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Journal of Tuberculosis Research 2016年第3期4卷 98-104页

作者： Muatsim Ahmed Mohammed Adam hamdan mustafa hamdan ali Eltahir Awad Gasim Khalil National Tuberculosis Reference Laboratory National Public Health Laboratory Federal Ministry of Health Khartoum Sudan National Tuberculosis Control Program Federal Ministry of Health Khartoum Sudan Tuberculosis Research Group/Institute of Endemic Diseases University of Khartoum Khartoum Sudan

Drug susceptibility testing (DST) plays a pivotal role in TB patients’ management leading to the selection of most effective drugs. This study aimed to determine resistance patterns to first line anti-TB drugs in Mycobacterium tuberculosis isolates from re-treated patients from Sudan. A total of 239 sputum specimens were collected from smear positive re-treatment TB patients during the period from July 2009 to July 2010. Specimens were pre-treated according to Petroff method. The recovered isolates were tested for sensitivity to first line anti-TB drugs by the 1% proportion method. One hundred and forty three (143/239, 59.8%) mycobacterial isolates were successfully recovered. The majority (98.6%, 141/143) of the isolates were Mycobacterium tuberculosis complex strains. Two strains (2/143, 1.4%) were identified as RIF/INH-resistant MOTT, while fifty four isolates (38.3%, 54/141) were MDR. Multi- drug resistant Mycobacterium tuberculosis complex (MDR-TB) among re-treatment patients from national referral centers for tuberculosis diagnosis and management was considerably high in the study isolates.

关键词： MDR Sudan TB Re-Treatment DST

A Deep-CNN Crowd Counting Model for Enforcing Social Distancing during COVID19 Pandemic: Application to Saudi Arabia’s Public Places

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Computers, Materials & Continua 2021年第2期66卷 1315-1328页

作者： Salma Kammoun Jarraya Maha hamdan Alotibi Manar Salamah ali Department of Computer Science FCITKing Abdulaziz UniversityJeddahSaudi Arabia MIRACL-Laboratory Sfax UniversitySfaxTunisia Department of Computer Science King Khalid UniversityAbhaSaudi Arabia

With the emergence of the COVID19 virus in late 2019 and the declaration that the virus is a worldwide pandemic,health organizations and governments have begun to implement severe health precautions to reduce the spread of the virus and preserve human *** enforcement of social distancing at work environments and public areas is one of these obligatory *** management is one of the effective measures for social *** reducing the social contacts of individuals,the spread of the disease will be immensely *** this paper,a model for crowd counting in public places of high and low densities is *** model works under various scene conditions and with no prior knowledge.A Deep CNN model(DCNN)is built based on convolutional neural network(CNN)structure with small kernel size and two *** increase the efficiency of the model,a convolutional neural network(CNN)as the front-end and a multi-column layer with Dilated Convolution as the back-end were ***,the proposed method accepts images of arbitrary sizes/scales as inputs from different *** evaluate the proposed model,a dataset was created from images of Saudi people with traditional and non-traditional Saudi *** model was also trained and tested on some existing *** to current counting methods,the results show that the proposed model has significantly improved efficiency and reduced the error *** achieve the lowest MAE by 67%,32%.and 15.63%and lowest MSE by around 47%,15%and 8.1%than M-CNN,Cascaded-MTL,and CSRNet respectively.

关键词： CNN crowd counting COVID19

Intra-articular interventions in osteoarthritis:Navigating the landscape of hyaluronic acid,mesenchymal stem cells,and plateletrich plasma

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World Journal of Orthopedics 2024年第8期15卷 704-712页

作者： Somar Soufan Joenne Al Khoury Zahi hamdan Mohamad ali Rida Department of Medicine University of BalamandBeirut 00000Lebanon Department of Internal Medicine Clemenceau Medical CenterBeirut 12345Lebanon

