Valerio Parisi | Searcharxiv

arXiv2026

A First-Order Assessment of Permanent Magnet Deflection for Space Radiation Protection

We present a preliminary feasibility assessment of a magnetic shield designed to protect a space probe from cosmic radiation via magnetic deflection using neodymium permanent magnets. This work is grounded in theoretical considerations whose preliminary indications are intended to serve as the basis for future Monte Carlo simulations and laboratory validation. The novelty of our approach lies in the use of a magnetic shield; its competitiveness with conventional passive absorbing shielding is not investigated here but warrants dedicated future work. The primary objective is to protect a spacecraft from the flux of charged particles emitted by the Sun. To this end, we combine theoretical modeling and numerical simulations, followed by the construction of a prototype for laboratory testing and, potentially, for future experimental validation at the CubeSat scale.

astro-ph.IM↗

arXiv2014

Uniform sampling of steady states in metabolic networks: heterogeneous scales and rounding

The uniform sampling of convex polytopes is an interesting computational problem with many applications in inference from linear constraints, but the performances of sampling algorithms can be affected by ill-conditioning. This is the case of inferring the feasible steady states in models of metabolic networks, since they can show heterogeneous time scales . In this work we focus on rounding procedures based on building an ellipsoid that closely matches the sampling space, that can be used to define an efficient hit-and-run (HR) Markov Chain Monte Carlo. In this way the uniformity of the sampling of the convex space of interest is rigorously guaranteed, at odds with non markovian methods. We analyze and compare three rounding methods in order to sample the feasible steady states of metabolic networks of three models of growing size up to genomic scale. The first is based on principal component analysis (PCA), the second on linear programming (LP) and finally we employ the lovasz ellipsoid method (LEM). Our results show that a rounding procedure is mandatory for the application of the HR in these inference problem and suggest that a combination of LEM or LP with a subsequent PCA perform the best. We finally compare the distributions of the HR with that of two heuristics based on the Artificially Centered hit-and-run (ACHR), gpSampler and optGpSampler. They show a good agreement with the results of the HR for the small network, while on genome scale models present inconsistencies.

cond-mat.stat-mech↗