(SI14-13) Study of Micropolar Ferrofluid in Non-Linear Stretching Sheet with Magnetic Field Effects

The present paper deals with mixed convection two-dimensional flow of micropolar ferrofluid when magnetic field presence in the system. The effects of different types of physical parameter like thermal radiation, thermophoresis and Brownian motion are considered in non-linear stretching sheet. The governing system of PDE is convert in dimensionless system of ODE using dimensionless variable . The suitable HAM is applied for solving the governing problems and obtained the results of linear velocity, micro rotational velocity, temperature, and concentration. Behaviors of different physical parameters effects are discussed through a graph. The Numerical values of skin friction, rate of heat transfer and rate of mass transfer are represented in table. From the numerical results, it is illustrated that the magnetic fields increase the fluid flow. It is also observed that the Brownian motion and thermophoresis parameter have positive impact in heat transfer process. The mass transfer process was delayed with Brownian motion whereas it improved with the thermophoresis parameter

(SI14-15) Heat Source/Sink and Chemical Reaction Effects on Micropolar MHD Nano Fluid Flow in Stretching/Shrinking Sheet

This paper deals with the effects of a non-uniform heat source/sink and chemical reaction on micropolar nanofluid flow in a stretching and shrinking sheet. The flow is considered as a laminar mixed convective two-dimensional steady flow. In this flow, water is considered as a base fluid, whereas iron oxide is considered to be a conventional fluid. The governing non-linear system of PDEs are transformed into a system of ODEs using the similarity transformation, and HAM is employed for obtaining solutions. For more understanding of the effects of various physical conditions, approximate results are obtained, and expressed graphically. From the results, it is concluded that the magnetic field tends to reduce the motion of the flow, whereas heat generation has a delay on it

Generative Artificial Intelligence Enhanced Deep Knowledge Tracing For Personalized Learning

In 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

(R2119) New Algorithms for Independent Component Analysis Based on a General Class of Dependence Criteria

The objective function of numerous well-established Independent Component Analysis (ICA) algorithms calculate based on specific dependence criteria. This study introduces a distinctive dependence criterion based on the cumulative distribution function (CDF) for characterizing the independence between two random variables and some of its properties are examined. Then, we propose a class of ICA algorithms based on the introduced dependence criterion. The performance of the algorithm is systematically compared to some previous similar algorithms. The results indicate that the suggested algorithm have fruitful performance rather than some similar previous known algorithms. Subsequently, the proposed algorithms are applied to real-time series data, serving as an effective pre-processing clustering method

Green Synthesis And Characterization Of Fe-Ti Mixed Nanoparticles For Enhanced Lead Removal From Aqueous Solutions

Heavy metal contamination in water resources presents a significant environmental and public health challenge, with lead a particular concern due to its toxicity and persistence. This study reports the green synthesis of Fe-Ti mixed oxide nanoparticles (NPs) using dextrose as a green source and investigates their effectiveness in lead removal from aqueous solutions. The synthesized NPs were characterized using XRD, FTIR, XPS, SEM-EDS, and BET analysis, revealing an amorphous structure with a high surface area (292.89 m² g¹) and mesoporous characteristics. XPS analysis confirmed the presence of mixed Fe³⁺/Fe²⁺ valence states in a Ti⁴⁺-rich framework, creating diverse binding sites for lead adsorption. The material exhibited optimal lead removal at pH 5, with adsorption following pseudo-second-order kinetics (R² \u3e 0.99) and a Langmuir isotherm model (R² \u3e 0.98). Maximum adsorption capacity reached 25.10 mg g⁻¹ at 40°C, showing endothermic behavior. The low point of zero charge (0.22) and surface hydroxyl groups enabled efficient lead binding may be through multiple mechanisms. Dose optimization studies established 6 g L⁻¹ as the optimal adsorbent concentration. The synergistic combination of iron\u27s affinity for heavy metals and titanium\u27s structural stability, coupled with environmentally friendly synthesis, resulted in a promising material for sustainable water treatment applications. Keywords: Nanoparticles, lead, adsorption, green synthesis, dextrose, heavy metal pollutio

Assessing Healthcare Insurance Policies in Mississippi: An Evaluation of Public and Private Options

Medicaid is the main payer for maternity care in the U.S., covering nearly half of all births. Federal law provides postpartum coverage for 60 days, but gaps exist, especially in non-expansion states. The American Rescue Plan Act of 2021 allows states to extend Medicaid postpartum coverage to 12 months. Mississippi, where Medicaid covers 65% of births, faces severe maternal and infant health challenges. Despite its surplus, extending postpartum care to 12 months could cost $7 million annually. This study examines private and public health insurance coverage for perinatal care in Mississippi. This involves analyzing how different insurance policies address the needs of pregnant women and their infants, and identifying gaps in coverage that affect health outcomes. Understanding the link between health insurance and perinatal care is essential to improving outcomes, policy, equity, and future research. Mississippi has made great strides to combat perinatal mental health among mothers/families with health insurance coverage through effective programs. This study examines the landscape of insurance coverage for perinatal care in Mississippi, galvanizes the implementation of policies pertinent to perinatal mental health challenges, and the benefits of promoting birthing families to thrive

Developing Humanistic Clinicians: A Review of Literature

Humanistic clinicians possess scientific and technical competence and demonstrate essential virtues like empathy, compassion, patience, love, and courage. Humanistic clinicians are trained with an emphasis on the sciences and the humanities. The humanistic clinician is therefore better equipped to care for patients, the patient’s family members, other clinicians, and themselves. Defining empathy can be problematic due to a lack of consistent definition. Relevant literature regarding empathy was reviewed before settling on a working definition. Several pedagogical approaches encourage the moral development of clinicians. The methods and purpose of those approaches are explained. Illness narratives or pathography is a particular type of narrative used to help clinicians develop humanistic qualities. The use of pathography in a narrative ethics framework is explored

Computational Learning Across Biological And Industrial Systems: Bayesian And Segmentation Models For Epigenomic And Manufacturing Data

In this thesis, we made a complete dual-domain investigation using a machine learning approach into two different scientific areas which were epigenetic analysis of seal species and pore identification in the work of additive manufacturing. The study showcased the flexibility and strength of modern computational tools in addressing complicated issues in various biological and industrial systems. We further investigated the epigenetics by using the Bayesian Neural Network and other machine learning methods to conduct a study on the DNA methylation pattern within three pinniped species (the northern elephant seal, Hawaiian monk seal, and Weddell seal) with perfect precision on species type identification and tissue origin differences. In the manufacturing field, we proposed U-SAMNet, an uncertainty-aware self-attention multi-task network, for the pore detection in Additive Manufacturing, conveying 99.83% accuracy and 91.11% F1-score in an efficient manner. The cross-domain comparison showed several shared challenges, such as the data imbalance, uncertainty quantification, and the requirement to design a robust pre-processing pipeline. In addition, this work introduced new methods to two different fields and it showed the potential translation of machine learning to scientific practice. Index Terms: Additive manufacturing, Bayesian neural networks, DNA methylation, epigenetics, machine learning, multi-task learning, uncertainty quantification