Digital Commons Network^™

Enhance Students’ Learning Outcomes By Redesigning Individual Learning Activities Into Group Activities For Introductory Level Physics Courses, Kalani Hettiarachchilage, Neel Haldolaarachchige

Publications and Research

The evolution of science education is a dynamic process driven by advances in pedagogy, technology, and especially, our understanding of how students learn. Educators are exploring innovative teaching and learning methodologies such as active learning, incorporated technology, interdisciplinary approaches, flipped classrooms, personalized teaching, and many more. The goal of all these evolving methodologies is to empower students with not only a strong foundation in scientific knowledge but also with the skills and mindset required to thrive in the future world. By adopting these innovative approaches, educators can help students become effective problem solvers, critical thinkers, and life-learning citizens. Our focus …

Investigating Efficiency Of Free-For-All Models In A Matchmaking Context, Emil Gensby, Bryan S. Weber, Anders H. Christiansen

Publications and Research

We explore several popular (and unpopular) systems for matchmaking and ranking in free-for-all (FFA) environments. The commonplace existing methods involve the reinterpretation of established two-player ranking systems (ie. Elo/Glicko) and decomposing multiplayer games into a set of multiple one-vs-one pairings. This decomposition, while commonplace, is not part of the intended use-case of these two-player ranking systems. We are the first to formally explore this ad-hoc usage and reassuringly find evidence that it converges to correct values. Second, we identify a method that appears to dominate what appears to be the most common publicly used method. At the same time, this …

Punishment And Resources, Mark D. White

Publications and Research

Philosophers of punishment have paid little attention to resource constraints, which have an undeniable impact on how various approaches to punishment work in the real world. It has fallen on economists, with their central focus on scarcity and opportunity cost, to analyze the resource demands of different philosophies of punishment. However, the utilitarian nature of mainstream economics limits the scope of economic theories of punishment to deterrence, which fits naturally into mathematical economic techniques, as opposed to retributivism, the principled nature of which resists quantification. This chapter explores the resource implications of punishment. It starts by identifying shortcomings of the …

Complex Dynamics Of Coral Gene Expression Responses To Low Ph Across Species, Veronica Z. Radice, Ana Martinez, Adina Paytan, Donald C. Potts, Daniel J. Barshis

Biological Sciences Faculty Publications

Coral capacity to tolerate low pH affects coral community composition and, ultimately, reef ecosystem function. Low pH submarine discharges (‘Ojo’; Yucatán, México) represent a natural laboratory to study plasticity and acclimatization to low pH in relation to ocean acidification. A previous >2‐year coral transplant experiment to ambient and low pH common garden sites revealed differential survivorship across species and sites, providing a framework to compare mechanistic responses to differential pH exposures. Here, we examined gene expression responses of transplants of three species of reef‐building corals (Porites astreoides, Porites porites and Siderastrea siderea) and their algal endosymbiont communities …

Season Influences Interspecific Responses Of Canopy-Forming Kelps To Future Warming And Acidification At High Latitude, Lauren E. Bell, Lily Westphal, Evan O' Brien, Jason A. Toy, Haleigh Damron, Kristy J. Kroeker

Biological Sciences Faculty Publications

Variability in primary producers' responses to environmental change may buffer higher trophic levels against shifts in basal resource composition. Then again, in instances where there is a lack of functional redundancy because consumers rely on a few species to meet their energetic requirements at specific times of the year, altered community production dynamics may significantly impact food web resilience. In high-latitude kelp forests, a complementary annual phenology of seaweed production supports coastal marine consumers' metabolic needs across large seasonal variations in their environment. Yet, marine consumers in these systems may face significant metabolic stress under the pronounced low pH conditions …

Deconstructing The Mangrove Carbon Cycle: Gains, Transformation, And Losses, M. F. Adame, N. Cormier, P. Taillardat, N. Iram, A. Rovai, T. M. Sloey, E. S. Yando, J. F. Blanco-Libreros, M. Arnaud, T. Jennerjahn, C. E. Lovelock, D. Friess, G. M. S. Reithmaier, C. A. Buelow, S. M. Muhammad-Nor, R. R. Twilley, R. A. Ribeiro

