A Study on Regional E-commerce Development in China and Its Influencing Factors

This paper defines the concept of regional e-commerce by analyzing the literature. According to the definition, this paper extracted the core indicators of regional e-commerce development, and used the principal component analysis to evaluate the development level of regional e-commerce. Finally, this paper analyzes the impact factors of regional e-commerce development from qualitative and quantitative perspective and puts forward relevant hypotheses. Regression analysis is used in this paper to validate the hypothesis in the analysis of the influencing factors of regional E-commerce. The empirical results has showed that the industrial structure, network coverage, logistics infrastructure are key factors of regional e-commerce development

Mortality of the Oldest Old in China

Objective: This study investigates the role of customary activities, both social and solitary, in mortality among the oldest old in China. Methods: The data come from the Chinese Longitudinal Healthy Longevity Survey.Weibull hazard models analyze the mortality risk of those 80 years of age and older within a 2-year period between 1998 and 2000. Results: Results show that solitary activities, either active or sedentary, are significantly associated with lower mortality risk. The effect of social activities on mortality gradually diminishes with age and is reversed at very old ages when physical exercise, health status, and sociodemographic characteristics are controlled. Discussion: Customary activities, which are less physically demanding, show independent effects on the elderly’s survival.Withdrawal from social contacts may be an adaptive response to challenges faced at very advanced ages. It is important to recognize the unique characteristics of this rapidly growing population

Modeling Online Learning Performance with Biometrics: Current Study and Future Directions

As the number of students and faculty involved in online learning in recent decades has increased, we recognize that there is a limited understanding of how learners react, interact, behave, and are served by the various components in the information delivery processes. When learning online without an instructor present in real-time, we need to understand the role of the learners’ cognitive load, emotions, and visual attention. This paper describes an experiment examining how engagement, cognitive load and visual attention mediate the effect of data representations and highlighting on learning performance. The results showed that in addition to tabular representations, highlighting significantly increased visual attention and decreased cognitive load, which was related to better learning performance

Towards Robust SDRTV-to-HDRTV via Dual Inverse Degradation Network

Recently, the transformation of standard dynamic range TV (SDRTV) to high dynamic range TV (HDRTV) is in high demand due to the scarcity of HDRTV content. However, the conversion of SDRTV to HDRTV often amplifies the existing coding artifacts in SDRTV which deteriorate the visual quality of the output. In this study, we propose a dual inverse degradation SDRTV-to-HDRTV network DIDNet to address the issue of coding artifact restoration in converted HDRTV, which has not been previously studied. Specifically, we propose a temporal-spatial feature alignment module and dual modulation convolution to remove coding artifacts and enhance color restoration ability. Furthermore, a wavelet attention module is proposed to improve SDRTV features in the frequency domain. An auxiliary loss is introduced to decouple the learning process for effectively restoring from dual degradation. The proposed method outperforms the current state-of-the-art method in terms of quantitative results, visual quality, and inference times, thus enhancing the performance of the SDRTV-to-HDRTV method in real-world scenarios.Comment: 10 page

The association and dose–response relationship between dietary intake of α-linolenic acid and risk of CHD: a systematic review and meta-analysis of cohort studies

Abstract Previous studies show inconsistent associations between α -linolenic acid (ALA) and risk of CHD. We aimed to examine an aggregate association between ALA intake and risk of CHD, and assess for any dose–response relationship. We searched the PubMed, EMBASE and Web of Science databases for prospective cohort studies examining associations between ALA intake and CHD, including composite CHD and fatal CHD. Data were pooled using random-effects meta-analysis models, comparing the highest category of ALA intake with the lowest across studies. Subgroup analysis was conducted based on study design, geographic region, age and sex. For dose–response analyses, we used two-stage random-effects dose–response models. In all, fourteen studies of thirteen cohorts were identified and included in the meta-analysis. The pooled results showed that higher ALA intake was associated with modest reduced risk of composite CHD (risk ratios (RR)=0·91; 95 % CI 0·85, 0·97) and fatal CHD (RR=0·85; 95 % CI 0·75, 0·96). The analysis showed a J-shaped relationship between ALA intake and relative risk of composite CHD ( χ 2 =21·95, P <0·001). Compared with people without ALA intake, only people with ALA intake <1·4 g/d showed reduced risk of composite CHD. ALA intake was linearly associated with fatal CHD – every 1 g/d increase in ALA intake was associated with a 12 % decrease in fatal CHD risk (95 % CI −0·21, −0·04). Though a higher dietary ALA intake was associated with reduced risk of composite and fatal CHD, the excess composite CHD risk at higher ALA intakes warrants further investigation, especially through randomised controlled trials

Robust Multimodal Failure Detection for Microservice Systems

Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based nomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the F1-score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches