Digital Commons Network^™

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Environmental And Socioeconomic Effects Of Roundtable On Sustainable Palm Oil Certification: A Systematic Review And Meta-Analysis, Kibrom T. Sibhatu, Matin Qaim

All Peer-Reviewed Publications

Palm oil is the most widely produced and consumed vegetable oil worldwide, but its production is associated with deforestation and other environmental and social problems. Roundtable on Sustainable Palm Oil (RSPO) is the only internationally-recognized voluntary certification standard aimed at mitigating such problems. Many studies examine RSPO’s effects on particular outcomes in specific contexts, but a consolidated global assessment is lacking. Here, we systematically review the literature and identify 53 original studies covering RSPO effects in various countries of Asia, Africa, and Latin America. Where data availability permits, we also conduct meta-analysis. Results reveal that RSPO can lead to environmental …

A Robust Hybrid Security Framework: Integrating Multi-Layered Text Encryption With Barcode-Based Steganography, Mohamed Sayed, Talaat M. Wahbi, Farooq Abdalwahab Haboub

BAU Journal - Science and Technology

The widespread use of the Internet is causing increasing security concerns regarding online communications. One method for achieving secure communication between authorized parties is steganography. We herein employ multilevel technologies, including compression, encryption, barcoding, and steganography to secure a secret text message. Type I multilevel steganography is used with a two-level setup. The first level uses enhanced least significant bit (secure LSB-L1) image steganography; the output is a stego-image file, the cover is an image file, and the secret data in this level is English text. The output from the first level is encrypted using the RSA algorithm, and the …

Unmasking Twitter Bots: An Applied Machine Learning Approach, Rayane El Raba’A, Layal Abu Daher

BAU Journal - Science and Technology

The rapid growth of social networks has led to increased challenges, such as fraud, cyberbullying, and the spread of automated accounts (bots). Detecting anomalies within these networks is essential to maintaining security and trust. This study explored machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Logistic Regression for anomaly detection in social networks, specifically focusing on Twitter bot identification, By applying AI-driven data mining techniques to a dataset of 37,438 Twitter bot accounts dataset, the research evaluates the effectiveness of these models in detecting unusual patterns. XGBoost achieved the highest accuracy (84.9%), with an ROA_AUC of 0.87, …

Coastal Flood Risk In New Hampshire: The Latest Science (2025), Lisa Wise, Hannah Volk, Kirsten Howard

UNH Cooperative Extension

This document provides a two-page summary of the NH Coastal Flood Risk Summary, Part I: Science report, which was updated in 2025.

Ai For Regression Analysis And More, Eli Snir

Generative AI Teaching Activities

Students use Copilot and NotebookLM to create a dataset and develop statistical analyses including regression.

Angularity Evaluation Of Coarse Aggregates Based On Local Feature Extraction Of Point Clouds, Ma Quanzhuo, Feng Ponan, Feng Zezhong, Lei Mingyu, Wang Zhenfeng, Li Jiange

Journal of China & Foreign Highway

The morphological characteristics of coarse aggregates significantly affect the mechanical properties of asphalt mixtures and thus directly influence their road performance and durability. This paper aims to innovatively propose an evaluation method for the angularity of coarse aggregates. A three-dimensional (3D) scanner was used to acquire the surface profile information of coarse aggregates, and local angularity features were extracted through point cloud data analysis. Subsequently, a comprehensive 3D angularity evaluation index for coarse aggregates (A3D) was proposed by combining local normal vectors and curvature characteristics. The results indicate that A3D can be more accurately and independently used for angularity evaluation. …

Optimization Design And Experimental Study On Strength Of Ternary Industrial Solid Waste Mixtures, Yang Shu, Ai Yi, Sheng Shanwang, Li Chuangmin

