Digital Commons Network^™

Wip-Monarch Accelerator Program To Engineering: A Reflection On The First Semester, Kristin Eden, Jeffrey W. Fergus, Stacie I. Ringleb

Mechanical & Aerospace Engineering Faculty Publications

This work in progress paper examines the Monarch Accelerator Program to Engineering (MAP2E) program. The MAP2E Program was developed to assist students who desire to become engineers but may need additional assistance in math and science. and it allows students to develop their math and science skills while creating a pathway to personal or professional enriching skills. Furthermore, the MAP2E program allows students to develop their math and science skills and create parallel pathways to personal or professionally enriching skills. For instance, a student may hope to one day start their own engineering firm. A business administration pathway would allow …

Optimal Control And Structurally-Informed Gradient Optimization Of A Custom 4-Dof Rigid-Body, Brock Marcinczyk, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

This work develops a control-centric framework for a custom 4-DOF rigid-body manipulator by coupling a reduced-order Pontryagin’s Maximum Principle (PMP) controller with a physics-informed Gradient Descent stage. The reduced PMP model provides a closed-form optimal control law for the joint accelerations, while the Gradient Descent module determines the corresponding time horizons by minimizing a cost functional built directly from the full Rigid-Body Dynamics. Structural-mechanics reaction analysis is used only to initialize feasible joint velocities—most critically the azimuthal component—ensuring that the optimizer begins in a physically admissible region. The resulting kinematic trajectories and dynamically consistent time horizons are then supplied to …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Cities, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

VMASC Publications

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained for one …

Age Of Information-Based Optimal Scheduling With Energy Cost Trade-Off For Smart Warehouse: A Deep Reinforcement Learning-Based Approach, Sandip Roy, Abhishek Bisht, Ashok Kumar Das, Sachin Shetty

VMASC Publications

Recent advances in the integration of high-speed mobile networks and real-time IoT devices have facilitated in building of smart warehouses, where a set of beacons and Internet of Things (IoT) devices (or source nodes) can monitor the status of various physical processes in a time-critical way. In real-time status monitoring systems, like smart warehouses, quantifying the freshness of the Internet of Things (IoT) data based on the age of information (AoI) metrics becomes quite crucial. As source nodes are battery-constrained, a balanced trade-off between AoI minimization and preservation of source node battery energy is essential. In this paper, in a …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

Perspectives Of Black Men On Their Representation In School Psychology, Erika Nicole Debra Thornton

PCOM Psychology Dissertations

This study examined Black male school psychologists' experiences, challenges, and contributions, focusing on the systemic barriers that limited their full participation in the field. A phenomenological approach was employed, with data collected through semi-structured interviews and analyzed using thematic analysis. Four key themes emerged: lack of early exposure to the profession, challenges during graduate training, the need for advocacy and representation, and the complexity of their individual identities. The findings highlighted the importance of proactive strategies to recruit and retain Black male school psychologists, stressing the need for diversity, cultural competence, and inclusivity in both professional and educational settings. Increased …

Characterization Of P38alpha And Nitric Oxide Synthase 3 Binding, Gillyan M. Jewett

Master's Theses

P38alpha is a member of the mitogen activated protein kinase (MAPK) family. This family is important for mediating signal transduction pathways through phosphorylation of downstream substrates. Previous work has described the ability of p38alpha to bind and phosphorylate nitric oxide synthase 3 (NOS3) at two sites, the autoinhibitory loop (AI) at S600 and the N-terminal MAPK binding site (NtMAP) at S114. P38alpha has two substrate binding domains, the common docking groove (CD-groove) and a hydrophobic pocket (DEF) that are important in binding and phosphorylating various substrates specific to each pocket. Utilizing biolayer interferometry, this work describes the importance of both …

5g-Practical Byzantine Fault Tolerance: An Improved Pbft Consensus Algorithm For The 5g Network, Xin Liu, Xing Fan, Baoning Niu, Xianrong Zheng

Information Technology & Decision Sciences Faculty Publications

The consensus algorithm is the core technology of blockchain systems to maintain data consistency, and its performance directly affects the efficiency and security of the whole system. Practical Byzantine Fault Tolerance (PBFT) plays a crucial role in blockchain consensus algorithms by providing a robust mechanism to achieve fault-tolerant and deterministic consensus in distributed networks. With the development of 5G network technology, its features of high bandwidth, low latency, and high reliability provide a new approach for consensus algorithm optimization. To take advantage of the features of the 5G network, this paper proposes 5G-PBFT, which is an improved practical Byzantine fault-tolerant …

