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Blade Wrap Angle Impact on Centrifugal Pump Performance:Entropy Generation and Fluid-Structure Interaction Analysis

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 109-137页

作者： hayder Kareem Sakran Mohd Sharizal Abdul Aziz Chu Yee Khor School of Mechanical Engineering Universiti Sains MalaysiaEngineering CampusNibong TebalPenang14300Malaysia Chemical Engineering Department Faculty of EngineeringAl-Muthanna UniversityMuthanna66001Iraq Faculty of Mechanical Engineering&Technology Universiti Malaysia Perlis(UniMAP)ArauPerlis02600Malaysia

The centrifugal pump is a prevalent power equipment widely used in different engineering patterns,and the impeller blade wrap angle significantly impacts its performance.A numerical investigation was conducted to analyze the influence of the blade wrap angle on flow characteristics and energy distribution of a centrifugal pump evaluated as a low specific speed with a value of 69.This study investigates six impellermodels that possess varying blade wrap angles(95°,105°,115°,125°,135°,and 145°)that were created while maintaining the same volute and other geometrical characteristics.The investigation of energy loss was conducted to evaluate the values of total and entropy generation rates(TEG,EGR).The fluid-structure interaction was considered numerically using the software tools ANSYS Fluent and ANSYSWorkbench.The elastic structural dynamic equation was used to estimate the structural response,while the shear stress transport k–ωturbulence model was utilized for the fluid domain modeling.The findings suggest that the blade wrap angle has a significant influence on the efficiency of the pump.The impeller featuring a blade wrap angle of 145°exhibits higher efficiency,with a notable increase of 3.76%relative to the original model.Variations in the blade wrap angle impact the energy loss,shaft power,and pump head.The model with a 145°angle exhibited a maximum equivalent stress of 14.8MPa and a total deformation of 0.084 mm.The results provide valuable insights into the intricate flow mechanism of the centrifugal pump,particularly when considering various blade wrap angles.

关键词： Centrifugal pump blade wrap angle entropy generation theory fluid-structure interaction hydraulic performance

In silico identification of antiviral compounds for the treatment of chikungunya virus infection:qsar modelling and md simulation analysis

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Medicine in Novel Technology and Devices 2024年第2期22卷 102-114页

作者： hayder M Abdulhamza Muthanna S.Farhan Sara.S Hassan Hany Aqeel Al-Hussainy Amjad Ibrahim Oriabi Department of Pharmacy Kut University CollegeAlkutWasit52001Iraq Department of Pharmaceutical Chemistry College of PharmacyUniversity of BaghdadBaghdadIraq Al-Manara College for Medical Sciences Department of PharmacyIraq Department of Pharmacy Hilla University CollegeBabylonIraq Dr Hany Akeel Institute Iraqi Medical Research CenterBaghdad10001Iraq

Chikungunya virus(CHIKV),transmitted by arthropods,has gained global recognition for its impact on public health.It has expanded globally,including Africa,Asia,and the Indian subcontinent,and has a helicase protein in its genome that is crucial for its replication.Thus,the study targeted the helicase protein of CHIKV with 745 antiviral compounds using an ML-based QSAR model and molecular docking.Top binders(5279172,78161839,6474310,and 5330286)were selected for MD simulation based on the control(Silvestrol).All compounds had the highest binding scores,with 78161839 showing the most consistent RMSD and the least conformational variation,indicating high stability.It also showed the lowest binding free energy(ΔG¼31.31 kcal/mol),indicating energetically favourable binding.PCA and FEL also depicted the stable complex confirmation of the protein and 78161839 complex during the 100 ns simulation.Overall,this study aimed to identify helicase function antiviral binders that could be experimentally tested for treating CHIKV.

