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Implications of electrode modifications in aqueous organic redox flow batteries

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Journal of Energy Chemistry 2024年第8期95卷 499-510,I0011页

作者： Zahid Manzoor Bhat Mohammad Furquan Muhammad Aurang Zeb Gul Sial Umair Alam Atif saeed alzahrani Mohammad Qamar Interdisciplinary Research Center for Hydrogen Technologies and Carbon Management(IRC-HTCM) King Fahd University of Petroleum&Minerals(KFUPM)Dhahran 31261Saudi Arabia Department of Materials Science and Engineering King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems(IRC-SES) King Fahd University of Petroleum and MineralsDhahran 31261Saudi Arabia

Aqueous organic redox flow batteries(RFBs)exhibit favorable characteristics,such as tunability,multielectron transfer capability,and stability of the redox active molecules utilized as anolytes and catholytes,making them very viable contenders for large-scale grid storage *** attention has been paid on the development of efficient redox-active molecules and their performance optimization through chemical substitutions at various places on the backbone as part of the pursuit for high-performance *** the fact that electrodes are vital to optimal performance,they have not garnered significant *** research has been conducted on the effects of electrode modifications to improve the performance of *** primary emphasis has been given on the impact of electrode engineering to augment the efficiency of aqueous organic *** overview of electron transfer at the electrode-electrolyte interface is *** implications of electrode modification on the performance of redox flow batteries,with a particular focus on the anodic and cathodic half-cells separately,are then *** each section,significant discrepancies surrounding the effects of electrode engineering are thoroughly examined and ***,we have presented a comprehensive assessment along with our perspectives on the future trajectory.

关键词： Redox flow batteries Electrode modification Organic redox molecules Outer sphere and inner sphere

Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan saeed alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

Performance Analysis of Curved Track G2T-FSO(Ground-to-Train Free Space Optical)Model under Various Weather Conditions

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Computer Modeling in Engineering & Sciences 2024年第12期141卷 2087-2105页

作者： Mohammed A.Alhartomi Mohammad F.L.Abdullah Wafi A.B.Mabrouk Mohammed S.M.Gismalla Ahmed Alzahmi saeed alzahrani Mohammad R.Altimania Mohammed S.Alsawat Department of Electrical Engineering University of TabukTabuk71491Saudi Arabia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Johor86400Malaysia Center for Communication Systems and Sensing King Fahd University of Petroleum and Minerals(KFUPM)Dhahran31261Saudi Arabia Department of Mechanical Engineering University of TabukTabuk71491Saudi Arabia

The demand for broadband data services on high-speed trains is rapidly growing as more people commute between their homes and ***,current radio frequency(RF)technology cannot adequately meet this *** order to address the bandwidth constraint,a technique known as free space optics(FSO)has been *** paper presents a mathematical derivation and formulation of curve track G2T-FSO(Ground-to-train Free Space Optical)model,where the track radius characteristics is 2667 m,divergence angle track is 1.5°for train velocity at V=250 km/*** transmitter configurations are proposed to maximize coverage range and enhance curve track G2T-FSO link performance under varying weather *** curved track G2T-FSO model was evaluated in terms of received power,signal-to-noise ratio(SNR),bit error rate(BER),and eye *** results showed maximum coverage lengths of 618,505,365,and 240 m for 4Tx/1Rx,3Tx/1Rx,2Tx/1Rx,and 1Tx/1Rx configurations,*** analyzed results demonstrate that the G2T-FSO link can be effectively implemented under various weather conditions.

关键词： Free space optics curve track ground-to-train communications geometrical loss weather conditions

Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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Computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

Sensitivity of Esophageal Scintigraphy (Milk Scans) in Detecting Gastro-Esophageal Reflux

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Open Journal of Medical Imaging 2015年第1期5卷 1-9页

作者： saeed Bafaraj Awad alzahrani Department of Diagnostic Radiology Faculty of Applied Medical Science King Abdulaziz University Jeddah Saudi Arabia

Gastro-esophageal scintigraphy (milk scan) is an accurate technique for detecting gastro-esophageal reflux that can be performed rapidly with minimal patient discomfort. A retrospective study was performed to find out to how much radionuclide milk scan is sensitive for detecting gastro-esophageal reflux (GER) for 89 patients who subjected to milk scan within Years 2010 to 2013. The study group age is ranging from one month old to four years old. Milk scan was performed using PHILIPS and SEIMENS machine. Tc 99-m sulfur colloid with milk administrated orally or through nasogastric tube to the patient. The highest frequency for milk scan study was in age group of 0 - 6 months old with 77% positive finding. Patients who suffer repeated vomiting represented the highest reason of examination for milk scan (46% of overall cases), while chocking attacks, pulmonary aspiration, and congenital heart disease, were 26%, 16%, 12% respectively of the overall cases. Seventy-nine percent of patients who prepared to be examine using nasogastric tube found with positive finding, 50% of GER patients have abnormal tracer activity in the mid of esophagus;on the other hand, 50% of GER patients divided equally between the lower and upper esophagus. The study show that infants especially in age of 0 - 6 months old and with repeated vomiting symptoms are strongly recommended to milk scan study rather than fluoroscopy to avoid unnecessary dose to the patients.

