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Efficacy and safety of treatment of hepatitis C virus infection in renal transplant recipients

World Journal of Gastroenterology 2012年第1期18卷 55-63页

作者： Abdulrahman A aljumah Mohamed A Saeed Ahmed I al flaiw Ibrahim H al Traif Abduljaleel M al alwan Salem H al Qurashi Ghormallah A al Ghamdi fayez f al hejaili Mohammed A al Balwi Abdulla A al Sayyari Division of Hepatology Department of Hepatobiliary Sciences and Liver Transplantation King Abdulaziz Medical City and King Saud bin Abdulaziz University for Health Sciences National Guard Health Affairs Division of Nephrology and Renal Transplantation Department of Medicine King Abdulaziz Medical City and King Saud bin Abdulaziz University for Health Sciences National Guard Health Affairs Division of Molecular Pathology and Genetics King Abdulaziz Medical City and King Saud bin Abdulaziz University for Health Sciences National Guard Health Affairs

AIM: To assess the efficacy and safety of combinedpegylated interferon and ribavirin therapy in hepatitis C virus (HCV) infection in renal transplant recipients. METHODS: This is a retrospective chart review of post renal transplant patients who were positive for anti-HCV and HCV-RNA, and who have received treatment with combination of pegylated interferon and ribavirin between October 2003 and December 2008. Only patients with stable graft function and absence of evidence of cirrhosis and who received the therapy for continuous 48 wk were included. Nineteen patients (13 male and 6 female) were identified and included. The patient's complete blood count, liver and kidney prof ile, and cal- culated glomerular f iltration rate (GfR) were monitored every 6-8 wk while on treatment. HCV-RNA was tested at 12 wk for early virological response, at 48 wk for end of treatment response (ETR), and then retested at 24, and 48 wk after completion of therapy for sustained virological response (SVR). Liver biopsies were obtained before treatment from all patients and graft kidney biopsies were performed as required. RESULTS: Of the entire cohort, 9 patients (47.4%) showed an ETR and 8 had SVR (42.1%). Of the 8 patients with abnormal alanine aminotransferase (alT) levels at baseline, 78.9% had their alT normalized (including the virological non responders). alT was normal in all responders at the end of therapy and at 24 wk post therapy (100%). Only one patient (5.3%) developed an increase in creatinine and decline in GfR from baseline towards the end of treatment. This patient's kidney biopsy revealed borderline rejection. There was no impact on response by HCV-genotype, initial HCV RNA load, age or sex of the patient or duration post transplant before commencement of therapy. all patients tolerated treatment in the same way as non-transplant with no unusual or increased occurrence of side effects. CONCLUSION: The combination of pegylated interferon and ribavirin is effective in su

关键词： allograft rejection Hepatitis C Pegylated interferon Ribavirin Renal transplant

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Automated algorithms for Detecting and Classifying X-Ray Images of Spine fractures

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Computers, Materials & Continua 2024年第4期79卷 1539-1560页

作者： fayez alfayez Department of Computer Science and Information College of ScienceMajmaah UniversityAL-Majmaah11952Saudi Arabia

This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spinefractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picturesegmentation, feature reduction, and image classification. Two important elements are investigated to reducethe classification time: Using feature reduction software and leveraging the capabilities of sophisticated digitalprocessing hardware. The researchers use different algorithms for picture enhancement, including theWiener andKalman filters, and they look into two background correction techniques. The article presents a technique forextracting textural features and evaluates three picture segmentation algorithms and three fractured spine detectionalgorithms using transformdomain, PowerDensity Spectrum(PDS), andHigher-Order Statistics (HOS) for *** an emphasis on reducing digital processing time, this all-encompassing method helps to create asimplified system for classifying fractured spine fractures. A feature reduction program code has been built toimprove the processing speed for picture classification. Overall, the proposed approach shows great potential forsignificantly reducing classification time in clinical settings where time is critical. In comparison to other transformdomains, the texture features’ discrete cosine transform (DCT) yielded an exceptional classification rate, and theprocess of extracting features from the transform domain took less time. More capable hardware can also result inquicker execution times for the feature extraction algorithms.