Designing A Tpack-Based Mobile Learning Model For Biology Education Using Nominal Group Techniques, Cik Jamaliah Abd Manaf, Shakinaz Desa, Muhammad Ikhwan Mat Saad, Helen Crompton

STEMPS Faculty Publications

Mobile learning (m-learning) enhances STEM education, particularly in biology, by supporting visualization and interaction. However, a structured framework integrating technological, pedagogical, and content knowledge (TPACK) is essential. This study designs a TPACK-based mobile learning model for biology using the Nominal Group Technique (NGT). A workshop with 10 experts identified seven key components and 67 elements, with nine elements rejected due to low consensus. The validated framework will guide model development using Interpretive Structural Modelling (ISM) to enhance critical thinking, collaboration, and digital citizenship, ultimately fostering effective mobile learning environments for biology education.

Atmospheric Chemistry Experiment (Ace) Winds, Matthew Wyatt, Peter F. Bernath, Chris Boone, Léo Lavy, Ryan Johnson

Chemistry & Biochemistry Faculty Publications

The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) uses limb geometry to measure transmittance spectra of Earth's atmosphere by solar occultation. Line-of-sight wind speeds can be derived via Doppler shifts of molecular lines in infrared spectra. The wind look direction angles relative to geodetic North are derived from geometry. We validate the new ACE version 5.3 (v.5.3) line-of-sight winds with MIGHTI and meteor radar vector wind observations and find a ±15 m/s sunrise/sunset shift above 80 km. We also compare line-of-sight winds from ACE-FTS v.5.2 and v.5.3 with vector winds from the MERRA-2, HWM14, and WACCM-X models. A ±15 m/s …

Evaluating The Chemical Reactivity Of Wildfire-Derived Dissolved Organic Molecules: Glutathione Binding Through Kendrick Mass Defect Analysis, Hannah M. Hamontree, Patrick G. Hatcher

Chemistry & Biochemistry Faculty Publications

The emerging risks to organisms of pyrogenic-derived dissolved organic matter (PyDOM) from forest fires are of concern due to its toxic and mutagenic potential (e.g., pro-oxidative responses in fauna through the depletion of glutathione, a nitrogen- and sulfur-containing tripeptide found in cells). This study simulates this phenomenon in a laboratory setting by identifying bonding between reduced l-glutathione and organic molecules in leachates from environmentally weathered biomass samples (charred and uncharred) using Kendrick Mass Defect (KMD) analysis from formula lists obtained from negative-mode electrospray ionization-Fourier transform-ion cyclotron resonance-mass spectrometry ((-)ESI-FT-ICR-MS). These formula lists reveal a 10-fold increase in nitrogen- and sulfur-containing …

The Importance Of Atomic Charges For Predicting Site-Selective Ir-, Ru-, And Rh-Catalyzed C-H Borylations, Shannon M. Stephens, Kyle M. Lambert

Chemistry & Biochemistry Faculty Publications

A supervised machine learning model has been developed that allows for the prediction of site selectivity in late-stage C-H borylations. Model development was accomplished using literature data for the site-selective (≥95%) C-H borylation of 189 unique arene, heteroarene, and aliphatic substrates that feature a total of 971 possible sp² or sp³ C-H borylation sites. The reported experimental data was supplemented with additional chemoinformatic descriptors, computed atomic charges at the C-H borylation sites, and data from parameterization of catalytically active tris-boryl complexes resulting from the combination of seven different Ir-, Ru-, and Rh-based precatalysts with eight different ligands. Of the over …

Challenges Associated With Pfas Detection Method In Africa, Abdullahi Tunde Aborode, Ridwan Olamilekan Adesola, Ibrahim Idris, Waheed Sakariyau Adio, Segun Olapade, Gladys Oluwafisayo, Israel Ayobami Onifade, Sodiq Fakorede, Taiwo Bakare-Abidola, Jelil Olaoye, Adedeji Daniel Ogunyemi, Oluwaseun Adeolu Ogundijo, Olamilekan Gabriel Banwo, Adetolase Azizat Bakre, Peter Oladoye, Grace Adegoye, Noimat Abeni Jinadu

