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Using Airborne LIDAR as an Approach for Detection of Understory Invasive Shrubs in Southern Illinois
No full textMapping and managing the spread of non-native invasive (NNI) shrub species across large areas can be highly time-consuming and resource intensive, often requiring extensive data collection periods and mitigation efforts to slow the spread. One potential solution for mapping NNI is LiDAR (Light Detection and Ranging), an active remote sensing method that uses laser pulses of light to measure environmental features such as topography and hydrology. While the LiDAR is typically targeting surface-level information such as elevational gradients, the laser interference with vegetation results in a three-dimensional characterization of forest structure that can be developed to assist land managers in the mapping of vegetation, potentially including NNI shrub species. We used LiDAR point cloud data collected by the state of Illinois to derive forest metrics based on a segmented height stratum to detect understory invasive shrub species, autumn olive (Elaeagnus umbellata) and bush honeysuckle (Lonicera maackii), in an upland hardwood forest in southern Illinois. To provide an assessment of LiDAR for detecting NNI shrubs, forest inventory data was collected at 91 circular plots (0.2 ha) during May to August 2024, which included identifying the native and NNI shrub species present and their associated average and maximum height, diameter at breast height (DBH), proportion area occupied, forest stand structure characteristics, and overstory canopy openness. Overstory forest conditions consisted of mixed hardwood species, dominated by Quercus spp. and Carya spp., while the midstory and understory were dominated by Ulmus spp. and Asimina triloba. To test the accuracy of LiDAR to predict occupancy of NNI species in forested stands, I first assessed five NNI occurrence thresholds within the focal height stratum to determine sensitivity of LiDAR to NNI presence (1%, 5%, 10%, 25%, and 50% occupancy). The 10% threshold—indicating that 10% of the LiDAR returns occurred in the focal stratum—was determined as best predictor for NNI occupancy based on its precision (0.87), accuracy (0.73), F1 harmonic mean (0.77), and Mathew’s Correlation Coefficient (MCC) (0.45). In addition to using LiDAR to predict where NNI species may be found, I also tested the relationship of two other metrics (canopy openness and land-use history) to NNI species coverage. The area occupied by NNI species was regressed against canopy openness within a plot using Poisson generalized linear regression, with results suggesting a canopy that is more open is likely to have a larger abundance of NNI present. Next, I assessed whether land-use history, as assessed by aerial imagery captured in 1937-1947, and documented those areas previously “Forested” had no significant, current indication of NNI presence (F3,87 = 1.19, p-value = 0.47), whereas areas previously “Non-Forested” did statistically indicate the presence of NNI (F3,87 = 1.19, p-value \u3c 0.01). Combined, these results suggest that LiDAR may be a promising method for detection and mapping of NNI species, particularly when it is combined with other forest characteristic measurements and land use history
DEBATING EMPIRE: THE YUCATAN CRISIS AND THE POLITICAL AFTERMATH OF THE MEXICAN-AMERICAN WAR
No full textThis study examines the impact of the Caste War of Yucatán had on the geopolitical relationship between the United States and Mexico in the aftermath of the Mexican-American War. The briefly independent Republic of Yucatán requested foreign intervention to end the Caste War. The United States debated intervention in Yucatán, while Mexico, still reeling from the loss of the Mexican-American War and dealing with internal political conflict, made steps to reincorporate Yucatán back into Mexico and to diplomatically resist American expansion. Examining the political landscape of the United States and Mexico in the spring of 1848 offers an alternate perspective of how differing political ideologies and nation-building projects in the United States and Mexico dealt with the Yucatán crisis
Using constructivist grounded theory study to understand the retention rate of the School of Automotive, Southern Illinois University Carbondale, Illinois
No full textThis study explored students’ reasons for returning and continuing their education in the School of Automotive. Therefore, this study developed an understanding of the higher-than-average retention rate for the School of Automotive at Southern Illinois University at Carbondale, Illinois. This study used a constructivist grounded theory approach, and the data came from semi-structured interviews. As this is a constructivist grounded theory study, the students and I co-created the theory behind this phenomenon
Teaching Functional Skills to Individuals with Autism Spectrum Disorder and Intellectual Disability using Behavior-Analytic Curricula
No full textESSAYS ON THE ECONOMIC IMPACT OF CLIMATE CHANGE IN BANGLADESH
