Digital Commons Network^™

Phytochemical Composition And Bioactivities Of Roselle (Hibiscus Sabdariffa L.) Extracts Obtained By Infusion, Ethanolic Maceration, And Maltodextrin-Assisted Drying, Risna Silvianti, Warsito Warsito, Elok Zubaidah, Zubaidah Ningsih, Aqidatul Izza

Karbala International Journal of Modern Science

Roselle (Hibiscus sabdariffa L.) is rich in phenolic compounds and anthocyanins, but the extraction method may influence its chemical composition and biological activity. This study compared hot-water infusion, ethanolic maceration, and maltodextrin-assisted drying. It aimed to examine the relationship between the extraction method, chemical profile, and bioactivity. The resulting extracts (EI, EM, and EP) were analyzed for total phenolic, flavonoid, and anthocyanin, LC-HRMS profiles, and antioxidant capacity by the FRAP assay. Antibacterial analysis was conducted against Escherichia coli (ATCC 25922) and Staphylococcus aureus (ATCC 25923) using agar well diffusion. Cytotoxicity tests (MTT, 24 h) were performed against HT-29, 4T1, …

Effects Of Warming On Growth And Leaf Colonization By Litter Mat-Forming Fungi In A Wet Tropical Forest In Puerto Rico, Ari E. Puentes, D. Jean Lodge, Deyaneira A. Ortiz-Iglesias, Tatiana Barreto-Vélez, Laura C. Rubio-Lebrón, Hieu Chu, Christine S. O'Connell, Sasha C. Reed, Tana E. Wood

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Wet tropical forests are experiencing rising temperatures and increased frequency and intensity of extreme climatic events, such as cyclones, which can increase rates of soil erosion and surface runoff. Fungal litter mats, formed by agaric decomposer fungi, play a crucial role in stabilizing slopes, preventing erosion, and aiding nutrient cycling; however, little is known about how warming affects litter mat growth and function. We investigated two litter mat-forming fungi, Gymnopus johnstonii and Marasmius aff. crinis-equi, in warmed (+4°C above ambient) and control plots in the Luquillo Experimental Forest, Puerto Rico. Growth and time-to-leaf colonization were monitored over 6 weeks …

Bridging Images And Language In Radiology: A Comprehensive Prisma Systematic Review Of Transformer Vision-Language Models And Clinical Readiness, Mohammad T. Khasawneh Dr., Sadaf Tabatabaee

Systems Science and Industrial Engineering Student Scholarship

Transformer vision-language models (VLMs) promise end-to-end automation of radiology reporting and related multimodal tasks. However, evidence remains fragmented across datasets, architectures, evaluation practices, and levels of clinical validation, limiting fair comparison and safe translation into practice. Following PRISMA 2020/PRISMA-S, search engines including PubMed, IEEE Xplore, Web of Science, and Google Scholar were systematically searched for peer-reviewed, English-language studies published between 2019 and 2025 that used paired radiology images and free-text reports. Dual reviewers screened records and extracted data using a locked schema covering datasets, modalities, architectures, training objectives, evaluation metrics, and indicators of clinical readiness. Free-text model descriptions were normalized …

Stoker-Fired Laboratory Boiler As A Tool For Optimising The Solid Fuel Combustion Process, Przemysław Rompalski, Justyna Guzy-Proc, Mariusz Kieczek

Journal of Sustainable Mining

This paper presents the results of quality testing performed on a stoker-fired laboratory boiler to present the technical potential of the boiler as a tool for optimising the combustion process of prepared solid fuels (granulate, energy blend) in the context of current demands related to the decarbonisation policy. The comprehensive testing of such fuels at a laboratory scale makes it possible to gain deep insights into the quality parameters of the fuel, flue gas and combustion waste, as well as to determine the combustion process parameters before the fuel is introduced for combustion in broader power engineering and household boilers. …

How Fe(Ii)/2-Oxoglutarate Oxygenase Chooses Chlorination Over Hydroxylation: Electric Field-Driven Ligand Exchange Governs C-Cl Formation, Simahudeen Bathir Jaber Sathik Rifayee, Midhun George Thomas, Anandhu Krishnan, Kritika Gupta, Carter Davis, Tatyana Karabencheva-Christova, Christo Z. Christov

