Digital Commons Network^™

Synthesis And Characterization Of A Biobased Polymer From Tung Oil And Pine Rosin & Characterization Of Polyhydroxybutyrate., Arijanet P. Osumah

College of Graduate Studies: Theses & Dissertations

This thesis presents two independents but thematically aligned research projects focused on the development and evaluation of sustainable polymers. The first involves the synthesis and characterization of a biobased polymer derived from tung oil and pine rosin. Tung oil, which is a triglyceride was chemically modified to produce tung oil diglyceride (TDG), which subsequently reacted with abietic acid-rich pine rosin to yield tung oil abietate (TOA), a biobased monomer. The TOA monomer was polymerized with butyl methacrylate (BMA) and divinylbenzene (DVB) using di-tert-butyl peroxide (DTBP) as a thermal initiator. The resulting polymer was thoroughly characterized using Fourier transform infrared spectroscopy …

Quantifying Inositol Phosphate Dephosphorylation To Understand The Role Of Recalcitrant Organic Phosphorus Forms On Harmful Algal Blooms In Freshwater Systems, Iffat Tasnim

College of Graduate Studies: Theses & Dissertations

Organophosphorus such as phytic acid, a surrogate species of inositol phosphate (IP), may serve as a source for orthophosphate (OP) in freshwater. In absence of OP, competent aquatic microorganisms upregulate the production of specialized enzymes to obtain growth-sustaining OP from organic phosphorus (org-P) forms. The contribution of recalcitrant org-P to the OP pool has been overlooked due to the lack of capable tools to measure OP production from org-P accurately. The objective of this study was to quantify OP production from phytic acid (a surrogate form of recalcitrant org-P) to assess the contribution of recalcitrant org-P forms to the total …

Assessment And Health Benefit Of Vitamin C Using A549 Cell, Yetunde Adepoju

College of Graduate Studies: Theses & Dissertations

Vitamin C (ascorbic acid) is a vital micronutrient recognized for its antioxidant capacity and essential bodily functions, such as collagen formation, neurotransmitter activity, and regulation of the immune system. This study explores how vitamin C can protect and potentially heal cells under oxidative stress and inflammation, using A549 cells, a human lung epithelial cell line, as the model system. The cells were exposed to varying concentrations of vitamin C dissolved in water, DMSO, and 5% DMSO. To assess the outcome, cell viability was measured with an MTT assay. In addition, the expression of key genes related to oxidative stress and …

Synthesis And Assessment Of Biobased Acrylated Epoxidized Soybean Oil Resins For Stereolithography 3d Printing Of Microfluidic Devices, Natalie S. Romero Figueroa

College of Graduate Studies: Theses & Dissertations

This research focuses on the development and characterization of a biobased photocurable resin synthesized from soybean oil for use in stereolithography (SLA) 3D printing of microfluidic devices. The study explores a sustainable alternative to petroleum-based resins by performing an acrylation reaction on epoxidized soybean oil (ESO) to produce acrylated epoxidized soybean oil (AESO), which was then formulated into a photocurable resin with the addition of a photo initiator. The fabrication process involved designing microfluidic molds in SolidWorks and printing them using an Elegoo Mars 4 SLA printer under various exposure and layer thickness parameters. The printed samples consisted of microchannels …

Ai Characterisations And Their Legal Implications, Jerrold Tsin Howe Soh

Research Collection Yong Pung How School Of Law

This chapter examines the difficult legal characterisation problems that artificially intelligent systems raise and explores how different characterisations of artificial intelligence (AI) shape practical legal outcomes. Three reasons are offered for the legal difficulty with characterising AI. First, answers to characterisation problems are inherently subjective and perspective-driven, particularly when the subject is an intangible technological system. Second, AI technology is especially difficult to define since the field typically proceeds on inexact anthropomorphic metaphors. Third, AI characterisation problems raise difficult sub-problems, particularly in determining how autonomous an AI system is. The chapter thus argues that a range of plausible AI characterisations …

