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ASSESSING THE VULNERABILITY OF PUBLIC WATER SYSTEMS TO DROUGHTS THROUGH INTEGRATED HYDROLOGICAL MODELING, REMOTE SENSING, AND SOCIAL SENSING
No full textDroughts rank among the most complex natural hazards due to their unpredictable onset, evolution, and cessation. It affects hydrological systems, ecosystems, and communities across different spatial and temporal scales. As climate change profoundly threatens water security, hydrologic models become a valuable tool for understanding the physical and social impacts of droughts and planning effective coping strategies. One of the critical factors that affects the performance of hydrological models at watershed scales is digital terrain data, also known as digital elevation models (DEMs). This research evaluated various DEM-related parameters (i.e., data sources, spatial scales, filtering, and flow barriers) on the Soil & Water Assessment Tool (SWAT) model’s goodness-of-fit in two gaging stations (Casey Fork and Rasey Creek). The results show that hydrologically enforced LiDAR-derived DEMs produced the lowest bias at the Casey Fork Creek for both monthly and daily models, but not at the Rasey Creek. Our results reveal that the coarser-resolution USGS 30m DEM outperformed the finer-resolution DEMs during the monthly calibration and validation phases at the Casey Creek gaging station, as indicated by the Nash-Sutcliffe Efficiency (NSE) and coefficient of determination (R²).Beyond evaluating model parameters, this study aimed to assess the vulnerability of Public Water Systems (PWS) to drought by integrating hydrological modeling, remote sensing, and social sensing. To achieve this, an interdisciplinary approach was adopted by applying a statistical model such as the Autoregressive Distributed Lag (ARDL) model to explore the dynamic relationships among hydro-meteorological indices, remote sensing indicators, and social sensing. Social media analysis of X (formerly known as Twitter) posts revealed that the frequency of drought-related tweets shows a significant correlation with short-term meteorological drought indices, such as SPEI. In contrast, vegetation indices, like EVI, were linked to delayed social responses. Public sentiment revealed a nuanced emotional response that was more context-dependent and indirect, showing a weak correlation with drought indicators. By integrating physical modeling with hydro-meteorological drought indices and social media data, our study offers a comprehensive framework for understanding drought dynamics and supporting more adaptive, community-informed water resource management
Mixed Methods Meta-Synthesis of Nutrition Education Programs For The Autism Spectrum Disorders’ Community
No full textAutism Spectrum Disorder (ASD) is growing in prevalence and diagnosis, highlighting the urgent need for more information on implementing interventions. Nutrition education interventions are crucial for the ASD community, including parents, adolescents, young adults, and children, due to many coexisting sensory and behavioral factors. This mixed-methods meta-synthesis evaluates current nutrition education interventions, revealing that while quantitative data related to improved intake is often lacking in significance, qualitative data shows that nutrition education can positively influence thoughts on nutrition and self-efficacy. Interventions that address both parents and children or adolescents are most effective for reinforcing nutrition education. Overall, these interventions must provide a safe environment and continuous education to support sustained behavior changes. This study emphasizes the need for comprehensive and tailored nutrition education to improve the well-being and quality of life for individuals with ASD. Despite challenges, nutrition education has the potential to significantly benefit the ASD community by addressing both immediate and long-term dietary habits
THE IMPACT OF FOREIGN AID ON AGRICULTURAL PRODUCTIVITY AND ITS RIPPLE EFFECTS THROUGH THE EXCHANGE RATES IN DEVELOPING COUNTRIES – A CASE STUDY IN WEST AFRICA
No full textThis study explores the impact of foreign agricultural aid on agricultural productivity and its subsequent effects on exchange rates, addressing a gap in the existing literature. While several studies have analyzed the effects of agricultural aid on productivity and poverty alleviation (e.g., Clemens et al., 2012; Rajan & Subramanian, 2005), the economic consequences of this aid on exchange rates have not been comprehensively examined. This research contributes to the literature by assessing both the direct and indirect effects of agricultural aid. The findings indicate that foreign aid has a statistically significant positive impact on agricultural productivity, with a $1 increase in aid corresponding to a 0.11% increase in productivity. However, the study also reveals that while foreign aid influences agricultural productivity, it has a negative, statistically insignificant impact on the exchange rate. These results suggest that while agricultural aid boosts productivity, its effect on exchange rate dynamics is limited and not statistically significant. This research provides valuable insights into the complex relationship between foreign aid, agricultural productivity, and exchange rate movements. The study also recommends that for aid to truly benefit recipient countries, it must be carefully targeted and allocated in ways that drive lasting improvements in the agricultural sector and overall economic stability
PREDICTORS OF CYBERBULLYING AND HELP-SEEKING BEHAVIORS IN RURAL MIDWESTERN SCHOOLS: A MULTILEVEL MODELING ANALYSIS