Biological Sciences Faculty Publications

Mangroves are one of the most carbon-dense forests on the Earth and have been highlighted as key ecosystems for climate change mitigation and adaptation. Hundreds of studies have investigated how mangroves fix, transform, store, and export carbon. Here, we review and synthesize the previously known and emerging carbon pathways in mangroves, including gains (woody biomass accumulation, deadwood accumulation, soil carbon sequestration, root and litterfall production), transformations (food web transfer through herbivory, decomposition), and losses (respiration as CO₂ and CH₄, litterfall export, particulate and dissolved carbon export). We then review the technologies available to measure carbon fluxes in …

Wild Sun Bears (Helarctos Malayanus) Exhibit Aseasonality In Parturition, Zachary A. David, Brian Crudge, Matt Hunt, Kirsty Officer, Vuthy Choun, Barbara Durrant, Megan A. Owen, Morokot Long, John P. Whiteman

Biological Sciences Faculty Publications

Seasonal reproduction can provide species with fitness advantages by allowing the birth of young to coincide with favorable environmental conditions, particularly in environments with high seasonal and intra-annual variation in these conditions. Seven of the eight species in the bear family (Ursidae) reproduce seasonally in both managed care and the wild; however, data for the eighth species, the sun bear (Helarctos malayanus), is unclear. Sun bears have been observed to reproduce throughout the year in managed care, yet currently there are no clear data of birth timing for wild sun bear cubs. Here we investigate the seasonality of …

Half Of Atlantic Reef-Building Corals At Elevated Risk Of Extinction Due To Climate Change And Other Threats, Luis Gutierrez, Beth Polidoro, David Obura, Francoise Cabada-Blanco, Christi Linardich, Emma Pettersson, Paul Pearce-Kelly, Krista Kemppinen, Juan Jose Alvarado, Lorenzo Alvarez-Filip, Anastazia Banaszak, Pilar Casado De Amezua, James Crabbe, Aldo Croquer, Joshua Feingold, Elizabeth Goergen, Stefano Goffredo, Bert Hoeksema, Danwei Huang, Emma Kennedy, Diego Kersting, Marcelo Kitahara, Petar Kružić, Margaret Miller, Flavia Nunes, Juan Pablo Quimbayo, Andrea Rivera-Sosa, Rosa Rodríguez-Martínez, Nadia Santodomingo, Michael Sweet, Mark Vermeij, Estrella Villamizar, Greta Aeby, Khatija Alliji, Daniel Bayley, Elena Couce, Benjamin Cowburn, C. Isabel Nuñez Lendo, Sean Porter, Kaveh Samimi-Namin, Tom Shlesinger, Bryan Wilson

Biological Sciences Faculty Publications

Atlantic reef-building corals and coral reefs continue to experience extensive decline due to increased stressors related to climate change, disease, pollution, and numerous anthropogenic threats. To understand the impact of ocean warming and reef loss on the estimated extinction risk of shallow water Atlantic reef-building scleractinians and milleporids, all 85 valid species were reassessed under the IUCN Red List Categories and Criteria, updating the previous Red List assessment of Atlantic corals published in 2008. For the present assessment, individual species declines were estimated based on the modeled coral cover loss (1989–2019) and projected onset of annual severe bleaching events (2020–2050) …

Ai And Ml-Based Risk Assessment Of Chemicals: Predicting Carcinogenic Risk From Chemical-Induced Genomic Instability, Ajay Vikram Singh, Preeti Bhardwaj, Peter Laux, Prachi Pradeep, Madleen Busse, Andreas Luch, Akihiko Hirose, Christopher J. Osgood, Michael W. Stacey

Biological Sciences Faculty Publications

Chemical risk assessment plays a pivotal role in safeguarding public health and environmental safety by evaluating the potential hazards and risks associated with chemical exposures. In recent years, the convergence of artificial intelligence (AI), machine learning (ML), and omics technologies has revolutionized the field of chemical risk assessment, offering new insights into toxicity mechanisms, predictive modeling, and risk management strategies. This perspective review explores the synergistic potential of AI/ML and omics in deciphering clastogen-induced genomic instability for carcinogenic risk prediction. We provide an overview of key findings, challenges, and opportunities in integrating AI/ML and omics technologies for chemical risk assessment, …

Abalones At Risk: A Global Red List Assessment Of Haliotis In A Changing Climate, Howard Peters, Gina M. Ralph, Laura Rogers-Bennett

