The Impact of Type Ia Supernovae in Quiescent Galaxies: I. Formation of the Multiphase Interstellar medium

A cool phase of the interstellar medium has been observed in many giant elliptical galaxies, but its origin remains unclear. We propose that uneven heating from Type Ia supernovae (SNe Ia), together with radiative cooling, can lead to the formation of the cool phase. The basic idea is that since SNe Ia explode randomly, gas parcels which are not directly heated by SN shocks will cool, forming multiphase gas. We run a series of idealized high-resolution numerical simulations, and find that cool gas develops even when the overall SNe heating rate $H$ exceeds the cooling rate $C$ by a factor as large as 1.4. We also find that the time for multiphase gas development depends on the gas temperature. When the medium has a temperature $T = 3\times 10^6$ K, the cool phase forms within one cooling time \tc; however, the cool phase formation is delayed to a few times \tc\ for higher temperatures. The main reason for the delay is turbulent mixing. Cool gas formed this way would naturally have a metallicity lower than that of the hot medium. For constant $H/C$, there is more turbulent mixing for higher temperature gas. We note that this mechanism of producing cool gas cannot be captured in cosmological simulations, which usually fail to resolve individual SN remnants.Comment: 16 pages, 11 figures, published by ApJ. This work is part of the SMAUG project, see more information at https://www.simonsfoundation.org/flatiron/center-for-computational-astrophysics/galaxy-formation/smaug/papersplash

Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model

In general, there are no long-term meteorological or hydrological data available for karst river basins. The lack of rainfall data is a great challenge that hinders the development of hydrological models. Quantitative precipitation estimates (QPEs) based on weather satellites offer a potential method by which rainfall data in karst areas could be obtained. Furthermore, coupling QPEs with a distributed hydrological model has the potential to improve the precision of flood predictions in large karst watersheds. Estimating precipitation from remotely sensed information using an artificial neural network-cloud classification system (PERSIANN-CCS) is a type of QPE technology based on satellites that has achieved broad research results worldwide. However, only a few studies on PERSIANN-CCS QPEs have occurred in large karst basins, and the accuracy is generally poor in terms of practical applications. This paper studied the feasibility of coupling a fully physically based distributed hydrological model, i.e., the Liuxihe model, with PERSIANN-CCS QPEs for predicting floods in a large river basin, i.e., the Liujiang karst river basin, which has a watershed area of 58 270 km-2, in southern China. The model structure and function require further refinement to suit the karst basins. For instance, the sub-basins in this paper are divided into many karst hydrology response units (KHRUs) to ensure that the model structure is adequately refined for karst areas. In addition, the convergence of the underground runoff calculation method within the original Liuxihe model is changed to suit the karst water-bearing media, and the Muskingum routing method is used in the model to calculate the underground runoff in this study. Additionally, the epikarst zone, as a distinctive structure of the KHRU, is carefully considered in the model. The result of the QPEs shows that compared with the observed precipitation measured by a rain gauge, the distribution of precipitation predicted by the PERSIANN-CCS QPEs was very similar. However, the quantity of precipitation predicted by the PERSIANN-CCS QPEs was smaller. A post-processing method is proposed to revise the products of the PERSIANN-CCS QPEs. The karst flood simulation results show that coupling the post-processed PERSIANN-CCS QPEs with the Liuxihe model has a better performance relative to the result based on the initial PERSIANN-CCS QPEs. Moreover, the performance of the coupled model largely improves with parameter re-optimization via the post-processed PERSIANN-CCS QPEs. The average values of the six evaluation indices change as follows: the Nash-Sutcliffe coefficient increases by 14 %, the correlation coefficient increases by 15 %, the process relative error decreases by 8 %, the peak flow relative error decreases by 18 %, the water balance coefficient increases by 8 %, and the peak flow time error displays a 5 h decrease. Among these parameters, the peak flow relative error shows the greatest improvement; thus, these parameters are of page1506 the greatest concern for flood prediction. The rational flood simulation results from the coupled model provide a great practical application prospect for flood prediction in large karst river basins

Proper Scaling of the Anomalous Hall Effect

Working with epitaxial films of Fe, we succeeded in independent control of different scattering processes in the anomalous Hall effect. The result appropriately accounted for the role of phonons, thereby clearly exposing the fundamental flaws of the standard plot of the anomalous Hall resistivity versus longitudinal resistivity. A new scaling has been thus established that allows an unambiguous identification of the intrinsic Berry curvature mechanism as well as the extrinsic skew scattering and side-jump mechanisms of the anomalous Hall effect.Comment: 5 pages, 4 figure

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks

Removal of sulfamethoxazole and sulfapyridine by carbon nanotubes in fixed-bed columns

Sulfamethoxazole (SMX) and sulfapyridine (SPY), two representative sulfonamide antibiotics, have gained increasing attention because of the ecological risks these substances pose to plants, animals, and humans. This work systematically investigated the removal of SMX and SPY by carbon nanotubes (CNTs) in fixed-bed columns under a broad range of conditions including: CNT incorporation method, solution pH, bed depth, adsorbent dosage, adsorbate initial concentration, and flow rate. Fixed-bed experiments showed that pH is a key factor that affects the adsorption capacity of antibiotics to CNTs. The Bed Depth Service Time model describes well the relationship between service time and bed depth and can be used to design appropriate column parameters. During fixed-bed regeneration, small amounts of SMX (3%) and SPY (9%) were irreversibly bonded to the CNT/sand porous media, thus reducing the column capacity for subsequent reuse from 67.9 to 50.4 mg g−1 for SMX and from 91.9 to 72.9 mg g−1 for SPY. The reduced column capacity resulted from the decrease in available adsorption sites and resulting repulsion (i.e., blocking) of incoming antibiotics from those previously adsorbed. Findings from this study demonstrate that fixed-bed columns packed with CNTs can be efficiently used and regenerated to remove antibiotics from water

Comment on "Single-mode excited entangled coherent states"

In Xu and Kuang (\textit{J. Phys. A: Math. Gen.} 39 (2006) L191), the authors claim that, for single-mode excited entangled coherent states $| \Psi_{\pm}(\alpha,m)>$, \textquotedblleft the photon excitations lead to the decrease of the concurrence in the strong field regime of $| \alpha | ^{2}$ and the concurrence tends to zero when $| \alpha | ^{2}\to \infty$". This is wrong.Comment: 4 apges, 2 figures, submitted to JPA 15 April 200