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After the first quasiperiodic eruption (QPE; GSN 069) was reported in 2019, four other sources have been identified as a QPE or a candidate. However, the physics behind QPEs is still unclear, although several models have been proposed. Pan et al. proposed an instability model for an accretion disk with magnetically driven outflows in the first QPE of GSN 069, which is able to reproduce both the light curve and the evolution of the spectra fairly well. In this work, we extend this model to all QPEs. We improve the calculations of the spectrum of the disk by introducing a hardening factor, which is caused by a deviation of opacity from a blackbody. We find that the light curves and evolution of the spectra of the four QPEs and candidates can all be well reproduced by our model calculations.

GSN 069 is a recently discovered quasi-periodic eruption (QPE) source recurring about every 9 hr. The mechanism for the QPEs of GSN 069 is still unclear. In this work, a disk instability model is constructed to explain GSN 069 based on Pan et al. (PLC21), where the authors proposed a toy model for the repeating changing-look active galactic nuclei. We improve the work of PLC21 by including a nonzero viscous torque condition on the inner boundary of the disk and adopting a general form for the viscous stress torque in the Kerr metric. It is found that the 0.4–2 keV light curves, the light curves at different energy bands, and the phase-resolved X-ray spectrum of GSN 069 can all be qualitatively reproduced by our model. Furthermore, the profiles of light curves in QPEs can be significantly changed by the parameter μ in the viscous torque equation, which implies that our model may also be applied to other QPEs.

It is suggested that the variation of the mass accretion rate in the accretion disk may be responsible for the occurrence of most changing-look active galactic nuclei (CL AGNs). However, the viscous timescale of a thin disk is far longer than the observed timescale of CL AGNs. Though this problem can be resolved by introducing the large-scale magnetic field, the mechanism for radio-quiet CL AGNs with a weak/absent large-scale magnetic field remains a mystery. In this work, we assume that the thin accretion disk is collapsed from the inner advection-dominated accretion flow (ADAF) instead of being formed from the outer thin disk through advection. This idea is tested by comparing the cooling timescale (tcool) of an ADAF with the observed timescale (ttran) of turn-on CL AGNs. We compile a sample of 102 turn-on CL AGNs from the archived data and calculate the cooling timescale of an ADAF with the critical mass accretion rate based on some conventional assumptions. It is found that tcool is much shorter than ttran in most of the CL AGNs, which validates our assumption, though tcool is not consistent with ttran (tcool < ttran). However, this is reasonable since most of the CL AGNs were observed only two times, indicating that the observed timescale ttran is the maximum value because the changing-look behavior can indeed happen before the second observation.

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease causing dementia and poses significant health risks to middle-aged and elderly people. Brain magnetic resonance imaging (MRI) is the most widely used diagnostic method for AD. However, it is challenging to collect sufficient brain imaging data with high-quality annotations. Weakly supervised learning (WSL) is a machine learning technique aimed at learning effective feature representation from limited or low-quality annotations. In this paper, we propose a WSL-based deep learning (DL) framework (ADGNET) consisting of a backbone network with an attention mechanism and a task network for simultaneous image classification and image reconstruction to identify and classify AD using limited annotations. The ADGNET achieves excellent performance based on six evaluation metrics (Kappa, sensitivity, specificity, precision, accuracy, F1-score) on two brain MRI datasets (2D MRI and 3D MRI data) using fine-tuning with only 20% of the labels from both datasets. The ADGNET has an F1-score of 99.61% and sensitivity is 99.69%, outperforming two state-of-the-art models (ResNext WSL and SimCLR). The proposed method represents a potential WSL-based computer-aided diagnosis method for AD in clinical practice.

Solid-state lithium (Li)–air batteries are recognized as a next-generation solution for energy storage to address the safety and electrochemical stability issues that are encountered in liquid battery systems 1 – 4 . However, conventional solid electrolytes are unsuitable for use in solid-state Li–air systems owing to their instability towards lithium metal and/or air, as well as the difficulty in constructing low-resistance interfaces 5 . Here we present an integrated solid-state Li–air battery that contains an ultrathin, high-ion-conductive lithium-ion-exchanged zeolite X (LiX) membrane as the sole solid electrolyte. This electrolyte is integrated with cast lithium as the anode and carbon nanotubes as the cathode using an in situ assembly strategy. Owing to the intrinsic chemical stability of the zeolite, degeneration of the electrolyte from the effects of lithium or air is effectively suppressed. The battery has a capacity of 12,020 milliamp hours per gram of carbon nanotubes, and has a cycle life of 149 cycles at a current density of 500 milliamps per gram and at a capacity of 1,000 milliamp hours per gram. This cycle life is greater than those of batteries based on lithium aluminium germanium phosphate (12 cycles) and organic electrolytes (102 cycles) under the same conditions. The electrochemical performance, flexibility and stability of zeolite-based Li–air batteries confer practical applicability that could extend to other energy-storage systems, such as Li–ion, Na–air and Na–ion batteries. Flexible, stable and energy-dense solid-state Li–air batteries are realised using ultrathin, chemically inert ion-conductive zeolite membranes as a solid electrolyte.