The Climate-Biodiversity-Pollution Nexus: The Pricing Of Environmental Credit Risks For European Industrial Polluters, Dominik Hirschbühl, Andrej Ceglar, Theodor Cojoianu, Tina Emambakhsh, Yifan Qi, Caterina Rho, Elsie Hu, Marco Petracco, Fabrizio Biganzoli, Alfred De Jager, Laura Garcia Herrero, Andrea Mandrici, Carlo Pasqua

Research Collection College of Integrative Studies

This study examines how euro area banks factor pollution-induced biodiversity risks into lending decisions, using data from 832 banks and 5,000 major polluters. Our results show that banks are increasingly pricing these risks by adjusting loan-to-value ratios and interest rates. Banks adjust lending conditions in line with EU pollution and biodiversity protection legislation, particularly for companies with large pollution footprints near biodiversity-protected areas or those contributing to Environmental Quality Standards failures of downstream surface waters. The former is driven primarily by banks’ adoption of biodiversity policies and public commitments to the Equator Principles, while the latter is a result of …

A Bibliographic And Topic Modeling Analysis Of The P-Adic Theory Literature Using Latent Dirichlet Allocation, Humberto Llinás, Ismael Gutiérrez, Anselmo Torresblanca, Javier De La Hoz, Brian Llinás

Computer Science Faculty Publications

P-adic analysis, introduced by Kurt Hensel in the early 20th century, has developed into a fundamental area of mathematical research with broad applications in number theory, algebraic geometry, and mathematical physics. This study aims to examine the thematic evolution and scholarly impact of p-adic research through a comprehensive topic modeling and bibliometric analysis. Using classical bibliometric techniques (e.g., performance analysis, co-authorship, and co-citation networks) combined with Latent Dirichlet Allocation (LDA), we analyzed 7388 peer-reviewed documents published between 1965 and 2024. The computational workflow was conducted using R (version 4.4.1) and VOSviewer (version 1.6.20), which enabled the identification of 20 distinct …

Deepssetracer 2.0: Improved Deep Learning Model Performance For Protein Secondary Structure Segmentation From Cryo-Em Maps, Bryan Hawickhorst, Thu Nguyen, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

DeepSSETracer is a method for segmenting protein secondary structure from medium-resolution (5-10Å) cryogenic electron microscopy (cryo-EM) density maps. We conducted experiments and ablation studies to examine the effects of normalization methods, max-pooling, activation functions, and loss calculation region on DeepSSETracer. By combining multiple technical improvements, the performance of the new version, DeepSSETracer 2.0, was significantly enhanced compared to DeepSSETracer 1.1. On a set of 77 test cases, the weighted average per-voxel F1 score increased from 62.1% to 70.3% for helix detection, and from 47.8% to 62.5% for β-sheet detection. While each of the five modifications in the network enhanced the …

Benchmarking And Improving Foundation Model Dietary Estimates From Meal Images, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Accurate quantifying dietary contents, such as calories, proteins, carbohydrates, and fats, from an image of a meal plate is vital for managing diabetes. Recently, Large Multimodal Models (LMMs) have excelled in complex vision-language tasks due to their use of very large, highly diverse data. This study benchmarked the use of seven LMMs that include full and lightweight models of GPT, Gemini, and Llama for nutrition estimation based on Google's Nutrition5k dataset and our own phone-collected DonateAndLearn dataset. We analyzed the performance of LMMs and the RGB-D fusion model, in which the RGB-D model was specifically trained using Nutrition5k data. On …

Heterogeneous Clustering Of Multiomics Data For Breast Cancer Subgroup Classification And Detection, Joseph Pateras, Musaddiq Lodi, Pratip Rana, Preetam Ghosh