No full textThe first chapter investigates the impact of climatic shocks on the land productivity of Bangladesh. In particular, the paper examines the effects of crop-specific rainfall shocks and temperature shocks on land productivity in Bangladesh. We find that climatic shocks have heterogeneous effects on productivity across crops. The second chapter examines the relationship between climatic shocks and income inequality in Bangladesh. We find that both rainfall shock and temperature shock reduce income inequality in Bangladesh. Finally, in the chapter, we explore the impact of climate change on wages of male and female workers in agriculture sector of Bangladesh
ENHANCED DEEP LEARNING NEURAL NETWORKS FOR CLASSIFICATION AND FORECASTING PROBLEMS IN EMBEDDED SYSTEMS
No full textThe widespread adoption of deep learning has led to a surge in demand for the efficient deployment of Deep Neural Networks (DNNs) on embedded and resource-constrained systems. These platforms are increasingly tasked with performing complex inference workloads in real time, yet they must do so under strict limitations on energy consumption, computation, and memory resources. As DNN models grow in complexity to support advanced applications such as time-series forecasting and image classification, ensuring their efficient execution without compromising performancebecomes a key design challenge. This calls for tailored algorithmic and architectural solutions that can meet application-specific constraints while maximizing the benefits of modern hardware accelerators.This dissertation proposes a set of algorithmic-based approaches aimed at enhancing the efficiency and adaptability of deep learning models for embedded systems. The research centers on three primary contributions: (i) a hybrid Long Short-Term Memory (LSTM)-Transformer architecture optimized for multi-step residential power load forecasting, integrating sequence modeling and attention mechanisms for improved accuracy under training time constraints, (ii) a lightweight hierarchical deep neural network enhancement that augments baseline classifiers through cascading binaryConvolutional Neural Networks (CNNs) and Vision Transformers, achieving higher classification accuracy and reduced inference time, and (iii) an energy-aware scheduling framework for DNN inference, featuring dynamic batch size selection, GPU frequency adjustment, and concurrent task mapping across multi-GPU systems to minimize energy use without violating real-time deadlines.In summary, this dissertation presents a comprehensive approach to designing and managing deep learning workflows on embedded platforms, emphasizing performance-efficiency trade-offs. The proposed methods demonstrated significantimprovements in both predictive accuracy and resource utilization, compared to existing solutions, offering practical pathways for deploying DNNs in sustainable, computing environments
ERROR ANALYSIS OF TURKISH EFL LEARNERS FROM A SYNTACTIC PERSPECTIVE
No full textThis study investigates the syntactic errors produced by Turkish EFL high school learners in their written English and explores the potential sources of these errors from both interlingual and intralingual perspectives. Drawing on data from three writing exams administered over the course of a semester, the study employs a descriptive error analysis approach to categorize errors into six major syntactic types: verb-related errors, article errors, preposition errors, pluralization errors, conjunction errors, and word order errors. The findings reveal that verb-related errors were the most frequent, followed by article and preposition errors. The sources of the errors were divided into two types: intralingual errors, which stem from the learners’ internal processing of the target language, and interlingual errors, which result from the influence of the learners’ native language. Although intralingual errors were more prevalent overall, many errors were also found to stem from interlingual transfer, particularly in cases involving articles and prepositions. The study highlights the significant role of syntactic differences between Turkish and English in shaping learners’ grammatical accuracy and highlights the pedagogical value of incorporating error analysis into L2 writing instruction. The results suggest that targeted feedback and contrastive grammar teaching can support learners in overcoming persistent syntactic challenges, thereby contributing to more effective writing instruction in EFL classrooms.Keywords: syntactic error analysis, Turkish EFL learners, interlingual errors, intralingual errors, second language writing, contrastive analysi
COMPOUND FLOODING DRIVERS ALONG THE ATLANTIC AND GULF COASTS OF THE UNITED STATES: QUANTIFYING CHANGES AND INTERACTIONS IN TIDAL RANGE, RELATIVE SEA-LEVEL, AND EXTREME PRECIPITATION