Journal of China & Foreign Highway

This paper aims to explore whether fly ash, lithium slag, and slag powder (ternary industrial solid wastes) can effectively replace cement as cementitious materials for interlocking large-particle crushed stone bases. A uniform design table U9(94) was used to design the test scheme for the mixing ratios of ternary industrial solid waste mixtures; the correlation between the mortar strength and the ternary industrial solid waste mixtures was analyzed by Pearson correlation; with the help of the mathematical statistical software SPSS 26.0, mathematical models for the flexural strength and compressive strength of the ternary industrial solid waste mixture mortar specimens were established. …

Teachers’ Awareness Of Family Engagement In Multilingual Education, Sedighe Zamani Roodsari

Journal of Multicultural Affairs

This study investigated public school teachers’ awareness of family engagement as a linguistic and cultural resource for multilingual students. Multilinguals are typically described as individuals who can communicate in more than one language, with English not being their native language, and their linguistic choices are influenced by societal norms and systems (Šifrar Kalan et al., 2024; Wei, 2008). Preparing pre-service teachers to enhance opportunities for multilingual students remains a critical need in teacher education programs, as they strive to move beyond monolingual ideologies in their teaching practices (Cárdenas Curiel et al., 2024; Kim & Choi, 2020; Williams & Ewing, 2019). …

Binding Energy Of Muonic Beryllium: Perturbative Versus All-Order Calculations, Shikha Rathi, Ulrich D. Jentschura, Paul Indelicato, Ben Ohayon

Physics Faculty Research & Creative Works

We compute the ground-state binding energy of muonic (Formula presented) (Formula presented) Be in two ways: first, the fully perturbative treatment of the nuclear-size effect often employed in light systems, and second, an approach that accounts for the finite-nuclear-size to all orders (and is inspired by calculations otherwise employed for heavy muonic ions). The results are compared term by term and show that both approaches agree to better than one part-per-million of the total energy. The objective of this work is twofold. The first is practical: to provide a parameterization that allows the extraction of the (Formula presented) (Formula presented) …

Using Low-Frequency Vibrational Spectroscopy To Develop Analytical Profiles For Polymorphic Organic Molecular Crystals, Salvatore Zarrella

Dissertations - ALL

Low-frequency vibrational spectroscopy provides a sensitive approach for characterizing polymorphism and intermolecular dynamics in organic molecular crystals. Terahertz time-domain spectroscopy (THz-TDS) and low-frequency Raman spectroscopy probe collective lattice vibrations arising from weak intermolecular interactions, offering direct insight into crystal packing, phase stability, and structural organization that is often inaccessible to conventional diffraction and mid-infrared techniques. To interpret these modes, experimental measurements are combined with periodic solid-state density functional theory (ss-DFT), which enables vibrational assignments to specific intermolecular motions, and crystal structure prediction (CSP), which maps accessible polymorphic energy landscapes. Together, these methods form a unified experimental–computational framework for understanding molecular …

(R2167) Global Stability Of Seirs Model With Single Dose Vaccination In Varying Population, Govind Jha, Prabhat Mandal, Sarita Jha

Applications and Applied Mathematics: An International Journal (AAM)

Understanding the evolution of infectious diseases within a dynamic population is essential for formulating effective public health strategies. This work introduces an enhanced SEIRS epidemic model that incorporates demographic variation and a single-dose vaccination strategy, supporting that immunity can be reduced over time. The model reflects diseases in which individuals may return to the susceptible state after recovery. Extending prior reduced three-dimensional models, this study develops a complete four-dimensional SEIRS framework, thereby increasing the model’s applicability to real-world scenarios. The inclusion of the full system presents greater analytical challenges. To overcome this, we extend the second additive compound matrix and …

Instructions To Authors, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Artificial Food Colors Are Not Needed To Attract Ruby-Throated Hummingbirds (Archilochus Colubris) To Feeders, A. Siddiqui, R. Kannan

Journal of the Arkansas Academy of Science

No abstract provided.

