Temporal, spatial and demographic distributions characteristics of COVID-19 symptom clusters from chinese medicine perspective: a systematic cross-sectional study in China from 2019 to 2023.

Background: The subtypes diagnosis of disease symptom clusters, grounded in the theory of "Treatment in Accordance with Three Categories of Etiologic Factors" and International Classification of Diseases 11th Revision (ICD-11), is a vital strategy for Chinese Medicine (CM) in treating unknown respiratory infectious diseases. However, the classification of disease symptom clusters continues to depend on empirical observations and lacks robust scientific evidence. Consequently, this study seeks to explore the temporal, spatial and demographic distributions characteristics of Corona Virus Disease 2019 (COVID-19) symptom clusters in China.

Design, Fabrication and Characterization of Disk Resonator Gyroscope with Vibration and Shock Resistance.

This paper presents a comprehensive optimization of an outer frame anchor disk resonator gyroscope (DRG) with enhanced resistance to vibration and shock, achieved by increasing the resonant frequency of the tub and translation modes. Furthermore, the wineglass mode retains a high quality factor, enhancing sensitivity and reducing the angle random walk (ARW). The performance of the proposed DRG is analyzed using dynamic equations, and its structural parameters are optimized through finite element analysis (FEA).

A decouple-decomposition noise analysis model for closed-loop mode-localized tilt sensors.

The development of mode-localized sensors based on amplitude output metrics has attracted increasing attention in recent years due to the potential of such sensors for high sensitivity and resolution. Mode-localization phenomena leverage the interaction between multiple coupled resonant modes to achieve enhanced performance, providing a promising solution to overcome the limitations of traditional sensing technologies. Amplitude noise plays a key role in determining the resolution of mode-localized sensors, as the output metric is derived from the measured AR (amplitude ratio) within the weakly coupled resonator system.

A Scale Factor Calibration Method for MEMS Resonant Accelerometers Based on Virtual Accelerations.

This paper presents a scale factor calibration method based on virtual accelerations generated by electrostatic force. This method uses a series of voltage signals to simulate the inertial forces caused by the acceleration input, rather than frequent and laborious calibrations with high-precision instruments. The error transfer model of this method is systematically analyzed, and the geometrical parameters of this novel micromachined resonant accelerometer (MRA) are optimized.

Learning Lightweight Dynamic Kernels With Attention Inside via Local-Global Context Fusion.

Traditional convolutional neural networks (CNNs) share their kernels among all positions of the input, which may constrain the representation ability in feature extraction. Dynamic convolution proposes to generate different kernels for different inputs to improve the model capacity. However, the total parameters of the dynamic network can be significantly huge.

Utilizing the Intrinsic Mode of Weakly Coupled Resonators for Temperature Compensation.

Accelerometers based on outputting amplitude ratios in weakly coupled resonators (WCRs) are attractive because their parametric sensitivity is higher by two or three orders of magnitudes than those based on outputting frequency. However, the impact of temperature on the coupler is a key factor in accelerometer applications. This paper proposed a novel mode-localized WCR accelerometer with a temperature compensation mechanism, with sensitive elements incorporating a double-ended tuning fork (DETF) resonator, clamped-clamped (CC) resonator, and a micro-lever coupler.

A Novel Self-Temperature Compensation Method for Mode-Localized Accelerometers.

Mode-localized sensing paradigms applied to accelerometers have recently become popular research subjects. However, the output of mode-localized accelerometers is influenced by environment temperature due to the difference in the thermal properties of the coupling resonators and the temperature dependence of coupling stiffness. To improve the performance of mode-localized accelerometers against temperature, we proposed an in situ self-temperature compensation method by utilizing the resonant frequency besides of amplitude ratios, which can be implied online.

Comparative analysis of the effects of opioids in angiogenesis.

Background: Angiogenesis, the formation of blood vessel from pre-existing ones, plays an important role in many pathophysiological diseases, such as cancer. Opioids are often used in clinic for the management of chronic pain in cancer patients at terminal phases. Here, we investigated and compared the effects and mechanisms of four opioids on angiogenesis.

