26419 research outputs found
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High-Energy η(′)π Photoproduction and the Nature of Exotic Waves
No full textThe observation of hybrid mesons in photoproduction experiments can provide essential insight into the inner workings of quantum chromodynamics in the strong coupling regime. In particular, the study of final η(′)π states is of great interest due to the presence of the lowest lying hybrid candidate with manifestly exotic quantum numbers, the π₁ (1600). In this work, a double-vector exchange model with Reggeized ρ and ω trajectories is developed to describe the photoproduction of η(′)π in the high-mass region. Results are presented for the differential cross sections and forward-backward asymmetries in the energy region of interest to the GlueX experiment. The model contains no free parameters, and reproduces the magnitude and t-dependence of existing CLAS data at Eγ = 5GeV. The model predicts a stronger asymmetry in the η′π channel than in the ηπ channel, consistent with what has previously been observed in pion beam experiments. This suggests that the sizeable production of exotic odd waves in η′π is not necessarily related to the presence of gluon-rich environments. Confirmation of these predicted asymmetries from forthcoming GlueX data would enable further predictions of the low-energy spectrum
EnLeM: Ensemble Learning-Based Model to Detect Phishing Websites
No full textPhishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in terms of performance but require more data and time. To tackle these challenges, we present EnLeM, an ensemble learning model designed specifically for phishing website detection. EnLeM brings together three well-known machine learning classifiers—decision tree, random forest, and k-nearest neighbor—using a hard voting mechanism, and further strengthens efficiency with Mutual Information–based feature selection. When tested on the UCI phishing dataset, EnLeM delivered strong results, reaching 97.21% accuracy and a 97.51% F1-score. Compared to individual ML classifiers, it consistently performed better, and it also proved more efficient than deep learning models such as CNN and LSTM. Notably, EnLeM maintained stable accuracy across different feature subsets while cutting execution time by roughly 13%. By striking a balance between accuracy, speed, and interpretability, EnLeM stands out as a practical and scalable solution for real-time phishing detection without the heavy resource demands of deep learning approaches
Doing Academic Research: A Practical Guide to Research Methods and Analysis (Second Edition)
No full textDoing Academic Research is an accessible guide to research focusing on the social sciences, humanities, and business. It approaches research as a process, steering readers through the beginnings of topic choice through choosing methods, samples, and then sharing their results.
The book guides readers through six methods in detail (interviews, focus groups, surveys, textual analysis, content analysis, and critical discourse analysis), providing enough information to successfully choose and conduct each method. It also covers observational methods like ethnography, experiments, and meta-synthesis, explaining when and why a researcher might choose those methods. It then shows readers how to analyze both qualitative and quantitative data, with tips and tricks to make the process more intuitive, and concludes with a discussion of how to effectively write, present, and publish research. The chapters also include additional material, ranging from discussions of ethics connected to specific methods, suggestions on how to work effectively with libraries, and ways to use artificial intelligence tools in the process. A selection of online resources are also available with websites, videos, selected bibliographies, and other guides to deepen understanding of the content.
This book is perfect for both undergraduate and graduate students, as well as professional academics and researchers looking to expand into additional methods or refine their understanding and approach to the ones they currently use.https://digitalcommons.odu.edu/libraries_books/1003/thumbnail.jp
Elizabeth Barry White
No full textPublicity photo submitted by author/presenter for ODU\u27s Annual Literary Festival 2025.https://digitalcommons.odu.edu/litfest_images/1013/thumbnail.jp
Crystal Wilkinson
No full textPublicity photo submitted by author/presenter for ODU\u27s Annual Literary Festival 2025.https://digitalcommons.odu.edu/litfest_images/1018/thumbnail.jp
Development of a Semi-Mechanistic Population Pharmacokinetic Model for Predicting Tenofovir Disoproxil Fumarate and Tenofovir Alafenamide Exposure in Plasma and Cellular Matrices During Pregnancy and Postpartum
No full textBackground and Objective
Tenofovir (TFV)-based regimens are backbones of both HIV treatment and pre-exposure prophylaxis during pregnancy. Multiple studies have shown up to one-third decreases in dried blood spot tenofovir-diphosphate concentrations during pregnancy among participants taking tenofovir disoproxil fumarate (TDF). Currently, there are no mechanism-based models describing the pharmacokinetics of tenofovir diphosphate (the active anabolite) in peripheral blood mononuclear cells (PBMCs) of pregnant individuals receiving TDF or tenofovir alafenamide (TAF), and the mechanisms behind observed differences between dried blood spots and PBMCs remain unclear.
Methods
To address this gap, we developed a semi-mechanistic model to simultaneously describe the pharmacokinetics of all clinically relevant TDF and TAF-derived moieties and conducted clinical trial simulations to compare TDF and TAF pharmacokinetics during pregnancy and postpartum.
Results
The pharmacokinetics of plasma TAF and TFV were best described by one-compartment and two-compartment models, respectively, with first-order absorption. A transit compartment was included to reflect the slower elimination rate of plasma TFV after receiving TAF. Cellular matrix PBMC and dried blood spots were included using a biophase model. For TDF, plasma TFV apparent clearance increased by 24.9% and 13.1% during the second and third trimesters of pregnancy, respectively, compared with non-pregnant populations. In the postpartum period, plasma TFV apparent clearance in pregnant women was 9.3% lower than in non-pregnant women. The bioavailability for TAF decreased by 17.3% and 5.1% during the second and third trimesters, respectively, and increased by 18% during the postpartum period relative to non-pregnant women. In pregnant women, simulations showed that TAF maintains approximately five times higher tenofovir diphosphate concentrations in PBMCs compared with TDF during the second and third trimesters, despite a decrease in PBMC tenofovir diphosphate concentrations for both drugs. This finding is consistent with the higher PBMC loading effect of TAF observed in non-pregnant populations.
Conclusions
Our semi-mechanistic model provides a framework for understanding pregnancy-associated pharmacokinetic changes and supports future research to refine dosing strategies for HIV treatment and prevention in pregnancy
Utilizing Machine Learning Techniques for Computer-Aided COVID-19 Screening Based on Clinical Data
No full textThe COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique