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Understanding the correlation between various pollutants and cancer across geographical clusters in the U.S.

Naik et al. | Sep 12, 2026

Understanding the correlation between various pollutants and cancer across geographical clusters in the U.S.

Here the authors investigate the relationship between environmental pollutants and cancer incidence rates across various geographical clusters in the United States from 2018 to 2020. By calculating Pearson correlation coefficients and t-statistics on CDC and EPA data, they demonstrated a strong correlation between specific pollutants and various cancers, offering insights that could help explain regional disparities in cancer rates and aid in preventing premature deaths.

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Study of PINN sensor layout in evaluating WSS with application to patient-specific carotid flow

Nie et al. | Sep 04, 2026

Study of PINN sensor layout in evaluating WSS with application to patient-specific carotid flow

Physics‑Informed Neural Networks (PINNs) offer a promising way to estimate blood‑flow behavior in arteries, especially near the vessel wall where traditional methods struggle. In this study, students tested how sensor density and placement affect PINN accuracy in modeling carotid artery flow and found that accuracy improves up to a moderate sensor density and depends strongly on how close sensors are placed to the arterial wall. Applying the optimized configuration to a patient‑specific carotid model produced velocity predictions closely matching computational fluid dynamics results, highlighting PINNs’ potential for future personalized cardiovascular assessment.

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