The tariffs of regulated public services under price cap regulation are variables defined in the tariff review process, subject to a major technical complexity and many restrictions. Given the complex scenario that involves the tariffs definition, the present paper seeks to verify the effectiveness of the implementation of a Linear Programming model to support decision-making in optimal tariffs definition. The Solver add-in of Microsoft Excel software was used to generate the optimal solution for the proposed model. The results indicated that the proposed model generated a reasonable result, incorporating the various restrictions for modeling and ensuring details at a sufficient level for modeling adjustments throughout the process, if necessary.
Social networks have become indispensable in people's lives. Despite the conveniences brought by social networks, the fake news on those online platforms also induces negative impacts and losses for users. With the development of deep learning technologies, detecting fake news in a data-driven manner has attracted great attention. In this paper, we give a brief survey that discusses the recent development of deep learning methods in fake news detection. Compared with previous surveys, we focus on the different data structures instead of the models they used to process those data. We give a new taxonomy that categorizes current models into the following three parts: models that formulate fake news detection as text classification, models that formulate fake news detection as graph classification, and models that formulate fake news detection as hybrid classification. The advantages and drawbacks of those methods are also discussed.
Visual variables are important factors for visualization in 2D maps, and the study of non-photorealistic visualization of 3D city models in virtual environments using visual variables is also necessary to represent the attributes of city such as energy consumption. This study proposed a set of visual variable mapping model based on 3D geographical environment and conducted user cognitive test. The goal of this research is to verify the applicability of visual variables in 3D space and explore the impact of disturbing visual variables on user cognition. The results show that visualization of compound visual variables in 3D space is more effective than single variable mapping, and it makes up for the shortcomings of single mapping. It is effective to study the visualization effect in three-dimensional geographical environment using virtual reality technology. The results from this case study provide guidance for 3D user-centric visualization technology and make contribute to the development of smart city.
The Efficient Global Optimization (EGO) optimizes the temperature curve of the reflow soldering process by constructing the Kriging model. Compared with the traditional Surrogate Assisted Optimization (SAO), it can meet two different requirements: exploitation of optima and exploration of uncertain areas. However, due to the inversion of the covariance correlation matrix and the solving of Kriging-related parameters, the Kriging approximation process for high-dimensional problems is time consuming and even impossible to construct. For this reason, a High-Dimensional Kriging Modeling method through Linear Discriminant Analysis (HDKRG-LDA) method is proposed by considering the correlation parameters and the design variables of a Kriging model. It uses LDA to transform a high-dimensional correlation parameter vector in Kriging into low-dimensional one, which is used to reconstruct a new correlation function. In this study, the HDKRG-LDA method assisted EGO algorithm is applied to optimize the temperature curve of the reflow soldering process. In the optimization results of the reflow soldering, the method can effectively predict the temperature curve, and provide guidance for the production process of the reflow soldering.
