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RELATIONSHIP BETWEEN SEMI-AQUATIC MAMMAL OCCUPANCY AND PARTICULATE CARBON, NITROGEN, AND PHOSPHORUS DYNAMICS IN FRESHWATER ECOSYSTEMS
No full textSemi-aquatic mammals such as beavers (Castor canadensis) and muskrats (Ondatra zibethicus) can influence carbon (C), nitrogen (N) and phosphorus (P) dynamics in freshwater systems. While the effect of beavers, in particular, are typically attributed to their dam and lodge building behaviors, these animals do not always build these structures. It’s possible that other, non-damming behaviors (e.g. foraging and burrowing) may influence the availability of nutrients as well. To assess this possibility, we surveyed 54 lentic and lotic waterbodies across Southern Illinois and investigated the possibility that beaver and muskrat occupancy improved our ability to explain variation in nutrient availability across these sites. We created occupancy models based on repeated sign surveys and camera trap data, and we used these to relate mammal presence, land cover and physicochemical gradients to log-transformed particulate C, N, P and their molar ratios (C:N, C:P, N:P). We found that wetland cover was correlated with an increase in particulate concentrations of C and N, whereas forest cover was correlated with a decrease of particulate concentrations of C, N, and P. Lentic sites consistently contain higher(include specific nutrients) than lotic sites. After accounting for these landscape controls, increasing beaver occupancy probability was significantly associated with lower particulate C:N, while C:P and N:P were unaffected by either mammal and only negatively impacted by site type. Our results demonstrate that even subtle beaver activity can affect particulate stoichiometry by enriching N relative to C, potentially enhancing downstream food quality and partially offsetting nutrient signals of landcover. Semi-aquatic mammal occupancy may be valuable predictors of nutrient cycling and their under-appreciated roles as biogeochemical engineers
COMPREHENSIVE DROUGHT ANALYSIS: MACHINE LEARNING-BASED METEROLOGICAL DROUGHT FORECASTING AND PCA-DRIVEN AGRICULTURAL DROUGHT MONITORING.
No full textDrought is a long-term natural disaster that affects many aspects of human life from health, water supply to ecosystems and agriculture. Drought’s occurrence and intensity has increased in recent years because of global warming, which urges for proper and accurate monitoring and forecasting of drought. For better understanding of droughts, this study uses two approaches. In first part, the study uses the historical temperature and precipitation data from period of 1960 to 2021 as input features for three different machine learning models – Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF). The research focuses on calculating the Standardized Precipitation Index(SPI) and Standardized Precipitation Evapotranspiration Index(SPEI) for temporal drought assessment of the watershed. Statistical parameters like the coefficient of determination (R2), Mean Absolute Error (MAE), Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) were determined to evaluate the model accuracy. Overall, the ANN and SVM models outperformed the RF model for forecasting long-term drought. The second approach utilizes Principal Component Analysis(PCA) for creating a Combined Drought Indictor (CDI-NM) for New Mexico State, using four agrometeorological variables- Vegetation Condition Index (VCI), temperature, Smoothed Normalized Difference Vegetation Index (SMN), and gridded rainfall data for the period of 2003-2019. The performance of the CDI-NM was evaluated with SPI-3, which results a strong correlation (R² \u3e 0.8 and RMSE=0.03) between both indices for the entire period of analysis. Negative correlation between crop yield and CDI-NM suggests the applicability of CDI-NM in drought monitoring. By combining machine learning models and PCA-based analysis this research enhances both meteorological drought forecasting and agricultural drought monitoring
EXPLORING THE ADOPTION OF QUALITY 4.0: A QUALITATIVE STUDY OF INDUSTRY PRACTICES AND BARRIERS
No full textThe emergence of Industry 4.0 has revolutionized quality management, introducing Quality 4.0 as a framework that integrates digital technologies with established quality principles. Despite its potential to enhance operational efficiency and decision-making, many organizations face challenges in adopting Quality 4.0, including skill gaps, resource constraints, and cultural resistance. This qualitative study explores industry practices, barriers, and enablers of Quality 4.0 implementation through surveys and interviews with quality professionals in southern Illinois manufacturing companies. Using a phenomenological approach, five major themes emerged: employee skillset, training, technology integration, quality tools, and supply chain management. Findings reveal that successful adoption depends on workforce readiness, leadership engagement, and strategic use of digital tools such as AI, machine learning, and data analytics. Based on these insights and a comprehensive literature review, the study proposes a practical framework for implementing Quality 4.0, emphasizing continuous improvement, cultural alignment, and digital transformation. The research contributes actionable guidance for organizations seeking to modernize quality systems and highlights future directions