An Improved Difference Temperature Compensation Method for MEMS Resonant Accelerometers.

Resonant accelerometers are promising because of their wide dynamic range and long-term stability. With quasi-digital frequency output, the outputs of resonant accelerometers are less vulnerable to the noise from circuits and ambience. Differential structure is usually adopted in a resonant accelerometer to achieve higher sensitivity to acceleration and to reduce common noise at the same time.

MGRNN: Structure Generation of Molecules Based on Graph Recurrent Neural Networks.

Molecular structure generation is a critical problem for materials science and has attracted growing attention. The problem is challenging since it requires to generate chemically valid molecular structures. Inspired by the recent work in deep generative models, we propose a graph recurrent neural network model for drug molecular structure generation, briefly called MGRNN (Molecular Graph Recurrent Neural Networks).

Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning.

Covalent organic frameworks (COFs) have the advantages of high thermal stability and large specific surface and have great application prospects in the fields of gas storage and catalysis. This article mainly focuses on COFs' working capacity of methane (CH). Due to the vast number of possible COF structures, it is time-consuming to use traditional calculation methods to find suitable materials, so it is important to apply appropriate machine learning (ML) algorithms to build accurate prediction models.

GAN-Based Key Secret-Sharing Scheme in Blockchain.

In this article, we propose a key secret-sharing technology based on generative adversarial networks (GANs) to address three major problems in the blockchain: 1) low security; 2) hard recovery of lost keys; and 3) low communication efficiency. In our scheme, the proposed network plays the role of a dealer and treats the secret-sharing process as a classification issue. The key idea is to view the secret as an image during the secret-sharing process.

Mask SSD: An Effective Single-Stage Approach to Object Instance Segmentation.

We propose Mask SSD, an efficient and effective approach to address the challenging instance segmentation task. Based on a single-shot detector, Mask SSD detects all instances in an image and marks the pixels that belong to each instance. It consists of a detection subnetwork that predicts object categories and bounding box locations, and an instance-level segmentation subnetwork that generates the foreground mask for each instance.

Parallel testing of vehicle intelligence via virtual-real interaction.

A self-driven closed-loop parallel testing system implements more challenging tests to accelerate evaluation and development of autonomous vehicles.

[Comparison of Clinical Outcomes of VATS and SBRT in the Treatment of NSCLC].

Background And Objective: More and more chest physicians chose video-assisted thoracoscopic surgery (VATS) to treat early stage non-small cell lung cancer (NSCLC). In recent years, there is still lack of a random trial comparing the clinical outcomes of VATS and stereotactic body radiotherapy (SBRT) in treating NSCLC. To provide a reference for the choice between VATS and SBRT, in the current meta-analysis, we compared the clinical outcomes of these two therapies in treating NSCLC.

Screening and Analysis of the Potential Bioactive Components of Poria cocos (Schw.) Wolf by HPLC and HPLC-MS(n) with the Aid of Chemometrics.

The aim of the present study was to establish a new method based on Similarity Analysis (SA), Cluster Analysis (CA) and Principal Component Analysis (PCA) to determine the quality of different samples of Poria cocos (Schw.) Wolf obtained from Yunnan, Hubei, Guizhou, Fujian, Henan, Guangxi, Anhui and Sichuan in China. For this purpose 15 samples from the different habitats were analyzed by HPLC-PAD and HPLC-MS(n).

Rapamycin ameliorates neuropathic pain by activating autophagy and inhibiting interleukin-1β in the rat spinal cord.

Autophagy acts as an important homoeostatic mechanism by degradation of cytosolic constituents and plays roles in many physiological processes. Recent studies demonstrated that autophagy can also regulate the production and secretion of the proinflammatory cytokine interleukin-1β (IL-1β), which plays a critical role in the development and maintenance of neuropathic pain. In the present study, the paw withdrawal threshold (PWT) and paw withdrawal latency (PWL) were significantly decreased after spinal nerve ligation (SNL), and the changes were accompanied by inhibited autophagy in the spinal microglia and increased mRNA and protein levels of IL-1β in the ipsilateral spinal cord.