Spatial damping of propagating sausage waves in coronal cylinders

Sausage modes are important in coronal seismology. Spatially damped propagating sausage waves were recently observed in the solar atmosphere. We examine how wave leakage influences the spatial damping of sausage waves propagating along coronal structures modeled by a cylindrical density enhancement embedded in a uniform magnetic field. Working in the framework of cold magnetohydrodynamics, we solve the dispersion relation (DR) governing sausage waves for complex-valued longitudinal wavenumber $k$ at given real angular frequencies $\omega$. For validation purposes, we also provide analytical approximations to the DR in the low-frequency limit and in the vicinity of $\omega_{\rm c}$, the critical angular frequency separating trapped from leaky waves. In contrast to the standing case, propagating sausage waves are allowed for $\omega$ much lower than $\omega_{\rm c}$. However, while able to direct their energy upwards, these low-frequency waves are subject to substantial spatial attenuation. The spatial damping length shows little dependence on the density contrast between the cylinder and its surroundings, and depends only weakly on frequency. This spatial damping length is of the order of the cylinder radius for $\omega \lesssim 1.5 v_{\rm Ai}/a$, where $a$ and $v_{\rm Ai}$ are the cylinder radius and the Alfv\'en speed in the cylinder, respectively. We conclude that if a coronal cylinder is perturbed by symmetric boundary drivers (e.g., granular motions) with a broadband spectrum, wave leakage efficiently filters out the low-frequency components.Comment: 6 pages, 2 figures, to appear in Astronomy & Astrophysic

Review of fan-use rates in field studies and their effects on thermal comfort, energy conservation, and human productivity

This paper is a literature review of field studies on fan-use rates and their effects on thermal comfort, energy conservation, and human productivity. In the assessed literature, fans are more popular in Asia, and more used in mixed-mode (MM) and naturally ventilated (NV) buildings than in air-conditioned (AC) buildings. On the basis of collected fan-use models, probit regression models of fan-use rates and ambient environments were obtained and indicate that the essential trigger of fan-use is a warm environment rather than building types. This result helps us to understand the control behaviors and comfort requirements of occupants. Also, fans could provide benefits in three aspects: widening neutral temperatures, saving energy, and improving occupants’ productivity. First, using fans in buildings elevates the neutral temperature and the upper limit of neutral zone (0.5 thermal sensation scale) averages by about 3 K in ranges from 25.7℃ to 28.7℃ and 27.5℃ to 30.7℃, respectively. Second, fan-use reduces AC-use rates in MM buildings in summer. The regression models based on the collected AC-use rate models illustrate that, on average, AC-use is expected to be reduced by about 15% in summer when fans are used. Third, providing occupants access to fans could improve occupants’ productivity. Based on the limited data available, a 3-K temperature extension is achieved by fans ensuring productivity not decreasing. This review could shed some light on the extension of the neutral temperature range, predictions of MM buildings’ energy consumptions, and methods to enhance productivity. Additionally, this review suggests some valuable directions for future research on fans

MSG-BART: Multi-granularity Scene Graph-Enhanced Encoder-Decoder Language Model for Video-grounded Dialogue Generation

Generating dialogue grounded in videos requires a high level of understanding and reasoning about the visual scenes in the videos. However, existing large visual-language models are not effective due to their latent features and decoder-only structure, especially with respect to spatio-temporal relationship reasoning. In this paper, we propose a novel approach named MSG-BART, which enhances the integration of video information by incorporating a multi-granularity spatio-temporal scene graph into an encoder-decoder pre-trained language model. Specifically, we integrate the global and local scene graph into the encoder and decoder, respectively, to improve both overall perception and target reasoning capability. To further improve the information selection capability, we propose a multi-pointer network to facilitate selection between text and video. Extensive experiments are conducted on three video-grounded dialogue benchmarks, which show the significant superiority of the proposed MSG-BART compared to a range of state-of-the-art approaches.Comment: 5 pages,3 figure