Biological Sciences Faculty Publications

There is increasing awareness that marine invertebrates such as abalones are at risk from the combined stressors of fishing and climate change. Abalones are an important marine fishery resource and of cultural importance to Indigenous and non-Indigenous people. A highly priced marine delicacy, they are inherently vulnerable: individuals are slow-growing and long-lived and successful reproduction requires dense assemblages. However, their global conservation status is poorly understood. Using IUCN Red List methodology, we assessed the extinction risk to all 54 species of abalone (genus Haliotis). Of the 21 fished commercially for human consumption either now and/or in the past, 15 …

State Of Coastal And Marine Resources: Sablayan, Occidental Mindoro, Ma Celina J. Añonuevo, Jemelyn Grace P. Baldisimo, Denmark B. Racamara, Mary Joyce P. Velos, Joseph Benedict T. Garcia, Frederico B. Sabban, Joey P. Cabasan, Rhea Mae Luciano, Darryl Anthony Valiño, Lovely Joy Heyres, Hazel O. Arceo

Biological Sciences Faculty Publications

[Introduction] Sablayan, situated on the western side of Mindoro Island facing the WPS (12.8564° N, 120.9101° E), is a first-class municipality in the province of Occidental Mindoro with a total land area of 2,188.8km² that is divided into 22 barangays, nine of which are along the coast. The coastline stretches for 115 km, with municipal waters covering approximately 750,000 km² (LGU-Sablayan 2008).

The Apo Reef Natural Park, one of its most prominent features, is located approximately 33 km off the coast (Figure 2.9-1) The Apo Reef Natural Park is a protected coral reef system under presidential proclamation 868 (1996) encompassing …

State Of Coastal And Marine Resources: Lian, Batangas, Joseph Benedict T. Garcia, Mary Joyce P. Velos, Jemelyn Grace P. Baldisimo, Joey P. Cabasan, Ma Celina J. Añonuevo, Denmark B. Recamara, Hazel O. Arceo

Biological Sciences Faculty Publications

[Introduction] Lian (13.9852° N, 120.6545° E) is a third-class municipality in the northwestern part of the province of Batangas. It is bounded on the north by the municipality of Nasugbu, on the east by the municipalities of Tuy and Balayan, on the west by the WPS, and on the south by the municipality of Catatagan (Figure 2.7-1). It has a total land area of 76.80km² that is divided into 19 barangays, six of which are coastal and two are integrated coastal barangays (LGU-Lian 2013). Its total population is 56,280 individuals in 15,991 households as of 2020 (PSA 2020).

The main …

Understanding The Importance Of Brand Equity On Higher Education: An Empirical Study Of Irish Public Universities, Javier Casanoves-Boix, Ana Cruz-Garcia, Sonia García-Lafuente, Mónica Pérez-Sánchez

Dept. of Marketing & International Business Publications

The today’s university education exposes that the argument that stu- dents have an authentic and valuable voice in the decisions that impact their learning and education has become a phenomenon known as 'stu-dent voice' (Matthews and Dollinger, 2023). Furthermore, Boy-murodova y Qosimova (2024) call to action for stakeholders in higher education to prioritize and invest in quality assurance initiatives as in-dispensable components of institutional effectiveness and academic ex-cellence. According to Lafuente, Cifuentes, and Faura (2024), higher education curricula must incorporate competencies that prepare gradu-ates with the necessary knowledge and values.

In Ireland, there are nine universities that account for a …

Reflecting On Addressing Academic Integrity Through Authentic Assessment, Sharon Lehane, Angela Wright

Conference Materials

With the advancement of Artificial Intelligence and chatbot technologies such as ChatGPT, little else is discussed currently by colleagues in Higher Education. Why? – because their potential disruption for assessment practices in higher education are so uncertain. Advances in these artificial intelligence technologies threaten the legitimacy of assessment and academic integrity, calling conventional assessment methods into question. The widespread utilisation of ChatGPT emphasizes the need for sustainable educational practices such as authentic assessments that focus on higher-order critical thinking and problem-solving skills. In terms of the methodology for this study, a critical examination of the association between authentic assessment design …

Snap-Ed New Mexico Outcome Evaluation Report 2024, Theresa H. Cruz, Lucinda V. Denatale, Robert Mccreary, Mahtab Soleimani, Camile R. Velarde, Belinda E. Vicuña, Yvonnie C. Baik

Reports & Documents

No abstract provided.

