Analysis of The Characteristics of emotional Experience in MOOC Learning

With the rapid development of massive open online courses (MOOCs), researchers have begun to pay attention to the experience of teachers and students in the MOOC classroom. Select the middle school mathematics curriculum standard and textbook research course in the MOOC platform of Chinese universities and collect 66 selfreported data of instantaneous experience and long-term experience of 10 learners on the course learning within two weeks. Qualitative and quantitative analyses were carried out in 8 sub-dimensions, including device usage preferences and problems, and platform tool application. The purpose of the research is to investigate the learning experience of MOOC learning platform more comprehensively and deeply. The research results show that mobile learning has become the main way of MOOC learning. The appearance, education and economy of the tools displayed on the platform directly affect the learning experience of learners. Demonstration tools are highly dependent, but the frequency of application, types and functions of tools are limited, and there is a lack of awareness and application of tools that promote advanced learning, deep learning, and reflective learning; the overall emotional experience of learners is in a positive emotional state and shows a distinct group characteristic. The learning experience of MOOC is directly related to the appearance, education and economy of the display tools in the platform; Learners have diverse experience of platform communication and cooperation tools, and are highly dependent on learning content display tools in the platform; Learners' emotional experience is both positive and negative, but it is dominated by positive emotions and shows distinct group characteristics

Remaining Useful Life Prediction of Rolling Bearings Using Electrostatic Monitoring Based on Two-Stage Information Fusion Stochastic Filtering

The accurate prediction of the remaining useful life (RUL) of rolling bearings is of great significance for a rational formulation of maintenance strategies and the reduction of maintenance costs. According to the two-stage nonlinear degradation characteristics of rolling bearing operation, this paper proposes a prognosis model based on modified stochastic filtering. First, multiple features reextracted from the time domain, frequency domain, and complexity angles, and the baseline Gaussian mixture model (GMM) is established using the normal operating data after spectral regression. The Bayesian-inferred distance (BID) is used as a quantitative indicator to reflect the bearing performance degradation degree. Then, taking multiparameter fusion results as input, the relationship between BID and remaining life is established by the two-stage stochastic filtering model to realize online dynamic remaining useful life prediction. The method in this paper overcomes the difficulty of accurately defining the failure threshold of rolling bearing. At the same time, it reduces the computational burden, avoiding the need of calculating the joint probability distribution for high-dimensional data. Finally, the proposed method has been verified experimentally to have high precision and engineering application value

Research on the Fault Diagnosis Method for Rolling Bearings Based on Improved VMD and Automatic IMF Acquisition

This paper proposes a novel method to improve the variational mode decomposition (VMD) method and to automatically acquire the sensitive intrinsic mode function (IMF). First, since fault signals are impulsive and periodic, a weighted autocorrelative function maximum (AFM) indicator is constructed based on the Gini index and autocorrelation function to serve as the optimization objective function. The mode number K and the penalty parameter α of VMD are automatically obtained through an optimal parameter searching process underpinned by the improved particle swarm optimization (PSO) algorithm with a variety of inertia weights. This improvement solves one of the major drawbacks of the conventional VMD method, that is, the need to manually set parameters. Then, an optimal IMF automatic selecting process is performed for single-failure faults and compound faults, according to the principles of the maximum weighted AFM indicator and maximum spectrum peak ratio (SPR), respectively. The sensitive IMFs are then subjected to an envelope demodulation analysis to obtain the fault characteristic frequency. The results of simulations and experiments show that the proposed method can effectively identify fault characteristics early, especially compound faults, demonstrating great potential for real-world applications

Roller Bearing Performance Degradation Assessment Based on Fusion of Multiple Features of Electrostatic Sensors

This paper presents a new method to assess the performance degradation of roller bearings based on the fusion of multiple features, with the aim of improving the early degradation detection ability of the electrostatic monitoring system. At first, a set of feature parameters of the electrostatic monitoring system indicating the normal state of the bearings are extracted from the perspective of the time domain, frequency domain and complexity. Then, the parameter set is processed to reduce the dimensions and eliminate the redundancy using spectral regression. With the processed features, a Gaussian mixed model is established to gauge the health of the bearing, providing the distance value obtained using Bayesian inference as a quantitative indicator for assessing the performance degradation. The method is applied to access the life of a bearing in which the mechanic fatigue is artificially accelerated. The test results show that the proposed method can better reflect the degradation process of the bearing compared to other evaluation methods. This enables the electrostatic monitoring technique to detect the degradation of the bearing earlier than the vibration monitoring, providing a powerful tool for the condition monitoring of roller bearings

