Due to the increase in the number of the satellite channels which resulted from the satellite broadcasting, the demand for the television drama production has increased dramatically, and there has been a significant competitive market, which encouraged the producers to adopt many carrots among them the inclusion of TV series large scenes whose main purpose is to show the volume of the production in the series, let alone achieving the dramatic function. The researcher called this type of scenes (the productive scenes), a name which has not been used before. The researcher studied these scenes and determined his research subject under the title (the production scenes in the TV series) where the research problem has been determined through

The researcher, in this academic research, seeks to highlight the role of the semantic production in the dramatic act and to know the extent of its work and importance, because drama is an act and the dramatic work is a group of acts. The dramatic meaning and act are two inseparable concepts that cannot be divided, because each dramatic act is a container of meanings through connotations, signs and symbols. The production of the connotations in the dramatic act is considered one of the important issues in the content language of communication between the sender and recipient in order to get to the intended meaning. These sign connotations engage the spectator in the codifying activity and get him immersed in the events. The research pres

The fractional order partial differential equations (FPDEs) are generalizations of classical partial differential equations (PDEs). In this paper we examine the stability of the explicit and implicit finite difference methods to solve the initial-boundary value problem of the hyperbolic for one-sided and two sided fractional order partial differential equations (FPDEs). The stability (and convergence) result of this problem is discussed by using the Fourier series method (Von Neumanns Method).

At the beginning of the twentieth century distorting handling totalitarian phenomena of art, which can be called the stage of the test, transition from the theory of ideal theory and other realistic is not cushy, since it requires vision and reading and other concepts, and in light of this dialectic manifested research problem by asking the following Is affected by the design idealism and realism. Through the above mentioned questions, the researcher found rationale for addressing this problem, the study through his research, which is marked (idealism and realism in a comparative study design). And demonstrated the importance of research in the identification of the concept and the effectiveness of the two theories idealism and realism a

This paper focuses on developing a self-starting numerical approach that can be used for direct integration of higher-order initial value problems of Ordinary Differential Equations. The method is derived from power series approximation with the resulting equations discretized at the selected grid and off-grid points. The method is applied in a block-by-block approach as a numerical integrator of higher-order initial value problems. The basic properties of the block method are investigated to authenticate its performance and then implemented with some tested experiments to validate the accuracy and convergence of the method.

This paper introduces a novel nonparametric hybrid cyber-intelligence-based statistical process control and anomaly detection framework in time series data. It is developed to overcome the shortcomings of the classical control schemes in dealing with complex, abnormal, and noisy input data, especially when it is autocorrelated. The proposed methodology combines three technical pillars: First, it utilizes a bidirectional long-short-term memory architecture (Bi-LSTM) to capture long-term time dependency and learn nonlinear patterns, leaving only true deviations as residuals that remove trends and noises from the market. Second, it adopts the Golden Eagle Optimizer (GEO) algorithm for optimal parameter selection. This intelligent algor

Background Due to the intermittent, nonlinear, and uncertain behavior of renewable energy sources (res) such as solar and wind, grid stability and reliability require very high forecasting and optimization skills as widely reported in the literature. Traditional optimization methods work very well in small or static systems but are suffer difficulty on large-scale, dynamic and stochastic renewable environment due to their NP-hard nature. Methods The framework introduces the concept of a Machine Learning-Assisted Hybrid Cuckoo Search (ML-HCS) that combines CS with a hybrid metaheuristic and integrates Long Short-Term Memory (LSTM) networks for forecasting based on both regression models of LSTMs and hybrid optimization algorithm