Analysis of the cores of these Improvements of Online Teaching System and Model－Based on the Evaluation and Feedback on the Online Teaching Model and Teaching Platform

In recent years, MOOC, SPOC and other online teaching modes have attracted widespread attention. Online teaching platforms such as Tencent Classroom and MOOC of Chinese universities have emerged in an endless stream. During the 2020 outbreak of COVID-19, schools at all levels actively engaged in online teaching. The problems and challenges faced in the course of this teaching process will push the research hotspots of modern educational technology to construct the online teaching model that meets the needs of colleges and universities in the context of Internet + Education . This study collected the evaluation and feedback of college students from different universities and different majors on online teaching mode and teaching platform, conducted a quantitative study of SPSS samples, and analyzed the influence of learners\u27 and teachers\u27 participation on online teaching effect. Results show that: there is a positive correlation between learners\u27 attitude towards online teaching and the effect of online teaching, and between learners\u27 participation and the effect of online teaching. There is also a positive correlation between teacher’s participation and effect of online teaching. There is no clear correlation between learners\u27 use of equipment and online teaching effectiveness. Through the interview, learners reported low self-evaluation in online learning, and there are some problems in the teaching process, such as lack of teaching experience, poor platform interaction ability, and low supervision ability of managers. This study argues that in the development of online teaching, learners\u27 and platform users\u27 sense of experience and teachers\u27 participation should be further improved and perfected

A Month in the Life of Groupon

Groupon has become the latest Internet sensation, providing daily deals to customers in the form of discount offers for restaurants, ticketed events, appliances, services, and other items. We undertake a study of the economics of daily deals on the web, based on a dataset we compiled by monitoring Groupon over several weeks. We use our dataset to characterize Groupon deal purchases, and to glean insights about Groupon's operational strategy. Our focus is on purchase incentives. For the primary purchase incentive, price, our regression model indicates that demand for coupons is relatively inelastic, allowing room for price-based revenue optimization. More interestingly, mining our dataset, we find evidence that Groupon customers are sensitive to other, "soft", incentives, e.g., deal scheduling and duration, deal featuring, and limited inventory. Our analysis points to the importance of considering incentives other than price in optimizing deal sites and similar systems.Comment: 6 page

Correlation Analysis of Lignin Accumulation and Expression of Key Genes Involved in Lignin Biosynthesis of Ramie (<i>Boehmeria nivea</i>)

The phloem of the stem of ramie (Boehmeria nivea) is an important source of natural fiber for the textile industry. However, the lignin content in the phloem affects the quality of ramie phloem fiber. In this study, the lignin content and related key gene expression levels were analyzed in the phloem and xylem at different developmental periods. The results showed that the relative expression levels of lignin synthesis-related key genes in the xylem and phloem of the stem gradually decreased from the fast-growing period to the late maturation period, but the corresponding lignin content increased significantly. However, the relative expression levels of a few genes were the highest during the maturation period. During all three periods, the lignin content in ramie stems was positively correlated with the expression of genes, including PAL, C4H and 4CL1 in the phenylpropanoid pathway, F5H and CCoAOMT in the lignin-specific synthetic pathway, and CAD in the downstream pathway of lignin synthesis, but the lignin content was negatively correlated with the expression of genes including 4CL3 in the phenylpropanoid pathway and UDP-GT in the shunt pathway of lignin monomer synthesis. The ramie 4CL3 recombinant protein prefers cinnamic acid as a substrate during catalysis, and it negatively regulates lignin synthesis. It is speculated that ramie 4CL3 is mainly involved in the synthesis of ramie flavonoid compounds, and that 4CL1 is mainly involved in lignin synthesis

Expression and clinical significance of tumor necrosis factor receptor superfamily 4 in laryngeal squamous cell carcinoma

Objective: To analyze the changes in tumor necrosis factor receptor superfamily (TNFRSF) 4 expression levels in laryngeal squamous cell carcinoma (LSCC) patients through multiple database data combined with clinical samples, and to study the role of TNFRSF4 in the occurrence and development of LSCC. Method: Based on Gene Expession Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database, the differentially expressed gene was screened out. The expression level prediction and survival analysis of TNFRSF4 were performed using Gene Expression Profiling Interactive Analysis (GEPIA) database. Western blot and qRT-PCR were performed on LSCC and adjacent tissue samples from three patients. K-M curve was drawn based on TCGA clinical data, and Cox regression analysis was performed. Finally, gene set enrichment analysis was performed to explore signaling pathways related to the role of TNFRSF4 in LSCC. Results: In the database and clinical samples, the expression of TNFRSF4 in the LSCC group was higher than that in the normal group (P＜0.05), and the survival rate of the high TNFRSF4 expression group was higher than that of the low TNFRSF4 expression group (P＜0.01). Multivariate Cox regression analysis showed that the expression level of TNFRSF4 (HR: 0.430, 95%CI: 0.229-0.806, P=0.009), gender (HR: 0.424, 95%CI: 0.204-0.882, P=0.022), lymph node staging (HR:2.010, 95%CI: 1.055-3.831, P=0.034) and distant metastasis (HR: 3.706, 95%CI: 1.152-11.922, P=0.028) collectively affect the overall survival time of patients. The results of gene set enrichment analysis showed that the most significantly correlated signaling pathways with TNFRSF4 expression included cell adhesion molecule, JAK-STAT signaling pathway, T cell receptor signaling pathway, B cell receptor signaling pathway, primary immunodeficiency, and autoimmune thyroid disease. Conclusion: High expression of TNFRSF4 may be a biomarker for good prognosis of patients with LSCC

