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. However, the amplitude noise originating from the weakly coupled resonator's closed-loop circuit has not yet been fully investigated. This paper presents a decouple-decomposition (DD) noise analysis model, which is applied to achieve high resolution in a mode-localized tilt sensor based on a weakly coupled resonator closed-loop circuit. The DD noise model separates the weakly coupled resonators using the decoupling method considering the nonlinearity of the resonators. By integrating the decoupled weakly coupled resonators, the model decomposes the weakly coupled resonator's closed-loop circuit into distinct paths for amplitude and phase noise analyses. The DD noise model reveals noise effects at various circuit nodes and models the system noise in the closed-loop circuit of the weakly coupled resonators. MATLAB/Simulink simulations verify the model's accuracy when compared to theoretical analysis. At the optimal operating point, the mode-localized tilt sensor achieves an input-referred instability of 3.91 × 10-4° and an input-referred AR of PSD of 2.01 × 10-4°/√Hz using the closed-loop noise model. This model is also applicable to other varieties of mode-localized sensors.

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. The experimental results demonstrate that, referring to the traditional earth's gravitational field tumble calibration method, the error of the scale factor calibration is 0.46% within ±1 g by using our method. Moreover, the scale factor is compensated by virtual accelerations. After compensation, the maximum temperature drift of the scale factor decreases from 2.46 Hz/g to 1.02 Hz/g, with a temperature range from 40 °C to 80 °C.

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. In this article, we propose a lightweight dynamic convolution method to strengthen traditional CNNs with an affordable increase of total parameters and multiply-adds. Instead of generating the whole kernels directly or combining several static kernels, we choose to "look inside", learning the attention within convolutional kernels. An extra network is used to adjust the weights of kernels for every feature aggregation operation. By combining local and global contexts, the proposed approach can capture the variance among different samples, the variance in different positions of the feature maps, and the variance in different positions inside sliding windows. With a minor increase in the number of model parameters, remarkable improvements in image classification on CIFAR and ImageNet with multiple backbones have been obtained. Experiments on object detection also verify the effectiveness of the proposed method.

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. The DETF out-of-phase mode is utilized, which is only sensitive to temperature, to measure the temperature change of WCRs and complete the temperature compensation using the compensation algorithm. This proposed method has no time delay in measuring the temperature of sensitive elements and no temperature difference caused by the uneven temperature field. The parametric sensitivity in amplitude ratio (AR) to acceleration drifting with temperature was theoretically analyzed, and the novel device was designed and fabricated by a silicon-on-glass process. Both simulation and experiment results demonstrated that the coupling stiffness drifted with temperature, which resulted in the drifts of its sensitivity to acceleration and zero-bias stability. Using the intrinsic mode of WCRs, in terms of the DETF out-of-phase mode, as an in situ thermometer and carrying out the temperature compensation algorithm, the drift of zero bias could be suppressed from 102 mg to 4.5 mg (g is the gravity acceleration), and the drift of the parameter sensitivity in AR was suppressed from 0.74 AR/g to 0.02 AR/g with the temperature range from 330 K to 370 K and acceleration range from 0 g to 0.2 g.

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. Experimental results showed that there were nearly 79-times and 87-times improvement in zeros bias and scale factor, respectively.

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. METHODS: We performed angiogenesis assays on human umbilical vein endothelial cells (HUVEC) that represent an in vitro model to assess the toxicity of drugs to endothelium. RESULTS: Morphine and oxycodone at 0.1 µM to 100 µM dose-dependently increased endothelial cell tube formation and proliferation. We observed the same in endothelial cells exposed to fentanyl at 0.1 µM to 10 µM but there was a gradual loss of stimulation by fentanyl at 100 µM and 1000 µM. Morphine and fentanyl reduced endothelial cell apoptosis-induced by serum withdrawal whereas oxycodone did not display anti-apoptotic effect, via decreasing Bax level. Oxycodone at the same concentrations was less potent than morphine and fentanyl. Different from other three opioids, codeine at all tested concentrations did not affect endothelial cell tube formation, proliferation and survival. Mechanism studies demonstrated that opioids acted on endothelial cells via µ-opioid receptor-independent pathway. Although we observed the increased phosphorylation of mitogen-activated protein kinase (MAPK) in cells exposed to morphine, fentanyl and oxycodone, the rescue studies demonstrated that the stimulatory effects of morphine but not fentanyl nor oxycodone were reversed by a specific MAPK inhibitor. CONCLUSION: Our work demonstrates the differential effects and mechanisms of 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. Ideally, a resonant accelerometer is only sensitive to external acceleration. However, temperature has a great impact on resonant accelerometers, causing unexcepted frequency drift. In order to cancel out the frequency drift caused by temperature change, an improved temperature compensation method for differential vibrating accelerometers without additional temperature sensors is presented in this paper. Experiment results demonstrate that the temperature sensitivity of the prototype sensor is reduced from 43.16 ppm/°C to 0.83 ppm/°C within the temperature range of -10 °C to 70 °C using the proposed method.

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). MGRNN combines the advantages of both iterative molecular generation algorithm and the efficiency of the training strategies. Moreover, MGRNN shows: (i) efficient computation for training; (ii) high model robustness for data; and (iii) an iterative sampling process, which allows to use chemical domain expertise for valency checking. Experimental results show that MGRNN is able to generate 69 % chemically valid molecules even without chemical knowledge and 100 % valid molecules with chemical rules.

