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In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section.

Myocarditis is a significant cardiovascular disease (CVD) that poses a threat to the health of many individuals by causing damage to the myocardium. The occurrence of microbes and viruses, including the likes of HIV, plays a crucial role in the development of myocarditis disease (MCD). The images produced during cardiac magnetic resonance imaging (CMRI) scans are low contrast, which can make it challenging to diagnose cardiovascular diseases. In other hand, checking numerous CMRI slices for each CVD patient can be a challenging task for medical doctors. To overcome the existing challenges, researchers have suggested the use of artificial intelligence (AI)-based computer-aided diagnosis systems (CADS). The presented paper outlines a CADS for the detection of MCD from CMR images, utilizing deep learning (DL) methods. The proposed CADS consists of several steps, including dataset, preprocessing, feature extraction, classification, and post-processing. First, the Z-Alizadeh dataset was selected for the experiments. Subsequently, the CMR images underwent various preprocessing steps, including denoising, resizing, as well as data augmentation (DA) via CutMix and MixUp techniques. In the following, the most current deep pre-trained and transformer models are used for feature extraction and classification on the CMR images. The findings of our study reveal that transformer models exhibit superior performance in detecting MCD as opposed to pre-trained architectures. In terms of DL architectures, the Turbulence Neural Transformer (TNT) model exhibited impressive accuracy, reaching 99.73% utilizing a 10-fold cross-validation approach. Additionally, to pinpoint areas of suspicion for MCD in CMRI images, the Explainable-based Grad Cam method was employed.

In this paper, by performing a two-dimensional particle-in-cell simulation, we investigate magnetic reconnection in the downstream of a quasi-perpendicular shock. The shock is nonstationary, and experiences a cyclic reformation. At the beginning of reformation process, the shock front is relatively flat, and part of upstream ions are reflected by the shock front. The reflected ions move upward in the action of Lorentz force, which leads to the upward bending of magnetic field lines at the foot of the shock front, and then a current sheet is formed due to the squeezing of the bending magnetic field lines. The formed current sheet is brought toward the shock front by the solar wind, and the shock front becomes irregular after interacting with the current sheet. Both the current sheet brought by the solar wind and the current sheet associated with the shock front are then fragmented into many small filamentary current sheets. Electron-scale magnetic reconnection may occur in several of these filamentary current sheets when they are convected into the downstream, and magnetic islands are generated. A strong reconnection electric field and energy dissipation are also generated around the X line, and high-speed electron outflow is also formed.

The concept of the Solar Ring mission was gradually formed from L5/L4 mission concept, and the proposal of its pre-phase study was funded by the National Natural Science Foundation of China in November 2018 and then by the Strategic Priority Program of Chinese Academy of Sciences in space sciences in May 2019. Solar Ring mission will be the first attempt to routinely monitor and study the Sun and inner heliosphere from a full 360-degree perspective in the ecliptic plane. The current preliminary design of the Solar Ring mission is to deploy six spacecraft, grouped in three pairs, on a sub-AU orbit around the Sun. The two spacecraft in each group are separated by about 30 degrees and every two groups by about 120 degrees. This configuration with necessary science payloads will allow us to establish three unprecedented capabilities: (1) determine the photospheric vector magnetic field with unambiguity, (2) provide 360-degree maps of the Sun and the inner heliosphere routinely, and (3) resolve the solar wind structures at multiple scales and multiple longitudes. With these capabilities, the Solar Ring mission aims to address the origin of solar cycle, the origin of solar eruptions, the origin of solar wind structures and the origin of severe space weather events. The successful accomplishment of the mission will advance our understanding of the star and the space environment that hold our life and enhance our capability of expanding the next new territory of human., Comment: To be published in Science China Technological Sciences, 20 pages, 9 figures, 3 tables

Decametric (DAM) radio emissions are one of the main windows through which one can reveal and understand the Jovian magnetospheric dynamics and its interaction with the moons. DAMs are generated by energetic electrons through cyclotron-maser instability. For Io (the most active moon) related DAMs, the energetic electrons are sourced from Io volcanic activities, and quickly trapped by neighboring Jovian magnetic field. To properly interpret the physical processes behind DAMs, it is important to precisely locate the source field lines from which DAMs are emitted. Following the work by Hess et al. [2008, 2010], we develop a method to locate the source region as well as the associated field lines for any given DAM emission recorded in a radio dynamic spectrum by, e.g., Wind/WAVES or STEREO/WAVES. The field lines are calculated by the state-of-art analytical model, called JRM09 [Connerney et al., 2018]. By using this method, we may also derive the emission cone angle and the energy of associated electrons. If multiple radio instruments at different perspectives saw the same DAM event, the evolution of its source region and associated field lines is able to be revealed. We apply the method to an Io-DAM event, and find that the method is valid and reliable. Some physical processes behind the DAM event are also discussed., Comment: accepted by Earth and Planetary Physics, 10 pages, 9 figures and 1 table

The expansion of hot electrons in flaring magnetic loops is crucial to understanding the dynamics of solar flares. In this paper we investigate, for the first time, the transport of hot electrons in a magnetic mirror field based on a 1-D particle-in-cell (PIC) simulation. The hot electrons with small pitch angle transport into the cold plasma, which leads to the generation of Langmuir waves in the cold plasma and ion acoustic waves in the hot plasma. The large pitch angle electrons can be confined by the magnetic mirror, resulting in the different evolution time scale between electron parallel and perpendicular temperature. This will cause the formation of electron temperature anisotropy, which can generate the whistler waves near the interface between hot electrons and cold electrons. The whistler waves can scatter the large pitch angle electrons to smaller value through the cyclotron resonance, leading to electrons escaping from the hot region. These results indicate that the whistler waves may play an important role in the transport of electrons in flaring magnetic loops. The findings from this study provide some new insights to understand the electron dynamics of solar flares., Comment: 17pages, 4 figures, accepted by ApJ