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The prediction of chaotic time series has been a popular research field in recent years. Due to the strong non-stationary and high complexity of the chaotic time series, it is difficult to directly analyze and predict depending on a single model, so the hybrid prediction model has become a promising and favorable alternative. In this paper, we put forward a novel hybrid model based on a two-layer decomposition approach and an optimized back propagation neural network (BPNN). The two-layer decomposition approach is proposed to obtain comprehensive information of the chaotic time series, which is composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD). The VMD algorithm is used for further decomposition of the high frequency subsequences obtained by CEEMDAN, after which the prediction performance is significantly improved. We then use the BPNN optimized by a firefly algorithm (FA) for prediction. The experimental results indicate that the two-layer decomposition approach is superior to other competing approaches in terms of four evaluation indexes in one-step and multi-step ahead predictions. The proposed hybrid model has a good prospect in the prediction of chaotic time series.

Throughout the series we see each commit a number of heinous crimes against people, including the Alliances drugging of an entire planet in an attempt to make the populace more compliant.1 The Alliance is not a succor to its people, but a hindrance, as revealed by its authoritarian nature and socioeconomic structure.2 The hypercapitalist verse seems offensive to Captain Malcolm (Mal) Reynolds sense of justice and his actions in Firefly show the audience how inescapable the brutality of such a system is. [...]a system places the acquisition of wealth and objects as overriding all else in a person's life: private, social, and leisure time become completely consumed in this pursuit, eroding the bonds between people in their communities and turning human interaction into an exchange of goods and services. [...]the Alliance's reaction to such a heinous tragedy caused by their own hubris inverts the expectation of the viewer. Alliance authorities claim they wish to spread the light of civilization to as many planets and people as possible, but it appears as if hunger for complete power and control over others motivates them, in the way Reavers hunger for human flesh.

Introduction This paper explores and critiques the typographic and design decisions made in the creation of the logo and brand for the 2oo5 film Serenity by Joss Whedon and how the choice of the Papyrus-inspired typeface used in the logo perpetuates the potential for orientalist, racialized stereotypes, and material dishonesty previously seen in the film's casting, props, and story critiqued prior in Serenity scholarly literature. [...]the Serenity logo takes on six different forms (Fig. i): 1. The American Marketing Association describes a brand as a \"name, term, sign, symbol, or design, or a combination of them, intended to identify the goods and services of one seller or group of sellers and to differentiate them from those of competition\" (American Marketing Association). The Serenity logo has succeeded in creating strong brand equity as 20 years later many Browncoats still adore the TV show, film, comics, books, and merchandise as much as ever.

In this work, information on COVID-19 confirmed cases is utilized as a dataset to perform time series predictions. We propose the design of ensemble neural networks (ENNs) and type-3 fuzzy inference systems (FISs) for predicting COVID-19 data. The answers for each ENN module are combined using weights provided by the type-3 FIS, in which the ENN is also designed using the firefly algorithm (FA) optimization technique. The proposed method, called ENNT3FL-FA, is applied to the COVID-19 data for confirmed cases from 12 countries. The COVID-19 data have shown to be a complex time series due to unstable behavior in certain periods of time. For example, it is unknown when a new wave will exist and how it will affect each country due to the increase in cases due to many factors. The proposed method seeks mainly to find the number of modules of the ENN and the best possible parameters, such as lower scale and lower lag of the type-3 FIS. Each module of the ENN produces an individual prediction. Each prediction error is an input for the type-3 FIS; moreover, outputs provide a weight for each prediction, and then the final prediction can be calculated. The type-3 fuzzy weighted average (FWA) integration method is compared with the type-2 FWA to verify its ability to predict future confirmed cases by using two data periods. The achieved results show how the proposed method allows better results for the real prediction of 20 future days for most of the countries used in this study, especially when the number of data points increases. In countries such as Germany, India, Italy, Mexico, Poland, Spain, the United Kingdom, and the United States of America, on average, the proposed ENNT3FL-FA achieves a better performance for the prediction of future days for both data points. The proposed method proves to be more stable with complex time series to predict future information such as the one utilized in this study. Intelligence techniques and their combination in the proposed method are recommended for time series with many data points.

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions. Recent works are focused only on confirmed COVID-19 cases or a specific country. In this work, the firefly algorithm designs an ensemble neural network architecture for each one of 26 countries. In this work, we propose the firefly algorithm for ensemble neural network optimization applied to COVID-19 time series prediction with type-2 fuzzy logic in a weighted average integration method. The proposed method finds the number of artificial neural networks needed to form an ensemble neural network and their architecture using a type-2 fuzzy inference system to combine the responses of individual artificial neural networks to perform a final prediction. The advantages of the type-2 fuzzy weighted average integration (FWA) method over the conventional average method and type-1 fuzzy weighted average integration are shown.