Enter into society: Digitalized livelihoods and prosumer labor for people with disabilities in China.

Background: The current debates on "digital labor" focus on a gorgeous and comprehensive experiential description and theoretical exposition but do not provide a thorough examination of the unique context and social structure. In China, the development of internet is closely tied to politics, the Chinese Government uses internet as a tool of social governance. More importantly, aside from desire-based communications produced by corporate ideology, the Chinese people's passion for the Internet also comes from individual survival, especially the middle and lower class of information represented by the disabled people. This means that analysis of the digital labor among people with disabilities in China must be done from a variety of angles, including politics, society and culture. Methods: This study combines life-history interviews and field research methods to explore the value and significance of digitalized livelihoods and free prosumer labor for people with disabilities in China through self-narration. Since 2020, researchers have been volunteering at two social organizations serving people with physical disabilities in Wuhan city, Hubei Province. We participated in 26 assistance activities for disabled groups which included three 14-day training camps, and interviewed 40 people with physical disabilities. Results: This study found that although the digitalized livelihoods practice of people with disabilities is still "precarious labor" in nature, whose self-expression in the cyberspace is easy to fall into the shackles of capital flow logic. However, digital labor practice provides them with the opportunity to "sit at home, enter the community and society," also enables them to "live independently." More importantly, this opportunity and possibility enable people with disabilities to experience a sense of value and self-esteem as "competent people." Therefore, in the realistic environment of structural obstacles in the social life of the people with disabilities in China, the possibility of "inclusiveness" brought by digital labor is the core value brought by digital society.

Users' Feedback on COVID-19 Lockdown Documentary: An Emotion Analysis and Topic Modeling Analysis.

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose "The lockdown: One month in Wuhan" YouTube documentary, and applied emotion analysis as well as a machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation (LDA) topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust (22.8%), joy (15.4%), and anticipation (17.6%) are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing.

A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer's disease.

Alzheimer's disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment.

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights.

Polyoxometalate-Bridged Cu(I)- and Ag(I)-Thiacalix[4]arene Dimers for Heterogeneous Catalytic Oxidative Desulfurization and Azide-Alkyne "Click" Reaction.

Two remarkable polyoxometalate-bridged Cu(I)- and Ag(I)-thiacalix[4]arene dimers, namely, [Cu4(SiW12O40)(L)2(DMF)2]·2EtOH·DMF (1-Cu) and [Ag4(PMo12O40)(L)2]·OH (1-Ag), were prepared by using a new thiacalix[4]arene, metal cation and polyoxometalate (L = tetra[2-(ethylthio)-1-methyl-1H-imidazole]-thiacalix[4]arene). In 1-Cu and 1-Ag, two thiacalix[4]arenes were linked together by one [SiW12O40]4- or [PMo12O40]3- anion via two metal cations to give a molecular dimer. Further, adjacent dimers were extended into a high-dimensional supramolecular architecture through hydrogen bonds. Markedly, these molecular dimers are exceedingly stable in organic solvents and then were employed as efficient catalysts for catalytic oxidation desulfurization as well as the azide-alkyne "click" reaction.

Ignitability of crude oil and its oil-in-water products at arctic temperature.

A novel platform and procedure were developed to characterize the ignitability of Alaska North Slope (ANS) crude oil and its water-in-oil products with water content up to 60% at low temperatures (-20-0°C). Time to ignition, critical heat flux, in-depth temperature profiles were investigated. It was observed that a cold boundary and consequent low oil temperature increased the thermal inertia of the oil/mixture and consequently the time to sustained ignition also increased. As the water content in the ANS water-in-oil mixture increased, the critical heat flux for ignition was found to increase. This is mainly because of an increase in the thermal conductivity of the mixture with the addition of saltwater. The results of the study can be used towards design of ignition strategies and technologies for in situ burning of oil spills in cold climates such as the Arctic.