A Review Of Herpetofaunal Distributions In Arkansas With Special Emphasis On Contributions From Citizen Science: Part Ii – Turtles, Lizards, And Alligators, L. Hobbs, E. Kelley, J. Jackson, J.D. Willson, K. G. Roberts, R. Kannan

Journal of the Arkansas Academy of Science

We update and present a comprehensive review of the distributions of the species of lizards (14), alligators (1), and turtles (16) in Arkansas. We compare historical and current distributions with citizen science data uploaded and verified in iNaturalist, a leading online citizen science biodiversity monitoring database. The Arkansas Herpetological Atlas, an online resource maintaining species occurrence records, is also used and compared. We highlight the role played by iNaturalist in enhancing our knowledge of the distributions of these species in Arkansas. As of 13 February 2025, verified and vetted Research Grade (RG) iNaturalist observations from Arkansas include 7,462 observations of …

A Scoping Review Of Sycophancy In Large Language Models: Operational And Theoretical Recognition, Kallen Zhou, Manning Littlejohn, Isabella Garrard

Endeavors: Mississippi State Undergraduate Research Journal

As large language models (LLMs) usage grows across different domains, sycophancy, the tendency for output to align with users, is increasingly being recognized as a primary issue arising from applying LLMs into critical areas. Current research has provided a variety of theoretical definitions, mitigation techniques, and quantification for sycophancy. However, there is little to no consistency across different papers. This scoping review seeks to connect different works on LLM sycophancy by identifying themes in theoretical definitions, measurement methods, and inducement techniques of sycophancy. By analyzing 26 papers (preprints, conference proceedings, and journal articles) from arXiv, ACL Anthology, and Scopus, this …

Laminarin-Loaded Solid-In-Oil Nanodispersion For Enhanced Non-Invasive Transdermal Immunization, Md. Shahin Ekka Sarker, Yoshirou Kawaguchi, Rie C. Wakabayashi, Noriho J. Kamiya, Muhammad Moniruzzaman, Masahiro Goto

Publications and Research

Simple and non-invasive transdermal vaccination is an attractive alternative to conventional injection-based immunization. However, the effectiveness of transdermal vaccines is often constrained by the stratum corneum barrier. Although the use of solid-in-oil (S/O) nanodispersion technology has successfully facilitated skin permeation to induce an immunological response, the antibody titers remain suboptimal. Herein, a dectin-1 selective ligand, laminarin, was used as an immunostimulatory adjuvant to enhance the immune response. S/O nanodispersions loaded with laminarin and ovalbumin (OVA) were systematically developed and characterized in terms of particle size, in vitro OVA release behavior, and skin permeation performance using excised mouse skin. In vivo …

Lafe0.925ti0.075o3 Perovskite Coated With Ethyl Cellulose: A High-Stability, Low-Hysteresis Platform For Advanced Humidity Sensing, Akhmad Futukhillah Fataba Alaih, Djoko Triyono, Rifqi Almusawi Rafsanjani

Makara Journal of Science

Humidity sensors are pivotal in numerous sectors, such as environmental monitoring, industrial automation, and health-care. This study presents the fabrication and detailed evaluation of an advanced humidity sensor using ethyl cellulose (EC)-coated LaFe_0.925Ti_0.075O₃ (LFTO) perovskite nanoparticles synthesized via the sol-gel method. The LFTO nanopar-ticles exhibited a mesoporous structure with an enhanced surface area, which significantly improved their moisture adsorption capabilities. The innovative application of EC coating addresses critical substrate adhesion issues, enhancing mechanical stability without compromising sensor sensitivity. Comprehensive performance evaluations revealed minimal hysteresis (

A Mathematical Model Of The Interactions Between A Collagen Lattice, Fibroblasts, And Fixed Surfaces With Varying Topographies, Mary Jenkins

Theses and Dissertations

Collagen is an important structural protein in the body, which plays a role in wound healing, particularly the contraction process. Collagen lattices have been studied for nearly 50 years to provide insight into wound contraction. In some cases, collagen interacts with surfaces that have fixed shapes, such as medical implants. Our model focuses on the interactions of a collagen lattice with such a fixed surface. We mathematically model a collagen lattice as a network of nodes connected by springs. We also model fixed surfaces that have various topographies. Our model includes fibroblast cells that connect to both surfaces and remodel …

An Analysis Of Heat Spread Through Smoldering Pine Needles, Timothy Keith

Theses and Dissertations

The goal of this thesis is to calculate the average speed of flame spread through smoldering pine needles. We present two different models for doing this: the first is a very large system of ODEs meant to represent the spread of flame through individual needles, the second is a macroscopic model based on the porous medium PDE. Simulations are run for each of these models and compared to each other as well as to existing models and experimental results.

Leveraging Spatial Statistics For Domain Adaptation Of Vision Language Models In Medical Vqa, Himanshu Raj

Master’s Dissertations

Recent advances in Vision–Language Models (VLMs) have demonstrated strong performance in Medical Visual Question Answering (Medical VQA) task. Although they perform very well within their domains, these models often experience issues with their generalization ability on unknown clinical distribution data because of different imaging technologies and patient groups used in various medical facilities. Generalization problems faced by these models make their practical application in the field of VLM-based medical VQA systems rather difficult. To overcome this limitation we proposed our method named Spatial Semantics Aware Domain Adaptation (SSADA), which is an integrated framework that combines both finetuning and prompt-based in-context …

Learning Trajectories Of Online Batch Selection Methods, Luke Green

Theses and Dissertations

Modern deep neural networks achieve strong performance on large-scale datasets, but often require substantial training time. Online batch selection methods seek to reduce this cost by updating models on informative subsets of each batch rather than on all available examples. Recently introduced methods leverage teacher models and report substantial speedups, particularly in noisy-label settings. However, comparisons are often based on the number of epochs required to reach a target test accuracy, a coarse metric that is sensitive to implementation details and may obscure important differences in learning dynamics. In this thesis, we implement several online batch selection methods in a …

Advancing Data Usability, Activity Modeling, And Stability Optimization In Computational Enzyme Design, Spencer Gardiner

Theses and Dissertations

A grand challenge of computational biology is to computationally design, in a single pass, a protein sequence that catalyzes an arbitrary chemical reaction at a high rate under specified conditions [1]. This work details advances in three essential areas on the path to that goal: data quality, activity prediction and modeling, and stability optimization. The structure and implementation of the Allotrope Simple Model (a FAIR data format for many scientific instruments) was examined in [2], setting the stage for training deep learning models on high-quality experimental datasets from diverse sources. In [3], the limits of physics-based and deep learning tools …

Beyond Single Images: A Comprehensive Benchmark For Album-Level Vision-Language Understanding, Shawn Huang

Theses and Dissertations

Automatic album organization has been studied extensively over the past decades due to significant progress in digital photography. Recent Vision-Language Models (VLMs) have shown strong performance on multi-image understanding, making them natural candidates for automating album organization workflows. While VLMs’ abilities in multi-image understanding have been widely studied, their performance on album organization remains underexplored. To bridge this gap, we introduce AlbumBench, the first comprehensive benchmark for automatic album organization. Specifically, we (1) define album organization tasks as photo selection for album-specific user objectives, photo rating according to how well user intents are fulfilled, and album-specific photo grouping given a …

Label-Flip Attack Detection Via Trust-Weighted Aggregation In Federated Learning For Underground Mine Security, Md Sazedur Rahman, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining operations are increasingly dependent on autonomous vehicles, robotic drilling systems, and intelligent inspection platforms operating in confined, GPS-denied tunnel environments. These systems rely on distributed perception models to interpret navigation cues, hazard warnings, and environmental signals in real time. While centralized deep learning can enhance model performance, transferring raw operational data across mining sites introduces serious confidentiality and security risks. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without sharing local datasets. However, deploying FL in underground mining introduces several critical challenges: (i) Training labels may be modified either maliciously by compromised clients or …

Where Evidence-Based Medicine Meets Ai: Promise, Pitfalls, And Practice, Sangil Lee, Joshua Davis, Ken Milne, Christina Shenvi, Lars K. Beattie, Martin Wegman, Laura Melville, Richard D. Shih, Bryan Kane

Department of Emergency Medicine Faculty Papers

No abstract provided.

