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China's financial regulatory system is crucial to the global economy, but is little understood. This book surveys and explicates the current status, the development, and planned reform of the Chinese financial supervision and regulatory system in a systematic way. From the shadow banking system to commercial banking, securities and the foreign exchange regime, the authors shed light on the different moving parts of the system; meanwhile, they show how reforms have changed the system in recent years, whether in free-trade zones, the Shanghai-Hong Kong stock market connection, or in the registration mechanisms required for new IPOs. The editors and authors are from the Chinese Academy of Social Sciences, the China Banking Regulatory Committee, the China Securities Regulatory Committee and other leading academic and policy organizations.

We extend the Exp-function method to fractional partial differential equations in the sense of modified Riemann-Liouville derivative based on nonlinear fractional complex transformation. For illustrating the validity of this method, we apply it to the space-time fractional Fokas equation and the nonlinear fractional Sharma-Tasso-Olver (STO) equation. As a result, some new exact solutions for them are successfully established.

Since both serum uric acid (SUA) and renal artery stenosis (RAS) are associated with atherosclerotic events and renal events, it is interesting to investigate whether SUA could predict long‐term outcome in patients with RAS. Patients were enrolled from inpatients from 2010 to 2014, must be ≥40‐year‐old. There were 3269 hypertensive patients enrolled, including 325 RAS patients. Endpoints included all‐cause death and new or worsening nephropathy (NNP). In analysis for all‐cause mortality, associations between SUA and risk of all‐cause mortality were an arising curve in total population, a U‐shape curve in non‐RAS population, and an arising curve in RAS population. When RAS was involved in multivariate analysis, association between SUA and risk of all‐cause mortality was still an arising curve in total population. In analysis for NNP, associations between SUA and risk of NNP were a declining curve in total population, not significant in non‐RAS population, and a U‐shape curve in RAS population. When RAS was involved in multivariate analysis, association between SUA and risk of NNP in total population was no longer significant. Not only association curve of SUA with mortality in non‐RAS patients is different from association curve in RAS patients, but also association curve of SUA with NNP in non‐RAS patients is different from association curve in RAS patients. The authors conclude that mechanisms of uric acid for mortality and NNP in RAS patients are different from non‐RAS patients. In addition to renal vascular obstruction, uric acid is another significant factor for NNP and death in RAS patients.

As an important means of connecting the physical world and the digital world, the reliability and security of the Internet of Things network are key issues. To raise the security of Internet of Things data, a research proposes an encryption technique that combines elliptic curve cryptosystem and hash functions. In this process, the radio frequency identification system is used as the central element for data encryption, with the elliptic curve cryptosystem employed to secure the transmitted data. Non-adjacent scalar representations are used to reduce the expected running time of scalar multiplication, and a bidirectional authentication protocol for Internet of Things encryption is designed using Hash functions. The experimental results showed that in the communication overhead test, the research method had a communication overhead of 242 bits when the Hash function output length was 40 bits in server communication. When analyzing the success rate of intercepting abnormal data access behavior, the research method achieved a success rate of 99.1% when the host file size was 100 Kb. In the analysis of scalar multiplication operation time, the research method only took 18 ms when the output length of the Hash function reached 340bits in a local area network environment. This illustrates that the raised method has a good encryption effect on the Internet of Things and can effectively ensure the security of Internet of Things communication. The research is expected to provide certain technical support for the development of the Internet of Things. Highlights An encryption method combining elliptic curve encryption and hash function is proposed to improve the security of IOT. A two-way authentication protocol is designed to verify identity to effectively prevent man in the middle attack. Non adjacent scalar algorithm is introduced to optimize ECC operation and improve operation speed.

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

Paroxysmal means PAVB is more dangerous than persistent third-degree AVB, as patients may experience prolonged ventricular asystole without warning, increasing the risk of syncope or sudden death.4 Electrophysiology tests often have a low diagnostic yield in such cases, making prolonged monitoring crucial for diagnosis. Extended ECG monitoring, including implantable loop recorders, can improve the detection rate of PAVB.5 Besides, timely pacemaker implantation is essential to prevent sudden death. Ethics approval This study involves human participants and was approved by the Ethical Review Board of Peking University Aerospace School of Clinical Medicine (Aerospace Center Hospital) and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

