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(SI15-064) Permutation Pentanomials over Finite Fields with Even Characteristic
No full textPermutation polynomials over finite fields constitute an active area of research and play an important role in diverse domains, including finite geometry, combinatorial design, coding theory, and cryptography. The study of these polynomials has a long history, and many results have been obtained in recent years. This paper presents new classes of permutation pentanomials based on permutation over the unit circle of finite fields with even characteristic that contribute to the theoretical development of permutation polynomials
(R2152) New Bivariate Type-2 Gumbel Distribution Based on the Farlie-Gumbel-Morgenstern Copula: Properties and its Application in Survival Analysis
No full textWe introduce a new bivariate probability distribution, termed the Bivariate FGM Type-2 Gumbel Distribution, constructed by combining the Farlie–Gumbel–Morgenstern (FGM) copula with the Type-2 Gumbel marginal distributions. This proposed distribution provides a flexible framework for modeling bivariate data and offers a viable alternative to several existing bivariate distributions, especially in scenarios where capturing dependence between variables is crucial. The theoretical properties of the distribution are thoroughly explored. We derive the marginal and conditional distributions, conditional expectations, moment generating function, and product moments. Procedures for random number generation from the distribution are discussed. Reliability-based characteristics, such as the survival function and hazard function, are also formulated, enhancing the applicability of the model in reliability analysis and life data modeling. For parameter estimation, we employ the maximum likelihood estimation (MLE) technique, and the estimators’ performance is examined through a comprehensive simulation study. To demonstrate practical utility, the proposed distribution is applied to a real-world dataset, where it shows a good fit and provides insightful results, underscoring its relevance for applied statistical modeling
(R2107) Analysis of a Bivalent Vaccine Model with Peer Influence Effect on Testing
No full textThe coronavirus caused havoc around the world. There was a terrible situation in villages and cities, and no one knew how to deal with it. Although the governments of each country tried their best to save the common people, vaccination programs and testing centers were built everywhere. However, people were not utilizing it due to fear. Because of this, the infection spread rapidly. The qualitative study of the mathematical model here is in context with the situation when bivalent vaccination and testing are available for an epidemic. The mathematical model combines the exposed period and influenza model with vaccination included under the peer effect in rural India under vital dynamics. The boundedness and positivity of the solution for the proposed model and its unique disease-free equilibrium point with its local and global stability are established. Threshold and sensitivity analysis of the model are also taken in context to study the role of parameters. Finally, simulation supports the established theoretical results
Generative Artificial Intelligence Enhanced Deep Knowledge Tracing For Personalized Learning
Full text linkIn today’s educational landscape, the demand for personalized learning experiences has gained significant attention, driven by advances in Artificial Intelligence (AI) and deep learning technologies. This dissertation investigated the integration of Generative Artificial Intelligence (GAI) with Deep Knowledge Tracing (DKT) to advance Personalized Adaptive Learning (PAL) systems, particularly within Historically Black Colleges and Universities (HBCUs). While HBCUs play a pivotal role in expanding educational opportunities, they often face challenges such as lower retention and graduation rates.
This research began by exploring the theoretical foundations of personalized learning and DKT, a data-driven technique that models learner knowledge acquisition over time. Using four years of educational data from Fall 2019 to Summer 2023 from Prairie View A&M University (PVAMU), this study aimed to enhance STEM education by predicting student course outcomes and identifying at-risk students. Multiple state-of-the-art (SOTA) DKT models, including DKT, DKT+, DKVMN, SAKT, and KQN, were employed to evaluate knowledge tracing performance. Results revealed that SAKT and KQN consistently achieved superior predictive accuracy, AUC, and F1 scores, enabling faculty and advisors to proactively support students through timely interventions.
A key advancement of this study was addressing the challenge of data scarcity, which often limits DKT effectiveness in resource-constrained environments like HBCUs. To overcome this, GAI models such as TABSYN, TabDDPM, and GReaT were used to generate synthetic datasets that augmented real student records. The integration of tabular GAI enhanced the robustness of DKT models, resulting in improved prediction accuracy and expanding the applicability of PAL systems across diverse educational contexts.
