A novel technique Sumudu transform Adomian decomposition method (STADM), is employed to handle some kinds of nonlinear time-fractional equations. We demonstrate that this method finds the solution without discretization or restrictive assumptions. This method is efficient, simple to implement, and produces good results. The fractional derivative is described in the Caputo sense. The solutions are obtained using STADM, and the results show that the suggested technique is valid and applicable and provides a more refined convergent series solution. The MATLAB software carried out all the computations and graphics. Moreover, a graphical representation was made for the solution of some examples. For integer and fractional order problems, solutio

: zonal are included in phraseological units, form metaphorical names for a person, give him various emotional and evaluative characteristics. This article examines the topic of zoomorphic metaphors that characterize a person in the Russian and Arabic languages in the aspect of their comparative analysis, since the comparative analysis of the metaphorical meanings of animalisms is an important method for studying cultural linguistics, since zoomorphic metaphors are a reflection of culture in a language.

A substantial percentage of the world’s energy consumption (almost 40%) and carbon dioxide (CO2) emissions (around 37%) come from the construction industry, especially schools. This work presents a new hybrid artificial intelligence (AI) engineering model that aims to maximize energy performance on campuses in a holistic way. Modules for data-driven forecasting, metaheuristic optimization, and real-time adaptive control are all part of the concept. A thorough energy simulation of a university campus building is used in conjunction with the AI model to assess its performance through a co-simulation framework. Findings show that yearly peak electricity demand may be reduced by 18.7% and total site energy consumption by 22.4% when co

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, rec

Introduction: Carrier-based gutta-percha is an effective method of root canal obturation creating a 3-dimensional filling; however, retrieval of the plastic carrier is relatively difficult, particularly with smaller sizes. The purpose of this study was to develop composite carriers consisting of polyethylene (PE), hydroxyapatite (HA), and strontium oxide (SrO) for carrier-based root canal obturation. Methods: Composite fibers of HA, PE, and SrO were fabricated in the shape of a carrier for delivering gutta-percha (GP) using a melt-extrusion process. The fibers were characterized using infrared spectroscopy and the thermal properties determined using differential scanning calorimetry. The elastic modulus and tensile strength tests were dete

       In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from      Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial

A nonlinear filter for smoothing color and gray images
corrupted by Gaussian noise is presented in this paper. The proposed
filter designed to reduce the noise in the R,G, and B bands of the
color images and preserving the edges. This filter applied in order to
prepare images for further processing such as edge detection and
image segmentation.
The results of computer simulations show that the proposed
filter gave satisfactory results when compared with the results of
conventional filters such as Gaussian low pass filter and median filter
by using Cross Correlation Coefficient (ccc) criteria.