Chitooligosaccharide (COS), which is acknowledged for possessing multiple functions, is a kind of low-molecular-weight polymer prepared by degrading chitosan via enzymatic, chemical methods, etc. COS has comprehensive applications in various fields including food, agriculture, pharmacy, clinical therapy, and environmental industries. Besides having excellent properties such as biodegradability, biocompatibility, adsorptive abilities and non-toxicity like chitin and chitosan, COS has better solubility. In addition, COS has strong biological functions including anti-inflammatory, antitumor, immunomodulatory, neuroprotective effects, etc. The present paper has summarized the preparation methods, analytical techniques and biological functions to provide an overall understanding of the application of COS.

Quasi-periodic eruptions (QPEs) are a class of X-ray repeating burst phenomena discovered in recent years. Although many models have been proposed to study this phenomenon, there remains significant debate regarding the physical origin of QPEs. In our previous work, we developed a disk instability model with a large-scale magnetic field and successfully reproduced the light curves and spectral characteristics of several QPE sources. We further investigate this model in this work, aiming to explain two key observational features: the dispersion in eruption periods and the peak temperatures during eruptions. This model reveals critical thresholds ( Ṁcrit , β1,crit) that separate systems into stable regimes with minimal period variations and unstable regimes where periods are highly sensitive to the accretion rate and magnetic field parameter, while peak temperatures remain nearly constant across the parameter space. This framework successfully explains both the regular eruptions observed in sources such as GSN 069 and the stochastic behavior in sources such eRO-QPE1, while simultaneously accounting for the observed temperature stability during long-term evolution of QPEs.

Based on the magnetization, an accretion disk with a large-scale magnetic field can be separated into either standard and normal evolution or magnetically arrested disk (MAD), which are difficult to identify from observations. It is still unclear whether all the radio-loud active galactic nuclei (RLAGNs) with a thin disk and strong radio emissions contain a MAD. We investigate this issue by utilizing the 3CRR catalog. We compile a sample of 35 quasars and 14 high-excitation radio galaxies powered by a thin accretion disk. In order to consistently compare with the MAD sample given by Li et al., the optical-UV emissions of our sample are all detected by the Hubble Space Telescope. It is found that the average X-ray luminosity (L X) of our sample is about 5.0 times higher than that of radio-quiet active galactic nuclei with matching optical-UV luminosity (L UV), in general accord with the factor of 4.5 times in MAD sample within the uncertainty. The relationship between radio (5 GHz) and X-ray (2 keV) luminosities in the 3CRR sources is also found to be consistent with the MAD sample. Furthermore, the jet efficiencies of 3CRR sources are consistent with those from the GRMHD simulations of MAD. Therefore, we suggest that probably all the quasars and at least a fraction of high-excitation radio galaxies in the 3CRR catalog, and perhaps all the RLAGNs with strong radio emissions contain a MAD.

This study proposes a novel multi-network architecture consisting of a multi-scale convolution neural network (MSCNN) with fully connected graph convolution network (GCN), named MSCNN-GCN, for the detection of musculoskeletal abnormalities via musculoskeletal radiographs. To obtain both detailed and contextual information for a better description of the characteristics of the radiographs, the designed MSCNN contains three subnetwork sequences (three different scales). It maintains high resolution in each sub-network, while fusing features with different resolutions. A GCN structure was employed to demonstrate global structure information of the images. Furthermore, both the outputs of MSCNN and GCN were fused through the concat of the two feature vectors from them, thus making the novel framework more discriminative. The effectiveness of this model was verified by comparing the performance of radiologists and three popular CNN models (DenseNet169, CapsNet, and MSCNN) with three evaluation metrics (Accuracy, F1 score, and Kappa score) using the MURA dataset (a large dataset of bone X-rays). Experimental results showed that the proposed framework not only reached the highest accuracy, but also demonstrated top scores on both F1 metric and kappa metric. This indicates that the proposed model achieves high accuracy and strong robustness in musculoskeletal radiographs, which presents strong potential for a feasible scheme with intelligent medical cases.

Theranostic nanoplatforms integrating diagnostic and therapeutic functions have received considerable attention in the past decade. Among them, hollow manganese (Mn)-based nanoplatforms are superior since they combine the advantages of hollow structures and the intrinsic theranostic features of Mn 2+ . Specifically, the hollow cavity can encapsulate a variety of small-molecule drugs, such as chemotherapeutic agents, photosensitizers and photothermal agents, for chemotherapy, photodynamic therapy (PDT) and photothermal therapy (PTT), respectively. After degradation in the tumor microenvironment (TME), the released Mn 2+ is able to act simultaneously as a magnetic resonance (MR) imaging contrast agent (CA) and as a Fenton-like agent for chemodynamic therapy (CDT). More importantly, synergistic treatment outcomes can be realized by reasonable and optimized design of the hollow nanosystems. This review summarizes various Mn-based hollow nanoplatforms, including hollow Mn x O y , hollow matrix-supported Mn x O y , hollow Mn-doped nanoparticles, hollow Mn complex-based nanoparticles, hollow Mn-cobalt (Co)-based nanoparticles, and hollow Mn-iron (Fe)-based nanoparticles, for MR imaging-guided cancer therapies. Finally, we discuss the potential obstacles and perspectives of these hollow Mn-based nanotheranostics for translational applications. Graphical Abstract Mn-based hollow nanoplatforms such as hollow Mn x O y nanoparticles, hollow matrix-supported Mn x O y nanoparticles, Mn-doped hollow nanoparticles, Mn complex-based hollow nanoparticles, hollow Mn-Co-based nanoparticles and hollow Mn-Fe-based nanoparticles show great promise in cancer theranostics.