Chemistry & Biochemistry Faculty Publications

Per- and polyfluoroalkyl substances (PFAS) are a group of man-made chemicals that are widely present in many industries. Monitoring and analyzing PFAS in Africa is challenging due to the limited availability of mass spectrometry (MS), which is an essential technique for detecting PFAS. This review assesses the scope and impact of the shortage of mass spectrometry instruments in Africa, emphasizing the resulting limitations in monitoring environmental and public health threats. The review analyzes the existing PFAS monitoring, the accessibility of MS instruments, and the technical capabilities within the continent. This study suggests that fewer African countries have sufficient MS instruments, …

Nucleotide-Derived Bacterial Alarmones Attenuate The Induction Of Type-I Interferon Responses In A Murine Macrophage Reporter Cell Line, Ryan P. Kilduff, Erin B. Purcell, Lisa M. Shollenberger

Chemistry & Biochemistry Faculty Publications

The stringent response is a well-studied phenomenon in many bacterial systems and regulates resource-consuming activities such as transcription, translation, and replication. The stringent response is a well-conserved signaling framework, as are the nucleotide-derived signaling mediators, collectively referred to as (p)ppGpp or as alarmones. There is a wealth of research evaluating nucleotide-derived alarmone signaling in bacterial models, however, their potential to modulate innate immune signaling has not yet been evaluated. Several common pathogen-synthesized molecules, such as lipopolysaccharide (LPS) and cyclic-di-AMP (c-di-AMP), act as pathogen-associated molecular patterns (PAMPs), which are common patterns that alert the innate immune system of bacterial infection. The …

Cellular Responses Of Astrangia Poculata (Ellis And Solander, 1786) And Its Symbiont To Experimental Heat Stress, Tyler E. Harman, Daniel Barshis, Briana Hauff Salas, Kevin B. Strychar

Biological Sciences Faculty Publications

Climate change has had devastating effects on tropical coral reefs; however, much less is known regarding how heat stress affects temperate coral. This research focuses on Astrangia poculata (Ellis and Solander, 1786) collected from Narragansett Bay, RI, during the summer and winter seasons and understanding the effect of experimental thermal extremes (i.e., 26 °C) on seasonally different populations. Photosynthetic efficiency (Fv/Fm), symbiont density (via an inverse relationship with pixel intensity), and oxidative stress via reactive oxygen species (ROS) concentrations were measured on symbiotic and aposymbiotic A. poculata. Higher Fv/Fm rates were observed in summer- vs. winter-collected corals (p ≤ …

Alternative Splicing In A Coral During Heat Stress Acclimation And Recovery, Nitin Baliga, Kathryn Stanklewicz, Jacob Valenzuela, Serdar Turkarsian, Wei-Ju Wu, Kelly Gomez-Campo, Nicolas Locatelli, Trinity Conn, Veronica Radice, Katherine Parker, Rachel Alderdice, Line Bay, Christian Voolstra, Daniel Barshis, Iliana Baums

Biological Sciences Faculty Publications

Climate change has caused drastic declines in corals. As sessile organisms, corals acclimate to environmental shifts through genome-wide changes in gene expression, epigenetic modifications, and alterations in microbiome composition. However, alternative splicing (AS), a conserved mechanism of stress response in many organisms, has been under-explored in corals. Using short-term acute thermal stress assays, we investigated patterns of AS in the scleractinian coral Acropora cervicornis during response to low (33°C), medium (35°C), and high (37°C) heat stress and subsequent overnight recovery. Our findings demonstrate reproducible dynamic shifts in AS of at least 40 percent of all genes during response to heat …

Selective Nutrient Incorporation May Underestimate Heterotrophy Of A Mixotrophic Reef-Building Coral, Connor R. Love, Marleen Stuhr, Michael D. Fox, Veronica Z. Radice, Maoz Fine, Kelton W. Mcmahon, David L. Valentine

Biological Sciences Faculty Publications

Mixotrophic reef-building corals acquire nutrition via photosynthate translocation from endosymbiotic microalgae and by heterotrophic prey/particle capture. Heterotrophy promotes resistance to, and recovery from, environmental stress, but quantifying coral heterotrophy remains difficult due to complex resource exchanges within the coral holobiont. We interrogated the response of multiple biomarkers to coral heterotrophy using fatty acid profiling and stable isotope analysis of Stylophora pistillata grown along a controlled feeding gradient from pure autotrophy to pure heterotrophy. We found that fatty acids and nitrogen were effectively incorporated into both coral host and symbiont tissues, while carbon, which is the primary target for conventional heterotrophy …

