Research on the Relationship between Online Reviews and Customer Purchase Intention: The Moderating Role of Personality Trait

As an important factor that affects customer purchase intention, online review has attracted the attention from both enterprises and researchers. According to persuasion theory, planned behavior theory and regulatory focus theory, combined with the three dimensions of online reviews, we construct a modified model of the influence of online reviews on customer purchase intention, and put forward relevant theoretical assumptions. Based on data from 252 samples, this paper studies the relationship between online reviews and customer purchase intention, and further reveals the moderating effect of personality traits

Quasi-period outflows observed by the X-Ray Telescope onboard Hinode in the boundary of an active region

Persistent outflows have recently been detected at boundaries of some active regions. Although these outflows are suggested to be possible sources of the slow solar wind, the nature of these outflows is poorly understood. Through an analysis of an image sequence obtained by the X-Ray Telescope onboard the Hinode spacecraft, we found that quasi-period outflows are present in the boundary of an active region. The flows are observed to occur intermittently, often with a period of 5-10 minutes. The projected flow speed can reach more than 200 km/s, while its distribution peaks around 50 km/s. This sporadic high-speed outflow may play an important role in the mass loading process of the slow solar wind. Our results may imply that the outflow of the slow solar wind in the boundary of the active region is intermittent and quasi-periodic in nature.Comment: 5 figures, accepted by RA

One-Shot Parameter Identification of the Thevenin's Model for Batteries: Methods and Validation

Parameter estimation is of foundational importance for various model-based battery management tasks, including charging control, state-of-charge estimation and aging assessment. However, it remains a challenging issue as the existing methods generally depend on cumbersome and time-consuming procedures to extract battery parameters from data. Departing from the literature, this paper sets the unique aim of identifying all the parameters offline in a one-shot procedure, including the resistance and capacitance parameters and the parameters in the parameterized function mapping from the state-of-charge to the open-circuit voltage. Considering the well-known Thevenin's battery model, the study begins with the parameter identifiability analysis, showing that all the parameters are locally identifiable. Then, it formulates the parameter identification problem in a prediction-error-minimization framework. As the non-convexity intrinsic to the problem may lead to physically meaningless estimates, two methods are developed to overcome this issue. The first one is to constrain the parameter search within a reasonable space by setting parameter bounds, and the other adopts regularization of the cost function using prior parameter guess. The proposed identifiability analysis and identification methods are extensively validated through simulations and experiments