Health State Estimation of On-Board Lithium-Ion Batteries Based on GMM-BID Model

As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process

Nomogram for Postoperative Headache in Adult Patients Undergoing Elective Cardiac Surgery

Background Postoperative headache (POH) is frequent after cardiac surgery; however, few studies on risk factors for POH exist. The aims of the current study were to explore risk factors related to POH after elective cardiac surgery and to establish a predictive system. Methods and Results Adult patients undergoing elective open‐heart surgery under cardiopulmonary bypass from 2016 to 2020 in 4 cardiac centers were retrospectively included. Two thirds of the patients were randomly allocated to a training set and one third to a validation set. Predictors for POH were selected by univariate and multivariate analysis. POH developed in 3154 of the 13 440 included patients (23.5%) and the overall mortality rate was 2.3%. Eight independent risk factors for POH after elective cardiac surgery were identified, including female sex, younger age, smoking history, chronic headache history, hypertension, lower left ventricular ejection fraction, longer cardiopulmonary bypass time, and more intraoperative transfusion of red blood cells. A nomogram based on the multivariate model was constructed, with reasonable calibration and discrimination, and was well validated. Decision curve analysis revealed good clinical utility. Finally, 3 risk intervals were divided to better facilitate clinical application. Conclusions A nomogram model for POH after elective cardiac surgery was developed and validated using 8 predictors, which may have potential application value in clinical risk assessment, decision‐making, and individualized treatment associated with POH

MicroRNA-150 Inhibits the Activation of Cardiac Fibroblasts by Regulating c-Myb

Background/Aims: Cardiac fibrosis is the primary cause of deteriorated cardiac function in various cardiovascular diseases. Numerous studies have demonstrated that microRNAs (miRNAs) are critical regulators of myocardial fibrosis. Specifically, many studies have reported that miR-150 is downregulated in cardiovascular diseases, such as acute myocardial infarction (AMI), myocardial hypertrophy and myocardial fibrosis. However, the exact role of miR-150 in these pathological processes remains unknown. Methods: We used the transverse aortic constriction (TAC) mouse model to study the role of miR-150 in cardiac fibrosis induced by pressure overload. After the TAC operation, qRT-PCR was used to measure the expression profiles of miR-150 in left ventricle tissues and populations of primary heart cell types. Then, we used both miR-150 knockout mice and wild type (WT) mice in the TAC model. Changes in cardiac function and pathology were measured using transthoracic echocardiography and pathological analysis, respectively. Furthermore, we predicted the target of miR-150 in cardiac fibroblasts (CFs) and completed in vitro CF transfection experiments using miR-150 analogs and siRNA corresponding to the predicted target. Results: We observed decreased expression levels of miR-150 in hearts suffering pressure overload, and these levels decreased more sharply in CFs than in cardiomyocytes. In addition, the degrees of cardiac function deterioration and cardiac fibrosis in miR-150-/- mice were more severe than were those in WT mice. By transfecting CFs with an miR-150 analog in vitro, we observed that miR-150 inhibited cardiac fibroblast activation. We predicted that the transcription factor c-Myb was the target of miR-150 in CFs. Transfecting CFs with c-Myb siRNA eliminated the effects of an miR-150 inhibitor, which promoted CF activation. Conclusion: These findings reveal that miR-150 acts as a pivotal regulator of pressure overload-induced cardiac fibrosis by regulating c-Myb

Polo-like kinase 1 promotes pulmonary hypertension

Abstract Background Pulmonary hypertension (PH) is a lethal vascular disease with limited therapeutic options. The mechanistic connections between alveolar hypoxia and PH are not well understood. The aim of this study was to investigate the role of mitotic regulator Polo-like kinase 1 (PLK1) in PH development. Methods Mouse lungs along with human pulmonary arterial smooth muscle cells and endothelial cells were used to investigate the effects of hypoxia on PLK1. Hypoxia- or Sugen5416/hypoxia was applied to induce PH in mice. Plk1 heterozygous knockout mice and PLK1 inhibitors (BI 2536 and BI 6727)-treated mice were checked for the significance of PLK1 in the development of PH. Results Hypoxia stimulated PLK1 expression through induction of HIF1α and RELA. Mice with heterozygous deletion of Plk1 were partially resistant to hypoxia-induced PH. PLK1 inhibitors ameliorated PH in mice. Conclusions Augmented PLK1 is essential for the development of PH and is a druggable target for PH