Learning Satisfaction of Online Art Education: A Case of Undergraduates in Public Colleges in Sichuan

Purpose: Online education is destined to become the development trend of education due to the rise of the COVID-19 epidemic. Therefore, this study aims to determine influencing factors of learning satisfaction of undergraduate students, majoring in online art education in public colleges in Sichuan Province, China. A conceptual framework proposes the causal relationship between system quality, information quality, service quality, perceived usability, perceived ease of use, self-efficacy, and learning satisfaction. Research design, data, and methodology: 494 undergraduates were surveyed as part of the study using a project questionnaire using both online and offline approaches. The sampling methods are judgmental sampling, stratified random and convenience sampling. In order to quantify the causal link and conduct a hypothesis test between the variables, the researcher utilized confirmatory factor analysis and a structural equation model. Results: The results demonstrate that all hypotheses are supported. Furthermore, self-efficacy has the strongest significant effect on perceived ease of use. Conclusion: This research can guide the relevant departments of art majors in public universities in Sichuan Province to integrate online learning and increase the effectiveness of that learning by enhancing students’ performance

Regression Analysis of Recurrent Gap Times with Time-Dependent Covariates

Individual subjects may experience recurrent events of same type over a relatively long period of time in a longitudinal study. Researchers are often interested in the distributional pattern of gaps between the successive recurrent events and their association with certain concomitant covariates as well. In this article, their probability structure is investigated in presence of censoring. According to the identified structure, we introduce the proportional reverse-time hazards models that allow arbitrary baseline function for every individual in the study, when the time-dependent covariates effect is of main interest. Appropriate inference procedures are proposed and studied to estimate the parameters of interest in the models. The proposed methodology is demonstrated with the Monte-Carlo simulations and applied to a well-known Denmark schizophrenia cohort study data set

Semiparametric Regression Analysis on Longitudinal Pattern of Recurrent Gap Times

In longitudinal studies, individual subjects may experience recurrent events of the same type over a relatively long period of time. The longitudinal pattern of the gaps between the successive recurrent events is often of great research interest. In this article, the probability structure of the recurrent gap times is first explored in the presence of censoring. According to the discovered structure, we introduce the proportional reverse-time hazards models with unspecified baseline functions to accommodate heterogeneous individual underlying distributions, when the ongitudinal pattern parameter is of main interest. Inference procedures are proposed and studied by way of proper riskset construction. The proposed methodology is demonstrated by Monte-Carlo simulations and an application to the well-known Denmark schizophrenia cohort study data se

Normal-mode splitting in the optomechanical system with an optical parametric amplifier and coherent feedback

Strong coupling in optomechanical systems is the basic condition for observing many quantum phenomena such as optomechanical squeezing and entanglement. Normal-mode splitting (NMS) is the most evident signature of strong coupling systems. Here we show the NMS in the spectra of the movable mirror and the output field in an optomechanical system can be flexibly engineered by a combination of optical parametric amplifier (OPA) and coherent feedback (CF). Moreover, the NMS could be enhanced by optimizing the parameters such as input optical power, OPA gain and phase, CF strength in terms of amplitude reflectivity of beam splitter.Comment: 8 pages, 7 figure

Deep Reinforcement Learning Based Optimal Energy Management of Multi-energy Microgrids with Uncertainties

Multi-energy microgrid (MEMG) offers an effective approach to deal with energy demand diversification and new energy consumption on the consumer side. In MEMG, it is critical to deploy an energy management system (EMS) for efficient utilization of energy and reliable operation of the system. To help EMS formulate optimal dispatching schemes, a deep reinforcement learning (DRL)-based MEMG energy management scheme with renewable energy source (RES) uncertainty is proposed in this paper. To accurately describe the operating state of the MEMG, the off-design performance model of energy conversion devices is considered in scheduling. The nonlinear optimal dispatching model is expressed as a Markov decision process (MDP) and is then addressed by the twin delayed deep deterministic policy gradient (TD3) algorithm. In addition, to accurately describe the uncertainty of RES, the conditional-least squares generative adversarial networks (C-LSGANs) method based on RES forecast power is proposed to construct the scenarios set of RES power generation. The generated data of RES is used for scheduling to obtain caps and floors for the purchase of electricity and natural gas. Based on this, the superior energy supply sector can formulate solutions in advance to tackle the uncertainty of RES. Finally, the simulation analysis demonstrates the validity and superiority of the method.Comment: Accepted by CSEE Journal of Power and Energy System

Short-term power load forecasting method based on CNN-SAEDN-Res

In deep learning, the load data with non-temporal factors are difficult to process by sequence models. This problem results in insufficient precision of the prediction. Therefore, a short-term load forecasting method based on convolutional neural network (CNN), self-attention encoder-decoder network (SAEDN) and residual-refinement (Res) is proposed. In this method, feature extraction module is composed of a two-dimensional convolutional neural network, which is used to mine the local correlation between data and obtain high-dimensional data features. The initial load fore-casting module consists of a self-attention encoder-decoder network and a feedforward neural network (FFN). The module utilizes self-attention mechanisms to encode high-dimensional features. This operation can obtain the global correlation between data. Therefore, the model is able to retain important information based on the coupling relationship between the data in data mixed with non-time series factors. Then, self-attention decoding is per-formed and the feedforward neural network is used to regression initial load. This paper introduces the residual mechanism to build the load optimization module. The module generates residual load values to optimize the initial load. The simulation results show that the proposed load forecasting method has advantages in terms of prediction accuracy and prediction stability.Comment: in Chinese language, Accepted by Electric Power Automation Equipmen

Some Essential Issues and Outlook for Industrialization of Cu-III-VI2 Thin-Film Solar Cells

The concept and method of in-line sputtering and selenization become the industrial standard for Cu-III-VI2 solar cell fabrication, but it is a difficult work to control and predict the electrical and optical performances, which are closely related to the chemical composition of the film. This chapter addresses the material design, device design, and process design using chemical compositions relating parameters. Compositional variation leads to change in the poisson equation, current equation and continuity equation governing the device design. To make the device design much realistic and meaningful, we have to build a model that relates the opto-electrical performance to the chemical composition of the film. The material and device structural parameters are input into the process simulation to give a complete process control parameters and method. We calculated neutral defect concentrations of non-stoichiometric CuMSe2 (M-In, Ga) under the specific atomic chemical potential conditions. The electrical and optical performance has also been investigated for the development of full function analytical solar cell simulator. Module instability and their origins are listed. After that progress of CZTS (Cu2ZnS4) is briefed on the future work of CIGS (CuInGaSe2). The future prospects regarding the development of CIGS thin-film solar cells (TFSCs) have also been discussed