Integration Of Intraoperative Data In Interpretable Machine Learning Models To Predict Postoperative Aki In Noncardiac Surgery Patients, Justin Do, Karan H. Shah, Melissa Xu, Andrew Hyunwoo Kim, Vivaswat Suresh, Nidhir Guggilla, Michael Li, Rishi Kothari

Department of Anesthesiology Faculty Papers

OBJECTIVES: We aimed to (1) quantify changes in discrimination when adding intraoperative data to preoperative data and (2) compare tabular machine learning with feature engineering against a time-aware LSTM-based model.

MATERIALS AND METHODS: Retrospective cohort of 46 204 adults undergoing 57 055 eligible noncardiac surgery in the INSPIRE database. We extracted 38 preoperative and 49 intraoperative variables; acute kidney injury (AKI) was defined by KDIGO serum creatinine criteria and modeled as stage 2/3 postoperative AKI. Models were trained on preoperative-only and combined pre- and intraoperative data. Intraoperative series were summarized using eight statistical features for tabular models or integrated directly …

Rheological Protocol To Assess And Optimize Latex Coagulation Dynamics For The Thin Glove Coagulant Dipping Process, Monday U. Okoronkwo, Kok Kong Ng, Wolfram Franke, Gaurav Sant

Chemical and Biochemical Engineering Faculty Research & Creative Works

Many products that directly impact the quality of human life today — gloves, catheters, condoms, and baby bottle teats — are made through the latex-dipping technology. While a variety of methods have been developed – e.g., particle counting, turbidimetry, microscopy, and light scattering – which are suitable for studying the coagulation of latex at very low concentrations, much less work has focused on methods suitable for in-situ characterization of latex coagulation in concentrated solutions (e.g., as relevant to the dipping process). This paper presents a process-relevant rheological protocol for assessing and optimizing latex coagulation dynamics for the thin glove coagulant …

An Empirical Study Of Rlvr Fine-Tuning For Mathematical Problem Solving In Llms, Rashmi Konnur

Master’s Dissertations

Large language models have shown immense improvement in coding and math performances thanks to reinforcement learning boosted algorithms. However, its true impact on broadening the reasoning and analytical capacities of an LLM is still contended. In this dissertation, we outline the foundations of Large Language Models, and delve into Reinforcement Learning with Verifiable Rewards (RLVR). We discuss various strategies to efficiently manipulate memory during a fine tuning update. We finally perform RLVR fine-tuning techniques on different models with varied use cases and compare their performances, which corroborate the efficiency of RLVR.

Data-Driven Electrochemistry Reveals The Impact Of Hydrophobicity On Aptamer Cross-Reactivity, Emily Carroll, Michael A. Pence, Elizabeth Winterholler, Taylor D. Sparks, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Electrochemical aptamer-based (E-AB) biosensors offer a promising platform for reagentless detection of molecular targets, yet aptamer recognition can be limited by cross-reactivity, particularly for hydrophobic analytes such as steroid hormones. To investigate how cross-reactivity influences E-AB sensor performance, we use automation and machine learning to screen a library of possible interferent molecules against a steroid-binding aptamer, with progesterone serving as a physiologically relevant test case. Here, we develop a label-free E-AB sensor for progesterone detection using a methylene blue-modified aptamer anchored with a hexanethiol linker. We then used an automated electrochemistry platform to perform reproducible and high-throughput characterization of our …