Long-term evolution of supercritical black hole accretion with outflows: a subgrid feedback model for cosmological simulations

We study the long-term evolution of the global structure of axisymmetric accretion flows onto a black hole (BH) at rates substantially higher than the Eddington value ($\dot{M}_{\rm Edd}$), performing two-dimensional hydrodynamical simulations with and without radiative diffusion. In the high-accretion optically-thick limit, where the radiation energy is efficiently trapped within the inflow, the accretion flow becomes adiabatic and comprises of turbulent gas in the equatorial region and strong bipolar outflows. As a result, the mass inflow rate decreases toward the center as $\dot{M}_{\rm in}\propto r^{p}$ with $p\sim 0.5-0.7$ and a small fraction of the inflowing gas feeds the nuclear BH. Thus, super-Eddington accretion is sustained only when a larger amount of gas is supplied from larger radii at $> 100-1000~\dot{M}_{\rm Edd}$. The global structure of the flow settles down to a quasi-steady state in millions of the orbital timescale at the BH event horizon, which is $> 10-100$ times longer than that addressed in previous (magneto-)RHD simulation studies. Energy transport via radiative diffusion accelerates the outflow near the poles in the inner region but does not change the overall properties of the accretion flow compared to the cases without diffusion. Based on our simulation results, we provide a mechanical feedback model for super-Eddington accreting BHs. This can be applied as a sub-grid model in large-scale cosmological simulations that do not sufficiently resolve galactic nuclei, and to the formation of the heaviest gravitational-wave sources via accretion in dense environments.Comment: 17 pages, 13 figures, 1 table (submitted to APJ

OLLIE: Derivation-based Tensor Program Optimizer

Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressions representable by a fixed set of predefined operators, missing possible optimization opportunities between general expressions. We propose OLLIE, the first derivation-based tensor program optimizer. OLLIE optimizes tensor programs by leveraging transformations between general tensor algebra expressions, enabling a significantly larger expression search space that includes those supported by prior work as special cases. OLLIE uses a hybrid derivation-based optimizer that effectively combines explorative and guided derivations to quickly discover highly optimized expressions. Evaluation on seven DNNs shows that OLLIE can outperform existing optimizers by up to 2.73$\times$ (1.46$\times$ on average) on an A100 GPU and up to 2.68$\times$ (1.51$\times$) on a V100 GPU, respectively

Vaccines for African swine fever: an update

African swine fever (ASF) is a fatal infectious disease of swine caused by the African swine fever virus (ASFV). Currently, the disease is listed as a legally notifiable disease that must be reported to the World Organization for Animal Health (WOAH). The economic losses to the global pig industry have been insurmountable since the outbreak of ASF. Control and eradication of ASF are very critical during the current pandemic. Vaccination is the optimal strategy to prevent and control the ASF epidemic, but since inactivated ASFV vaccines have poor immune protection and there aren’t enough cell lines for efficient in vitro ASFV replication, an ASF vaccine with high immunoprotective potential still remains to be explored. Knowledge of the course of disease evolution, the way of virus transmission, and the breakthrough point of vaccine design will facilitate the development of an ASF vaccine. In this review, the paper aims to highlight the recent advances and breakthroughs in the epidemic and transmission of ASF, virus mutation, and the development of vaccines in recent years, focusing on future directions and trends

CSST forecast: impact from non-Gaussian covariances and requirements on systematics-control

The precise estimation of the statistical errors and accurate removal of the systematical errors are the two major challenges for the stage IV cosmic shear surveys. We explore their impact for the China Space-Station Telescope (CSST) with survey area $\sim17,500\deg^2$ up to redshift $\sim4$. We consider statistical error contributed from Gaussian covariance, connected non-Gaussian covariance and super-sample covariance. We find the super-sample covariance can largely reduce the signal-to-noise of the two-point statistics for CSST, leading to a $\sim1/3$ loss in the figure-of-merit for the matter clustering properties ($\sigma_8-\Omega_m$ plane) and $1/6$ in the dark energy equation-of-state ($w_0-w_a$ plane). We further put requirements of systematics-mitigation on: intrinsic alignment of galaxies, baryonic feedback, shear multiplicative bias, and bias in the redshift distribution, for an unbiased cosmology. The $10^{-2}$ to $10^{-3}$ level requirements emphasize strong needs in related studies, to support future model selections and the associated priors for the nuisance parameters.Comment: submitted to MNRA

Construction of a one-step multiplex real-time PCR assay for the detection of serogroups A, B, and E of Pasteurella multocida associated with bovine pasteurellosis