关键词： Chikungunya virus Machine learning based QSAR Molecular dynamic solution Helicase

Bendamustine and rituximab as frontline therapy in extranodal marginal zone lymphoma:a single-institution experience

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Oncology Research 2024年第6期32卷 1031-1036页

作者： CONSTANTINE N.LOGOTHETIS NATHAN P.HORVAT TONY KURIAN CELESTE BELLO JULIO CHAVEZ LEIDY ISENALUMHE BIJAL SHAH LUBOMIR SOKOL hayder SAEED JAVIER PINILLA SAMEH GABALLA Department of Internal Medicine University of South FloridaTampaFL33602USA College of Medicine University of South Florida MorsaniTampaFL33602USA H.Lee Moffitt Cancer Center and Research Institute TampaFL33612USA

Extranodal marginal zone lymphoma(EMZL)encompasses 70%of cases of marginal zone lymphoma.Frontline bendamustine and rituximab(BR)were derived from trials involving other indolent non-Hodgkin’s lymphomas.Only one trial has evaluated frontline BR prospectively in EMZL.This retrospective study reports outcomes among EMZL patients receiving frontline BR.Twenty-five patients were included with a median age of 69 years(40–81).Five(20.0%)patients had stage Ⅰ/Ⅱ disease,and 20(80.0%)had stage Ⅲ/Ⅳ disease.The median number of cycles was 6.0(3.0–6.0).Maintenance rituximab was administered to 10(41.7%)individuals.Overall response rate(ORR)was 100.0%(60.0%complete response,40.0%partial response).Medians of overall survival and progression-free survival were not reached.The estimated 2-year progression-free survival was 85.2%and overall survival was 100.0%.Four(16.6%)patients had infections related to treatment;3(12.0%)transformed to diffuse large B-cell lymphoma;5(20.8%)had a relapse or progression of EMZL;and 3(12.0%)died unrelated to BR.BR is an efficacious and well-tolerated front-line regimen for EMZL with response data consistent with existing literature.

关键词： Extranodal marginal zone lymphoma Bendamustine Rituximab Front-line therapy

Childhood Papillary Thyroid Carcinoma: A Case Report

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Case Reports in Clinical Medicine 2024年第4期13卷 108-114页

作者： Alma Al Mansour hayder Makki Hamadi Mahmoud Elshafey Mohamed Maryam Alkhatry Alqassemi Hospital Emirates Health Services Sharjah UAE Ibrahim Bin Hamad Obaidulla Hospital Ras Al Khaimah UAE

Background: Thyroid cancer is a rare disease yet the most common endocrine malignancy in pediatrics. Unlike adult patients, children with thyroid nodules typically don’t complain of pain, soreness, or difficulty swallowing. Additionally, using the recommended therapy for adults to treat paediatrics is not appropriate. There is an unmet need for updated unique guidelines for the management of papillary thyroid carcinoma (PTC) in paediatrics and adolescents. Case Report: A 12-year-old girl had an atypical presentation of metastatic PTC in lymph nodes. She was treated initially with hemi-thyroidectomy, followed by total thyroidectomy. A multidisciplinary team followed her up till successful results were found. Conclusion: Due to the difference in pathophysiology between thyroid tumors in children and adults, a unique approach to PTC management is to be implemented. Further trials are required for a better understanding of risk factors, the likelihood of recurrence, and the long-term side effects of the chosen management plan.

关键词： Papillary Thyroid Carcinoma Childhood Thyroidectomy Case Report

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

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Computer Systems Science & Engineering 2023年第5期45卷 1517-1530页

作者： Wisam hayder Mahdi Necmi Taşpınar University of Diyala Department of Communications EngineeringDiyalaBaquba32001Iraq Erciyes University Department of Electrical and Electronics EngineeringKayseri38039Turkey

Multicast device-to-device(D2D)communication technology is considered as one of the new technologies in the fifth generation(5G)networks that directly addresses the need for content sharing among internet users.In fact,when direct communication is available between devices,the spectral efficiency is improved by reusing the licensed cellular spectrum.The current studies show that D2D communication increases network capacity and reduces latency.In order to achieve the alternate capabilities,coordination is required to implement interference management.We considered subcarrier allocation for the uplink,in addition to the power control that takes place on the underlay network.The completed data rate in single multicast communication is significantly reduced and limited by nodes with lower channel quality.In this paper,we used Shuffled Frog Leaping Algorithm(SFLA)for resource allocation(RA)in D2D multicast communications.We compared the results of the SFLA algorithm with the Firefly Algorithm(FA),Ant Colony Optimization(ACO)and Particle Swarm Optimization(PSO);in terms of D2D user throughput,Cellular User(CU)throughput,network average throughput,network interference and signal interference noise ratio(SINR)target.The simulation results show that SFLA clearly outperforms other algorithms in terms of data rate under the high pressure of infeasibility.

