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As one of the nearest and most dormant supermassive black holes (SMBHs), M31* provides a rare but promising opportunity for studying the physics of black hole accretion and feedback at the quiescent state. Previous Karl G. Jansky Very Large Array (VLA) observations with an arcsecond resolution have detected M31* as a compact radio source over centimeter wavelengths, but the steep radio spectrum suggests optically thin synchrotron radiation from an outflow driven by a hot accretion flow onto the SMBH. Aiming to probe the putative radio outflow, we conducted milliarcsecond-resolution very long baseline interferometric (VLBI) observations of M31* in 2016, primarily at 5 GHz and combining the Very Long Baseline Array, Tianma 65 m, and Shanghai 25 m radio telescopes. Despite the unprecedented simultaneous resolution and sensitivity achieved, no significant (≳3 σ) signal is detected at the putative position of M31* given an rms level of 5.9 μ Jy beam−1, thus ruling out a pointlike source with a peak flux density comparable to that (∼30 μ Jy beam−1) measured by the VLA observations taken in 2012. We disfavor the possibility that M31* has substantially faded since 2012, in view that a 2017 VLA observation successfully detected M31* at a historically high peak flux density (∼75 μ Jy beam−1 at 6 GHz). Instead, the nondetection of the VLBI observations is best interpreted as the arcsecond-scale core being resolved out at the milliarcsecond scale, suggesting an intrinsic size of M31* at 5 GHz larger than ∼300 times the Schwarzschild radius. Such extended radio emission may originate from a hot wind driven by the weakly accreting SMBH. [ABSTRACT FROM AUTHOR]

• End-to-end single-lead ECG diagnosis with high accuracy and efficiency. • Multi-scale convolutions and hybrid RNNS for multi-angle ECG insights. • Imbalanced clinical datasets: realistic data distribution. • Segmentation and recombination: for large-scale datasets with varying lengths. Arrhythmia is a prevalent cardiovascular disease that can unveil various heart health issues. In recent times, the rise of wearable devices has garnered attention towards the advantages of portable single-lead ECG devices in heart health monitoring. Achieving end-to-end ECG automatic diagnosis through deep learning is essential due to the time-consuming and labour-intensive characteristics of manual diagnosis of ECG records. This paper presents the design of a hybrid residual recurrent convolutional neural network (HRRCNN) for automatically diagnosing single-lead ECG with multiple labels. Firstly, the convolutional layers and residual networks extract the spatial and short-term temporal features. Subsequently, a hybrid recurrent neural network (RNN) comprised of a double-layer bidirectional gated recurrent unit (GRU) and a bidirectional long short-term memory (LSTM) is employed to capture long and short-term temporal features. Finally, an output layer, comprising an attention layer and a fully connected layer, converts the extracted features into classification results. To evaluate the performance of HRRCNN, a dataset from the Shanghai First People's Hospital Affiliated to Shanghai JiaoTong University(Shanghai General Hospital, SGH) is utilized. The experimental results demonstrate that HRRCNN achieves an accuracy of 95.08 %, F1-macro of 87.22 %, Sensitivity of 88.51 %, and a positive prediction rate of 86.13 %. Comparative analysis with existing methods underscores the superior performance of HRRCNN, thereby establishing its potential as an out-of-hospital heart health monitoring tool and an auxiliary tool in clinical settings. [ABSTRACT FROM AUTHOR]

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban–suburban–rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5. [ABSTRACT FROM AUTHOR]