Biotime 2.0: Expanding And Improving A Database Of Biodiversity Time Series, Maria Dornelas, Laura H. Antão, Amanda E. Bates, Viviana Brambilla, Jonathan M. Chase, Cher F. Y. Chow, Ada Fontrodona-Eslava, Anne E. Magurran, Inês S. Martins, Faye Moyes, Alban Sagouis, Samuel Adu-Acheampong, Daniel Acquah-Lamptey, Dušan Adam, Penelope A. Ajani, Aitor Albaina, Pablo Almaraz, Jeongseop An, Roger Sigismund Anderson, Madelaine Jean Robertson Anderson, Alexsander Z. Antunes, Ivan Arismendi, Linda Armbrecht, Pedro Aros-Mardones, Sreejith Kalpuzha Ashtamoorthy, Narayanan Ayyappan, Gal Badihi, Joseph J. Bailey, Andrew H. Baird, Mark Edward Baird, Sreekumar Vadakkethil Balakrishnan, José António L. Barão-Nóbrega, Adi Barash, Miguel Barbosa, Jos Barlow, Claus Bässler, Matthieu Beaumont, Natalie Beenaerts, Tiago Octavio Begot, Wallace Beiroz, Ricardo Beldade, David M. Bell, Alecia Bellgrove, Jonathan Belmaker, Lisandro Benedetti-Cecchi, Cassandra E. Benkwitt, Pamela Medina-Van Berkum, Brandon T. Bestelmeyer, Matthew C. Betts, Maxwell Kelvin Billah, Anne D. Bjorkman, Magdalena Błażewicz, Christopher P. Bloch, Shane A. Blowes, Antonio Bode, Juliano A. Bogoni, Thomas Bolger, Timothy C. Bonebrake, Erik Bonsdorff, Roberta Bottarin, Luke N. Brokensha, Rob W. Brooker, Andrew J. Brooks, Helge Bruelheide, Thiago Almeida Bueno, Claire Laguionie, Mariana Lopes Campagnoli, James Cant, Erica Pellegrini Caramaschi, Alexandre Caron, Tadhg Carroll, Tancredi Caruso, Juan Carvajal-Quintero, Giuseppe Castaldelli, Edward Castañeda-Moya, Pedro V. Castilho, Sonia Zanini Cechin, Shahar Chaikin, Uchangi Manjunatha Chandrashekara, Tory J. Chase, Chaolun Allen Chen, Jorge José Cherem, Sei-Woong Choi, Erica M. Christensen, Alexander V. Christianini, Jackson Wing Four Chu, Peter Coad, Carl Van Colen, Lise Comte, Elisabeth J. Cooper, J. Hans C. Cornelissen, Eddy Cosson, Unai Cotano, Luc Crevecoeur, Shannan Kyle Crow, Graeme S. Cumming, Vanessa S. Daga, Gabriella Damasceno, Gergana N. Daskalova, Claire H. Davies, Robert A. Davis, Frank P. Day, Sussy De-La-Zerda, Amy Elizabeth Deacon, Indradatta De Castro-Arrazola, Steven Degraer, Kharran Deonarinesingh, Juan C. Diaz-Ricaurte, Christopher R. Dickman, Tara Dirilgen, Ciaran John Dolan, J. Emmett Duffy, Timothy E. Dunn, Giselda Durigan, Ciara Dwyer, Steven Earl, Dor Edelist, Graham John Edgar, Sally Edmonson, Ashley K. Elgin, Kari Elsa Ellingsen, Sarah C. Elmendorf, Ruth S. Eriksen, S. K. Morgan Ernest, Ruben Escribano, Paula Cabral Eterovick, Brian S. Evans, Jason D. Everett, Vesela Evtimova, Dan A. Exton, Andrew J. Fairbairn, Filipe Moreli Fantacini, Fabiano Turini Farah, Fábio Zanella Farneda, Mario E. Favila, Philippe Fernandez-Fournier, Braulio Fernández-Zapata, Diogo F. Ferreira, Carola Ferronato, Christopher R. Du Feu, Alessandra Fidelis, David A. Fifield, Vilmar Picinatto Filho, Walter Mesquita Filho, Robert N. L. Fitt, Carlos A. H. Flechtmann, William R. Fraser, Donna L. Fraser, Lídia Freixas, John Fryxell, Garrett J. Fundakowski, Scott Stanley Gabara, Elise Gallois, Mariana Garcia Criado, Emili