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[Objective] The Belt and Road Initiative is the core of China’s foreign policy and future opening-up and international cooperation. It is of great significance to analyze the pattern and efficiency of fossil energy trade between China and countries along the Belt and Road to promote the high-quality development of energy along the Belt and Road and ensure China’s energy security. [Methods] Based on the fossil energy trade data of China’s import countries along the Belt and Road from 2000 to 2020 in the CHRTD resource trade database, this study explored the spatial and temporal change patterns and efficiency of fossil energy trade between China and these countries based on the ordered clustering method, survival analysis model, and stochastic frontier gravity model. [Results] The main results are as follows: From 2000 to 2020, China’s fossil energy import experienced obviously different stages, the number of trading countries was increasing, and the import volume was increasing year by year. Among them, the proportion of natural gas imports increased significantly; energy trade was relatively stable, and the survival rate of trade relations was high, but there were still some low energy trade activities. GDP, economic structure, and spatial distance were important factors that affected China’s energy imports. Energy import efficiency was generally low, and high-value countries was in small number and spatially scattered. Among the energy sources where the time-varying model is applicable, the trade efficiency of coal imports was decreasing year by year, while the import value and volume of natural gas were on the rise. [Conclusion] China’s fossil energy import trade cooperation with countries along the Belt and Road has gradually deepened, but the overall trade efficiency is still low. Further improving regional connectivity and formulating more refined trade management policies are important ways to reduce energy trade risks.

Background Long COVID is a common problem in the recovery period of coronavirus disease (COVID-19). The prevention and treatment of long COVID has become the focus of the medical fields of COVID-19. It is important to clarify the situation of long COVID in China and find out the follow-up research route, thus providing evidence-based evidence for clinical practice. Objective To explore the characteristics of long COVID in China, aiming to provide references for the follow-up research. Methods From January 2023 to August 2023, a self-made survey questionnaire was used to investigate the current situation of long COVID in China. The questionnaire included general information, such as gender and age, treatment expectations, symptoms and signs in acute and recovery period, etc. Results A total of 1 001 questionnaires were collected, including 901 people infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and 585 (64.9%) people with long COVID. Binary Logistic regression analysis showed that female (OR=2.000, 95%CI=1.477-2.705, P

Decoding semantic concepts for imagination and perception tasks (SCIP) is important for rehabilitation medicine as well as cognitive neuroscience. Electroencephalogram (EEG) is commonly used in the relevant fields, because it is a low-cost noninvasive technique with high temporal resolution. However, as EEG signals contain a high noise level resulting in a low signal-to-noise ratio, it makes decoding EEG-based semantic concepts for imagination and perception tasks (SCIP-EEG) challenging. Currently, neural network algorithms such as CNN, RNN, and LSTM have almost reached their limits in EEG signal decoding due to their own short-comings. The emergence of transformer methods has improved the classification performance of neural networks for EEG signals. However, the transformer model has a large parameter set and high complexity, which is not conducive to the application of BCI. EEG signals have high spatial correlation. The relationship between signals from different electrodes is more complex. Capsule neural networks can effectively model the spatial relationship between electrodes through vector representation and a dynamic routing mechanism. Therefore, it achieves more accurate feature extraction and classification. This paper proposes a spatio-temporal capsule network with a self-correlation routing mechaninsm for the classification of semantic conceptual EEG signals. By improving the feature extraction and routing mechanism, the model is able to more effectively capture the highly variable spatio-temporal features from EEG signals and establish connections between capsules, thereby enhancing classification accuracy and model efficiency. The performance of the proposed model was validated using the publicly accessible semantic concept dataset for imagined and perceived tasks from Bath University. Our model achieved average accuracies of 94.9%, 93.3%, and 78.4% in the three sensory modalities (pictorial, orthographic, and audio), respectively. The overall average accuracy across the three sensory modalities is 88.9%. Compared to existing advanced algorithms, the proposed model achieved state-of-the-art performance, significantly improving classification accuracy. Additionally, the proposed model is more stable and efficient, making it a better decoding solution for SCIP-EEG decoding. [ABSTRACT FROM AUTHOR]

This paper presents an innovative cross-coupled differential drive rectifier for dual-band Radio frequency identification (RFID) tags. A self-compensating rectifier with enhanced power-conversion efficiency (PCE) at low and high RF power is proposed. The proposed rectifier utilizes a self-compensating circuit, the extra two cross-couple transistors, to increase the gate voltage of transistors to control the conduction of the rectifying transistors. In order to adapt high frequency (HF) and ultrahigh frequency (UHF) rectification, the matching circuit is designed with parasitic cancellation technology. Moreover, the cascading power management circuits are added to generate a stable output voltage. The multi-standard rectifier is designed and simulated in 0. 1 8 μ m CMOS process. The simulated result shows that the proposed three-stage rectifier achieves a PCE 74% (at 5 0 0 k Ω load) when receiving a 915 MHz signal with average power of − 2 5 dBm. Moreover, the maximum efficiency achieves 56% at HF 13.56 MHz, and the final output voltage can be stabilized at a specific voltage of 1.052 V. [ABSTRACT FROM AUTHOR]

Introduction: Limb motor dysfunction is one of the challenges in rehabilitation after cerebral ischemic stroke (CIS) and greatly affects the quality of life of patients. This study aims to investigate the central mechanisms of the curative effect with multimodal magnetic resonance imaging (MRI), which will provide additional evidence to support the application of Xingnao Kaiqiao (XNKQ) acupuncture., Methods and Analysis: This trial is a randomized controlled trial. Patients who meet the criteria will be recruited and randomly divided into 2 groups. One group will receive acupuncture treatment and another group will not receive acupuncture treatment. Both groups will receive conventional treatment. In addition, 20 healthy individuals will be recruited who will not receive any treatment. The total course of treatment is 14 days. The primary outcome is multimodal MRI analysis. For safety assessment, adverse events will be observed and recorded., Ethics and Dissemination: The study involving human subjects was reviewed and approved by the Ethics Committee of IRB of The First Teaching Hospital of Tianjin University of TCM (TYLL2023[K]031). This study complied with the Declaration of Helsinki. Written informed consent about this study was provided by the participants. The results of this study will be published in a peer-reviewed journal., Clinical Trial Registration: Chinese Clinical Trial Registration Center (ChiCTR2300078315) https://www.chictr.org.cn/., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Tian, Xiao, Li, Chang, Lv, Li, Zhao and Dai.)

In recent years, the number of functional magnetic resonance imaging (fMRI) research in acupuncture grows increasingly. However, due to the differences in acupoint selection, acupuncture technique and sample size, the problems get more prominent in terms of the diverse results and the lack of common rules of acupuncture among researches. By taking the fMRI research for post-stroke motor dysfunction (PSMD) treated with acupuncture as the example, this paper introduces the fMRI Meta-analysis technology for integrating the relevant research results and extracting the common rules, namely image-based Meta-analysis (IBMA) and coordinate-based Meta-analysis (CBMA). Considering the higher feasibility of CBMA, three available CBMA methods are explained specially, including activation likelihood estimation (ALE), kernel density analysis (KDA), and seed-based d mapping (SDM). Focusing on the precautions and operation procedure of CBMA, the review is conducted systematically on the type of fMRI research, task design, analytical method, and the thinking integrity of fMRI Meta-analysis, and the review findings are collated in charts. It aims to assist readers to understand the abstract and complex theories and practical information of this technology efficiently, conveniently and systematically, and hopes to provide the references for the future learning and the application.