This study aimed to identify the employment of the social networking platform «Twitter» in the 2016 presidential campaign led by the Republican candidate, Donald Trump; and analyse his tweets through his personal account on «Twitter»  for the period from: 10/ 8/2016 to: 11/ 8/2016 which represents the last month of the election campaign.

The study belongs to the type of descriptive studies using the analytical method through an analysis index that includes sub-categories and other secondary categories. The research has adopted the ordinary unit of information material (tweet) as an analysis unit for this purpose.

The dynamical behavior of a two-dimensional continuous time dynamical system describing by a prey predator model is investigated. By means of constructing suitable Lyapunov functional, sufficient condition is derived for the global asymptotic stability of the positive equilibrium of the system. The Hopf bifurcation analysis is carried out. The numerical simulations are used to study the effect of periodic forcing in two different parameters. The results of simulations show that the model under the effects of periodic forcing in two different parameters, with or without phase difference, could exhibit chaotic dynamics for realistic and biologically feasible parametric values.

The design studies, especially in their contemporary stages, have not been isolated from the subjects that underpin advanced ideas in the field of technical treatments that enrich and improve the social life of users through those technological innovations that have enhanced and changed life styles, Especially in the field of general institutional techniques, including cinemas, as the ideal places for people and visitors to this space, which can contribute to the promotion of cultural, social and technical aspects within this millennium. DONC as reflected on the positive communication and communication between the intellectual and cognitive develop individuals, including cultural and technical side of the individual. The first chapter co

This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

The present work elucidates the utilization of activated carbon (AC) and activated carbon loaded with silver nanoparticles (AgNPs-AC) to remove tetracycline (TC) from synthetically polluted water. The activated carbon was prepared from tea residue and loaded with silver nanoparticles. Scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and Brunauer-Emmett-Teller (BET) were used to characterize the activated carbon (AC) and silver nanoparticles-loaded activated carbon (AgNPs-AC). The impact of various parameters on the adsorption effectiveness of TC was examined. These variables were the initial adsorbate concentration (Co), solution acidity (pH), adsorption time (t), and dosag

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar

This paper is specifically a detailed review of the Spatial Quantile Autoregressive (SARQR) model that refers to the incorporation of quantile regression models into spatial autoregressive models to facilitate an improved analysis of the characteristics of spatially dependent data. The relevance of SARQR is emphasized in most applications, including but not limited to the fields that might need the study of spatial variation and dependencies. In particular, it looks at literature dated from 1971 and 2024 and shows the extent to which SARQR had already been applied previously in other disciplines such as economics, real estate, environmental science, and epidemiology. Accordingly, evidence indicates SARQR has numerous benefits compar

. In recent years, Bitcoin has become the most widely used blockchain platform in business and finance. The goal of this work is to find a viable prediction model that incorporates and perhaps improves on a combination of available models. Among the techniques utilized in this paper are exponential smoothing, ARIMA, artificial neural networks (ANNs) models, and prediction combination models. The study's most obvious discovery is that artificial intelligence models improve the results of compound prediction models. The second key discovery was that a strong combination forecasting model that responds to the multiple fluctuations that occur in the bitcoin time series and Error improvement should be used. Based on the results, the prediction a