García-Berthou, Joaquim Garrabou, Andrew R. Gates, Roberto Cazzola Gatti, Anna Gavioli, Tal Gavriel, Benoit Gendreau-Berthiaume, Xingli Giam, Carina Gjerdrum, Michael Glemnitz, Jasmin Annica Godbold, Daniel Gómez-Gras, Rodrigo Barbosa Gonçalves, Andy Goold, Richard R. Gordon, Menachem Goren, Fernando Vilas Boas Goulart, William G. Gould, Meagan M. Graboski, Nicholas A. J. Graham, Maurício Eduardo Graipel, Laura J. Grange, Aaron C. Greenville, Gary D. Grossman, Valeria A. Guinder, Peter Haase, Gary N. Haskins, Kris Havstad, Luise Hermanutz, Michael Julian Hames Hickford, Pamela Hidalgo, Pedro Higuchi, Andrew S. Hoey, Gert Van Hoey, Annika Hofgaard, Kristen T. Holeck, Robert D. Hollister, Richard T. Holmes, Mia Odell Hoogenboom, Joaquín Hortal, Tammy Horton, Chih-Hao Hsieh, Christine L. Huffard, Ida-Maria Huikkonen, Allen H. Hurlbert, Julian Hynes, Pascal Irz, Natalia Macedo Ivanauskas, Akemi Iwayama, Darren K. James, Ute Jandt, Anna M. Jażdżewska, Merlijn Jocque, Sophie T. Johnston, Samuel E. I. Jones, Faith A. M. Jones, Julia A. Jones, Edite Jucevica, Ugis Kagainis, Maiko Kagami, Jungwon Kang, Xuejia Ke, Erin Colleen Keeley, Rebecca Kinnear, Kari Klanderud, Uwe Klinck, Roel Van Klink, Stefan Klotz, Carolien Kockaert, Halvor Knutsen, Matti Koivula, Alessandra Kortz, Peter Kriegel, Chao-Yang Kuo, David J. Kushner, Rosina Kyerematen, Raphaël Lagarde, Lesley T. Lancaster, Ori Frid Landau, Wouter Van Landuyt, Eric R. Larson, Mai Lazarus, Cheol Min Lee, Jonathan S. Lefcheck, Jonas J. Lembrechts, Renato A. Ferreira De Lima, Romullo Guimarães Lima, Nathália G. S. Lima, Cristina Linares, Sandra C. Lindstrom, Francisco Lloret, John David Lloyd, Cleonice Maria Cardoso Lobato, David M. Lodge, Peter Richard Long, Celeste López-Abbate, Adrià López-Baucells, Julio Louzada, Maite Louzao, Antonella Lugliè, Micheli Ribeiro Luiz, S. Ellen Macdonald, Joshua S. Madin, André Lincoln Barroso Magalhães, Rajindra Mahabir, David Maphisa, Thomas Edward Martin, Marcio Martins, Patrick T. Martone, Silvia Matesanz, Shin-Ichiro S. Matsuzaki, Thomas J. Matthews, Iain Mccombe Matthews, Connie J. Maxwell, Kent P. Mcfarland, Brian J. Mcgill, Diane Marie Mcknight, Michael J. Mcwilliam, Jason Meador, Henning Meesenburg, Kristin Meier, Viesturs Melecis, Peter L. Meserve, Christoph F. J. Meyer, Anders Michelsen, Natali Olivia Roman Miiller, Marco Milardi, Nataliya Milchakova, Robert J. Miller, Jonathan Millett, Tom Moens, Luciano F. A. Montag, Jon Moore, Jörg Müller, Akhil Murali, Shauna Ann Murray, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nakamura, Sasi Nayar, Francis Neat, James A. Nelson, Michael Paul Nelson, Boris P. Nikolov, Rym Nouioua, Collins Ayine Nsor, Michael O' Connor, Edward Adzesiwor Obodai, Amy Marie Offland, Romà Ogaya, Hisako Ogura, Thomas A. Okey, Julian D. Olden, Luiz Gustavo Rodrigues Oliveira-Santos, Jeffrey C. Oliver, Esben Moland Olsen, Vladimir G. Onipchenko, Daniel Oro, Dais Ozolins, Krzysztof Pabis, Bachisio Mario Padedda, Facundo X. Palacio, Alain Paquette, Sinta Trilestari Pardede, David M. Patersib, Sarah Pausina, Raphaël