关键词： Resource Allocation(RA) SFLA D2D 5G

Hybrid Deep Learning Enabled Load Prediction for Energy Storage Systems

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Computers, Materials & Continua 2023年第5期75卷 3359-3374页

作者： Firas Abedi hayder M.A.Ghanimi Mohammed A.M.Sadeeq Ahmed Alkhayyat Zahraa H.Kareem Sarmad Nozad Mahmood Ali Hashim abbas Ali S.Abosinnee Waleed Khaild Al-Azzawi Mustafa Musa Jaber Mohammed Dauwed Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity of Warith Al-AnbiyaaKarbalaIraq ITM Department Technical College of AdministrationDuhok Polytechnic UniversityDuhokIraq College of Technical Engineering The Islamic UniversityNajafIraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkuk36013Iraq College of Information Technology Imam Ja’afar Al-Sadiq UniversityAl-Muthanna66002Iraq Altoosi University College NajafIraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBagdadIraq Department of Medical Instruments Engineering Techniques Al-Turath University CollegeBaghdad10021Iraq Department of Medical Instruments Engineering Techniques Al-Farahidi UniversityBaghdad10021Iraq Department of Medical Instrumentations Techniques Engineering Dijlah University CollegeBaghdadIraq

Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.

关键词： Load forecasting smart grid energy storage system electricity load forecasting artificial intelligence clustering

A novel multi-stable sinusoidal chaotic map with spectacular behaviors

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Communications in Theoretical Physics 2023年第11期75卷 3-14页

作者： Ahmed M Ali Ali Sridevi Sriram hayder Natiq Atefeh Ahmadi Karthikeyan Rajagopal Sajad Jafari Department of Electronics Techniques Babylon Technical InstituteAl-Furat Al-Awsat Technical UniversityBabylon51001Iraq Al-Mustaqbal University College Babylon51001Iraq Centre for Computational Biology Chennai Institute of TechnologyChennai600069Tamil NaduIndia Department of Computer Technology Engineering College of Information TechnologyImam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq Department of Biomedical Engineering Amirkabir University of Technology(Tehran Polytechnic)TehranIran Centre for Nonlinear Systems Chennai Institute of TechnologyChennai600069Tamil NaduIndia Department of Electronics and Communications Engineering and University Centre of Research and Development Chandigarh UniversityMohali140413PunjabIndia Health Technology Research Institute Amirkabir University of Technology(Tehran Polytechnic)TehranIran

Chaotic behavior can be observed in continuous and discrete-time systems.This behavior can appear in one-dimensional nonlinear maps;however,having at least three state variables in flows is necessary.Due to the lower mathematical complexity and computational cost of maps,lots of research has been conducted based on them.This paper aims to present a novel one-dimensional trigonometric chaotic map that is multi-stable and can act attractively.The proposed chaotic map is first analyzed using a single sinusoidal function;then,its abilities are expanded to a map with a combination of two sinusoidal functions.The stability conditions of both maps are investigated,and their different behaviors are validated through time series,state space,and cobweb diagrams.Eventually,the influence of parameter variations on the maps’outputs is examined by one-dimensional and two-dimensional bifurcation diagrams and Lyapunov exponent spectra.Besides,the diversity of outputs with varying initial conditions reveals this map’s multi-stability.The newly designed chaotic map can be employed in encryption applications.

