submit and allow the jurors to be exposed too. Future research might manipulate verbal evidence auditorily, rather than through a written medium, or manipulate what legal party introduces gruesome evidence
How propaganda and intersectionality influenced the American Revolution
Get PDFThe overall research question for this thesis is how both propaganda and intersectionality play their respective roles within the American Revolution era in the American colonies. Many historians have brought forward different aspects of this era, ranging from 1763 through 1783, and give others a more well-rounded perspective of this time. Other historians have also discussed some of the propaganda, while others have focused on bringing forward the discussion of the biographies of those involved, specifically Abigail Adams, Phillis Wheatley and Molly Brant.There was the possibility of not finding the information regarding propaganda and intersectionality playing roles within the American Revolution, and if that had been the case, this would be an entirely different thesis. The research shows that both propaganda and intersectionality played different roles within this era, as primary and secondary sources show the usage of both persuasion through propaganda and the roles that Adams, Wheatley, and Brant found themselves in throughout this time. Both propaganda and intersectionality played roles in decisions that were made as the years continued towards and through the American Revolution. This research is important as it expands the overall knowledge and understanding of the American Revolution era, and how other factors played a role in the outcome of the war
Public opinion toward Russia’s war against Ukraine: investigatingwartime attitudes in Kazakhstan and Kyrgyzstan
Get PDFHow have Central Asian publics reacted to Russia’s war against Ukraine? This study assesses overall attitudes toward the war in Kazakhstan and the Kyrgyz Republic using survey data from the early days of the war. We find that respondents in Kyrgyzstan are more likely to hold pro-Russian attitudes regarding the war than their counterparts in Kazakhstan. We then examine the relationship between political attitudes and correlates commonly linked to geopolitical preferences generally and pro-Russian attitudes specifically: ethnolinguistic identity, remittances, and media use. Results suggest that ethnic identity holds the strongest and most consistent link to wartime sentiments, with ethnic Kyrgyz and Kazakhs showing less pro-Russian attitudes compared to ethnic Russians. Further results indicate that language and media use are somewhat associated with pro-Russian wartime attitudes, though this relationship varies by context and issue area. Finally, we find limited support for the argument that receiving remittances is associated with political preferences.Ye