Michigan Tech Publications

Non-heme Fe(II)/2-oxoglutarate (2OG)-dependent halogenases catalyze highly selective C-H halogenation. BesD is a non-heme Fe(II)/2OG halogenase that performs regio- and stereoselective chlorination of the l-lysine (l-Lys) substrate. Understanding the mechanism by which halogenation is favored over canonical hydroxylation is essential for guiding enzyme engineering efforts aimed at converting hydroxylases into halogenases. Here, we combine molecular dynamics (MD) and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations to elucidate the origin of chlorination selectivity in BesD and variants derived from a homologous hydroxylase. Our results indicate that, although the initial inline Cl-Fe(III)-OH intermediate is inherently predisposed toward hydroxylation, it undergoes a two-step isomerization in …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Uncovering Discrete States From Multimodal Psychophysiological Data Using Gaussian Latent Dirichlet Allocation (Glda), Congyu Wu, Aaron Fisher, David Schnyer

Northeast Journal of Complex Systems (NEJCS)

In this article we explore and validate the utility of an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for discovering discrete states from repeated, multimodal psychophysiological samples collected from multiple individuals. Psychology and medical research heavily involves measuring potentially related but individually inconclusive variables from a cohort of participants to derive diagnosis, necessitating clustering analysis for state identification. Traditional probabilistic clustering models such as Gaussian Mixture Model (GMM) assume a global mixture of component distributions, which may not be realistic for observations from different patients. The GLDA model borrows the individual-specific mixture structure from a popular topic model Latent …

Replication And Architectural Enhancement Of Omni Aggregation Networks, Matteo Calviello

Rose-Hulman Undergraduate Research Publications

Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, a capability valuable across many application domains. This thesis focuses on the replication and architectural enhancement of the Omni Aggregation Network (OAN), a lightweight super-resolution architecture designed to balance reconstruction quality with computational efficiency through a novel omni-axis self-attention mechanism.

Independent replication remains a persistent challenge in machine learning research, and this work addresses that issue directly. Initial replication attempts trailed the original reported results by approximately 6-10% in performance metrics before several undocumented implementation details were identified. After correcting these discrepancies, the replicated implementation achieved results within 4% of …

Replication And Architectural Enhancement Of Omni Aggregation Networks, Matteo Calviello

Senior Projects - Computer Science & Software Engineering

Single Image Super-Resolution (SISR) reconstructs high-resolution images from low-resolution inputs, a capability valuable across many application domains. This thesis focuses on the replication and architectural enhancement of the Omni Aggregation Network (OAN), a lightweight super-resolution architecture designed to balance reconstruction quality with computational efficiency through a novel omni-axis self-attention mechanism.

Independent replication remains a persistent challenge in machine learning research, and this work addresses that issue directly. Initial replication attempts trailed the original reported results by approximately 6-10% in performance metrics before several undocumented implementation details were identified. After correcting these discrepancies, the replicated implementation achieved results within 4% of …

Computational-Experimental Synergy As A Predictive And Interpretive Tool Through Metal-Organic Framework Investigations, Cristian Sayers

Graduate Student Theses and Dissertations

In the search for tunable materials platforms capable of addressing both clean energy conversion and viable post-combustion carbon capture, metal-organic frameworks (MOFs) have emerged as a uniquely versatile class of porous solids offering high specific surface area, structural tunability, and chemically addressable metal nodes and organic linkers. Realizing their potential requires understanding behavior that spans atomic-scale electronic structure and bulk-scale property response, a regime in which neither computation nor experiment alone is sufficient. This work attempts to develop a tighter integration between density functional theory (DFT) and laboratory investigation, treating the two modalities as mutually informing probes of the same …

Productive Armature, Gabriel Ofem Peter

Masters Theses

What if water was all you had, what if your only source of survival was a body of water? I shudder to think that some people get to be put in this situation in the world we live in. As I understand the importance of water to survival, it is obvious that water can be the only lifeline of survival for a community. This is the experience for residents of Makoko, Lagos. Their survival depends on the Lagos Lagoon, a large body of water flowing in from the Atlantic Ocean. This research looks beyond the surface of what this community …