Pélissier, Steven C. Pennings, Josep Penuelas, Felipe Walter Pereira, Nivaldo Peroni, Sergio Picó, Francesca Pilotto, Hudson Tercio Pinheiro, Oscar Pizarro, Roberto Pizzolotto, Francesco Pomati, Paulo Santos Pompeu, Dominique Ponton, Eric Post, Nicolas Poulet, Juha Pöyry, Steven J. Presley, Herbert H. T. Prins, Pieter Provoost, Kathleen L. Prudic, Vignesh Punjayil, Petr Pyšek, Pascal Querner, Juan Pablo Quimbayo, Indar W. Ramnarine, Daniel C. Reed, Peter Bernard Reich, Suzanne M. Remillard, Cerren Richards, Anthony James Richardson, Itai Van Rijin, Victor H. Rivera-Monroy, Christian Rixen, Kevin Peter Robinson, Ricardo Rocha, Ricardo R. Rodrigues, Cassy Rodrigues, Bjørn De Roos, Denise De C. De Rossa-Feres, Loreta Rosselli, Peter Charles Rothlisberg, Ana Rubio, Lars G. Rudstam, Catalina S. Ruz, Nancy B. Rybicki, Gunther Van Ryckegem, Andrew L. Rypel, Jon P. Sadler, Victor Satoru Saito, Sofia Sal, Renato Portela Salomão, Nathan J. Sanders, Flavio A. M. Santos, Tiago Gomes Dos Santos, Swapan Kumar Sarker, Sara E. Scanga, Marcus Schaub, Jochen Schmidt, Inger Kappel Schmidt, Robert L. Schooley, Alfred Schultz, Alberto Scotti, Amanda Serpell-Stevens, Filipe C. Serrano, Elizabeth H. Shadwick, Matthew Shaft, Thomas W. Sherry, Erika Mayumi Shimabukuro, Jacek Siciński, Caya Sievers, Fernando Rodrigues Da Silva, Ana Carolina Da Silva, Juliana M. Silveira, Tadeu Siqueira, Arunkumar Kavidapadinjattathil Sivadasan, Prasad Theruvil Parambil Sivan, Agnija Skuja, Amalia L. Slaughter, Jasper A. Slingsby, Joseph R. Smith, Bruno Eleres Soares, Martin Solan, Flaviana Maluf Souza, Gabriel B. G. Souza, Joshua L. Sprague, Ulrich Stachow, J. John Stadt, Christopher D. Stallings, Radoslav Hristov Stanchev, Emily H. Stanley, Brian M. Starzomski, Jose Mauro Sterza, Maarten Stevens, F. Gary Stiles, Stefan Stoll, Rick D. Stuart-Smith, Yzel Rondon Súarez, Laura Super, Sarah R. Supp, Tapio Sutela, Iain M. Suthers, Anna Suuronen, Kerrie M. Swadling, Daniel K. Szydlowski, Hisatomo Taki, Sara Jeanne Snell Taylor, Pablo A. Tedesco, Nils Teichert, Akira Terui, Gary P. Thiede, Anne Thimonier, Oliver Thomas, Peter Allan Thompson, Simon Thorn, Jeremy S. Tiemann, Luís Felipe Toledo, Anne Tolvanen, Maria Teresa Zugliani Toniato, Ignasi Torre, Marcos Adriano Tortato, Kumiko Totsu, Andrew Trant, Robert R. Twilley, Hirokazu Urabe, Pierre Valade, Nelson Valdivia, Martha Isabel Vallejo, Thomas J. Valone, Jan Vanaverbeke, Tiago Silveira Vasconcelos, Teppo Vehanen, Fábio Venturoli, Hans M. Verheye, Hendrik Jannes Wietse Vermeulen, Arne Verstraeten, Marcelo Vianna, Rui Vieira, João Paulo Santos Vieira-Alencar, Marc Vilella, Jean Ricardo Simões Vitule, Lien Van Vu, Robert B. Waide, Paige S. Warren, Joseph Paul Wayman, Sara L. Webb, Benjamin Weigel, Ellen A. R. Welti, Fritha West, Fulgor Westermann, Matthew A. Whalen, Ethan P. White, Claire E. Widdicombe, Richard Williams, Mark Williamson, Michael R. Willig, Sonja Wipf, Eric J. Woehler, Alje Woldering, Kerry D. Woods, Wu-Bing Xu, Ruthy Yahel, Zeren Yang, Kyle J. A. Zawada, Camila Zornosa-Torres, Assaf Zvuloni

