Fitchburg State University

ABSTRACT This paper introduces a novel application of the Lotka-Volterra model to analyze the dynamic interplay between military and civilian government spending in the United States from 1960 to 2019. Drawing on the classical ‘guns versus butter’ framework, we conceptualize military spending as a predator and civilian spending as prey, both constrained by GDP as a shared resource. By constructing a system of nonlinear differential equations, we model inter-sectoral competition, long-term memory effects, and time-varying growth and carrying capacities. The model reveals strong asymmetries in budgetary interaction: military expenditures exert a disproportionately negative impact on civilian spending, while being more sensitive to shared constraints, suggesting a prey-like vulnerability. Using a genetic algorithm for parameter estimation, we demonstrate that shifts in growth rates and sectoral capacities correspond closely with historical inflection points, such as the Cold War and the War on Terror. Our findings offer compelling evidence of Lotka-Volterra-like oscillatory behavior in public budget allocation, underscoring the strategic dominance of military priorities over social investments. This dynamic modeling approach provides new insights into the systemic trade-offs embedded in fiscal policy and raises critical questions about the sustainability of militarized economic strategies.

The growing demand for reliable resource management at the computational edge has created a pressing need for adaptable orchestration frameworks. This paper proposes a hybrid orchestration framework that combines cloud-native paradigms with edge-specific optimizations. Through extensive simulations using CloudSim/iFogSim, the approach demonstrates an 84% reduction in latency, 50% lower energy consumption, and support for 500+ nodes with 12.7s average failover times. The framework integrates federated learning for privacy-preserving collaboration and maintains O(log N) decision-making complexity. Experimental validation shows significant improvements over existing top-down, bottom-up, and alternative edge orchestration platforms, providing a scalable and resilient solution for distributed edge environments.

Social and Web Analytics is becoming important towards analyzing online user behavior and data-informed business decisions of others. In the e-commerce domain, understanding customer behavior on shopping websites provides valuable insights into purchasing patterns and user intent. Despite the abundance of user interaction data, accurately predicting whether a visitor will enter the site and complete a transaction remains a critical challenge for online retailers. This study aims to analyze the user’s intentions through machine learning classification to predict purchasing behavior and website abandonment. The UCI Online Shoppers Purchasing Intention Dataset is used, which contains $\mathbf{1 8}$ features like revenue, visitor types, and days of return. Models-Logistic Regression, Random Forest, and XGBoost-are used to test the dataset. Model performance is measured by accuracy, recall, precision, and F1-score, with Synthetic Minority Over-sampling Technique (SMOTE) applied to address severe class imbalance. The results demonstrate that Random Forest can capture complex, non-linear correlations in user behavior, outperforming the other models with the best accuracy and F1score. The findings offer e-commerce platforms insights for improving high-intend customer targeting and reducing cart abandonment rates.

Associations between single-nucleotide polymorphisms (SNPs) and phenotype are an important research paradigm in genomics and have been extensively studied, especially for the purpose of bettering our understanding of he emergence and development of disease. Various methodologies accommodating different data types, data qualities and genomic properties have been developed. In this work, we focus on a specific aspect of the analysis, namely the computation of an association score using a statistical procedure. We argue that Bayesian inference using a mixture prior that is learned from the data leads to better predictions than existing approaches, and suggest its incorporation into existing tools. To demonstrate its power, we implement the new procedure and compare its performance to popular tools on simulated data. We also detect associations between disease and SNPs using a real, modest-size RNA-Sequencing dataset, showing that the method can produce useful insights and has broad applicability.

Large-scale data processing frameworks, such as Apache Spark, frequently encounter severe memory constraints that degrade system throughput due to excessive memory pressure and extended garbage collection cycles. This work introduces a smart memory of

This essay considers how open educational resources (OER), particularly open textbooks, can address barriers of cost, representation, and access in teaching medieval literature. Beginning with the adaptation of existing free digital materials, the author outlines the benefits and challenges of curating resources that emphasize disability studies and diverse cultural perspectives. The discussion then turns to the creation of original open textbooks, such as Heritages of Change, which integrate Universal Design for Learning principles and cultural heritage frameworks into first-year writing and literature courses. These initiatives expand the accessibility of medieval content by eliminating financial burdens, embedding multiple perspectives, and offering flexible pedagogical approaches. The essay argues that the labor of developing OER is offset by the ability to provide equitable, inclusive, and up-to-date course materials. Ultimately, open textbooks not only increase student access to medieval studies but also foster connections between the past and present through inclusive and socially responsive teaching practices.

Efficient elasticity is fundamental for achieving high performance and cost-effectiveness in cloud environments. Traditional approaches often encounter difficulties in handling dynamic workload variations, which may result in either resource over-prov

ABSTRACT Urbanization profoundly impacts stream ecosystems, leading to altered hydrology, degraded water quality, and shifts in biological communities, a phenomenon known as the “urban stream syndrome.” This study investigated the taxonomic and functional responses of benthic macroinvertebrate assemblages to urban land cover and local habitat conditions across 22 stream sites in the Nashua River watershed of Massachusetts and New Hampshire. Using a multi‐scale approach, we analyzed land use at watershed, riparian, and reach scales and assessed in‐stream environmental factors, including water quality, substrate composition, and canopy openness. Results indicate that while urban land cover was correlated with increased conductivity and pH, macroinvertebrate taxonomic and functional diversity was more strongly influenced by local habitat conditions such as dissolved oxygen, nitrate levels, and stream width. Sensitive taxa declined in urbanized areas, while opportunistic species with traits favoring rapid reproduction increased. These findings underscore the importance of localized habitat management in maintaining stream biodiversity and highlight the utility of functional trait analysis in urban stream ecology.

This paper investigates the evolving landscape of the insurance industry in response to digital technologies. It examines how digitalization influences insurance companies' strategies, organizational structures, risk management practices, workforce, a