Frictional Intelligence, Posheng Cheng

Masters Theses

This is an experimental interaction design project that challenges anthropomorphism in human-computer interaction. In particular, the recent advancement of artificial intelligence technologies like Large Language Models has taken anthropomorphism to new heights. The conversational chatbot interface of AI prioritizes mimicking an inherently human communication medium to maximize human-likeness. However, anthropomorphism has several downsides. Conversational interfaces obscure the limitations and the tangible cost of the technology. They also imply fictional moral status and human-level cognitive capabilities, which means general public sentiment focuses on the ``overhyped'' excitement and fear rather than on other socio-ethical and capacity questions that are far more urgent …

Optimizing Gated Rnns, Joshua Paul Fechete

Honors Projects

Gated recurrent neural networks such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) help fix instability present in normal recurrent neural networks. This allows them to be used for various real-world tasks, and due to their architecture, they are uniquely qualified to handle variable sized input such as text. However, even before training can begin on a machine learning model, various hyperparameters must be chosen to decide how the model will be architectured. Choosing good hyperparameters is vital for creating a model that performs well but is not larger and more computationally expensive to run than it needs …

Second Annual Advances In Business Education Conference 2026 Proceedings, Kelsey Metz

Advances in Business Education (ABE) Conference Proceedings

Conference Overview: The Second Annual Advances in Business Education (ABE) Conference was held on May 21–22, 2026, at Lincoln Memorial University in Harrogate, Tennessee. Hosted by the LMU School of Business, the ABE Conference exists to promote teaching excellence, scholarly inquiry, and regional engagement through innovation and collaboration in business education.

With a focus on fostering meaningful dialogue among educators, researchers, students, and industry professionals, the conference welcomed more than 70 attendees representing 11 institutions from across the Appalachian region and beyond.

The event was structured around four key tracks:

Pedagogy and Teaching Excellence: Showcasing innovative teaching methods, instructional technologies, …

Modeling Of Penicillin Fermentation Process Based On A Multi-Stage Lhs-Eprcc Method, Quan Li, Peng Su, Haiying Wan, Chengxi Zhang, Zhijian He, Yiyang Ni

Journal of System Simulation

This paper focused on the modeling of microbial fermentation processes under varying production environments and proposed a novel approach. Considering that the dynamic characteristics of microorganism s differ across growth stages, we introduced the concept of multi-stage sensitivity analysis, in which each stage was investigated separately. The fuzzy C-means (FCM) algorithm was employed to cluster process data under nominal conditions, thereby dividing the penicillin fermentation process into distinct growth stages. Based on this division, the Latin hypercube sampling with partial rank correlation coefficient (LHS-EPRCC) method was applied to conduct sensitivity analysis for each stage, identifying an importance parameter set (IPS) …

Power Flow Calculation Based On Block-Encoded Adiabatic Quantum Newton-Raphson Method, Shengchao Jiang, Yunqing Pei, Hongying Zhai, Guojian Wu, Fang Gao

Journal of System Simulation

To overcome the efficiency bottleneck of the traditional Newton-Raphson (NR)method in high- dimensional power flow calculations for modern power systems and the constraints of variational quantum algorithm frameworks, this paper proposed a power flow calculation framework integrating block encoding technology and adiabatic quantum computing principles. Based on block encoding technology, adiabatic quantum theory, and the NR method, a block-encoded adiabatic quantum power flow calculation framework (BQ-NR) was constructed. The NR correction equations were mapped to a quantum system, and the quantum state encoding of the correction equations was realized by constructing an extended Hermitian matrix and a projection operator; a …

Asymmetric Opinion Formation Of Emotional Excitable Agents, Irene Ferri, Emanuele Cozzo, Aleix Nicolás-Olivé, Albert Díaz-Guilera, Luce Prignano

Northeast Journal of Complex Systems (NEJCS)

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a decreasing function of their emotional arousal, an additional independent variable distinct from the opinion value. Additionally, our framework accounts for agents' ability to broadcast messages, with interactions influencing the timing of each other's message emissions. Our findings underscore the significant role of synchronization in shaping consensus formation. …

Innovation Strategies For Small And Medium-Sized Construction Companies In Nigeria, Olabode Akinkunmi Akindele

Walden Dissertations and Doctoral Studies

No abstract provided.