Fake Malware Generation Using Gans As Api Calls, Saieswar Reddy Vaka

Master's Projects

With malware threats on the rise, they have also grown more complicated and subtle. Consequently, incorporating cutting-edge machine learning into cybersecurity defenses has never been more crucial. Nevertheless, building resilient machine-learning models is a significant challenge due to the need for existing diversified and complete malware datasets. This project will relieve this difficulty by employing a Generative

Adversarial Network (GAN) to develop artificial malware samples featuring an Appli- cation Programming Interface (API) call series. While traditional generative modeling

has primarily been limited to image-based fields, we offer an “outside the box” domain – malware signature generation – as an API …

Base Station Selection Based On The Predicted Dwell Time In 5g-V2x Handover, Anushree Jayesh Shah

Master's Projects

Vehicle-to-Everything (V2X) networks have facilitated smooth communication in vehicles and their surroundings via 5G technology. As vehicles move into different coverage areas, they tend to switch between cellular base stations in order to talk to these "things". Handovers are crucial to maintaining these networks’ effectiveness. To ensure uninterrupted connectivity and seamless handovers, it is extremely important to select a base station that is most appropriate. A vehicle’s dwell time is the duration it stays connected to a base station before handoff. This research focuses on predicting dwell time using regression techniques to proactively choose the most suitable base station. We …

Nft Price Prediction Using Machine Learning, Akhil Patil Bagili

Master's Projects

In the evolving cryptocurrency marketplace, Non Fungible Tokens (NFT) pose a unique challenge when it comes to predicting the prices due to their high volatility and fluctuating nature. This project aims to create a model that utilizes deep learning techniques to accurately forecast NFT prices. Based on the real time data and the transaction history, the model uses Convolutional Neural Networks (CNNs) and Long Term Short Memory Networks (LSTM) to analyze and make effective predictions about future prices. The methodology involves gathering data from two online marketplaces, Dune and Opensea to create a dataset that enhances the model’s predictive capabilities. …

Optimized Community Detection Across Distributed Heterogeneous Servers, Akash Narang

Master's Projects

The exploration of community detection is crucial across various fields, including marketing, and biological research. This area has evolved from non-overlapping communities to recognize nodes as part of multiple overlapping communities. Current research continues to uncover these dynamics. The main challenge is identifying overlapping communities in graphs with billions of nodes and edges. This paper aims to enhance methodologies for community detection in parallel for unprecedentedly large and complex networks. We introduce the HeteroNodesAdapter algorithm, which supports heterogeneous worker nodes and optimized load distribution in graph stream processing. Additionally, we propose the TailBalancedCommunitySize algorithm to find an optimum community size, …

Detecting Fake Reviews Using Aspect Based Sentiment Analysis And Graph Convolutional Networks, Prathana Phukon

Master's Projects

Online reviews significantly influence consumer behavior and business reputa- tions. Detecting fake reviews is important for maintaining trust and integrity in

these platforms. In this project, an application of Aspect-Based Sentiment Analy- sis (ABSA) called FakeDetectionGCN is introduced to distinguish genuine feedback

from deceptive content. The idea is to analyze sentiments related to specific aspects (features) within reviews. Graph Convolutional Networks (GCNs) are used to model the complex contextual dependencies in the review texts. Additionally, SenticNet, an external semantic resource, is integrated to enhance the understanding of sentiments in the reviews. This model is capable of identifying both human-generated as …

Multirelational Twitter Bot Detection Using Graph Neural Networks, Ketan Jadhav

Master's Projects

Social media is a key resource in modern human communication as well as for information. Ease of access and global reach is a primary factor to the popularity of several social media platforms like Twitter. Social Media Bots are automated programs which are developed for social engagement. These bots, however, are being used with malicious intent as well, to spread fake news and manipulate the masses. Identification of social media bot accounts has become crucial since social media has become one of the primary sources of news and information for a lot of people. This project aims to propose Multirelation …

Community Detection Using Deep Learning: Variational Graph Autoencoder Enhanced With Leiden And K-Truss Techniques, Jyotika Hariom Patil

Master's Projects

Community detection in networks is essential for understanding the complex structures of connected systems. Traditional deep learning (DL) methods such as Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs) have shown promised results in supervised tasks, like classification, but often fail in unsupervised tasks like community detection because of the lack of labels. Self- supervised approaches where we integrate crucial community information offer a solution. This project seeks to explore DL methods for community detection, focusing specifically on using Graph Variational Autoencoders (VGAEs). While classical approaches can efficiently handle small to medium-sized networks, they typically struggle with larger-sized structures. …

Opinion Graphs Construction For Reviews Using Transfer Learning And Large Language Models, Yichen Lin