No full textCyberbullying is a significant problem throughout the United States. Many risk factors have been identified for cyberbullying victimization, including experiencing traditional bullying victimization, social media usage, gender, and underrepresented racial/ethnic status, although the results are inconclusive. In response to cyberbullying, youth may choose to engage in or avoid help-seeking behaviors. Individual characteristics such as age, gender, frequency of victimization, race and ethnicity, and student perceptions of school staff can influence whether a youth seeks help. School level variables, including anti-bullying policies and teacher awareness and perceptions of cyberbullying may also impact help-seeking behaviors. However, few studies have empirically assessed their effects. Moreover, few recent studies have examined these factors for youth in rural schools. This study concurrently evaluated individual and school level predictors of cyberbullying victimization and help-seeking behaviors among rural youth. The results of this study indicated that experiencing traditional bullying victimization and social media usage were statistically significant predictors of cyberbullying victimization. Surprisingly, results demonstrated that grade, gender, and racial/ethnic status did not significantly predict victimization. Consistent with some previous research, the current study found that identifying as female, in a lower grade, White, and as having high frequency of victimization, predicted overall help-seeking behaviors. Conversely, student perceptions of school personnel and teacher awareness and perceptions of cyberbullying did not significantly predict overall help-seeking behaviors or help-seeking behaviors from school personnel. However, for some schools, anti-bullying policies were significant predictors. Overall, the findings suggest the need for schools to encourage help-seeking behaviors when a cyberbullying event occurs. Implications for prevention and intervention programming are discussed
It’s Still Happening: Conversion Therapy Laws Leaving College Students and More At Risk
No full textConversion Therapy is still legal for adults across the United States, and for minors in many states. The laws and case law facilitating or preventing this discredited practice hang in the balance of whichever direction the Federal political climate moves at any given times. Even in states where Conversion Therapy is celebrated as banned , populations of Americans that include the uniquely vulnerable students at religious universities are still subjected to it. This exploration and policy proposal solution prompts the total ban of Conversion Therapy and explains how a true legislative movement to that aim could begin
Data Set for Fipronil Insecticide Response Spectrum Model for Juvenile Chinook Salmon
No full textThis spreadsheet is a compilation of the data sets corresponding to the following manuscript:
Development of a Fipronil Insecticide Response Spectrum Model for Juvenile Chinook Salmon (Oncorhynchus tshawytscha)
Kara E. Huff Hartz, Katie J. Knaub, Louise Cominassi, Giovanni S. Molinari, Shane Power, Mia Arkles, S. D. M. Chinthaka, Andrea Chandler, Md. Habibullah-Al-Mamun, Gregory Whitledge, Amelie Segarra, Richard E. Connon, Shawn Acuña, Michael J. Lydy
Environmental Pollution, Volume 385, November 2025, 127122
Nitrogen Fertilization Strategies for Corn in Southern Illinois: Effects of Application Timing, Starter Fertilizer, and Cereal Rye on Yield and Nutrient Balance
No full textEffective nitrogen management is crucial for both maximizing yields and minimizing nitrogen residues, particularly in poorly drained clay soils common in southern Illinois. The studies in this thesis evaluated multiple site-year field studies addressing nitrogen rate, nitrogen timing, and cover crop management to minimize environmental impacts and maximize economic benefits. In the first study, eight different pre-plant nitrogen rates (0-394 kg N per ha) were evaluated in 12 site-year studies. The economic optimum nitrogen rate, EONR, was found to range from 167 to 361 across different years and field conditions. The Maximum Return to Nitrogen (MRTN) determined for Southern Illinois was only 17%, and N leaching was found to be greater than EONR for all site-years. In the second study, two different nitrogen applications were compared in a 10-site-year corn trial: pre-planting and sidedress. The results showed that the EONR of sidedress nitrogen application was 8% lower than that of pre-planting nitrogen. It was concluded that N-based leaching losses were reduced by 30 kg N per hectare without any yield loss. The third study examined the impact of integrating cereal rye as a cover crop into corn production on yield. In the study areas where cover crops were used, although higher EONR values were found due to N immobilization, no statistically significant loss was observed. This thesis study found that N timing and the integration of cover crops into corn production reduced losses from environmentally harmful nitrogen and increased effective fertilizer efficiency. These findings highlight the need for the development of MRTN-based tools that integrate factors such as timing, weather, and soil variability in Southern Illinois
SYNTHESIS AND CHARACTERIZATION OF A NOVEL MALDI MATRIX, DTB CHCA, AND AN AUTOMATED QUANTITATIVE DATA COLLECTION PROCESS FOR MALDI-MS