A Predictive Correlational Study Of User Acceptance Of Electronic Medical Record Systems In Nursing Higher Educational Institutions, Clara Awosika

Doctoral Dissertations and Projects

The purpose of this quantitative, predictive correlational study was to examine the extent to which perceived usefulness and perceived ease of use predict behavioral intention and actual usage of electronic medical record systems among nurses who have passed the National Council Licensure Examination for Registered Nurses. Using the Technology Acceptance Model, this study sought to address a gap in the literature regarding the level of user acceptance and utilization of EMR systems among nursing students during their training in higher education institutions. Participants included 85 actively licensed nurses who used EMR systems during their academic preparation. Data were collected using …

Storyteller: Training-Free Narrative Grounding And Forseebench: Evaluation For Long Form Audio Description, Seung Hyun Hahm

Dartmouth College Master’s Theses

Understanding long-form video requires tracking events, motivations, and relationships across time rather than describing isolated frames. However, existing video--language models (VLMs) and audio description (AD) systems often generate short-horizon descriptions that omit narrative context, causal intent, and story continuity, limiting accessibility for blind and low-vision (BLV) audiences. This thesis investigates how long-form AD can be grounded in narrative memory without relying on expensive supervised training pipelines or heavily curated annotations.

We propose StoryTeller, a training-free retrieval-augmented framework for long-form audio description. Instead of depending solely on frame-level perception, StoryTeller summarizes observations into structured narrative facts that capture who did what …

Machine Learning For Modeling In An Elementary Differential Equations Class, Nathan Albin, Andrew G. Bennett, Abhinav Chand

CODEE Journal

Mixing machine learning with modeling is an area of increasing importance. This paper presents a lesson where students model a spring-mass system both using traditional analysis with linear damping and using machine learning to learn the damping from real data. The machine learning is implemented in a Jupyter notebook hosted on Google Colab, allowing students to train the neural network without requiring the students to carry out coding. Students get experience with how machine learning can fail, how it can work, and the time and data requirements for machine learning to succeed, and are asked to apply this knowledge to …

3d Puzzle Generation Beyond Voxelized Parts, Iris Xia

Computer Science Theses

Burr puzzles are interlocking assemblies whose pieces must be inserted and removed through tightly constrained motions. Designing them is difficult because geometric fit, interlocking behavior, and disassembly order are tightly coupled, while existing computational methods remain largely limited to voxelized or template-based constructions.

This work presents a framework for 3D puzzle generation beyond voxelized parts. The method replaces local mobility heuristics with a certified search over geometry edits. Starting from a topological contact specification, it constructs signed distance fields for individual parts, applies complementary local edits, and validates each candidate using exact geometric checks and a kernel disassembly graph. The …

Robert, Powers; Unl Chemistry; Nmr-Assisted Drug Discovery, Mark Griep, Robert Powers

Department of Chemistry: Faculty Interviews

Dr. Robert Powers became a chemistry professor at the University of Nebraska-Lincoln in 2003 and is about to retire after 23 years. Prior to UNL, Bob was a drug discovery researcher for 11 years at American Cyanamid, which eventually became Wyeth and is now part of Pfizer. Bob was born in Jersey City, New Jersey. Something in his youth must have sparked an interest in chemistry because he earned a bachelor's in that subject from Rutgers University in New Brunswick. Then he traveled 1200 miles west to Purdue University in Indiana where he earned his doctorate. Next, he did postdoctoral …

Real-Time Instruction Internalization For Large Language Models, Brenden Smith

Theses and Dissertations

For the end user, Large Language Models (LLMs) are programs that process natural language inputs into natural language outputs. In popular usage, this tends to take the form of conversation: a user asks a question, provides information, or gives instructions, and the LLM (hopefully) replies in a manner we would expect of an informed and compliant person. While convenient and intuitive for users, this natural conversational format encourages the misconception that LLMs are learning from conversations, when they do not. This work presents the benefits and practicality of a language model paradigm that meets this user expectation -- that is, …

Spectral Classification Of Diverse Lithologies Using Multi- And Hyperspectral Satellite Data: A Comparative Study, Önder Gürsoy, Emre Özelkan, Rutkay Atun, Ayşe Betül Çalişkan, Ahmet Efe