Mining social lending motivations for loan project recommendations

International audienceOnline social lending has facilitated the ability of borrowers to reach lenders for financing support. With the increasing number of social lending projects, it is becoming very difficult for lenders to find appropriate projects to invest in, and for borrowers to get the funds they need. Project recommendation techniques provide a promising way to solve this problem to some degree, by recommending borrowers’ projects to lenders who are able to invest. Unfortunately, current loan project recommendations only explore some structured information to match borrowers and lenders, so they cannot achieve a satisfactory way to solve the problem very well. In this study, we innovatively mine a huge amount of unstructured data, the text data of borrowers’ and lenders’ motivations, to provide loan project recommendations that solve the problem of mismatches between borrowers and lenders. We present a motivation-based recommendation approach that uses text mining and classifier techniques to identify borrowers’ and lenders’ motivations. Using a dataset from the well-known social lending platform Kiva, our experiment results show that, compared with prior works, the proposed approach improves project recommendations in inactive lender groups and unpopular loan groups, which shows the superiority of the proposed approach in addressing data sparsity and cold start problems in loan project recommendations. This study thus initiates an attempt to solve the information overload problem and improve matching between borrowers and lenders through mining big unstructured text data found in a large number of P2P platforms

Data Augmentation for Motor Imagery Signal Classification Based on a Hybrid Neural Network

As an important paradigm of spontaneous brain-computer interfaces (BCIs), motor imagery (MI) has been widely used in the fields of neurological rehabilitation and robot control. Recently, researchers have proposed various methods for feature extraction and classification based on MI signals. The decoding model based on deep neural networks (DNNs) has attracted significant attention in the field of MI signal processing. Due to the strict requirements for subjects and experimental environments, it is difficult to collect large-scale and high-quality electroencephalogram (EEG) data. However, the performance of a deep learning model depends directly on the size of the datasets. Therefore, the decoding of MI-EEG signals based on a DNN has proven highly challenging in practice. Based on this, we investigated the performance of different data augmentation (DA) methods for the classification of MI data using a DNN. First, we transformed the time series signals into spectrogram images using a short-time Fourier transform (STFT). Then, we evaluated and compared the performance of different DA methods for this spectrogram data. Next, we developed a convolutional neural network (CNN) to classify the MI signals and compared the classification performance of after DA. The Fr&eacute;chet inception distance (FID) was used to evaluate the quality of the generated data (GD) and the classification accuracy, and mean kappa values were used to explore the best CNN-DA method. In addition, analysis of variance (ANOVA) and paired t-tests were used to assess the significance of the results. The results showed that the deep convolutional generative adversarial network (DCGAN) provided better augmentation performance than traditional DA methods: geometric transformation (GT), autoencoder (AE), and variational autoencoder (VAE) (p &lt; 0.01). Public datasets of the BCI competition IV (datasets 1 and 2b) were used to verify the classification performance. Improvements in the classification accuracies of 17% and 21% (p &lt; 0.01) were observed after DA for the two datasets. In addition, the hybrid network CNN-DCGAN outperformed the other classification methods, with average kappa values of 0.564 and 0.677 for the two datasets

Experimental demonstration of 9.6 Gbit/s polar coded infrared light communication system

A new inter-frame polar coded modulation scheme is proposed and experimentally demonstrated in an infrared light communication (ILC) system. The scheme utilizes the Monte Carlo (MC) method to jointly design an inter-frame polar code with 16-ary quadrature-amplitude modulation (16QAM) and orthogonal frequency-division multiplexing (OFDM). The indoor transmission of 9.6 Gbit/s 16QAM OFDM signal is experimentally achieved over a 3.2 km single-mode fiber and 0.8 m free space. The experiment results show that the proposed scheme employing a polar code of length 1024 and cyclic redundancy check aided successive cancellation list (CA-SCL) decoding with a list size of 2 resulted in no errors over 107 bits. Moreover, the proposed scheme requires negligible extra decoding complexity with respect to its classical counterpart, MC-constructed polar coded modulation. To the best of our knowledge, this is the first experimental demonstration of a polar coded modulation based infrared light communication system