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Context.The heating of the solar corona and solar wind, particularly through suprathermal particles and kinetic Alfvén waves (KAWs) within the 0–10 R_Sunrange, has been a subject of great interest for many decades. This study investigates and explores the acceleration and heating of charged particles and the role of KAWs in the solar corona. Aims.We investigate how KAWs transport energy and accelerate and heat the charged particles, focusing on the behavior of perturbed electromagnetic (EM) fields, the Poynting flux vectors, net power transfer through the solar flux loop tubes, resonant particles’ speed, group speed, and the damping length of KAWs. The study examines how these elements are influenced by suprathermal particles (κ) and the electron-to-ion temperature ratios (T_e/T_i). Methods.We used kinetic plasma theory coupled with the Vlasov-Maxwell model to investigate the dynamics of KAWs and particles. We assumed a collisionless, homogeneous, and low-beta electron-ion plasma in which Alfvén waves travel in the kinetic limits; that is,m_e/m_i ≪ β ≪ 1. Furthermore, the plasma incorporates suprathermal high-energy particles, necessitating an appropriate distribution function to accurately describe the system. We adopted the Kappa distribution function as the most suitable choice for our analysis. Results.The results show that the perturbed EM fields are significantly influenced byκand the effect of T_e/T_i. We evaluate both the parallel and perpendicular Poynting fluxes and find that the parallel Poynting flux (S_z) dissipates gradually for lowerκvalues. In contrast, the perpendicular flux (S_x) dissipates quickly over shorter distances. Power deposition in solar flux tubes is significantly influenced byκand T_e/T_i. We find that particles can heat the solar corona over long distances (R_Sun) in the parallel direction and short distances in the perpendicular direction. The group velocity of KAWs increases for lowerκvalues, and the damping length, L_G, is enhanced under lowerκ, suggesting longer energy transport distances (R_Sun). These findings offer a comprehensive understanding of particle-wave interactions in the solar corona and wind, with potential applications for missions such as the Parker Solar Probe, (PSP), and can also apply to other environments where non-Maxwellian particle distributions are frequently observed. 

Abstract The phase landscape of UTe₂features a remarkable diversity of superconducting phases under applied pressure and magnetic field. Recent quantum oscillation studies at ambient pressure have revealed the quasi-2D Fermi surface of this material. However, the pressure–dependence of the Fermi surface remains an open question. Here we track the evolution of the UTe₂Fermi surface as a function of pressure up to 19.5 kbar by measuring quantum interference oscillations. We find that in sufficient magnetic field to suppress both superconductivity at low pressures and incommensurate antiferromagnetism at higher pressures, the quasi-2D Fermi surface found at ambient pressure smoothly connects to that at 19.5 kbar, with no signs of a reconstruction over this pressure interval. We observe a smooth increase in oscillatory frequency with increasing pressure, indicating that the warping of the cylindrical Fermi sheets continuously increases with pressure. By computing a tight-binding model, we show that this enhanced warping indicates increasedf-orbital contribution at the Fermi level – up to and beyond the critical pressure at which superconductivity is truncated. These findings highlight the value of high-pressure quantum interference measurements as a sensitive probe of the electronic structure in heavy fermion materials. 

The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retrieval-Augmented Generation (RAG), which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. First, limited retrieved contexts might not cover all necessary information, while excessive retrieval can introduce irrelevant and inaccurate references, interfering with the model’s generation. Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers. To address these issues, we propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the calibrated selection of the number of retrieved contexts. Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model, balancing its dependence on inherent knowledge and retrieved contexts for generation. We demonstrate the effectiveness of RAFE on three medical VQA datasets, achieving an average improvement of 20.8% in factual accuracy.