Large time blow up for a perturbation of the cubic Szeg\H{o} equation

We consider the following Hamiltonian equation on a special manifold of rational functions, i\p\_tu=\Pi(|u|^2u)+\al (u|1),\ \al\in\R, where $\Pi$ denotes the Szeg\H{o} projector on the Hardy space of the circle \SS^1. The equation with \al=0 was first introduced by G{\'e}rard and Grellier in \cite{GG1} as a toy model for totally non dispersive evolution equations. We establish the following properties for this equation. For \al\textless{}0, any compact subset of initial data leads to a relatively compact subset of trajectories. For \al\textgreater{}0, there exist trajectories on which high Sobolev norms exponentially grow with time.Comment: page number:1

A trend deduction model of fluctuating oil prices

Crude oil prices have been fluctuating over time and by a large range. It is the disorganization of oil price series that makes it difficult to deduce the changing trends of oil prices in the middle- and long-terms and predict their price levels in the short-term. Following a price-state classification and state transition analysis of changing oil prices from January 2004 to August 2009, this paper first verifies that the observed crude oil price series during the soaring period follow a Markov Chain. Next, the paper deduces the changing trends of oil prices by the limit probability of a Markov Chain. We then undertake a probability distribution analysis and find that the oil price series have a log-normality distribution. On this basis, we integrate the two models to deduce the changing trends of oil prices from the short-term to the middle- and long-terms, thus making our deduction academically sound. Our results match the actual changing trends of oil prices, and show the possibility of re-emerging soaring oil prices.Oil price; Log-normality distribution; Limit probability of a Markov Chain; Trend deduction model; OPEC

A Trend Deduction Model of Fluctuating Oil Prices

Crude oil prices have been fluctuating over time and by a large range. It is the disorganization of oil price series that makes it difficult to deduce the changing trends of oil prices in the middle- and long-terms and predict their price levels in the short-term. Following a price-state classification and state transition analysis of changing oil prices from January 2004 to April 2010, this paper first verifies that the observed crude oil price series during the soaring period follow a Markov Chain. Next, the paper deduces the changing trends of oil prices by the limit probability of a Markov Chain. We then undertake a probability distribution analysis and find that the oil price series have a log-normality distribution. On this basis, we integrate the two models to deduce the changing trends of oil prices from the short-term to the middle- and long-terms, thus making our deduction academically sound. Our results match the actual changing trends of oil prices, and show the possibility of re-emerging soaring oil prices.Oil Price, Log-normality Distribution, Limit Probability of a Markov Chain, Trend Deduction Model, OPEC

Analysis Of Undergraduate Students’ Behavioral Intentions and Usage Behavior of Online Learning Platforms in Chengdu, Sichuan, China

Purpose: This study examines the factors affecting behavioral intention and usage behavior of online learning platforms among undergraduate students in Xihua University in Chengdu, Sichuan, China. A conceptual framework is developed through the Theory of Planned Behavior (TPB), the technology acceptance model (TAM) and its extended Model (TAM2), and the unified theory of technology acceptance and use (UTAUT). The researcher determines key variables which are social influence, perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control behavioral intention and usage behavior. Research design, data, and methodology: The target population is 500 participants. The study applied quantitative method to distribute online questionnaires. The sampling method used are purposive and convenience sampling. The data were analyzed by Confirmation factor analysis (CFA) to test the validity and reliability. In addition, structural equation modeling (SEM) model was used to evaluate the hypotheses. Results: The results showed that behavioral intention and use behavior were significantly influenced by social influence, perceived usefulness, perceived ease of use, attitudes, subjective norms, and perceived behavioral control. Conclusions: The findings imply that users’ behavioral intentions are crucial to online learning adoption and suggests that platform designers should fully improve and upgrade online learning platform systems

Innovative Approach to Anti-BEPS and the Coherence of International Tax Law

This dissertation is comprised of three articles: Avi-Yonah, Reuven,. co-author. Evaluating BEPS: A Reconsideration of the Benefits Principle and Proposal for UN Oversight. H. Xu, co-author. Harv. Bus. L. Rev. 6, no. 2 (2016): 185-238 Reuven S. Avi-Yonah & Haiyan Xu, A Global Treaty Override? The New OECD Multilateral Tax Instrument and Its Limits, 39 Mich. J. Int\u27l L. 155 (2018). Avi-Yonah, Reuven S. China and BEPS. Haiyan Xu, co-author. Laws 7, no. 1 (2018): 4-30

Influencing Factors Of Behavioral Intention and Use Behavior of Online Learning Platforms Among Public College Students in Chengdu, Sichuan Province, China

Purpose: The rapid development of Internet technology and the rapid popularization of mobile terminals have promoted the vigorous development of online education. Based on the theory of technology acceptance models, his study highlights the factors influencing the behavioral intention and use behavior of Chinese public vocational school students to use online learning platforms. In this framework, the researchers examined social influence, perceived usefulness, perceived ease of use, attitudes, subjective norms, and the relationship between perceived behavioral control and behavioral intention and use behavior. Research design, data, and methodology: This quantitative study employed 500 vocational school students who have been using online learning platforms in Chengdu, Sichuan Province, China. The sampling techniques involve purposive and convenience sampling. IOC validation ensured the content validity and the pilot test (n=30) with Cronbach’s alpha reliability (CA) test results were approved. Statistical analyses were conducted by confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: The results showed that social influence, perceived ease of use, perceived usefulness, subjective norms, perceived behavioral control, attitude significantly influence behavioral intention, and usage behavior. Conclusions: Finally, relevant suggestions are made for the improvement and development of online learning platforms in order to increase students’ willingness to use them