CHINA'S K12 ONLINE EDUCATION RESEARCH UNDER THE EPIDEMIC : Satisfaction Analysis on K12 Distance Learning

The value that the Internet brings to the K12 education industry is reflected in four aspects: supply chain, efficiency improvement, experience optimization and clear output. In this research, we first briefly introduce the K12 online education and its history of development. Additionally, we discuss the general features of the online educations according to analyzing the comments under the relevant applications. Then we analyze the market of K12 online education in China, including the dominant applications in the current market, the business model, and the supply chain of the online education. Next, we analyze the evaluation framework for online education which is proposed by Quality Matters as a model. Even though it is still very difficult to design a model that can perfectly evaluation the quality of online education, however, at least, according to the analysis, we see the possibility. To analyze the satisfaction of the distance learning in K12, we design a questionnaire and receive responses from 218 student and more than 100 responses from their parents. Based on the data, we find the result is relatively positive. Evidence shows the potential bright future of online education. Therefore, we use SWOT model to analyze and predict the future development of K12 online education from different dimensions. Finally conclude the research

Network Traffic Classification Based on External Attention by IP Packet Header

As the emerging services have increasingly strict requirements on quality of service (QoS), such as millisecond network service latency ect., network traffic classification technology is required to assist more advanced network management and monitoring capabilities. So far as we know, the delays of flow-granularity classification methods are difficult to meet the real-time requirements for too long packet-waiting time, whereas the present packet-granularity classification methods may have problems related to privacy protection due to using excessive user payloads. To solve the above problems, we proposed a network traffic classification method only by the IP packet header, which satisfies the requirements of both user's privacy protection and classification performances. We opted to remove the IP address from the header information of the network layer and utilized the remaining 12-byte IP packet header information as input for the model. Additionally, we examined the variations in header value distributions among different categories of network traffic samples. And, the external attention is also introduced to form the online classification framework, which performs well for its low time complexity and strong ability to enhance high-dimensional classification features. The experiments on three open-source datasets show that our average accuracy can reach upon 94.57%, and the classification time is shortened to meet the real-time requirements (0.35ms for a single packet).Comment: 12 pages, 5 figure

Surface morphology and mechanical properties of conventional and selfadhesive resin cements after aqueous aging

The stable long-term performance of resin cement under oral environmental conditions is a crucial factor to obtain a satisfactory success of the allceramic dental restoration. Objective: This study aimed at evaluating and comparing the surface morphology and mechanical property of conventional and self-adhesive resin cement after aqueous aging. Materials and Methods: Disc-shaped specimens of 3 conventional (C1: Multilink N, C2: Duolink, C3: Nexus 3) and 3 self-adhesive (S1: Multilink Speed, S2: Biscem, S3: Maxcem) types of resin cements were subjected to irradiation. After 24 h, the Knoop microhardness of each resin cement was evaluated. The specimens were immersed separately in distilled water and maintained at 37°C. A total of 5 specimens of each resin cement were collected at the following time intervals of immersion: 1, 6, 12 and 18 months. The samples were used to evaluate the Knoop parameters of microhardness, sorption and solubility. The surface morphology of the specimens after 18 months of immersion was observed by scanning electron microscopy. The sorption and solubility data were analyzed by two-way ANOVA. The Knoop microhardness was tested by the ANOVA repeated measures (P<0.05). Results: The sorption and solubility parameters of C1 and S1 exhibited significant fluctuations during the aqueous aging. The hardness of the S1 and S2 specimens decreased significantly after an 18-month water immersion. The S1, S2 and S3 specimens indicated higher filler exposure and stripping and apparent pores and cracks compared to specimens C1, C2 and C3, respectively. Conclusion: The surface of selfadhesive resin cements is more susceptible to aqueous damage than that of the conventional resin cements

Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval

Most existing cross-modal retrieval methods employ two-stream encoders with different architectures for images and texts, \textit{e.g.}, CNN for images and RNN/Transformer for texts. Such discrepancy in architectures may induce different semantic distribution spaces and limit the interactions between images and texts, and further result in inferior alignment between images and texts. To fill this research gap, inspired by recent advances of Transformers in vision tasks, we propose to unify the encoder architectures with Transformers for both modalities. Specifically, we design a cross-modal retrieval framework purely based on two-stream Transformers, dubbed \textbf{Hierarchical Alignment Transformers (HAT)}, which consists of an image Transformer, a text Transformer, and a hierarchical alignment module. With such identical architectures, the encoders could produce representations with more similar characteristics for images and texts, and make the interactions and alignments between them much easier. Besides, to leverage the rich semantics, we devise a hierarchical alignment scheme to explore multi-level correspondences of different layers between images and texts. To evaluate the effectiveness of the proposed HAT, we conduct extensive experiments on two benchmark datasets, MSCOCO and Flickr30K. Experimental results demonstrate that HAT outperforms SOTA baselines by a large margin. Specifically, on two key tasks, \textit{i.e.}, image-to-text and text-to-image retrieval, HAT achieves 7.6\% and 16.7\% relative score improvement of Recall@1 on MSCOCO, and 4.4\% and 11.6\% on Flickr30k respectively. The code is available at \url{https://github.com/LuminosityX/HAT}.Comment: Accepted at ACM Multimedia 202

The inter-comparison of AATSR aerosol optical depth retrievals from various algorithms

The project aerosol-CCI as part of European Space Agency (ESA) Climate Change Initiative (CCI) has provided three aerosol retrieval algorithms for the Advanced Along-Track Scanning Radiometer (AATSR) aboard on ENVISAT. For the purpose of estimating different performance of these three algorithms in Asia, in this paper we compared the Aerosol Optical Depth (AOD) of L2 data (10km×10km) including FMI AATSR Dual-view ADV algorithm, the Oxford RAL Aerosol and Cloud retrieval (ORAC) algorithm and the Swansea University AATSR retrieval (SU) algorithm with the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing Network (CARSNET) data separately. The result shows that the algorithms of ADV and SU have good performance on the retrieval of AOD, and the ORAC algorithm has relative lower precision than other two algorithms

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning

Effect of short-term exercise intervention on cardiovascular functions and quality of life of chronic heart failure patients: A meta-analysis

AbstractObjectiveThe purpose of this study was to comprehensively evaluate the effect of short-term exercise intervention on the cardiovascular functions and quality of life (QoL) of patients with chronic heart failure (CHF).MethodsThis meta-analysis was analyzed using RevMan5.3 and Stata 13.0. The parameters of cardiovascular functions and QoL were assessed. Weighted mean differences and their corresponding 95% confidence intervals (CIs) were computed for continuous variables.ResultsData from 2533 CHF patients enrolled in 28 published studies of randomized controlled trials (RCTs) were collated. There were significant differences in VO2 max prior to and after exercise intervention in CHF patients who are 50–55 years old (5 RCTs; 95% CI, −4.86 to −2.29; I2 = 50.5%), 60–65 years old (10 RCTs; 95% CI, −2.66 to −2.04; I2 = 0%), and 69–75 years old (5 RCTs; 95% CI, −1.88 to −0.34; I2 = 38.5%). VO2 max was significantly increased by aerobic exercise (9 RCTs; 95% CI, −3.45 to −1.92; I2 = 37.7%) and combined aerobic resistance exercise (4 RCTs; 95% CI, −4.41 to −0.26; I2 = 76.6%). There were significant differences in cardiac output (n = 303; 95% CI, −0.25 to −0.02; I2 = 12%) and QoL (n = 299; 95% CI, 3.19 to 9.70; I2 = 17%) prior to and after short-term exercise.ConclusionAerobic exercise and aerobic with resistance exercise can significantly improve the aerobic capacity of CHF patients, whereas resistance exercise cannot. The improvement in aerobic capacity caused by aerobic exercise and aerobic with resistance exercise decreases with age. Systolic blood pressure and ventricle structures and functions of CHF patients show no significant changes after the short-term exercise intervention

TRIM21 Restricts Coxsackievirus B3 Replication, Cardiac and Pancreatic Injury via Interacting With MAVS and Positively Regulating IRF3-Mediated Type-I Interferon Production

