Research on Privacy Paradox in Social Networks Based on Evolutionary Game Theory and Data Mining

In order to obtain social benefits, social networks have started taking benefits from private information of network users. While having increased concerns about the risk of privacy disclosure, users still generally disclosed under high privacy concerns, which directly formed the privacy paradox. The expansion and generalization of privacy paradox indicate that the implementation of privacy protection in social networks is still in a dilemma. Studying and solving the problem of privacy paradox is conducive to ensure the healthy development of social network industry. Based on this, this study has designed a research system that analyzes the privacy paradox of social networks from three dimensions: cause, existence and form. After studying existing research of privacy paradox in social networks, evolutionary game theory is determined to be introduced into the procedure of cause analysis, while data mining is used as a data analysis method for empirical research. Within the whole research process, the evolutionary game model of privacy paradox in social networks is built up first, while the necessary conditions for the generation of privacy paradox is addressed, which is derived from the evolutionary stable strategy. Secondly, the questionnaire survey method is used to collect private data of active users of both Weibo and WeChat. Lastly, Apriori and CHAID algorithm are used to determine the relationship of user privacy concerns, privacy behavior, and other factors, which then confirms the existence of privacy paradox on two social networks and makes a comparison between their forms of privacy paradox in specific. This research systematically makes a useful an in-depth analysis to the privacy paradox in social networks and is meaningful for establishing a hierarchical protection system of users\u27 privacy for enterprises

3D porous Ti3C2 MXene/NiCo-MOF composites for enhanced lithium storage

To improve Li storage capacity and the structural stability of Ti3C2 MXene-based electrode materials for lithium-ion batteries (LIBs), a facile strategy is developed to construct three-dimensional (3D) hierarchical porous Ti3C2/bimetal-organic framework (NiCo-MOF) nanoarchitectures as anodes for high-performance LIBs. 2D Ti3C2 nanosheets are coupled with NiCo-MOF nanoflakes induced by hydrogen bonds to form 3D Ti3C2/NiCo-MOF composite films through vacuum-assisted filtration technology. The morphology and electrochemical properties of Ti3C2/NiCo-MOF are influenced by the mass ratio of MOF to Ti3C2. Owing to the interconnected porous structures with a high specific surface area, rapid charge transfer process, and Li+ di. © 2020 by the authors.National Natural Science Foundation of ChinaNational Natural Science Foundation of China [51702098]; National Key R&D Program of China [2016YFE0131200]; International Cooperation Project of Shanghai Municipal Science and Technology Committee [18520744400

Field Emission Properties of Carbon Nanotubes with Boron Doping and H 2

Gas adsorption and atom doping usually present when preparing carbon nanotubes (CNTs) and can affect the field emission properties of carbon nanotubes. H2O molecule and boron atom are the most important adsorbates, respectively. Using ab-initio calculations, we have investigated the electron field emission performance of CNTs simultaneously adsorbed with one H2O molecule and doped with one boron atom (BCNT+H2O) in this paper. The results indicate that the electrons localize at the top of BCNT+H2O and the electronic density of states (DOS) around the Fermi level is enhanced obviously. It is expected that BCNT+H2O will be more propitious to the field emission of electrons based on the calculations of DOS, HOMO/LUMO, and Mulliken charge population

Application progress of CT radiomics in gastrointestinal stromal tumor

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor in the gastrointestinal tract, with complex biological characteristics and varying risks, and the treatment methods and prognosis of patients with different risks are quite different; therefore, early diagnosis and risk assessment are crucial for its precision treatment. In recent years, CT radiomics, as an emerging imaging technology, can transform traditional CT image features into a large number of data, thereby reflecting the inherent heterogeneity of GIST and even correlating with its gene expression features. This paper reviews the research progress of CT radiomics in the diagnosis and prediction of GIST with the help of machine learning. The current CT radiomics can not only be used for the differential diagnosis of GIST and other gastric diseases, but also for the risk evaluation of GIST. Furthermore, pathological analysis and gene diagnosis can be performed based on CT images, and then the first-line treatment effect and long-term prognosis can be predicted. At present, various prediction models constructed by combination of CT radiomics and clinical information have been well verified in the specific practice of different clinical problems, showing broad application prospects. However, in the specific clinical application process, different methods of sample data collection and processing, differences in the selection of machine learning algorithms, and the selection of 2D or 3D images all affect the specific effectiveness of CT radiomics. Hence, unified and standardized application rules for radiomics has to be established

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biology. Here, we present Sub-Compartment Identifier (SCI), an algorithm that uses graph embedding followed by unsupervised learning to predict sub-compartments using Hi-C chromatin interaction data. We find that the network topological centrality and clustering performance of SCI sub-compartment predictions are superior to those of hidden Markov model (HMM) sub-compartment predictions. Moreover, using orthogonal Chromatin Interaction Analysis by in-situ Paired-End Tag Sequencing (ChIA-PET) data, we confirmed that SCI sub-compartment prediction outperforms HMM. We show that SCI-predicted sub-compartments have distinct epigenetic marks, transcriptional activities, and transcription factor enrichment. Moreover, we present a deep neural network to predict sub-compartments using epigenome, replication timing, and sequence data. Our neural network predicts more accurate sub-compartment predictions when SCI-determined sub-compartments are used as labels for training

La-modified sba-15/h2o2 systems for the microwave assisted oxidation of organosolv beech wood lignin

In this manuscript, the influence of organosolv beech wood lignin (LOB) on its oxidative conver-sion to high added-value phenolic aldehydes is discussed. Environmental friendly and low-cost H2O2 was used as the oxygen atom donor. The catalyst was prepared by immobilizing Lanthanum com-pounds onto the periodic mesoporous channels of siliceous SBA-15. The activity of the La/SBA-15 was investigated towards oxidation of LOB in the presence of hydrogen peroxide as oxidant with microwave irradiation. Considering the characteristics of LOB, an unexpected low syringaldehyde concentration at 10min of reaction time (1.47 g/L, corresponding to 15.66% yield) was obtained; the other major product was vanillin at 25min (0.78 g/L, i.e., 9.94% yield). The high reactivity of syringyl nuclei may be pointed out as the main reason for the faster production and degradation of syringaldehyde in oxida-tion. Other low molecular weight phenolic products were found: vanillic acid, syringic acid and minor quantities of aceto-derivatives. The profile of products concentration with the reaction time of catalytic oxidation with microwave irradiation are shown and discussed with reference to the investigated lignin features. The mechanism of the microwave catalytic oxidation for LOB under alkaline conditions was proposed