Master's Projects

With the rapid development of the Internet, reading online reviews before making a purchase, booking a hotel, or making a restaurant reservation has become a part of daily life. Customers often consider reviews as crucial supplementary information before making decisions on how to spend their money. However, reading many reviews to gain helpful information takes time and effort. This project proposes a new method OpinionGraphGenerator that aims to create opinion graphs from hotel reviews to reduce the high volume of text in reviews while preserving essential insights. In an opinion graph, vertices are semantically similar opinions, where each opinion consists …

Skin Cancer Detection Using Reinforcement Learning, Vikas Chercadu

Master's Projects

Advances in medical diagnostics have increasingly harnessed the power of artificial

intelligence, offering substantial improvements in early and accurate disease identifi- cation. This paper elaborates on a novel integration of Multi-Agent Reinforcement

Learning (MARL) with Deep Learning for the early detection of skin cancer, one of the most prevalent and lethal forms of cancer when left unchecked. Our project capitalizes on the sophisticated VGG16 Network for the extraction of detailed features from the widely-utilized HAM10000 dermatoscopic dataset, enhancing these features with additional color, texture, and shape analysis. Utilizing a custom-designed MARL environment, we facilitate a collaboration among multiple intelligent agents, …

Frame Rate Enhancement Using Gans: A Deep Learning Approach, Shanmukah Sri Harsha Anivilla

Master's Projects

Videos are sequences of frames that are displayed continuously within a time frame, which creates the illusion. FPS is defined as the number of frames per second, and is crucial to determine the smoothness of motion or scene changes in the video. To improve the appearance of the videos, we can a technique called Frame Rate Enhancement. This is an approach to augment generated frames between pairs of frames using Generative Adversarial Networks. There are a few traditional techniques using Convolution Neural Networks and Optical Flow based methods, but they create unwanted artifacts such as blurring or ghosting and might …

Intelligent Caching Using Continuous Machine Learning In Named Data Networking, Sai Sameer Yanamandra

Master's Projects

Our project focuses on improving Named Data Networking (NDN), an alternative network architecture to traditional IP networks, particularly in unstable conditions where connections frequently drop, and data movement is unpredictable. In NDN, data is cached at various routers across the network, enhancing accessibility even amidst unstable connections. A key challenge we address is determining the optimal level of data redundancy in unstable scenarios. We aim to balance the need for data availability with the risk of excessive data duplication. Our solution involves developing a novel data caching approach for the NDN’s content store based on continuous machine learning. This method …

Image Segmentation By Convolutional Neural Networks In Coral Resilience Research, Jennifer Benbow

Master's Projects

As ocean temperatures rise, coral bleaching is becoming more frequent and severe. Selective breeding experiments show promise for enhancing coral resilience, but scaling these projects is hindered by the labor-intensive nature of taking numerous time series measurements as corals grow. Automating this process with computer vision is one solution to this bottleneck, and to our knowledge, no such tool exists at present. To fill this gap, we have trained a set of machine learning models, based on the Mask R-CNN framework, for segmenting juvenile corals in lab-based coral resilience research. This work shows that retraining the Mask R-CNN architecture through …

Investigating Uncertainty In Gaussian Process Models, Wilson Strasilla

Master's Projects

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Employing Large Language Models And Retrieval Augmented Generation For Enhanced Predictive Flexibility In Cancer Mortality Prediction, Mridang Kejriwal

Master's Projects

Today, cancer is a major health risk to thousands of people, and there are over a two-hundred different types of cancer. Luckily, over the past several years, the outcomes and survival rates have increased, all thanks to machine learning, specifically Recurrent Neural Networks (RNN) and Long Short-Term memory (LSTM) networks. However, the current prognostic models don’t allow healthcare professionals to adapt the variables to mimic all the different features of every type of cancer, resulting in a model that works but is not as accurate as it could be. This study explores improving the accuracy and adaptability of the current …

Intelligent Caching In Named Data Networking, Deep Pradipbhai Shah

Master's Projects

Named Data Networking (NDN) has a built-in caching capability that is enabled with the help of its Content Store. Caching in NDN has several benefits, such as reducing overhead on the producer side, avoiding a single point of failure, and reducing network load. The primary caching policy of the NDN architecture is to leave copies everywhere. However, this scheme induces significant cache redundancy. Existing advanced cache techniques either periodically share the entire list of cached content at a node or make a caching decision without knowing the cached content at other nodes in the network. We propose an intelligent cache …