No full textCompound flooding caused by the influence of multiple flood drivers has emerged as one of the most pressing challenges in coastal areas of the United States. Traditional single-driver flood models often fail to capture the complexity of these events. Therefore, evaluating the impact of different factors to compound flooding is important. Given the high population density, economic significance, and low-lying topography of both the Atlantic and Gulf Coasts, a comprehensive understanding of compound flood vulnerability is vital for informed adaptation and risk mitigation. This research presents a comprehensive analysis of three key flood drivers: relative sea level, tidal range, and precipitation, across eight tide gauge stations and corresponding HUC-8 coastal watersheds (five on the Atlantic coast and three on the Gulf coast) from 1979 to 2022. The study aims to fulfill two primary objectives: (1) identify long-term trends in individual flood drivers on both annual mean and 90th percentile levels, and (2) assess the co-occurrence of the flood drivers to evaluate their potential contribution to compound flooding. Both of the objectives are evaluated on an annual and seasonal scale. Findings reveal a statistically significant and consistent increase in the relative sea level across all stations and seasons. Precipitation trends demonstrate localized intensification, particularly in the mid-Atlantic region, with seasonal peaks in winter, summer, and fall. Tidal range trends are largely insignificant, except at Galveston, TX, which shows an increasing pattern. The co-occurrence analysis indicates that fall is the most active season for joint exceedance of sea level and precipitation, likely influenced by hurricane activity, followed by winter in the Atlantic region and summer in the Gulf. These results highlight distinct spatial and seasonal hotspots of compound flood potential, emphasizing the need for region-specific and seasonally adaptive flood risk frameworks
Fifty Years of Water Research Projects in California: Keyword Analysis and Qualitative Coding with Natural Language Processing (NLP) Models
Get PDFGlobal freshwater resources are increasingly strained in many regions, driven by agricultural expansion, population growth, energy production, and climate change. Research in water science and management seeks to address challenges that industrialized societies face to ensure water of sufficient quality and quantity. What themes have been prominent in water research in past decades and how have these themes changed over time? While the field of water management has often relied on expert judgement to identify research needs, recent analytical tools provide novel opportunities to evaluate the evolution of research priorities in water management. This paper presents a thematic analysis of water research projects in California using keyword analysis with Natural Language Processing models and a database of fifty years of funded research. Results indicate that some themes, such as groundwater management, have remained consistent over time, while others, including aquatic ecosystem management, have emerged more recently with recognized environmental degradation. Research has appeared to respond to changes in water policy priorities and climate variability, with drought-related research projects corresponding to periods of significant drought in California. The analysis demonstrates a replicable methodology for evaluating research themes and outcomes in water research using inductive thematic analysis, which can be applied to more examples from Water Resources Research Act funded projects and other water research initiatives
External and Internal Institutions’ Impact on Agricultural Development
Get PDFThis thesis examines the key determinants of agricultural productivity in West Africa, emphasizing both external and internal influences. It seeks to understand why agriculture—a sector vital to economic growth and societal welfare—continues to underperform in West Africa relative to other developing regions in an increasingly interconnected world. The study critically evaluates the impact of structural adjustment programs on agricultural development. It also explores the legacy of import substitution industrialization policies adopted in the post-independence era alongside the foundational structures that shaped Africa’s developmental trajectory.
Qualitative findings reveal that internal factors have contributed significantly to advancing the agricultural sector, while external factors have a negative contribution to West Africa\u27s agricultural development. Qualitative analyses indicate that external factors, represented by openness, have negative effects on agricultural outcomes, whereas internal factors such as government size, public expenditure, subsidies, and legal and property rights have played a more significant role in influencing agricultural outcomes. However, quantitative analysis using Ordinary Least Squares (OLS) and Pooled Models paints a nuanced picture of internal factors exhibiting mixed results, with variables such as fertilizer consumption and subsidies showing a weaker correlation with agricultural performance. The two results, OLS and pooled, suggest that external factors emerge as the primary contributors to agricultural underdevelopment in West Africa