Supervised, semi-supervised, and unsupervised learning of the Domany-Kinzel model

The Domany Kinzel (DK) model encompasses several types of non-equilibrium phase transitions, depending on the selected parameters. We apply supervised, semi-supervised, and unsupervised learning methods to studying the phase transitions and critical behaviors of the (1 + 1)-dimensional DK model. The supervised and the semi-supervised learning methods permit the estimations of the critical points, the spatial and temporal correlation exponents, concerning labelled and unlabelled DK configurations, respectively. Furthermore, we also predict the critical points by employing principal component analysis (PCA) and autoencoder. The PCA and autoencoder can produce results in good agreement with simulated particle number density

Web Mining For Financial Market Prediction Based On Online Sentiments

Financial market prediction is a critically important research topic in financial data mining because of its potential commerce application and attractive profits. Previous studies in financial market prediction mainly focus on financial and economic indicators. Web information, as an information repository, has been used in customer relationship management and recommendation, but it is rarely considered to be useful in financial market prediction. In this paper, a combined web mining and sentiment analysis method is proposed to forecast financial markets using web information. In the proposed method, a spider is firstly employed to crawl tweets from Twitter. Secondly, Opinion Finder is offered to mining the online sentiments hidden in tweets. Thirdly, some new sentiment indicators are suggested and a stochastic time effective function (STEF) is introduced to integrate everyday sentiments. Fourthly, support vector regressions (SVRs) are used to model the relationship between online sentiments and financial market prices. Finally, the selective model can be serviced for financial market prediction. To validate the proposed method, Standard and Poor’s 500 Index (S&P 500) is used for evaluation. The empirical results show that our proposed forecasting method outperforms the traditional forecasting methods, and meanwhile, the proposed method can also capture individual behavior in financial market quickly and easily. These findings imply that the proposed method is a promising approach for financial market prediction

Numerical Analysis of Space Effect on the Pile-Anchor Bracing Deep Foundation Pit

Volume 8 Issue 1 (January 201

Therapeutic potential of fatty acid amide hydrolase, monoacylglycerol lipase, and N-acylethanolamine acid amidase inhibitors

Fatty acid ethanolamides (FAEs) and endocannabinoids (ECs) have been shown to alleviate pain and inflammation, regulate motility and appetite, and produce anti-cancer, anxiolytic, and neuroprotective efficacies via cannabinoid receptor type 1 (CB1) or type 2 (CB2), or via peroxisome proliferator-activated receptor α (PPAR-α) stimulation. FAEs and ECs are synthesized by a series of endogenous enzymes, including N acylphosphatidylethanolamine-phospholipase D (NAPE-PLD), diacylglycerol lipase (DAGL), or phospholipase C (PLC), and their metabolism is mediated by several metabolic enzymes, including fatty acid amide hydrolase (FAAH), monoacylglycerol lipase (MAGL), Nacylethanolamine acid amidase (NAAA), or cyclooxygenase-2 (COX-2). Over the last decades, increasing the concentration of FAEs and ECs through the inhibition of degrading enzymes has been considered to be a viable therapeutic approach to enhance their anti-nociceptive and anti-inflammatory effects, as well as protecting the nervous system