In conclusion, this dissertation advances the field of DKT by integrating innovative approaches that enhance PAL systems at HBCUs. It demonstrated how combining DKT with GAI for synthetic data augmentation can improve educational outcomes. Moreover, it highlighted the importance of collaboration between AI researchers and educators to develop data-driven techniques that support students through improved resource allocation, timely interventions, and refined support strategies.
Index Terms: deep knowledge tracing, educational data mining, generative artificial intelligence, historically Black colleges and universities, personalized adaptive learning, synthetic data generation
Unsupervised Machine Learning Techniques To Categorize Genomic Islands
Full text linkUnsupervised Machine Learning (ML) techniques are powerful tools for identifying similarity patterns and can be utilized to categorize the data into related groups. This study showcased the applications of unsupervised methods, namely hierarchical clustering to carefully determine related groups of newly identified genomic islands (GIs). Genomic islands are mobile genetic elements integrated into bacterial chromosomes. Gis can impact the evolution of bacteria for example by carrying virulence or metabolic genes. Precisely identifying Gis calls for a sophisticated process which we recently implemented as an already-published tool called TIGER which stands for Target/Integrative Genetic Element Retriever (TIGER). TIGER identifies mobile DNAs in each genome and identifies genomic islands with high accuracy. We employed TIGER to identify approximately 131,000 Gis in E. coli bacteria. FastANI is a bioinformatics tool used to estimate the average nucleotide identity (ANI) between two bacterial genomes. ANI is defined as the mean nucleotide identity of orthologous gene pairs shared between two microbial genomes. A 131,000 by 131,000 Gis similarity matrix was obtained by analyzing 131k by 131k pairs of Gis sequences using FastANI, storing in a sparse matrix which is utilized to record large scaled dataset. To identify the similarities among the E. coli Gis, the hierarchical clustering algorithms and our novel heuristical approaches successfully categorized 131k Gis into relevant groups. The dendrogram, which is the output of the hierarchical clustering, was created to display the closeness among the Gis and identify related groups of Gis as well as singleton Gis. To obtain the ideal number of clusters, two initial grouping methods were implemented: quantity customized clustering and cutline-based clustering. Three cluster optimization approaches were used to improve the clustering algorithm, including ANI score-based optimization, Gis site type-based optimization, and dendrogram height-based optimization. Height-based optimization identified better performance and well separated the meaningful clusters. Purity, Dunn’s Index, and alignment ratios were used to further verify and narrow down the clusters. Our results provide a promising method for categorizing large DNA segments that can be compared using a similarity measure and be categorized into more precise clusters for further analysis.
Index Terms – Bioinformatics, genomic islands, hierarchical clustering, high-performance computing, phylogenetic trees, unsupervised learning
College Leaders’ Perspectives Of Diversity At A Community College System In Texas: A Phenomenological Study Of Validation
Full text linkThe United States has undergone dramatic changes in student demographics over the last 40 years. Nowhere is this reflected more than in the field of education (Center for Public Education, 2012; Fabina et al., 2023). Disaggregation of student demographics is a useful and common method for measuring student outcomes, and in the United States, non-majority students consistently have lower student outcomes (Gay, 2013; Howard, 2010; Ocay et al., 2023).
Ma and Baum (2016) and McNair et al. (2015) stated that the role of community colleges is pivotal in postsecondary education. As such, college leaders and administrators have focused attention on ensuring the campus climate is conducive to meeting the needs of their diverse student population in order to improve completion rates. What has received less attention in recent years is an exploration of the policies and practices of community college administrators designed to create educational environments that promote success in tangible ways that affirm and support all students’ persistence and completion. Therefore, this qualitative phenomenological study, using Rendon’s (1994) Validation Theory, sought to illuminate, from a leadership perspective, the creation and institutionalization of a community college campus climate in Texas that fostered success for the range of diverse students they enroll.
