Lan Jin | Searcharxiv

arXiv2026

PASCHEN-1D: A one-dimensional fluid plasma solver with multi-mechanism surface emission and flexible external circuit coupling

We present PASCHEN-1D (Plasma Advanced Solver with Coupled High-fidelity Emission and external Network), a one-dimensional time-dependent fluid plasma solver developed for self-consistent simulation of gas discharges and plasma breakdown with coupled electrode surface emission and flexible external circuit networks. The code solves drift-diffusion continuity equations for electrons and ions together with Poisson's equation. It is dynamically coupled to lumped RLC circuits, which self-consistently treat plasma transport, plasma-surface interaction, dielectric effects, and circuit response within a single framework. The electrode emission module includes ion-induced secondary electron emission, Fowler-Nordheim and Murphy-Good field emission, Richardson-Dushman thermionic emission, and photoemission based on a general, exact quantum mechanical emission theory. A finite-volume formulation with Kurganov-Tadmor fluxes, explicit diffusion, and fourth-order Runge-Kutta time integration is employed to ensure stable transient (sometimes ultrafast) evolution across breakdown and glow regimes. The solver is validated against multiple benchmark cases, including nanosecond pulsed dielectric-barrier discharges, DC breakdown and glow transitions, and Paschen curve construction for argon and nitrogen, with results consistent with published studies. With high-fidelity emission physics and a flexible circuit-coupling framework, PASCHEN-1D provides a versatile and efficient tool for modeling breakdown and transient discharge phenomena.

physics.plasm-ph↗

arXiv2023

Application of Novel PACS-based Informatics Platform to Identify Imaging Based Predictors of CDKN2A Allelic Status in Glioblastomas

Gliomas with CDKN2A mutations are known to have worse prognosis but imaging features of these gliomas are unknown. Our goal is to identify CDKN2A specific qualitative imaging biomarkers in glioblastomas using a new informatics workflow that enables rapid analysis of qualitative imaging features with Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty nine patients undergoing GBM resection with CDKN2A status determined by whole-exome sequencing were included. GBMs on magnetic resonance images were automatically 3D segmented using deep learning algorithms incorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. GBMs without CDKN2A alterations were significantly larger (64% vs. 30%, p=0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no CDKN2A alteration (OR: 4.3; 95% CI:1.5-12.1; p<0.001). We developed a novel integrated PACS informatics platform for the assessment of GBM molecular subtypes and show that tumors with HOMDEL are more likely to have radiographic evidence of pial invasion and less likely to have deep white matter invasion or subependymal invasion. These imaging features may allow noninvasive identification of CDKN2A allele status.

q-bio.NC↗