Computer Science Faculty Publications

The rapid growth of diverse -omics datasets has made multiomics data integration crucial in cancer research. This study adapts the expectation–maximization routine for the joint latent variable modeling of multiomics patient profiles. By combining this approach with traditional biological feature selection methods, this study optimizes latent distribution, enabling efficient patient clustering from well-studied cancer types with reduced computational expense. The proposed optimization subroutines enhance survival analysis and improve runtime performance. This article presents a framework for distinguishing cancer subtypes and identifying potential biomarkers for breast cancer. Key insights into individual subtype expression and function were obtained through differentially expressed gene …

A Data-Driven Sliding-Window Pairwise Comparative Approach For The Estimation Of Transmission Fitness Of Sars-Cov-2 Variants And The Construction Of The Evolution Fitness Landscape, Md Jubair Pantho, Richard Annan, Landen Alexander Bauder, Sophia Huang, Letu Qingge, Hong Qin

Computer Science Faculty Publications

Estimating the transmission fitness of SARS-CoV-2 variants and understanding their evolutionary fitness trends are important for epidemiological forecasting. Existing methods are often constrained by their parametric natures and do not satisfactorily align with the observations during COVID-19. Here, we introduce a sliding-window data-driven pairwise comparison method, the differential population growth rate (DPGR) that uses viral strains as internal controls to mitigate sampling biases. DPGR is applicable in time windows in which the logarithmic ratio of two variant subpopulations is approximately linear. We apply DPGR to genomic surveillance data and focus on variants of concern (VOCs) in multiple countries and regions. …

Coldstartcpi: Induced-Fit Theory-Guided Dti Predictive Model With Improved Generalization Performance, Qichang Zhao, Haochen Zhao, Linyuan Gao, Kai Zheng, Yajie Li, Qiao Ling, Jing Tang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Predicting compound-protein interactions (CPIs) plays a crucial role in drug discovery. Traditional methods, based on the key-lock theory and rigid docking, often fail with novel compounds and proteins due to their inability to account for molecular flexibility and the high sparsity of CPI data. Here, we introduce ColdstartCPI, a framework inspired by induced-fit theory, which leverages unsupervised pre-training features and a Transformer module to learn both compound and protein characteristics. ColdstartCPI treats proteins and compounds as flexible molecules during inference, aligning with biological insights. It outperforms state-of-the-art sequence-based models, particularly for unseen compounds and proteins, and shows strong generalization capability …

Securing The Internet Of Wetland Things (Iowt) Using Machine And Deep Learning Methods: A Survey, Guma Ali, Wamusi Robert, Maad M. Mijwil, Malik Sallam, Jenan Ayad

Mesopotamian Journal of Computer Science

Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The …

Anila: Adaptive Neuro-Inspired Learning Algorithm For Efficient Machine Learning, Ai Optimization, And Healthcare Enhancement, Ismael Khaleel, Wijdan Noaman Marzoog, Ghada Al-Kateb

Mesopotamian Journal of Computer Science

The Adaptive Neuro-Inspired Learning Algorithm (ANILA) offers a breakthrough in the realm of machine learning by drawing inspiration from the biological processes of the human brain. Developed to address limitations in conventional models such as CNNs and RNNs, ANILA enhances real-time responsiveness, energy efficiency, and system adaptability. By emulating neurobiological behaviors particularly sparse coding and synaptic plasticity ANILA allows systems to process data dynamically, adjust to novel inputs without retraining, and scale effectively across environments like IoT and healthcare diagnostics. Performance evaluations highlight significant reductions in latency, increases in energy efficiency (up to 92%), and exceptional adaptability to changing data …

The Next Frontier In Computer Science, Trends And Research Opportunities, Mostafa Abdulghafoor Mohammed, Z. T. Al-Qaysi, Tahsien Al-Quraishi

Mesopotamian Journal of Computer Science

No abstract provided.

Woa-Covid-19: Whale Optimization Algorithm For Selection Of Multi-Examination Features Based On Covid-19 Infections, Karrar Hameed Abdulkareem, Mazin Abed Mohammed, Zaid Abdi Alkareem Alyasseri, Dawood Zahi Khutar, Osama Ahmad Alomari

Mesopotamian Journal of Computer Science

Since its emergence in late 2019, COVID-19 (Coronavirus Disease 2019) has become one of the most critical global health threats, claiming millions of lives and placing many more at serious risk. The complexity of diagnosing COVID-19 lies in the wide range of clinical and examination features involved, prompting researchers to explore various advanced diagnostic methods. However, one of the main challenges is identifying the most relevant features that can streamline and improve diagnostic accuracy. In this study, we propose a feature selection approach based on the Whale Optimization Algorithm (WOA) to identify key examination indicators associated with COVID-19. We used …

A Review Of Image Steganography Based On Metaheuristic Optimization Algorithms, Fatima Abdulhussain Khalil, Ammar Ali Neamah, Hasanen Alyasiri

Mesopotamian Journal of Computer Science

Due to the widespread popularity of digital images on the Internet, image-based steganography has become a widely adopted technique for embedding secret information into everyday visual content. In parallel, steganalysis plays a vital role in digital forensics and information security by seeking to uncover hidden content within these images. Although steganographic techniques—particularly those employing adaptive embedding strategies—have made significant progress, many steganalysis approaches still struggle to generalize effectively across different image types and embedding methods. This contrast highlights the need for more intelligent, flexible, and robust analysis frameworks. This review examines steganographic techniques for digital images and the application of …

Agentic Ai-Enhanced Virtual Reality For Adaptive Immersive Learning Environments, Indra Kishor, Udit Mamodiya, Mohammed Almaayah, Amer Alqutaish, Rami Shehab, Theyazn H. H. Aldhyani

Mesopotamian Journal of Computer Science

Immersive learning using Virtual Reality (VR) has gained prominence for delivering experiential, engaging education. However, most VR learning environments lack real-time adaptability, personalization, and cognitive responsiveness. This study presents an Agentic AI-enabled VR framework that autonomously adjusts pedagogical content, interaction style, and challenge level based on learner behavior, emotions, and performance feedback. The proposed system integrates a reinforcement learning-based agent with a virtual reality module to form an intelligent tutor capable of independent decision-making. A neuro-symbolic model processes multi-modal feedback (gesture, speech, gaze, performance) to determine context-aware pedagogical strategies. The system employs a self-evolving curriculum logic that adapts in real …

Bioaccumulation Of Legacy And Novel Pfas In The Environment, Hesham Taher, Rainer Lohmann

Graduate School of Oceanography Faculty Publications

The bioaccumulation of per- and polyfluoroalkyl substances (PFAS), both legacy and novel, in the environment presents significant ecological and health risks. PFAS are a diverse group of synthetic chemicals known for their persistence and bioaccumulation, which can cause widespread environmental contamination and health risks. Strong carbon-fluorine bonds give these compounds unparalleled stability, preventing them from degrading and enabling them to endure in a range of environmental matrices, including water, soil, and biota. Legacy PFAS, including perfluorooctanoic acid (PFOA) and perfluorooctane sulfonic acid (PFOS), have been extensively studied and regulated, resulting in lower concentrations in some environmental media. However, novel PFAS, …

Corporate ‘Capture Strategies’ Impacting Human And Ecosystem Health, Alex T. Ford, Marlene Ågerstrand, Michael G. Bertram, Miriam L. Diamond, Rainer Lohmann, Andreas Schäffer, Martin Scheringer, Gabriel Sigmund, Anna Soehl, Maria Clara V.M. Starling, Noriyuki Suzuki, Marta Venier, Penny Vlahos

Graduate School of Oceanography Faculty Publications

The concept of regulatory capture has been extensively studied in academic literature, primarily within the social sciences. This phenomenon has been increasingly discussed in the environmental sciences as the impacts of regulatory capture on human and ecosystem health have become increasingly apparent. Regulatory capture is just one tactic employed by vested interests in the strategy of delaying, weakening, or abolishing policies designed to protect the public interest. Here, we define capture strategies as ‘the act of influencing individuals, organizations, or governments to prioritize corporate interests over those of human and ecosystem health’. Similar to the evolution of terms like whitewashing …

Avoiding Racial Equity Detours: Racial Equity Trainers’ Visions Of Racially Equitable Residential Environmental Education, Anna Mooney Ph.D.

Antioch University Dissertations & Theses

This study investigated how racial equity trainers envision racially equitable residential environmental education (REE). While addressing racial inequities has been central to environmental scholarship, equity detours undergirded by systemic power structures continue to pose significant barriers in addressing inequities in practice. The research explored the question: “How do racial equity trainers envision racially equitable residential environmental education?” Using constructivist grounded theory and drawing on portraiture’s “search for goodness,” the study conducted 1-hour virtual interviews with six racial equity trainers, employing iterative coding through the constant comparative method. A 2-hour virtual focus group and subsequent 30-minute “dissertation reality check” provided additional …

Canary In The Alpine: Shifts In Snowbed Plant Communities Over Time In The Adirondack Mountains Of New York, Katie Rhodes

Antioch University Dissertations & Theses

Due to their unique species diversity and vulnerability, alpine snowbed communities have the potential to serve as early detection or ‘canary’ communities that can inform scientific understanding of the timing and physical manifestation of a changing alpine ecosystem. In the summer of 2008, fifteen alpine snowbed vegetation community transects over 5 mountain summits were established and studied in the High Peaks Wilderness Complex within the Adirondack State Park by the New York Natural Heritage Program. Each transect was 5 meters long and encompassed five 1x1 meter subplots. These transects were relocated and surveyed again in the summer of 2024 in …

Behavioral Response Of Wild Greater Caribbean Manatees (Trichechus Manatus Manatus) To Playbacks Of Conspecifics In Maya Beach, Belize, Phoebe Sonia Hodson

Antioch University Dissertations & Theses

The behavioral response of the wild Greater Caribbean manatee (Trichechus manatus manatus) to the acoustic vocalizations of their closely related subspecies, the Florida manatee (Trichechus manatus latirostris), was examined for the first time in Placencia Lagoon, Maya Beach, Belize. The main goal of this study was to determine how vocalizations from conspecifics influence the behavior of the wild Greater Caribbean manatee and how this behavioral change may give insight into the functionality of manatee vocalizations. Three call types (squeak, squeal and high squeak) and chewing sounds were played to varying size aggregations of manatees. Broad behavioral …

21st-Century Stewardship: Infusing Environmental Education With Global Citizenship, Ava Y. Goodale

Antioch University Dissertations & Theses

In today's interconnected world, where socioecological issues transcend political borders, integrating environmental stewardship education with global citizenship education offers valuable opportunities for student empowerment. The goal of this multi-article dissertation was to conceptualize and operationalize 21st-century stewardship in educational settings. Twenty-first-century stewardship is a pedagogical approach that recognizes the limitations of environmental stewardship education as it is traditionally taught and is defined in Chapter II as an updated stewardship model that is infused with global citizenship’s multiscalar perspectives, global compacts, and layered identities to achieve its maximum impact in our interconnected world. This goal was first accomplished in Chapter II …

Mapping Kirtland’S Warbler (Setophaga Kirtlandii) Stationary Non-Breeding Habitat: Characterizing Land Cover Within The South-Central Bahamas And Evaluating The Impacts Of Sea Level Rise, Cole Anthony Scrivner

Antioch University Dissertations & Theses

Understanding the spatial distribution of current and future habitat for the Kirtland’s Warbler (Setophaga kirtlandii) and other threatened species is critical for guiding conservation planning in The Bahamas. Our study mapped land cover and impacts of sea level rise across the south-central Bahamian islands, as an initial step to determine suitable areas for Kirtland’s Warblers in the region. We used a Random Forest (RF) classification of multispectral satellite image bands and indices from Landsat and Sentinel-2 image composites to predict land cover. The classification identified tropical dry forest communities (broadleaved, semi-evergreen trees and shrubs known as coppice) as …

A Biogeochemical Perspective On Acidification And Buffering Capacity In The Piscataqua Estuary, Sanjana Varanasi

Master's Theses and Capstones

Coastal acidification is influenced not only by rising atmospheric CO2 and river-ocean mixing, but also by metabolic processes that alter seawater carbonate chemistry and buffering capacity. This study examines how sedimentary biogeochemical processes contribute to carbonate system variability in the Piscataqua Estuary, a tidally dynamic channel connecting Great Bay to the Gulf of Maine. The biogeochemical processes considered include sedimentary aerobic respiration, denitrification, sulfate reduction, and carbonate dissolution or precipitation. Two incubation experiments were conducted in September and October of 2024 at the University of New Hampshire’s Coastal Marine Laboratory (CML) to quantify changes in pH, dissolved inorganic carbon (DIC), …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

Carbon Sequestration Through Conservation Tillage In Sandy Soils Of Arid And Semi-Arid Climates: A Meta-Analysis, Samantha Lynn Colunga, Leila Wahab, Alejandro Fierro-Cabo, Engil Isadora Pujol Pereira

School of Earth, Environmental, & Marine Sciences Faculty Publications

Highlights

• Under CST, SOC increased by 12.74 ± 1.46 % in sandy soils with 5 of the 6 practices contributing to this rise.
• SOC increased up to 15 years post-CST conversion, but more research is needed to assess long-term effects.
• SOC increased by 13% in soils with over 56% sand content under CST.
• SOC increased by 15% in the 0-20 cm depth under CST, with no significant increase at greater depths.
• SOC increased under CST when nitrogen was applied at rates up to 100 kg N ha⁻¹.

Abstract

This meta-analysis assessed soil organic carbon (SOC) percent changes in sandy soils, …

Examining The Presence And Effects Of Coherence And Fragmentation In The Gulf Of Maine Fishery Management Network, Derek A. Katznelson, Antonia Sohns, Dongkyu Kim, Evelyn Roozee, William Donner, Andrew M. Song, Jasper R. De Vries, Owen Temby, Gordon M. Hickey

School of Earth, Environmental, & Marine Sciences Faculty Publications

Natural resource management networks cohere due to mutual dependencies and fragment, in part, due to the perceived risks of interaction. However, research on these networks has tended to accept coherence a priori rather than problematizing dependence, and few studies exist on interorganizational risk perception. This article presents the results of a study operationalizing these concepts and measuring the distribution of three types of dependence (capital, legitimacy, and regulatory) and two types of perceived risk (performance and sanction) among nearly fifty stakeholder groups and organizations participating in the management of fisheries in the binational Gulf of Maine. The analysis reveals an …

Nesting Population Trend Of The Leatherback Sea Turtle In Bocas Del Toro Province And Comarca Ngäbe-Buglé, Panama For The Period 2002–2022, Sonia Gutiérrez Parejo, Susan E. Piacenza, Raúl García, Cristina Ordóñez, Roldán A. Valverde

School of Earth, Environmental, & Marine Sciences Faculty Publications

Sea turtle biologists have made sustained efforts to understand the global status of leatherback sea turtle populations. However, despite progress in assessments, demographics, and ecology, key uncertainties persist in tracking leatherback population trends. Trend analyses have historically focused on nesting beaches, with nest counts providing a widely used index for population abundance. Here, we analysed 20 years of annual nest abundance at four main nesting beaches (Soropta, Bluff, Playa Larga and Chiriquí) in Bocas del Toro province and the Comarca Ngäbe-Buglé, Panama, which constitute the largest nesting leatherback sea turtle population in Central America. We conducted daily nest counts during …