The research questions were: (1) What specific policies and practices do community college administrators consider integral to creating a validating campus climate? and (2) What specific policies and practices have community college administrators developed to promote interaction among diverse groups on campus? Two themes emerged from the data: Current Climate Mask Efforts and Personal, Not Institutional.
Given the present realities of the current political and social discourse concerning diversity, the net effect of the two themes may represent the quandary some community college leaders, indeed, all college leaders, face. Although data is not always destiny, data is instructive. The United States is more diverse today than it has ever been in its history. Relatedly, diversity encompasses more than race/ethnicity. As such, all American institutions are microcosms of society. The findings are instructive for postsecondary educational leaders navigating real and imagined minefields.
Keywords: community college and campus environment, non-majority student populations, Validation Theor
Educating Nurse Practitioners On Music-Based Interventions: An Evidence-Based Project
Full text linkBackground: Arts-based interventions (ABIs) harness the transformative power of art to enhance processes or situations, particularly those related to physiological, emotional, and psychological well-being. There are three types of arts-based interventions: visual, literary, and performing arts. Among these, music therapy, a form of performing arts intervention, has gained recognition as an effective approach for addressing mental health challenges. Music therapy has shown promise in reducing anxiety symptoms, offering a non-invasive and accessible form of support within ABI strategies. Purpose: This project assessed changes in nurse practitioners\u27 knowledge, attitudes, and practices, as well as their intentions to utilize music-based interventions in clinical practice. PICO Question: In Nurse Practitioners, how will a music-based therapy educational session impact post-intervention KAP scores compared to pre-intervention KAP scores and influence their intention to utilize music-based interventions for patients with anxiety? Theoretical Framework: The RE-AIM framework was
employed for this project due to its ability to evaluate the effectiveness and sustainability of interventions. Methods: A knowledge, attitudes, and practices (KAP) survey was utilized pre- and post-intervention to capture differences in participants’ responses. Data Analysis: The Intellectus Statistics software program was used to analyze the data. Two-tailed paired samples t-tests and Wilcoxon signed-rank tests were performed to determine the significance of participants’ scores before and after the intervention. Results: The pre-intervention knowledge median was 2.33. The post-intervention knowledge median was 4.83. The pre-intervention median attitude score was 3.33, and the post-intervention median score was 4.33. The pre-intervention intention to utilize score was 4.00, and the post-intervention median was 4.50. Conclusion: This DNP project validated that an educational session for nurse practitioners on music-based interventions increased the knowledge, attitudes, practices, and change in intention to utilize. Recommendations for Future Research: Conduct longitudinal studies, establish a control group, expand implementation, examine the impact of music-based interventions on patient clinical outcomes, and assess the change in practice post-intervention.
Keywords: anxiety, arts-based interventions, knowledge, attitudes and practices, music-based interventions, nurse practitioner
(SI15-067) AHP and MOORA Decision Making Methods on Bipolar Fuzzy Sets
No full textDecision making, in the present contemporary world, has been inherently complicated. Nowadays, the major challenge is the selection of an appropriate option. The arrival of numerous brands and models makes a purchase challenging. Hence, in this paper, four different models of the same branded laptops are considered, for the selection of an appropriate option, using the Analytic Hierarchy Process (AHP) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), with bipolar fuzzy sets, which help decision makers arrive at the most logical choice based on their preferences. The bipolar fuzzy Analytic Hierarchy Process efficaciously supports decision making in cases where problems are complex and helps to understand and define them elaborately. A bipolar scale is defined in this process to demonstrate the relative significance of two criteria. However, the validity of a scale is the underlying base for an appropriate decision. The bipolar fuzzy Multi-Objective Optimization method provides an authorizing solution between better and worse attributes, taking into consideration beneficial and non-beneficial criteria.Weights are found using both the bipolar fuzzy Analytic Hierarchy and entropy methods, which help in achieving a better decision by comparison
Improving Post-Resuscitation Debriefing Completion Rates In An Acute Care Community Hospital: A Quality Improvement Project