Tripartite motif-containing 21 (TRIM21) is a regulator of tissue inflammation and pro-inflammatory cytokine production, and has been implicated in negative regulation of IRF3-dependent type I interferon signaling. However, the antiviral activity of TRIM21 varies among diverse viruses and its role on regulation of type I interferon remains inconsistent in different microbial infections. Here, we investigate the potential role for TRIM21 in controlling Coxsackievirus B3 (CVB3) replication and susceptible organ pathology. We found that CVB3 infection up-regulated the expression of TRIM21 in hearts of mice and cardiomyocytes at early phase of infection. Knock-down of TRIM21 resulted in increased viral replication, while overexpression led to increased phosphorylation and dimerization of IRF3, increased IFN-β transcription and reduced viral replication in vitro. We demonstrate that TRIM21 promotes the activation of IRF3 in CVB3-infected cells via interacting with MAVS and catalyzing the K27-linked polyubiquitination of MAVS, thereby enhancing type I interferon signaling. The RING domain of ubiquitin ligase activity and PRY-SPRY domain of TRIM21 are critical for its anti-viral effect. In vivo overexpression of TRIM21 significantly protected mice against viral myocarditis by suppressing CVB3 replication and reducing cardiac inflammatory cytokine production. While TRIM21 deficient mice exhibited a decreased IFN-β production, an increased cardiac and pancreatic CVB3 replication, and aggravated pancreatic injury as well as myocarditis during acute infection. Thus, our results demonstrate TRIM21 as a positive regulator of IFN-β signaling by targeting MAVS during CVB3 infection and suggest it as a potent host defense against CVB3 infection and viral-induced injury in hearts and pancreas

Blocking interaction between SHP2 and PD‐1 denotes a novel opportunity for developing PD‐1 inhibitors

Small molecular PD‐1 inhibitors are lacking in current immuno‐oncology clinic. PD‐1/PD‐L1 antibody inhibitors currently approved for clinical usage block interaction between PD‐L1 and PD‐1 to enhance cytotoxicity of CD8+ cytotoxic T lymphocyte (CTL). Whether other steps along the PD‐1 signaling pathway can be targeted remains to be determined. Here, we report that methylene blue (MB), an FDA‐approved chemical for treating methemoglobinemia, potently inhibits PD‐1 signaling. MB enhances the cytotoxicity, activation, cell proliferation, and cytokine‐secreting activity of CTL inhibited by PD‐1. Mechanistically, MB blocks interaction between Y248‐phosphorylated immunoreceptor tyrosine‐based switch motif (ITSM) of human PD‐1 and SHP2. MB enables activated CTL to shrink PD‐L1 expressing tumor allografts and autochthonous lung cancers in a transgenic mouse model. MB also effectively counteracts the PD‐1 signaling on human T cells isolated from peripheral blood of healthy donors. Thus, we identify an FDA‐approved chemical capable of potently inhibiting the function of PD‐1. Equally important, our work sheds light on a novel strategy to develop inhibitors targeting PD‐1 signaling axis

Validation of an adaptive transfer function method to estimate the aortic pressure waveform

Aortic pulse wave reflects cardiovascular status, but, unlike the peripheral pulse wave, is difficult to be measured reliably using noninvasive techniques. Thus, the estimation of aortic pulse wave from peripheral ones is of great significance. This study proposed an adaptive transfer function (ATF) method to estimate the aortic pulse wave from the brachial pulse wave. Aortic and brachial pulse waves were derived from 26 patients who underwent cardiac catheterization. Generalized transfer functions (GTF) were derived based on the autoregressive exogenous model. Then, the GTF was adapted by its peak resonance frequency. And the optional peak resonance frequency for an individual was determined by regression formulas using brachial systolic blood pressure. The method was validated using the leave-one-out cross validation method. Compared with previous studies, the ATF method showed better performance in estimating the aortic pulse wave and predicting the feature parameters. The prediction error of the aortic systolic blood pressure and pulse pressure were 0.2 ± 3.1 and -0.9 ± 3.1 mmHg, respectively. The percentage errors of augmentation index, percentage notch amplitude, and ejection duration were -2.1 ± 32.7%, 12.4 ± 9.2%, and -2.4 ± 3.3%, respectively