Biological Sciences Faculty Publications

Motivation: Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database.

Main Types of Variables Included: The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised …

Tidal Love Numbers For A Non-Rotating Two-Fluid Neutron Star: Interior Solution, Gregory G Jaurequi

Dissertations and Theses

We investigate the tidal Love numbers for a non-rotating dark matter admixed neutron star (NS). Tidal Love numbers, which quantify a star's response to the tidal field of a companion object, are an important feature in the gravitational wave (GW) signal and vary depending on the star’s equation of state and dark matter profiles. By determining how the internal properties of the NS affect the GW signal, we hope that improvements in gravitational wave detection will help constrain these parameters by providing additional data from neutron star mergers with other compact objects. To this end, we focus on solving the …

Synthesis And Luminescence Studies Of Europium Doped Zinc Lead Germanate Phosphors, Anika Nawar Chowdhury

Dissertations and Theses

Phosphors are luminescent materials that emit light upon excitation, which have significant uses in optoelectronics, bioimaging, sensors, security, and other fields. Inorganic luminescent materials are well known for their stability and broad applications. Among these, germanate based phosphors have recently caught attention because of their inertness, excellent chemical and thermal stability, structural flexibility, and unique properties. For optimizing the properties of the materials, impurities are added to the host intentionally, which are known as dopants. Rare earth elements are often applied as dopants because of their unique f-f transitions. Eu3+ is one of the broadly studied rare-earth dopants among these, …

Comparative Analysis Of Estimation Techniques On Human Performance In Seek-And-Place Tasking, Adam J. Wehr

Graduate Thesis and Dissertation post-2024

Accurate parameter estimation is fundamental to statistical modeling, shaping the validity of inference, prediction, and decision-making. Although Maximum Likelihood Estimation (MLE) and Bayesian methods are well established theoretically, their performance relative to alternative estimators in small-sample, skewed-data contexts remains less clear. This thesis addresses this gap through a systematic evaluation of estimation techniques applied to human response time data from a controlled seek-and-place jigsaw puzzle task, where distributions are characteristically right-skewed.

A performance-modified lognormal model was developed to capture the relationship between task complexity and response times. Four estimation approaches were investigated: MLE, Method of Moments (MoM), Bayesian estimation under …

Least Squares Support Vector Data Description With Adaptive Optimizers, Daniel Markwei

Graduate Thesis and Dissertation post-2024

One-class classification has emerged as a powerful technique for data description, enabling a model to learn exclusively from data belonging to a single (target) class. By focusing solely on patterns within this class, this classification procedure is implemented in order to successfully assess whether incoming input deviates or is part of the target group. This approach develops a sophisticated understanding of the defining characteristics of the target data in relation to other groups. Consequently, any data point that deviates from the target group is flagged as an anomaly because it diverges from the learned distribution. One way to explore the …

Fast Differentiable Projection Layers Onto High-Dimensional Polytopes For Large-Scale Predictive Modeling, Camilo Gomez

Graduate Thesis and Dissertation post-2024

Recent trends in statistical machine learning have revealed the power of integrating optimization procedures as first-class components within end-to-end large-scale predictive systems. These structured computations—often formulated as projections onto geometric objects like polytopes—enable models to inject domain-specific inductive biases and enforce desirable properties in the learned representations useful in downstream classification or regression tasks. This dissertation explores the role of such projections in enhancing learning performance and generalization, particularly in the context of predictive modeling with data intensive systems.

Motivated by the limitations of traditional surrogate loss functions such as logistic loss for classification or cross-entropy for multiclass problems, this …

Domain-Constrained Clustering: A Two-Stage Framework For Muscle Fiber Type Identification, Fanchao Yi

Graduate Thesis and Dissertation post-2024

This dissertation introduces a novel Two-Stage Clustering Framework for the automated identification of human skeletal muscle fiber types using fluorescence microscopy image intensity data. By integrating two complementary clustering methods, this framework overcomes the subjectivity and labor-intensive nature of traditional manual methods, offering an objective and efficient solution for large-scale muscle physiology research. The initial framework combined Density Peaks Clustering (DPC) with a Gaussian Mixture Model employing a t-distribution (GMM-t). This version was validated by comparing its clustering results with manual counts at the individual subject level. We successfully applied this framework in two published studies \cite{brennan2020,hinkley2023}, demonstrating its practical …

