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CFD Simulation of Particles Mixing and Segregation in a Rotary Coating Disk: Influence of Drag Models and Restitution Coefficient

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Open Journal of Applied Sciences 2022年第3期12卷 267-283页

作者： Salma Awad Nouh Ku Zilati Bt Ku Shaari Lau Kok Keong Shafirah Bt Samsuri tigabwa yosef ahmed Department of Chemical Engineering Universiti Teknologi PETRONAS Seri Iskandar Malaysia

In this paper, the dynamics of a transverse plane of a rotary coating disk of a binary mixture system comprising sand and urea particles were simulated using the two-fluid model along with the kinetic theory of granular flow in Fluent 19.1. Although some parameters relating to the material properties and size of the rotary coating disk have been researched, the effects of both drag force and restitution coefficient on the flow characteristics have yet to be examined. Thus, this paper numerically examines the effect of the inclusion of drag models and particle-particle restitution coefficients on particle dynamics in a rotary disk operating in the rolling regime of the granular bed. Three particle-particle drag models were considered: the Schiller-Naumann, Syamlal-O’Brien, and Gidaspow. The Syamlal-O’Brien and Gidaspow models were both able to successfully simulate particle segregation in a perfect rolling regime, whereas the Schiller-Naumann drag model appeared to be unable to predict the segregation of the particles and the rolling flow regime under the assumed conditions. Four different values of the restitution coefficient were also investigated: 0.7, 0.8, 0.9, and 0.95. The higher restitution values of 0.9 and 0.95 were found to substantially affect flow characteristics, ensuring suitable rolling regime behaviour for the bed during the rotational movement. The lower restitution coefficients of 0.7 and 0.8, on the other hand, indicated that irregular velocity vectors could be obtained in the active region of the granular bed.

关键词： Rolling Mode Drag Model Restitution Coefficient CFD

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New stem cell autophagy surrogate diagnostic biomarkers in earlystage hepatocellular carcinoma in Egypt:A pilot study

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World Journal of Hepatology 2021年第12期13卷 2137-2149页

作者： Tarek yosef Wesam ahmed Ibrahim Marwa Matboli Amina ahmed Swilam Sarah El-Nakeep Gastroenterology and Hepatology Unit Department of Internal MedicineFaculty of MedicineAin Shams UniversityCairo 11591Egypt Department of Biochemistry Faculty of MedicineAin Shams UniversityCairo 11591Egypt Department of Internal Medicine Health Affair DirectorateMinistry of Health and PopulationCairo 11591Egypt

BACKGROUND Stem cell autophagy disruption is responsible for the development of hepatocellular carcinoma(HCC).Many non-coding RNAs are linked to the activation and inhibition of certain *** SQSTM1 gene controls stem cell autophagy as shown in previous *** upregulation of SQSTM1 is associated with the inhibition of autophagy in cancerous stem cells in patients with *** To determine whether serum microRNA,hsa-miR-519d,is linked to SQSTM1 gene and whether they could be used as diagnostic biomarkers for early-stage *** In silico analysis was performed to determine the most correlated genes of autophagy with ***1 and hsa-miR-519d were validated through this pilot clinical *** study included 50 Egyptian participants,who were classified into three subgroups:Group 1 included 34 patients with early-stage HCC,Group 2 included 11 patients with chronic liver disease,and Group 3(control)included 5 healthy *** patients were subjected to full laboratory investigations,including viral markers and alpha-fetoprotein(AFP),abdominal ultrasound,and clinical assessment with the Child–Pugh score ***,the patients with HCC underwent triphasic computed tomography with contrast to diagnose and determine the tumor site,size,and *** real-time polymerase chain reaction was used to assess hsa-miR-519d-3p and SQSTM1 in the serum of all the study *** Hsa-miR-519d-3p was significantly upregulated in patients with HCC compared with those with chronic liver disease and healthy subjects with an area under the curve(AUC)of 0.939,with cutoff value 8.34,sensitivity of 91.2%,and specificity of 81.8%.SQSTM1 was upregulated with an AUC of 0.995,with cutoff value 7.89,sensitivity of 97.1%,and specificity of 100%.AFP significantly increased in patients with HCC with an AUC of 0.794,with cutoff value 7.30 ng/mL,sensitivity of 76.5%,and specificity of 72.7%.CONCLUSION This study is the first to show a direct re

关键词： Autophagy Hepatocellular carcinoma miRNA miR-519d Stem cell SQSTM1

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Big Data Flow Adjustment Using Knapsack Problem

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Journal of Computer and Communications 2018年第10期6卷 30-39页

作者： Eyman yosef ahmed Salama M. Elsayed Wahed Department of Mathematics Faculty of Sience Bour Said University Bour Said Egypt Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

The advancements of mobile devices, public networks and the Internet of creature huge amounts of complex data, both construct & unstructured are being captured in trust to allow organizations to produce better business decisions as data is now pivotal for an organizations success. These enormous amounts of data are referred to as Big Data, which enables a competitive advantage over rivals when processed and analyzed appropriately. However Big Data Analytics has a few concerns including Management of Data, Privacy & Security, getting optimal path for transport data, and Data Representation. However, the structure of network does not completely match transportation demand, i.e., there still exist a few bottlenecks in the network. This paper presents a new approach to get the optimal path of valuable data movement through a given network based on the knapsack problem. This paper will give value for each piece of data, it depends on the importance of this data (each piece of data defined by two arguments size and value), and the approach tries to find the optimal path from source to destination, a mathematical models are developed to adjust data flows between their shortest paths based on the 0 - 1 knapsack problem. We also take out computational experience using the commercial software Gurobi and a greedy algorithm (GA), respectively. The outcome indicates that the suggest models are active and workable. This paper introduced two different algorithms to study the shortest path problems: the first algorithm studies the shortest path problems when stochastic activates and activities does not depend on weights. The second algorithm studies the shortest path problems depends on weights.

关键词： 0 - 1 Knapsack Problem Big Data Big Data Analytics Big Dao Ta Inconsistencies

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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

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Computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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