SARS-CoV-2 has been spreading around the world for the past year. Recently, several variants such as B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma), which share a key mutation N501Y on the receptor-binding domain (RBD), appear to be more infectious to humans. To understand the underlying mechanism, we used a cell surface-binding assay, a kinetics study, a single-molecule technique, and a computational method to investigate the interaction between these RBD (mutations) and ACE2. Remarkably, RBD with the N501Y mutation exhibited a considerably stronger interaction, with a faster association rate and a slower dissociation rate. Atomic force microscopy (AFM)-based single-molecule force microscopy (SMFS) consistently quantified the interaction strength of RBD with the mutation as having increased binding probability and requiring increased unbinding force. Molecular dynamics simulations of RBD–ACE2 complexes indicated that the N501Y mutation introduced additional π-π and π-cation interactions that could explain the changes observed by force microscopy. Taken together, these results suggest that the reinforced RBD–ACE2 interaction that results from the N501Y mutation in the RBD should play an essential role in the higher rate of transmission of SARS-CoV-2 variants, and that future mutations in the RBD of the virus should be under surveillance.

Deep T-wave inversion (TWI) in the mid- to lateral precordial leads is a recognized electrocardiographic marker of apical hypertrophic cardiomyopathy (ApHCM), although its temporal trajectory is variable. We report a 39-year-old hypertensive, non-athlete male who exhibited stable, low-amplitude biphasic T waves for four years, followed by rapid progression in the fifth year to giant, asymmetric TWI (10 mm). At the onset of this escalation, transthoracic echocardiography revealed relatively increased apical wall thickness compared with basal segments, although still within normative limits, which may represent an early phenotypic cue of ApHCM that was not clinically recognized. Subsequent echocardiography confirmed ApHCM, and cardiac magnetic resonance (CMR) substantiated isolated apical hypertrophy, indicating electro-anatomical conversion during the fifth year despite well-controlled blood pressure. This case highlights that rapid TWI progression following prolonged quiescence should prompt clinical suspicion for ApHCM. Given the potential for false-negative echocardiographic findings, close surveillance and timely CMR are essential for definitive diagnosis.

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists’ markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well.

Background Non-operatively managed shoulder conditions, including rotator cuff tendinopathy, subacromial pain syndrome, adhesive capsulitis, and non-displaced proximal humerus fractures, frequently cause pain and restricted mobility. While traditional rehabilitation is effective, access to in-person therapy can be hindered by various factors. Telerehabilitation, which leverages telecommunication technologies, is a promising alternative to traditional in-person rehabilitation. However, its overall efficacy remains uncertain due to inconsistent findings in prior studies. Methods This systematic review and meta-analysis assessed the effectiveness of telerehabilitation for non-operatively managed shoulder conditions, concentrating on randomized controlled trials (RCTs). The control group received standard in-person rehabilitation or home-based exercise programs. The primary outcomes assessed were pain (using the visual Analog Scale [VAS]), range of motion (ROM) including flexion, abduction, external rotation and internal rotation, as well as functional outcomes evaluated through the Shoulder Pain and Disability Index [SPADI], Disabilities of the Arm, Shoulder, and Hand [DASH], and Quick DASH scores. Results Eight randomized controlled trials (RCTs) were included. Data synthesis employed random-effects or fixed-effects models based on heterogeneity, with the risk of bias evaluated via the Cochrane Collaboration tool. Telerehabilitation over 12 weeks significantly reduced pain compared to in-person rehabilitation (MD = -1.06, 95% CI -1.84 to -0.29, P  = 0.007; Certainty of evidence: very low) whereas shorter durations showed limited effectiveness. Significant improvements in ROM were observed for flexion (MD = 4.01, 95% CI 2.48 to 5.54, P  < 0.001; Certainty of evidence: low), abduction (MD = 4.61, 95% CI 2.63 to 6.60, P  < 0.001; Certainty of evidence: low), and external rotation (MD = 3.69, 95% CI 0.77 to 6.62, P  = 0.01; Certainty of evidence: low). However, no significant improvement was observed for internal rotation. The functional outcomes, as measured by the SPADI, significantly improved (MD = -13.32, 95% CI -21.40 to -5.23, P  = 0.001; Certainty of evidence: low), whereas the DASH scores did not significantly differ (MD = -0.66, 95% CI -3.17 to 1.85, P  = 0.60; Certainty of evidence: low). Conclusion Telerehabilitation may reduce pain and improve range of motion in patients with non-operatively managed shoulder conditions, particularly when interventions are sustained for 12 weeks or longer. However, the certainty of evidence remains low due to methodological limitations, highlighting the need for further high-quality trials to confirm these findings.