No full textBackground: Post-resuscitation debriefing improves team dynamics and CPR quality and can identify systemic barriers to quality care delivery. However, completion rates remain low in acute care settings. Purpose: This quality improvement project evaluated whether a one-hour training session for rapid response nurses and charge nurses, combined with weekly reminder emails, would increase completion rates of post-resuscitation debriefings and influence nurses’ perceptions of their usefulness in an acute care community hospital. PICOT Questions: Will a one-hour training session (I) with rapid response nurses and unit charge nurses (P), along with a weekly reminder system (I), increase the completion rate (O) of post-resuscitation debriefings in an acute care community hospital in three months (T)? Will a one-hour training session (I) with rapid response nurses and unit charge nurses (P) change their perceptions of the usefulness of post-resuscitation debriefings (O) in an acute care community hospital? Theoretical Framework:The PDSA framework was utilized to guide this project due to its structured methodology and wide acceptance in healthcare quality improvement. Methodology: A pre-post, quasi-experimental design was implemented at a 293-bed acute care community hospital. Forty nurses participated in a one-hour virtual educational session and then received weekly automated reminder emails. Pre- and post-surveys (n = 18) assessed respondents’ perceptions of debriefing usefulness, and debriefing completion rates were audited over a three-month period and compared with the same period in the prior year. Statistical analyses included chi-square testing for completion rates and Wilcoxon signed-rank testing for perception data. Results: The results of the post-intervention analysis demonstrated significant increases in debriefing completion rates (χ² [1]=31.65, p\u3c .001) and perceived usefulness of post-resuscitation debriefing (V=4.00, z=−2.67, p=.008). Conclusion: A one-hour educational session combined with structured reminders significantly increased both the completion rate and perceived value of post-resuscitation debriefings. These findings highlight the potential of these low-resource strategies to improve adherence to post-resuscitation debriefing. A longitudinal study is warranted to assess the long-term sustainability of this post-resuscitation debriefing intervention and examine changes in specific patient outcomes, such as neurological deficits or survival rates, with increased debriefings.
Keywords: clinical event debriefing, post-resuscitation debriefing, post-code debriefing, team dynamics, reminder systems, electronic reminder systems, clinical reminder system
Biochar And Manure Levels: Impact On Selected Soil Hydro-Physical Parameters And Sorghum Canopy Temperature
No full textClimate-smart agriculture (CSA) enhances crop yields and maintains healthy soils while also contributing to strategies that mitigate climate change. One of the main aspects of CSA is its impact on soil and environmental conditions, such as structural stability, moisture retention, temperature regulation, and canopy development, which are fundamental to sustainable crop production and land management. Soil organic amendments, e.g., biochar, and chicken and dairy manure applications, substantially improve soil hydrological properties and structure. This thesis reports on the impact of three organic amendment types and rates on selected soil hydrological and physical properties, including water holding capacity, heat capacity, aggregate stability, compaction, and crop canopy temperature. The following objectives are used to achieve the goal of the study, including quantifying the response of soil aggregate stability, moisture, heat capacity, and crop canopy temperature to two biochar levels (low and high) and chicken and dairy manures applied at three rates (none, recommended, and double the recommended rates). The 13 treatments were replicated three times to account for the effect of the spatial variability.
The Analysis of Variance (ANOVA) revealed that among the amendments tested, only biochar significantly influenced soil aggregate stability, highlighting its positive effect on improving the soil structure and lowering its erosion. However, manure types and rates and their interaction did not significantly affect soil aggregate stability. Similarly, the same factors did not have any statistically significant effect on the measured soil health indicators and sorghum canopy temperature. Multiple seasonal analyses are necessary to confirm these results under different rainfall and weather conditions. Simulating the experiment using a numerical model that simulates the soil-plant-air continuum will help understand the long-term effects of soil amendments on soil health, hydro-physical properties, subject to varying environmental conditions, and can also enhance the generalizability of the findings of the study.
Keywords: Soil aggregate stability, soil moisture, soil temperature, soil compaction, canopy temperatur