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In the design of multiple response parameters optimization, weighted principal component analysis (weighted PCA) is used to build the relationship between the response variables and controllable factor model by linear regression. But in the complicated nonlinear production process, the fit of the linear regression model is not high that cannot satisfy the requirement of the parameter design model. This study proposed an improved weighted PCA based on RBF neural network prediction model. In this paper, RBF neural network was used to construct nonlinear prediction model of production process and to adjust the weighted PCA algorithm by adding the predict ability index of neural network model. In the design of multiple response parameters, this approach improve the effect of process parameters optimization. And applied this method to multiple response parameters optimization design of metallization polypropylene film capacitor thermal polymerization process, the results show that capacitance value and the loss tangent are all improved, and the effect of optimization parameters is achieve to satisfactory results.

In this article, we focus on the left (translation) actions on noncommutative compact connected Lie groups S U (2) × T n . We define the rotation vectors of the left actions induced by the elements in the maximal tori of S U (2) × T n , and utilize rotation vectors to give the complete topologically conjugate classifications of left actions. Algebraic conjugacy and smooth conjugacy are also considered. [ABSTRACT FROM AUTHOR]

In this article, we focus on the left (translation) actions on noncommutative compact connected Lie groups S U (2) × T n . We define the rotation vectors of the left actions induced by the elements in the maximal tori of S U (2) × T n , and utilize rotation vectors to give the complete topologically conjugate classifications of left actions. Algebraic conjugacy and smooth conjugacy are also considered. [ABSTRACT FROM AUTHOR]

The limited resources of fossil fuels and bio energies, along with the environmental pollution caused by their combustion, make it necessary to search for renewable and clean alternative sources and to optimize the available current energies specifically for required energy for using in buildings. This is mandatory for a solution of future energy production issues as huge consumption of energy in buildings and structures is unavoidable in buildings for instance China's higher education buildings consume huge amounts of bioenergy. Most of the attention in the production of biofuel has been focused on the use of plant biomass, agricultural waste, solid waste, and sewage treatment sludge. Today, there are renewable resources to replace fossil fuels such as biofuels for usage in buildings required energy; however, in the last decade, the cultivation of microalgae has been proposed as another option for biomass production. Utilization of algae biomass is more cost-effective than vegetable crops in terms of water consumption and cultivated area and reduces costs and greenhouse gas emissions by replacing fossil fuels. Artificial intelligence is a tool to help for this utilization and becoming much better in recent researches. Many types of microalgae, due to their high ability to consume organic carbon and inorganic nitrogen and phosphorus, can grow in different water environments, including urban, industrial, agricultural wastewaters, and wastewaters containing animal waste, which contain large amounts of organic and inorganic carbon. There are nitrogen, phosphorus and other elements that act as a biological filter that with extensive studies and inventing new methods, cost-effective production of algae-biofuels can be achieved. This review goals to run over the latest innovations and artificial intelligence approaches for biohydrogen generation from algal towards sustainable energy optimization and greener environment for buildings required energy. The current state of the art, existing and upcoming challenges, latest technologies, and solutions to overcome existing limitation are presented in details. • This review cover the latest innovations for biohydrogen generation from algal. • The sustainable energy and greener environment aspect are considered. • Latest innovations of AI for biohydrogen generation from algal are reviewed. • Current literature, existing and future challenges and technologies are presented. [ABSTRACT FROM AUTHOR]