关键词： Gastro-Esophageal Scintigraphy Milk Scan Gastro-Esophageal Reflux

Perceived Stress and Coping Styles among the General Population in Saudi Arabia during COVID-19 Pandemic

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International Journal of Mental Health Promotion 2022年第3期24卷 361-373页

作者： Waleed A.Alghamdi Sami H.alzahrani saeed S.Shaaban Naseem A.Alhujaili Department of Medicine Faculty of MedicineKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medicine Division of PsychiatryRabigh Faculty of MedicineKing Abdulaziz UniversityJeddah25732Saudi Arabia Department of Family Medicine Faculty of MedicineKing Abdulaziz UniversityJeddah21589Saudi Arabia

This article examines the stress levels,coping responses,and influence of adaptive and maladaptive coping styles on stress in Saudi Arabia during the COVID-19 *** online cross-sectional survey was distributed to the attendees of a pandemic-related awareness *** questionnaire assessed demographic and clinical characteristics,coping strategies(the brief COPE inventory),and stress levels(the Perceived Stress Scale).A multivariate linear regression analysis was conducted to assess the predictors of *** highest adaptive and maladaptive coping styles were reported for religion and *** groups were independently more vulnerable to experience stress,including young-and middle-aged adults,females,students,and those with a lowto-middle income,with a personal or familial history of mental conditions,and living with someone with COVID-19 in their close *** levels of stress were also predicted by lower levels of using positive coping styles,particularly positive reframing,planning,and returning to religion,and higher levels of negative coping styles,such as venting,self-blame,and *** psychotherapeutic interventions through the media and digital solutions are warranted to mitigate and manage stress by targeting vulnerable groups,promoting active coping styles,and discouraging negative coping strategies.

关键词： Anxiety coronavirus public health pandemic mental health chronic illness

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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Computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

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Computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.alzahrani saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character ***, many features usually create difficulties due to high dimensionality *** this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this *** Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

Impact of COVID-19 on endourology surgical practice in Saudi Arabia:A national multicenter study

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Asian Journal of Urology 2021年第4期8卷 416-423页

作者： Abdel Raheem Ali Yahya Ghazwani Ibrahim Alowidah Raed A.Azhar Mohammed Alomar Ahmed alzahrani Ossamah Alsowayan Wissam Kamal Abdulrahman Alalayet Ahmad Bugis Abdulaziz MAlthunayan Malzahrani Tarek Kamel Fadaak Aiman Al-Solumany saeed Bin Hamri Urology Department King Saud Medical CityRiyadhSaudi Arabia Urology Department Faculty of MedicineTanta UniversityTantaEgypt Devision of Urology King Abdullah International Medical Research CenterCollege of MedicineKing Abdulaziz Medical CityKing Saud Bin Abdulaziz University for Health ScienceDivision of UrologyKing Abdulaziz Medical CityRiyadhSaudi Arabia Urology Department Faculty of MedicineKing Abdulaziz UniversityJeddahSaudi Arabia Department of Surgery College of MedicineKing Saud University and King Saud University Medical CityRiyadhSaudi Arabia Urology Department Price Sultan Military Medical CityRiyadhSaudi Arabia Urology Department King Fahd HospitalImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia Urology Department King Fahd HospitalJeddahSaudi Arabia

Objective:To identify the impact of COVID-19 on endourology surgical practice in Saudi ***:A retrospective study of seven tertiary hospitals from January 2019 to April 2019,and from January 2020 to April 2020 was *** of urology outpatient department(OPD)visits and endourology procedures in the first third of 2020 were analyzed and compared with those in the first third of 2019,as well as,during the full curfew time,*** 2020 versus April ***:Number of OPD visits in the first third of 2020 and 2019 were 19499 and 26594,respectively(p75%,including OPD visits and elective endourology *** hospitals have changed their strategic preventive measures by increasing the rate of teleclinics and day surgeries.

关键词： COVID-19 Endourology Saudi Arabia