St. Louis Community College

Abstract Childhood diarrheal disease remains a leading cause of morbidity and mortality among children under five years in sub-Saharan Africa, particularly in settings affected by inadequate sanitation, climate variability, malnutrition, and limited healthcare access. Conventional forecasting approaches are often constrained by sparse surveillance data, weak spatial representation, and limited incorporation of mechanistic disease dynamics. This study presents a Physics-Informed Multimodal Artificial Intelligence Digital Twin framework that integrates Physics-Informed Neural Networks, Graph Neural Networks, diffusion-reaction epidemiological modeling, multimodal fusion learning, and Digital Twin simulation to estimate and predict childhood diarrheal disease burden in Kenya, Somaliland, and Zimbabwe. Using public epidemiological, environmental, climate, sanitation, and synthetic proof-of-concept datasets, the framework modeled temporal disease dynamics, spatial transmission, pathogen-attributed burden, and outbreak trajectories while enforcing epidemiological consistency through physics-informed optimization. Results demonstrated robust forecasting performance, enhanced spatial transmission modeling, uncertainty-aware predictions, and realistic outbreak simulations across the three countries. Rotavirus, Shigella, and Cryptosporidium were identified as major contributors to modeled mortality burden, while unsafe water exposure, poor sanitation, malnutrition, and climate-sensitive transmission substantially increased disease risk. Compared with a Bayesian baseline model, the multimodal framework achieved superior nonlinear risk characterization, geospatial learning, and temporal prediction. These findings highlight the potential of scientific machine learning and digital twin systems for infectious disease surveillance, outbreak forecasting, climate-health analytics, and evidence-based public health decision-making in low-resource African settings.

Date presented: April 25, 2026 Primary Author and Speaker: Rose McAndrew, rmcandrew80@gmail.com

To determine the chronology of the imagery and iconography at Picture Cave in the Missouri River Valley, we radiocarbon dated 16 additional pictographs using plasma oxidation and accelerator mass spectrometry. Bayesian chronological modeling estimates that pictograph production began between cal AD 85 and 530 and ended in cal AD 1325-1630, with a duration of 905-1480 years (95.4% probability). Although the majority of pictographs were produced during the Late Woodland and Early Mississippian periods, one age is within the Middle Woodland period, and two of the pictographs were created during the Middle Mississippian or Late Mississippian periods. X-ray diffraction identified graphitic carbon as pyrolyzed carbon for black paintings and drawings and hematite and goethite iron minerals for red pictographs on the sandstone rock substrate. Given its multiple visitations over time, Picture Cave is a persistent sacred place within the landscape. Para determinar la cronolog & iacute;a de las im & aacute;genes e iconograf & iacute;a de la Picture Cave en el valle del r & iacute;o Missouri, datamos por radiocarbono 16 pictograf & iacute;as adicionales mediante oxidaci & oacute;n por plasma y espectrometr & iacute;a de masas con acelerador. El modelado cronol & oacute;gico bayesiano estima que la producci & oacute;n de pictograf & iacute;as comenz & oacute; entre los a & ntilde;os 85 y 530 dC y finaliz & oacute; entre los a & ntilde;os 1325 y 1630 dC, con una duraci & oacute;n de entre 905 y 1480 a & ntilde;os (con una probabilidad del 95,4%). Si bien la mayor & iacute;a de las pictograf & iacute;as se realizaron durante los periodos Woodland Tard & iacute;o y Mississippian Temprano, una de ellas corresponde al periodo Woodland Medio y dos fueron creadas durante los periodos Mississippian Medio o Mississippian Tard & iacute;o. La difracci & oacute;n de rayos X identific & oacute; carbono graf & iacute;tico como carbono pirolizado en las pinturas y dibujos negros, y hematita y goetita, minerales de hierro, en las pictograf & iacute;as rojas sobre el sustrato de arenisca. Dada la evidencia de m & uacute;ltiples visitas a la cueva a lo largo del tiempo, la Cueva de las Pinturas constituye un lugar sagrado de gran importancia en el paisaje.

This study examined student perspectives on flipped learning (FL), a pedagogy that shifts lectures outside the classroom to promote active, hands-on engagement during class. Twenty-four Science, Technology, Engineering, and Mathematics (STEM) faculty members from a 4-year university and a 2-year community college implemented FL after receiving professional development. The research employed a longitudinal quasi-experimental design, tracking the implementation of FL over a 4-year period. A sample of 1,466 students enrolled in these flipped courses provided feedback via end-of-course surveys. Survey data were analyzed using ordinal logistic regression to assess the impact of gender, race/ethnicity, institution type, and academic level on FL perceptions. Results revealed variations across demographics: male respondents expressed higher confidence, whereas nonbinary students strongly endorsed FL across metrics. Female students admitted having higher engagement and favorable classroom environments. The highest levels of positive perception were observed among Asian students, and non-White and senior-level (fourth-year) students reported stronger agreement on FL benefits. Perceptions were more favorable in the 2-year institution, highlighting FL’s unique impact in this environment. Overall, FL was positively associated with increased engagement and confidence across diverse student groups, suggesting that the model is a vital, resilient, and inclusive framework for STEM education.

Abstract Objectives: To develop and evaluate a deployable deep learning system with Gradient-weighted Class Activation Mapping (Grad-CAM) for tuberculosis screening from chest radiographs and to assess its classification performance and explainability across desktop and mobile deployment platforms. Materials and methods: This study used publicly available chest X-ray datasets containing Normal and Tuberculosis images. A DenseNet121-based transfer learning model was trained using stratified training, validation, and test splits with data augmentation and class weighting. Model performance was evaluated using accuracy, precision, recall, F1 score, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC). Grad-CAM was used to visualize regions influencing model predictions. The trained model was converted to TensorFlow Lite and deployed in both a Windows desktop application and a Flutter-based mobile application for offline inference and visualization. Results: The model demonstrated strong classification performance on the independent test dataset, with high accuracy and AUC values indicating effective discrimination between Normal and Tuberculosis cases. Grad-CAM visualizations showed that the model focused primarily on anatomically relevant lung regions, particularly the upper and mid-lung fields in Tuberculosis cases. Deployment testing confirmed consistent prediction outputs and Grad-CAM visualizations across both Windows and mobile platforms. Conclusion: The proposed deployable deep learning system with Grad-CAM provides accurate and interpretable tuberculosis screening from chest radiographs and demonstrates feasibility for offline mobile and desktop deployment. This approach has potential as an artificial intelligence-assisted screening and decision support tool in radiology, particularly in resource-limited and remote healthcare settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research received no external funding. The study was conducted using institutional and personal resources, and no financial support was received from any funding agency, commercial entity, or non-profit organization. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets used and analyzed in this study are publicly available from Kaggle and can be accessed at: https://www.kaggle.com/. The original contributions presented in this study are included in the article and its Supplementary Material. Further inquiries can be directed to the corresponding author.