No full textThe initial development of matrix assisted laser desorption/ionization mass spectrometry (MALDI-MS) was empirical in nature, resulting in a constant search for new and improved matrices. Cinnamic acid derivatives, which are also well known for their use as natural antioxidants, have been commonly utilized as MALDI matrices since its inception, but the inclusion of tert-butyl groups on the phenolic ring, known to improve antioxidant performance of phenols, has not been tested for a MALDI matrix. In this work, the novel MALDI-MS matrix alpha-cyano-3,5-di-tert-butyl-4-hydroxycinnamic acid (DTB-CHCA) was synthesized and its performance with peptides, polymers, and proteins was compared with commercially available alpha-cyano-4-hydroxycinnamic acid (CHCA) and sinapinic acid (SA). The inclusion of di-tert-butyl groups onto the CHCA base structure improves the performance of the compound as a MALDI-MS matrix, particularly with large proteins and hydrophobic and acidic analytes.Additionally, quantitation via MALDI-MS is complicated due to the inhomogeneous nature of sample crystals resulting in inconsistent desorption. Previous work from our lab has demonstrated the ability to generate quantitative data by manually collecting mass spectra based on a fixed analyte peak width. In this work, a novel automated quantitative data collection method was developed which speeds up the time of analysis and removes the potential for user bias. This method utilizes the AutoXecute software bundled with the Bruker FlexControl software and functions by accepting spectra based on the matrix ion signal full width at half maximum and a minimum ion signal intensity. The software is demonstrated to be able to collect quantitative spectra (R2 \u3e 0.99) over 1 to 2 orders of magnitude for a range of analytes from small organic molecules to peptides and proteins, and peptide mixtures. Improvements in sample reproducibility are demonstrated by coupling the automated data collection process with an airbrush sample preparation technique to produce more homogenous samples
CHARACTERIZING THE MOLECULAR FUNCTION OF ISWI CHROMATIN REMODELING COMPLEX FACTORS IN NORMAL SKELETAL MUSCLE AND RHABDOMYOSARCOMA
No full textRhabdomyosarcoma (RMS) is an aggressive pediatric cancer arising from skeletal muscle precursor cells. This thesis investigates the role of the ISWI chromatin remodeler subunit SMARCA1 in normal myogenesis and RMS pathogenesis. Overexpression of SMARCA1 in C2C12 cells inhibits the expression of myogenic markers (Myod1, Myog, Lmod1, Acta1, Tnni2) and impairs myotube formation, as assessed by immunostaining for myosin heavy chain. These effects are partially rescued by Wnt signaling activation using 10 mM LiCl treatment. Conversely, SMARCA1 is upregulated in RMS cell lines (RH30, RH28, RD, RD2) compared to C2C12 cells. CRISPR-Cas9-mediated knockout of SMARCA1 in RH30 cells leads to a significant downregulation of TGF-β pathway components (TGFBR1), EMT-related genes (SNAIL1, WNT11, KRAS, MRAS, TCF7L1) as demonstrated by RT-qPCR. Also, a slight downregulation of HDAC2 protein expression, and downregulation of MYOD1 and MYOG were observed by Western blot. These findings suggest that SMARCA1 acts as a regulator of muscle differentiation and plays a critical role in maintaining the expression of genes involved in TGF-β signaling and EMT in RMS cells, highlighting its potential as a therapeutic target
Microgrid Approach with Data-Driven Monitoring to Enhance the Resilience of Water Distribution Systems
No full textExisting centralized water supply systems are critically threatened by the joint effects of climate and socioeconomic changes, extreme weather events, and physical degradation due to aging, and their changeability. The uncertain and changing drivers pose challenges for water supply systems in providing sustainable water services during disruptions. The energy field dealing related issues has endorsed the microgrid approach with a decentralized energy supply with dispersed local energy sources. The application of energy microgrids has demonstrated their resiliency for sustainable energy supply. For improved resiliency of a water distribution system, this study investigated the application of a microgrid approach and the detection of subtle anomalies in its operation. This thesis explores the setup of a lab scale water distribution model addressing the following queries: 1) 1. Can the microgrid approach enhance water distribution system resilience more effectively than the traditional centralized and decentralized water distribution systems? 2) Can the subtle anomalies in microgrid operation be detected to enhance resilience? To acknowledge the questions and test the corresponding hypotheses, this study built a lab-scale water distribution model, which can demonstrate centralized, decentralized and water microgrid systems under various disruption scenarios – e.g., power off due to extreme weather conditions, pipe leaks/bursts due to aging or freezing. Here, water microgrid systems consider the interactive operations between central and local water systems, while decentralized systems lack operative interactions. The performance of the system was evaluated by functionality-based resilience for each disruption scenario. The quantitative resilience analysis reveals that the microgrid configuration showed reduced degradation of water supply performance resulting on improved resilience during disruptions. Further, a widely used machine learning model for anomaly detection, Autoencoder was applied to detect the minor changes in water use in the lab scale model. Results highlight the ability of autoencoder on detection of subtle anomalies. However, the noise on the datasets plays a vital role in its performance. The findings suggest engineering insights into the upgrade of current centralized water systems with a microgrid approach to maximize sustainable water supply service under uncertain and extreme disruptions