Osteoarthritis(OA)poses a substantial burden on patients,leading to pain,functional decline,and reduced quality of *** conventional treatments focus on symptom management,disease-modifying interventions are yet to be *** review explores the efficacy of intra-articular interventions,particularly hyaluronic acid(HA),mesenchymal stem cells(MSCs),and platelet-rich plasma(PRP),in the context of OA *** injections,with diverse formulations like Hylan G-F20,sodium hyaluronate,and hyaluronan,present varying outcomes,necessitating a nuanced understanding of their effectiveness and *** therapy,derived from adipose tissue,umbilical cord,or bone marrow,shows promising results in clinical improvement,with adipose-derived MSCs demonstrating efficacy in maintaining benefits over 6 ***,bone-marrow-derived MSCs show limited effectiveness,highlighting the need for sourcespecific *** has emerged as a superior option for long-term pain reduction and quality of life improvement,with leukocyte-poor formulations and a critical platelet count of 10 billion demonstrating optimal *** comprehensive analysis underscores the potential of intra-articular interventions in OA management,emphasizing the need for personalized and evidence-based approaches to enhance treatment efficacy and patient outcomes.

关键词： Osteoarthritis,Viscosupplementation Platelets Hyaluronic acid Stem cell

Wear Resistance and Non-Magnetic Layer Formation on 316L Implant Material with Plasma Nitriding

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Journal of Bionic Engineering 2014年第4期11卷 620-629页

作者： ali Fatih Yetim mustafa Yazici Department of Mechanical Engineering Faculty of Engineering and Architecture Erzurum Technical University Erzurum 25700 Turkey Department of Nanoscience and Nanoengineering Ataturk University Erzurum 25240 Turkey

In this study, the applicability of plasma nitriding treatment in the production of non-magnetic and corrosion resistant layer on 316L stainless steel implant material was investigated. 316L stainless steel substrates were plasma nitrided at temperatures of 350 ℃, 375 ℃, 400 ℃, 425 ℃ and 450 ℃ for 2 h in a gas mixture of 50% N2-50% H2, respectively. It was determined that the treatment temperature is the most important factor on the properties of the corrosion resistant layer of 316L stainless steel. The results show that s-phase formed at the temperatures under 400 ℃, and at the temperatures above 400 ℃, instead of s-phase, CrN and y＇-Fe4N phases were observed in the modified layer. The electrical resistivity and surface roughness of the modified layer increase with treatment temperature. Under 400 ℃ the corrosion resistance increased with the temperature, above 400 ℃ it decreased with the increase in treatment temperature. It was analyzed that the electrical resistivity and the soft （ideal） ferro- magnetic properties of 316L stainless steel increased with treatment temperature during nitriding treatment. Also, plasma ni- triding at low temperatures provided magnetic behavior close to the ideal untreated 316L stainless steel.

关键词： 316L stainless steel biomaterials plasma nitriding magnetic properties wear electrical resistivity

Deep Learning-Based Robust DC Fault Protection Scheme for Meshed HVDC Grids

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2423-2434页

作者： Muhammad Zain Yousaf Hui Liu ali Raza ali mustafa the School of Electrical Engineering Guangxi UniversityNanning 530004China the School of Electrical Engineering University of LahoreLahore 39161Pakistan the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China

Fast and reliable detection of faults is a significant technical challenge in transient-based protection for a modular multi-level converter(MMC)based high voltage direct current(HVDC)*** is because existing protection schemes rely heavily upon setting a complicated protective threshold,the failure of which causes high DC-fault currents in HVDC grids,and MMC is prone to such strong transient *** this context,this paper proposes a DC-line fault diagnosis technique based on a tuned long-short-term memory(LSTM)algorithm to improve the response and accuracy of transient-based *** discrete wavelet transform(DWT)extracts the transient features of DC-line voltages in the frequency-time *** healthy and faulty samples are incorporated during training even by considering the noise *** training,numerous test samples are run to evaluate the proposed algorithm’s robustness under various fault *** results show the proposed algorithm can detect DC faults and has a high recognition accuracy of 98.6%.Compared to contemporary techniques,it can perform well to identify DC-line faults because of the efficient training of characteristic features.