SCHOLARLY PROGRAM NOTES FOR THE MASTERS RECITAL OF WESLEY CAMDON CAULKINS
No full textIn this paper, I will disseminate biographical and historical data and share my own musical analysis of the songs and arias I performed on my Master’s recital. Included are the following: Blue Mountain Ballads, by Paul Bowles (1910-1999), Don Quichotte à Dulcinée, by Maurice Ravel (1875-1937), “Oh, What a Beautiful Mornin’!”- Oklahoma! (1943) Richard Rodgers (1902-1979) and Oscar Hammerstein (1895-1960), “Sibillar gli angui d’aletto”- Rinaldo, by George Fredrick Handel (1685-1759), “Madamina! Il catalogo e questo”- Don Giovanni, by Wolfgang Amadeus Mozart (1756-1791), Drei Gesange von Metastasio, Franz Schubert (1797-1828), selections from Fünf Lieder op. 47, no. 1, 3, and 4, by Johannes Brahms (1833-1897), and “Unexpressed”, by John Bucchino (b. 1952)
FISCAL DOMINANCE AND BANK FRAGILITY IN FRONTIER ECONOMIES: THEORY AND EVIDENCE FROM SUB-SAHARAN AFRICA
No full textIs fiscal dominance a threat to financial stability? This dissertation examines how fiscal dominance threatens banking-sector stability in frontier economies, focusing on Sub-Saharan Africa. A dynamic stochastic general equilibrium (DSGE) model with macro-financial linkages, calibrated to Ghana, shows that a one-standard-deviation, money-financed fiscal shock raises inflation by 0.1 percentage points, widens credit spreads by 10 basis points, reduces bank net worth by 0.4 percent within two quarters, and increases the bank leverage ratio by 0.8 percent, illustrating how fiscal dominance amplifies financial vulnerabilities by weakening bank balance sheets and tightening financial conditions. Empirically, Bayesian vector autoregression (BVAR) and local-projection techniques applied to Ghanaian data from 2006 to 2022 show that inflation shocks linked to monetary financing initially stimulate private credit but eventually drive up borrowing costs. As credit becomes more expensive, borrowers face growing repayment challenges, leading to a rise in non-performing loans. This deterioration in loan performance erodes banks’ capital adequacy and weakens their resilience to future shocks over a 20- to 24-month horizon. Fixed-effects panel regressions across Ghana, Kenya, Nigeria, and South Africa (1998–2020) show that a one-percent increase in the public-debt-to-GDP ratio is associated with a 0.34-percent rise in NPLs, while rapid money growth and exchange-rate depreciation further erode loan quality. These findings underscore the systemic risks that fiscal dominance poses to financial stability and highlight the need for credible fiscal–monetary policy coordination, strengthened macro-prudential regulation, and structural reforms to safeguard banking systems and support sustainable economic development in frontier economies
Dimension Reduction for High-Dimensional Multivariate Heteroskedastic Time Series via Envelope Methods
No full textMultivariate time series data, prevalent in fields such as finance, economics, and neuroscience, often display complex features including heteroskedasticity and dynamic structural changes. Ignoring these factors can lead to inefficient and biased estimation as well as inaccurate forecasting. Although traditional models, such as vector autoregressive (VAR) and matrix autoregressive (MAR) frameworks, are widely used, they often assume constant volatility or rely on oversimplified covariance structures, limiting their applicability and interpretability in high-dimensional, time-varying contexts.This dissertation addresses these limitations by developing a unified set of models that integrate envelope methods and reduce subspace techniques to efficiently handle heteroskedasticity, dimensionality reduction, and time-varying dynamics in high-dimensional multivariate time series. We introduce the unconditional heteroskedastic Envelope VAR (HEVAR) models, which link the mean function to time-varying unconditional covariances through a minimal reducing subspace, thereby improving estimation stability and forecasting accuracy. Extending this concept to matrix-valued time series, we propose the unconditional heteroskedastic Envelope MAR (HEMAR) models, which preserve the inherent row-column structures while modeling both high dimensionality and heteroskedasticity. Furthermore, we extend this framework to dynamic brain connectivity analysis by creating a reducing subspace framework for multivariate GARCH (MGARCH) models, enabling improved functional connectivity estimation and facilitating brain health classification from fMRI data. Finally, we propose a time-varying MAR (TV-MAR) model that accommodates smoothly evolving structures in both the coefficient matrices and innovation covariance components, further enhancing model flexibility for structured high-dimensional time series and capturing rich temporal dependencies often overlooked by static models. Across all proposed models, we establish theoretical and asymptotic properties, develop (quasi-)maximum likelihood estimation procedures, and demonstrate the practical advantages through extensive simulations and real-world applications. Together, this work contributes new tools for modeling, inference, forecasting, classification, and uncovering complex structures in high-dimensional, heteroskedastic, and dynamically evolving multivariate time series
Effects of Processing on Bitter Melons’ Antidiabetic, Antioxidant and Sensory Qualities.
