In The Name of Allah Most Gracious Most Merciful

This is a study dedicated to explaining the conditions of the end of heaven, stage by stage; And to draw the final picture of it, through the facts of the Qur’an, and to make the contextual integration of the verses of the end of heaven, and their arrangement in an intentional arrangement, a path that guides us to knowing the integrated picture of it on the Day of Resurrection, according to scientific interpretation.

The importance and usefulness of the research lies in:

Extracting the order of the stages of the end of heaven, from its first state to its last, which came in separate verses of the Qur’an, each one of which was placed in a surah that was consistent w

The efficient removal of dissolved organic compounds (DOC) from wastewater has become a major environmental concern because of its high toxicity even at low concentrations. Therefore, a technique was needed to reduce these pollutants. Ion exchange technology (IE) was used with Amberlite^TM IR120 Na, Amberlite^TM IR96RF, and Amberlite^TM IR402, firstly by using anion and mixed bed system, where the following variables are investigated for the process of adsorption: The height of the bed in column (8,10 and 14 cm), different concentrations of (DOC) content at constant flow rate. The use of an ion exchanger unit (continuous system) with three columns (cation, anion, and mixed bed) was studied.

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

Organohalosilanes conslitute an important subject ١٦؛ the chemistry oforganosilicon compound؛. Being starting materials and intermediates in the synthesis of a large number of various compounds so it is very important to get such materials in its highest purity ,but the separation of rathylchlorosilanes was still a big^oblem, duet^the great similarity in their physical and chemical properties, making its analysing verydifficult, ^or this reason tteir must be a good method o^e^r^iondealing^ththe^compounds, gas- liquid chromatography proved that it was the best, specially when (m- nitrotoluene) was used as a stationary liquid phase, it gave a complete separation and a good statistical results

Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ

In this paper, a procedure to establish the different performance measures in terms of crisp value is proposed for two classes of arrivals and multiple channel queueing models, where both arrival and service rate are fuzzy numbers. The main idea is to convert the arrival rates and service rates under fuzzy queues into crisp queues by using graded mean integration approach, which can be represented as median rule number. Hence, we apply the crisp values obtained to establish the performance measure of conventional multiple queueing models. This procedure has shown its effectiveness when incorporated with many types of membership functions in solving queuing problems. Two numerical illustrations are presented to determine the validity of the

Liquid-Liquid Extraction of Cu(II) ion in aqueous solution by dicyclohexyl-18-crown-6 as extractant in dichloroethane was studied .The extraction efficiency was investigated by a spectrophometric method. The reagent form a coloured complex which has been a quantitatively extracted at pH 6.3. The method obeys Beer`s law over range from (2.5-22.5) ppm with the correlation coefficient of 0.9989. The molar absorptivity the stoichiometry of extracted complex is found to be 1:2. the proposed method is very sensitive and selective.