Bovine pasteurellosis, caused by serogroups A, B, and E of Pasteurella multocida (Pm), is mainly manifested as bovine respiratory disease (BRD) and hemorrhagic septicemia (HS). The disease has caused a great economic loss for the cattle industry globally. Therefore, identifying the Pm serogroups is critical for optimal diagnosis and subsequent clinical treatment and even epidemiological studies. In this study, a one-step multiplex real-time PCR assay was established. Three pairs of specific primers were prepared to detect the highly conserved genomic regions of serogroups A (HyaD), B (bcbD), and E (ecbJ) of Pm, respectively. The results depicted that the method had no cross-reaction with other bovine pathogens (Mannheimia hemolytica, Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, Salmonella Dublin, Mycobacterium paratuberculosis, infectious bovine rhinotracheitis virus, and Mycoplasma bovis). The linear range (107 to 102 copies/μL) showed the R2 values for serogroups A, B, and E of Pm as 0.9975, 0.9964, and 0.996, respectively. The multiplex real-time PCR efficiency was 90.30%, 90.72%, and 90.57% for CartA, CartB, and CartE, respectively. The sensitivity result showed that the serogroups A, B, and E of Pm could be detected to be as low as 10 copies/μL. The repeatability result clarified that an intra-assay and an inter-assay coefficient of variation of serogroups A, B, and E of Pm was < 2%. For the clinical samples, the detection rate was higher than the OIE-recommended ordinary PCR. Overall, the established one-step multiplex real-time PCR assay may be a valuable tool for the rapid and early detection of the serogroups A, B, and E of Pm with high specificity and sensitivity

Adaptive Flutter Suppression for a Fighter Wing via Recurrent Neural Networks over a Wide Transonic Range

The paper presents a digital adaptive controller of recurrent neural networks for the active flutter suppression of a wing structure over a wide transonic range. The basic idea behind the controller is as follows. At first, the parameters of recurrent neural networks, such as the number of neurons and the learning rate, are initially determined so as to suppress the flutter under a specific flight condition in the transonic regime. Then, the controller automatically adjusts itself for a new flight condition by updating the synaptic weights of networks online via the real-time recurrent learning algorithm. Hence, the controller is able to suppress the aeroelastic instability of the wing structure over a range of flight conditions in the transonic regime. To demonstrate the effectiveness and robustness of the controller, the aeroservoelastic model of a typical fighter wing with a tip missile was established and a single-input/single-output controller was synthesized. Numerical simulations of the open/closed-loop aeroservoelastic simulations were made to demonstrate the efficacy of the adaptive controller with respect to the change of flight parameters in the transonic regime

Co-utilization of two coal mine residues

In this study, the experiments were carried out in a circulating fluidized bed with different coal bed methane and coal gangue mixing ratios. The results show that bed temperature distribution becomes well-proportioned and the combustion efficiency increases when coal bed methane was introduced. The NO emission increases along with the excess air coefficient rise. The SO 2 emission reduces first and then increases with the rising bed temperature and there is an optimum temperature corresponding to the lowest SO 2 emission. At the same time, the effects of the bed temperature and excess air coefficient on pollutant emissions are more obvious when coal bed methane and coal gangue mixing ratio is less than 0.3. In the experiments, the best operation conditions have been found at coal bed methane and coal gangue mixing ratio of 0.2 and excess air coefficient of less than 1.3. The results show that the co-combustion of coal bed methane and coal gangue in circulating fluidized bed is feasible and provides some references for the combustion optimization

Evaluating the Prognostic Accuracy of Biomarkers for Glioblastoma Multiforme Using The Cancer Genome Atlas Data

Background: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor. Previous studies on GBM biomarkers focused on the effect of the biomarkers on overall survival (OS). Until now, no study has been published that evaluates the performance of biomarkers for prognosing OS. We examined the performance of microRNAs, gene expressions, gene signatures, and methylation that were previously identified to be prognostic. In addition, we investigated whether using clinical risk factors in combination with biomarkers can improve the prognostic performance. Methods: The Cancer Genome Atlas, which provides both biomarkers and OS information, was used in this study. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic accuracy. Results: For prognosis of OS by 2 years from diagnosis, the area under the ROC curve (AUC) of microRNAs, Mir21 and Mir222, was 0.550 and 0.625, respectively. When age was included in the risk prediction score of these biomarkers, the AUC increased to 0.719 and 0.701, respectively. The SAMSN1 gene expression attains an AUC of 0.563, and the “8-gene” signature identified by Bao achieves an AUC of 0.613. Conclusions: Although some biomarkers are significantly associated with OS, the ability of these biomarkers for prognosing OS events is limited. Incorporating clinical risk factors, such as age, can greatly improve the prognostic performance