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 (CH4). 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. A major obstacle for the use of ML algorithms is that the performance of an algorithm may be affected by many design decisions. Finding appropriate algorithm and model parameters is quite a challenge for nonprofessionals. In this work, we use automated machine learning (AutoML) to analyze the working capacity of CH4 based on 403,959 COFs. We explore the relationship between 23 features such as the structure, chemical characteristics, atom types of COFs, and the working capacity. Then, the tree-based pipeline optimization tool (TPOT) in AutoML and the traditional ML methods including multiple linear regression, support vector machine, decision tree, and random forest that manually set model parameters are compared. It is found that the TPOT can not only save complex data preprocessing and model parameter tuning but also show higher performance than traditional ML models. Compared with traditional grand canonical Monte Carlo simulations, it can save a lot of time. AutoML has broken through the limitations of professionals so that researchers in nonprofessional fields can realize automatic parameter configuration for experiments to obtain highly accurate and easy-to-understand results, which is of great significance for material screening.

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. If the user's private key is text, we can covert the key text into an image called the original image. Specifically, we first divide the original image into original subimages by the image segmentation. Next, we encode each original subimage by DNA coding. Finally, we train the proposed network to find the key secret-sharing results. Our proposed scheme is not only a significant extension of the GANs but also a new direction for the key secret-sharing technology. The simulation results show that the scheme is secure, and both flexible and efficient in communication.

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. In the detection subnetwork, multi-scale and feedback features from different layers are used to better represent objects of various sizes and provide high-level semantic information. Then, we adopt an assistant classification network to guide per-class score prediction, which consists of objectness prior and category likelihood. The instance-level segmentation subnetwork outputs pixel-wise segmentation for each detection while providing the multi-scale and feedback features from different layers as input. These two subnetworks are jointly optimized by a multi-task loss function, which renders Mask SSD direct prediction on detection and segmentation results. We conduct extensive experiments on PASCAL VOC, SBD, and MS COCO datasets to evaluate the performance of Mask SSD. Experimental results verify that as compared with state-of-the-art approaches, our proposed method has a comparable precision with less speed overhead.

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. METHODS: Five major medical databases, CNKI, CPVIP (http://www.cqvip.com/), PubMed, Embase, and ISI web of science were systematically searched to identify all studies from January 2010 to February 2016 on VATS and SBRT therapies. Finally, original English or Chinese publications of stage I and II NSCLC with adequate patients and adequate SBRT doses were enrolled. A multivariate random effects model was used to perform a meta-analysis to compare overall survival and disease free survival between VATS and SBRT while adjusting for median age and operable patient numbers. RESULTS: Fourteen VATS studies (included 3,482 patients) and nineteen SBRT studies (included 3,997 patients) published in the same period were eligible. The median age and follow-up duration were 64 years and 43.4 months for VATS patients and 74 years and 29.5 months for SBRT patients, respectively. The mean unadjusted overall survival rates at 1, 2, 3, and 5 years with VATS were 93.5%, 84.9%, 77.0% and 76.3% compared to 89.0% 73.3% 59.0% and 36.7% with SBRT. The mean unadjusted disease free survival rates at 1, 2, 3, and 5 years with VATS were 93.6%, 88.6%, 85.6% and 75.6% compared to 79.3%, 72.1%, 64.9% and 58.9% with SBRT. While, after adjusted for proportion of operable patients and median age, the estimate overall survival rates at 1, 2, 3, and 5 years with VATS were 94%, 92%, 84% and 71% compared to 98%, 95%, 87% and 83% with SBRT. And the estimate disease free survival rates at 1, 2, 3, and 5 years with VATS were 97%, 94%, 85% and 75% compared to 88%, 81%, 74% and 63% with SBRT. CONCLUSION: Before adjustment, the SBRT group showed worse clinical outcomes (overall survival and disease free survival) than VATS group. When take consider of median age and operability, the patients with SBRT differ substantially from patients treated with VATS. After adjustment of median age and operability, there are no significant differences between these two therapy 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). Twenty-three compounds were detected by HPLC-MS(n), of which twenty compounds were tentatively identified by comparing their retention times and mass spectrometry data with that of reference compounds and reviewing the literature. The characteristic fragmentations were summarized. 3-epi-Dehydrotumulosic acid (F13), 3-oxo-16α,25-dihydroxylanosta-7,9(11),24(31)-trien-21-oic acid (F4), 3-oxo-6,16α-dihydroxylanosta-7,9(11),24(31)-trien-21-oic acid (F7) and dehydropachymic acid (F15) were deemed to be suitable marker compounds to distinguish between samples of different quality according to CA and PCA. This study provides helpful chemical information for further anti-tumor activity and active mechanism research on P. cocos. The results proved that fingerprint combined with a chemometric approach is a simple, rapid and effective method for the quality discrimination of P. cocos.

Rapamycin ameliorates neuropathic pain by activating autophagy and inhibiting interleukin-1ß 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-1ß (IL-1ß), 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-1ß in the ipsilateral spinal cord. We then investigated the antinociceptive effect of rapamycin, a widely used autopahgy inducer, on SNL-induced neuropathic pain in rats and found that treatment with intrathecal rapamycin significantly attenuated the mechanical allodynia and thermal hyperalgesia. Moreover, rapamycin significantly enhanced autophagy in the spinal microglia, whereas it reduced the mRNA and protein levels of IL-1ß in the ipsilateral spinal cord. Our results showed that rapamycin could ameliorate neuropathic pain by activating autophagy and inhibiting IL-1ß in the spinal cord.