关键词： chaotic map discrete chaos sinusoidal map multi-stability dynamical analysis

Chimp Optimization Algorithm Based Feature Selection with Machine Learning for Medical Data Classification

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Computer Systems Science & Engineering 2023年第12期47卷 2791-2814页

作者： Firas Abedi hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University for WomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering the Islamic UniversityNajafIraq

Datamining plays a crucial role in extractingmeaningful knowledge fromlarge-scale data repositories,such as data warehouses and databases.Association rule mining,a fundamental process in data mining,involves discovering correlations,patterns,and causal structures within datasets.In the healthcare domain,association rules offer valuable opportunities for building knowledge bases,enabling intelligent diagnoses,and extracting invaluable information rapidly.This paper presents a novel approach called the Machine Learning based Association Rule Mining and Classification for Healthcare Data Management System(MLARMC-HDMS).The MLARMC-HDMS technique integrates classification and association rule mining(ARM)processes.Initially,the chimp optimization algorithm-based feature selection(COAFS)technique is employed within MLARMC-HDMS to select relevant attributes.Inspired by the foraging behavior of chimpanzees,the COA algorithm mimics their search strategy for food.Subsequently,the classification process utilizes stochastic gradient descent with a multilayer perceptron(SGD-MLP)model,while the Apriori algorithm determines attribute relationships.We propose a COA-based feature selection approach for medical data classification using machine learning techniques.This approach involves selecting pertinent features from medical datasets through COA and training machine learning models using the reduced feature set.We evaluate the performance of our approach on various medical datasets employing diverse machine learning classifiers.Experimental results demonstrate that our proposed approach surpasses alternative feature selection methods,achieving higher accuracy and precision rates in medical data classification tasks.The study showcases the effectiveness and efficiency of the COA-based feature selection approach in identifying relevant features,thereby enhancing the diagnosis and treatment of various diseases.To provide further validation,we conduct detailed experiments on a benchmark medical dataset,revealing the superiority of th

关键词： Association rule mining data classification healthcare data machine learning parameter tuning data mining feature selection MLARMC-HDMS COA stochastic gradient descent Apriori algorithm

Computational Intelligence Driven Secure Unmanned Aerial Vehicle Image Classification in Smart City Environment

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Computer Systems Science & Engineering 2023年第12期47卷 3127-3144页

作者： Firas Abedi hayder M.A.Ghanimi Abeer D.Algarni Naglaa F.Soliman Walid El-Shafai Ali Hashim abbas Zahraa H.Kareem Hussein Muhi Hariz Ahmed Alkhayyat Department of Mathematics College of EducationAl-Zahraa University forWomenKarbalaIraq Biomedical Engineering Department College of EngineeringUniversity ofWarith Al-AnbiyaaKarbalaIraq Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt College of Information Technology Imam Jaafar Al-Sadiq UniversityAl-Muthanna66002Iraq Department of Medical Instrumentation Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Computer Engineering Department Mazaya University CollegeDhi QarIraq College of Technical Engineering The Islamic UniversityNajafIraq

Computational intelligence(CI)is a group of nature-simulated computationalmodels and processes for addressing difficult real-life problems.The CI is useful in the UAV domain as it produces efficient,precise,and rapid solutions.Besides,unmanned aerial vehicles(UAV)developed a hot research topic in the smart city environment.Despite the benefits of UAVs,security remains a major challenging issue.In addition,deep learning(DL)enabled image classification is useful for several applications such as land cover classification,smart buildings,etc.This paper proposes novel meta-heuristics with a deep learning-driven secure UAV image classification(MDLS-UAVIC)model in a smart city environment.Themajor purpose of the MDLS-UAVIC algorithm is to securely encrypt the images and classify them into distinct class labels.The proposedMDLS-UAVIC model follows a two-stage process:encryption and image classification.The encryption technique for image encryption effectively encrypts the UAV images.Next,the image classification process involves anXception-based deep convolutional neural network for the feature extraction process.Finally,shuffled shepherd optimization(SSO)with a recurrent neural network(RNN)model is applied for UAV image classification,showing the novelty of the work.The experimental validation of the MDLS-UAVIC approach is tested utilizing a benchmark dataset,and the outcomes are examined in various measures.It achieved a high accuracy of 98%.

关键词： Computational intelligence unmanned aerial vehicles deep learning metaheuristics smart city image encryption image classification