A Unified Thermodynamic Framework For Regime Discovery, Entropy-Aware Optimization, And Emergent Leadership In Flocking Systems, Victor Nkwocha

Graduate Thesis and Dissertation post-2024

This thesis presents a unified framework for modeling, optimizing, and analyzing collective behavior in flocking systems through a thermodynamic and statistical lens. In the first study, we employ entropy-regularized Gaussian Mixture Models (GMM) to identify distinct thermodynamic regimes—ordered, transitional, and disordered—based on the energy and entropy profiles of simulated agent-based flocks. The second study introduces the Organic Adaptive Flocking Algorithm (OAFA), a biologically inspired, entropy-aware extension of the Boids model. OAFA dynamically adjusts rule weights based on local entropy signals and is optimized via a multi-objective NSGAII framework to balance cohesion, separation, energy efficiency, and disorder. Results show that entropy …

Clustering And Classification Methods For Forensic Science Applications, Slun Booppasiri

Graduate Thesis and Dissertation post-2024

Finite mixture models have been widely used to cluster data consisting of homogeneous subpopulations. In forensic palynology, pollen is used as a proxy to link individuals or items to a crime scene. Mixtures of pollen data-including willow and mustard and blank samples-were analyzed using flow cytometry. Willow and mustard clusters tend to have multivariate normal distributions, while a background cluster has multivariate non-normal distribution. We propose a finite mixture model capable of handling the mixtures of pollen in terms of univariate and multivariate distribution. The proposed methods are applied in simulated and mixture of pollen datasets.

Finite mixture models typically …

Robust Multiple Change Point Detection For Streaming Data, Randyll Pandohie

Graduate Thesis and Dissertation post-2024

The growing prevalence of high-frequency, high-dimensional data streams in domains such as finance, cybersecurity, and industrial monitoring has intensified the demand for real-time multiple change point detection methods. These methods are expected to identify distributional shifts as they occur while also determining the number and locations of change points—even in the presence of noise, outliers, and limited labeled data. Traditional batch-based approaches and many existing machine learning models fall short in streaming contexts due to their reliance on static datasets and sensitivity to contamination.

This thesis proposes a unified framework for robust multiple change point detection in streaming environments. The …

Exploring Adaptive University President Narratives To Generate Growth And Sustainability, Ryan Charles Weber

Theses and Dissertations

This mixed-methods study explores how university presidents leverage adaptive leadership principles and learning organization frameworks to generate institutional growth and sustainability in higher education. Through analysis of presidential narratives, the research examines the intersection of Heifetz et al.’s (2009) adaptive leadership theory and Senge’s (2006) five disciplines of learning organizations, revealing patterns in leadership communication that drive change. The study analyzed narratives from eight university presidents who demonstrated success through their institutions’ minimum 30% endowment growth over five years. Using thematic analysis and frequency counts, the research identified 198 references to learning organization disciplines, 90 adaptive leadership phases, and 100 …

Demystifying Misconceptions In Introductory Undergraduate Chemistry Education With 3d Printed Molecular Orbitals, Hiwot Endeshaw, Daniel J. O'Leary

Pomona Senior Theses

Spatial abilities are invaluable skills for the science, technology, engineering, and mathematics (STEM) subjects. However, women, people from low socioeconomic backgrounds, and ethnic minorities are more likely to have less developed spatial abilities, which is one of the many contributing factors for the lack of diversity in STEM subjects like chemistry. Teaching spatial skills to undergraduate chemistry students may address this issue and help increase the diversity of students who go on to continue their education in the subject. 3D printing technology in higher education has exploded in recent years, presenting a unique opportunity to teach “hard to grasp” concepts …

Modified N-Heterocyclic Carbene Catalysts For Polyurethane Synthesis, Charise D. Young

Pomona Senior Theses

Polyurethanes are widely used polymers synthesized from diisocyanate and diol molecules. They are ubiquitous polymeric materials with important industrial applications. Their increasing market value prompts the need for selective and efficient production which can be achieved through N-Heterocyclic carbene (NHC) catalysis. N-heterocyclic carbenes are heterocyclic molecules containing a carbene atom and at least one nitrogen adjacent to the carbene center. The nitrogen substituents on the adjacent nitrogen play an important role in influencing their steric and electronic properties and resultant reactivity.^1,2 There is a lack of a comprehensive understanding of the steric and electronic properties of NHCs and how …