Turkish Journal of Earth Sciences

Accurate lithological mapping requires selecting the appropriate remote sensing data and classification methods. This study evaluates the performance of four satellite datasets—Landsat 8 OLI, Sentinel-2A, ASTER, and Hyperion EO-1—using three spectral classification techniques: Matched Filtering (MF), Spectral Angle Mapper (SAM), and Spectral Information Divergence (SID). The study area is located between the Zara and Koyulhisar districts in eastern Türkiye and comprises diverse lithological units. A total of 49 rock samples collected in the field were used for validation. The results indicate that MF consistently outperformed the other methods, achieving the highest accuracy with Landsat 8 (Kappa = 94.2%). ASTER data …

Hyperspectral Image Classification Using Novel 1-D And 2-D Deep Neural Networks, Özlem Polat, Zümray Dokur, Tamer Ölmez

Turkish Journal of Earth Sciences

Hyperspectral image (HSI) classification is of critical importance in many fields including agriculture, geology, environmental monitoring, and urban planning. In recent years, many researchers have utilized deep neural networks (DNNs), known for their high performance in the classification of HSIs. When 2-D/3-D convolutional neural networks are used in HSI classification, filters are applied using input patches typically larger than 11 × 11. This allows spectral and spatial features to be evaluated together. However, this combination creates several problems. Because HSIs have low spatial resolution, they often do not contain strong texture details. Furthermore, features with little relevance to classification make …

A Shallow Landslide Hazard Assessment Using A Probabilistic Approach: An Example From Northeastern Türkiye (Beşikdüzü, Trabzon), Kübra Tezel, Aykut Akgün

Turkish Journal of Earth Sciences

The objective of this study was to identify and assess shallow landslide hazard in both spatial and temporal terms within the boundaries of Beşikdüzü District in northeastern Türkiye. The workflow was initiated with the development of a detailed multitemporal mass‑movement inventory map derived from satellite imagery provided on the Google Earth platform,1 covering the period between 2000 and 2018. Inventory mapping was complemented by extensive field verification campaigns to identify discrepancies, confirm spatial accuracy, and document additional morphological details that could not be detected from imagery alone. A 10-m spatial resolution digital elevation model (DEM) was generated from 1:25,000-scale digital …

Improving Explainability And Interpretability Of Neural Networks Via Hyperparameter-Extended Influence Functions, William Breslin

Dissertations and Theses

Understanding how model predictions and training outcomes vary with changes in data, features, and modeling choices is central to explainable artificial intelligence. This dissertation introduces a unified framework for explainability by generalizing classical influence functions to encompass user-defined hyperparameters embedded in the training loss, model architecture, or data representation. By extending influence functions in this way, the framework broadens their applicability and integrates multiple explainability techniques into a single, coherent approach. It provides a common mathematical foundation linking data impact, feature importance, and model design analysis, and supports a broad class of additional explainability analyses beyond these settings. The demonstrated …

Geometric Structure In High-Dimensional Representations: Theory And Applications To Language, Jiayi Chen

Dartmouth College Ph.D Dissertations

This thesis develops a geometric perspective on high-dimensional representations, motivated by applications to language. Rather than treating representations solely as inputs to predictive models, we view them as structured objects whose geometry encodes meaningful information. In particular, we argue that such representations exhibit organization at multiple scales: at a global level, metric and clustering structure capture relationships such as genre, authorship, and discourse; at a local level, geometric quantities such as intrinsic dimension and curvature describe how these relationships vary across the space.

To study these phenomena, we combine empirical analysis with theoretical development. On the empirical side, we examine …

Unquaint Pigs’ Stakeholder Perspectives On Wild Swine In Central Western Florida, Tyler Koerner

USF Tampa Graduate Theses and Dissertations

The Anthropocene presents important questions for how to address the continuous transferal of new species into new spaces and the cascading impacts of environmental transformations. Novel ecosystems present one such outline for conservation that acknowledges the modification of historic ecosystems and the social entanglements that arrive with new species. I argue that novel ecosystems can benefit from further anthropological integrations that aid in describing the entanglements of introduced species and the pursuit of best managerial outcomes. For wild swine in Florida, the adoption of a novel approach is especially relevant because of entanglements with historic and modern Floridian stakeholders and …