关键词： High voltage direct current(HVDC) longshort-term-memory(LSTM) primary protection voltage source converters(VSC)

Formation of air-entraining vortices at horizontal intakes without approach flow induced circulation

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Journal of Hydrodynamics 2016年第1期28卷 102-113页

作者： mustafa GOGUS Mete KOKEN ali BAYKARA Civil Engineering Department Middle East Technical University

The aim of this experimental study is to investigate the effects of hydraulic parameters on the formation of air-entraining vortices at horizontal intake structures without approach flow induced circulation. Six intake pipes of different diameters were tested in the study. The intake pipe to be tested was horizontally mounted to the front side of a large reservoir and then for a wide range of discharges experiments were conducted and critical submergences were detected with adjustable approach channel sidewalls. Empirical equations were derived for the dimensionless critical submergence as a function of the relevant dimensionless parameters. Available data is also checked for the possible scale effect. Then, these obtained equations were compared with the similar ones in the literature which showed a quite good agreement.

关键词： air-entraining vortex horizontal intakes model scale effect sidewall clearance vortex

Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

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Computer Systems Science & Engineering 2022年第12期43卷 1255-1270页

作者： Nasebah Almufadi ali mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computing School of Electrical Engineering and Computer ScienceNational University of Sciences and Technology(NUST)IslamabadPakistan

Currently,mobile communication is one of the widely used means of ***,it is quite challenging for a telecommunication company to attract new *** recent concept of mobile number portability has also aggravated the problem of customer *** need to identify beforehand the customers,who could potentially churn out to the *** the telecommunication industry,such identification could be done based on call detail *** research presents an extensive experimental study based on various deep learning models,such as the 1D convolutional neural network(CNN)model along with the recurrent neural network(RNN)and deep neural network(DNN)for churn *** use the mobile telephony churn prediction dataset obtained from ***,containing the data for around 100,000 individuals,out of which 86,000 are non-churners,whereas 14,000 are churned *** imbalanced data are handled using undersampling and *** accuracy for CNN,RNN,and DNN is 91%,93%,and 96%,***,DNN got 99%for ROC.

关键词： Deep learning machine learning churn prediction convolutional neural network recurrent neural network

Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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Computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

Application of grey relational analysis based on Taguchi method for optimizing machining parameters in hard turning of high chrome cast iron

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Advances in Manufacturing 2018年第4期6卷 419-429页

作者： ali Kalyon mustafa Giinay Dursun Ozyiirek Department of Manufacturing Engineering Karabiik University Karabiik 078050 Turkey Department of Mechanical Engineering. Karabiik University Karabuk 078050 Turkey

High chrome white cast iron is particularly preferred in the production of machine parts requiring high wear resistance. Although the amount of chrome in these materials provides high wear and corrosion resistances, it makes their machinability difficult. This study presents an application of the grey relational analysis based on the Taguchi method in order to optimize chrome ratio, cutting speed, feed rate, and cutting depth for the resultant cutting force (Fr) and surface roughness (Ra) when hard turning high chrome cast iron with a cubic boron nitride (CBN) insert. The effect levels of machining parameters on Fr and Ra were examined by an analysis of variance (ANOVA). A grey relational grade (GRG) was calculated to simultaneously minimize Fr and Ra. The ANOVA results based on GRG indicated that the feed rate, followed by the cutting depth, was the main parameter and contributed to respo ses. Optimal levels of parameters were found when the chrome ratio, cutting speed, feed rate, and cutting depth were 12%, 100m/min, 0.05mm/r, and 0.1mm, respectively, based on the multiresponse optimization results obtained by considering the maximum signal to noise (SIN) ratio of GRG. Confirmation results were verified by calculating the confidence level within the interval width.

关键词： High chrome cast iron Hard turning Grey relational analysis Multi-response optimization Taguchi method

Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

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Computer Systems Science & Engineering 2023年第3期44卷 1973-1988页

作者： Aishah Alrashidi ali mustafa Qamar Department of Computer Science College of ComputerQassim UniversityBuraydahSaudi Arabia

Electrical load forecasting is very crucial for electrical power systems’planning and *** electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load *** meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi *** factory load and meteorological data used in this study are recorded hourly between 2016 and *** data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in *** applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory *** addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this *** outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning ***,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

关键词： Electricity load forecasting meteorological data machine learning feature selection modeling real-world problems predictive analytics