No full textBitter melon (Momordica charantia) is a functional food known for its antidiabetic and antioxidant properties, largely attributed to its bioactive compounds like charantin, polypeptide-p, and flavonoids. However, the influence of different food processing methods on these properties remains underexplored. Given the rising prevalence of type 2 diabetes, especially in high-risk populations, this study examined how processing methods such as air drying, freeze drying, pickling, canning, blanching & freezing, and stir frying affect the antidiabetic enzyme inhibition (α-amylase and α-glucosidase), antioxidant activity (DPPH assay), and consumer acceptability of bitter melon. Methanol extracts from fresh and processed bitter melon samples demonstrated varying degrees of DPPH scavenging activity, with stir-fried, freeze-dried, and pickled (180 mL vinegar) samples retaining the highest antioxidant activity (~76–82%). All samples exhibited α-amylase and α-glucosidase inhibition to varying extents, but thermal processing generally reduced bioactivity. Drying studies showed that higher drying temperatures (155°F≈68.3°C) reduced moisture content and water activity faster but also lowered antioxidant activity. Sensory analysis revealed strong preference for freeze-dried stir-fried samples, especially among Black or African American males, a demographic with elevated diabetes prevalence in national statistics (CDC, 2022). Processing had a significant impact on bitter melon’s bio functional and sensory properties. Freeze drying and pickling (with moderate vinegar levels) were optimal for preserving antioxidant potential and enzyme inhibitory activity. These findings provide a basis for recommending processed bitter melon as a nutritionally functional food for diabetes management. Further work is needed to assess long-term stability and clinical efficacy in vivo.Keywords: Bitter Melon, Momordica charantia, α-amylase, α-glucosidase, DPPH, Functional foods, Stir fry, Vinegar pickling, Antioxidant activity, Diabetes management, GA
EXPERIMENTAL STABILITY OF Al-O-H PHASES AT THE NANOSCALE
No full textThere is a lack of available data for the size-dependent controls of Al-O-H mineral stability, and factors like temperature and time will inevitably affect the crystallization sequence. Initial predictions indicate a crossover in stability between the bulk stable phase of corundum to γ-Al2O3 and Al-OH minerals like nordstrandite at the nanoscale. Crystallization experiments are conducted using a ~4.6 pH AlCl3-NaOH-H2O solution, heated at lengths of 3, 12, and 24 hours from 75-250°C. Precipitated solids are analyzed via XRD, FTIR, SEM-EDS, BET, and TEM methods. Lack of detection for predicted oxide phases implies this study operates within a hydrated system, due to higher temperature requirements for transformation to a dehydrated oxide structure. Results indicate the dominant phase at all 3 and 12-hr temperatures is a previously undescribed alumina compound with some Na and Cl and assumed isometric structure. Methods yield crystalline size estimations for the unknown phase that increase in size with longer heating times, but SEM/TEM images show the dodecahedral morphologies decrease in definition and size at higher temperatures. At 24-hr times, this phase is only detected up to 225°C. A transition occurs somewhere between 12-24 hr and 225-250°C from the unknown compound to boehmite as rods with widths \u3c100 nm. Boehmite may form as the first Al-OH phase due to Ostwald step-rule, being closest to the initial unknown phase in total enthalpy and overpowering any present size-dependent controls at the nanoscale
Transferable Targeted Attack for Generating Adversarial Examples Across Convolutional and Transformer-Based Vision Models
No full textAdversarial attacks have revealed the fragility of deep neural networks, but most targeted attacks remain model-specific and struggle to generalize across different model architectures. In this paper, we propose a transferable targeted attack framework that unifies momentum, input diversity, and translation invariance within a Projected Gradient Descent (PGD) optimization scheme. Our method is designed to craft adversarial examples that not only mislead individual models but transfer to unseen architectures while steering predictions toward a fixed target class. We evaluate the proposed method on five diverse image classification models: ResNet-50, EfficientNet-B0, DenseNet-121, Swin Transformer, and MobileViT, using CIFAR-10 dataset as the benchmark. Through a leave-one-out ensemble strategy, we achieve a consistent improvement in targeted attack success rate on held-out models, demonstrating the method’s improved transferability. This work exposes critical vulnerabilities in both convolutional and transformer-based vision models and offers a scalable blueprint for designing stronger, more generalizable adversarial attacks
Introduction to the Special Issue: Celebrating 60 Years of the Water Resources Research Act
Get PDFWater Resources Research Act (WRRA) of 1964 created the network of water research centers and institutes now known as the National Institutes for Water Resources (NIWR). Through its unique model of shared federal, state, and local investment water in NIWR centers and institutes at public universities across the nation, the WRRA ushered in six decades of locally- tailored research, education, and outreach on water resource challenges as they continued to evolve. This special issue highlights the rich history and wide-ranging impacts of the work of NIWR centers and institutes. Appropria