关键词： feature reduction image classification X-ray images

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Constructing a small organic molecule by using the derivative of oxadiazole for photovoltaic applications

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Optoelectronics Letters 2024年

作者： HUSSEIN K.MEJBEL ABDIULRSOOL H.al-TAHER fayez A.NAMAH LAfY f.al-BADRY Department of Physics College of Education for Pure Science University of Thi-Qar Directorate-General for Education Dhi Qar Department of Physics College of Science University of Thi-Qar

Because of their elasticity， light weight， little cost， and wide-area applications， besides that a notable increase in power conversion efficiency （PCE）， organic solar cells have recently caught the concern of numerous researchers. In the current project， we created a variety of organic small compounds as donor materials by using Gaussian 09. Our design approach entails swapping the core part， which is benzothiadiazole （BT）， based on a derivative of oxadiazole， as the terminals on both sides of the prototype compound （RM） with a variety of fragments to improve the electronic and optic characterization and performance for PCE in organic solar cells. Careful theoretical consideration under density-functional theory DfT and time-dependent density functional theory TD-DET at the B3LYP/6-31G（d，p） basis set was made to find the maximum absorption wavelengthλmax， the efficiency of light harvesting （LHE）， excitation energy (EX)， HOMO and LOMO levels， energy band gap (Eg)， dipole moment （ρ）， the DOS， ionization potential （LP）， electron affinity （EA）， fill factor （Ef）， and open circuit voltage (Voc) of molecules RM and TSstudied in a methodical manner. Calculated values revealed that proposed compounds T1-T4 can be encouraging materials for manufacturing organic solar cells as a donor tiny compound with acceptor PC61BM to predict a rise in power conversion efficiency （PCE） owing to their possession of proper values for some optical and electronic features， like the lowest HOMO level， narrower band gaps， and a greater absorption wavelength compared with the reference model RM.

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AMachine Learning Approach to Cyberbullying Detection in Arabic Tweets

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Computers, Materials & Continua 2024年第7期80卷 1033-1054页

作者： Dhiaa Musleh Atta Rahman Mohammed Abbas alkherallah Menhal Kamel al-Bohassan Mustafa Mohammed alawami Hayder ali alsebaa Jawad ali alnemer Ghazi fayez al-Mutairi May Issa aldossary Dalal A.aldowaihi fahd alhaidari Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Networks and Communications College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

With the rapid growth of internet usage,a new situation has been created that enables practicing *** has increased over the past decade,and it has the same adverse effects as face-to-face bullying,like anger,sadness,anxiety,and *** the anonymity people get on the internet,they tend to bemore aggressive and express their emotions freely without considering the effects,which can be a reason for the increase in cyberbullying and it is the main motive behind the current *** study presents a thorough background of cyberbullying and the techniques used to collect,preprocess,and analyze the ***,a comprehensive review of the literature has been conducted to figure out research gaps and effective techniques and practices in cyberbullying detection in various languages,and it was deduced that there is significant room for improvement in the Arabic *** a result,the current study focuses on the investigation of shortlisted machine learning algorithms in natural language processing(NLP)for the classification of Arabic datasets duly collected from Twitter(also known as X).In this regard,support vector machine(SVM),Naive Bayes(NB),Random forest(Rf),Logistic regression(LR),Bootstrap aggregating(Bagging),Gradient Boosting(GBoost),Light Gradient Boosting Machine(LightGBM),Adaptive Boosting(AdaBoost),and eXtreme Gradient Boosting(XGBoost)were shortlisted and investigated due to their effectiveness in the similar ***,the scheme was evaluated by well-known performance measures like accuracy,precision,Recall,and ***,XGBoost exhibited the best performance with 89.95%accuracy,which is promising compared to the state-of-the-art.

关键词： Supervised machine learning ensemble learning cyberbullying Arabic tweets NLP

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Tuning the optoelectronic properties for small organic compounds as organic solar cell applications

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Optoelectronics Letters 2024年第7期20卷 412-417页

作者： Hussein K.Mejbel Lafy f.al-Badry Department of Physics College of Education for Pure Science University of Thi-Qar Department of Physics College of Science University of Thi-Qar

Recently, organic solar cells have attracted the attention of many researchers owing to flexibility, low cost, light weight and large-area applications, and significant improvement in the power conversion efficiency（PCE）. In this work, we designed chains from organic compounds as donors and replaced the core unit in each series with a variety of acceptors in order to enhance their optical and electronic characterization and performance for power conversion efficiencies in organic solar cells. We utilized density functional theory（DfT） and time-dependent density functional theory（TD-DfT） to investigate the geometry optimization by using the Gaussian 09 program. Both electronic and optical properties were determined, which involve the lowest unoccupied molecular orbital（LUMO） and the highest occupied molecular orbital（HOMO） levels, the band gap energy, maximum absorption wavelength(λ;max;), open circuit voltage(V;oc;), and light harvesting efficiency（LHE）. Our evaluation denotes that the small compounds suggested are predicted to exhibit the best performance compared to the first series（SC1）, like a lower band gap energy, a lower HOMO energy level, a greater absorption range, and a larger PCE.

关键词： A

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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu fayez alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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RoGRUT: A Hybrid Deep Learning Model for Detecting Power Trapping in Smart Grids

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Computers, Materials & Continua 2024年第5期79卷 3175-3192页

作者： farah Mohammad Saad al-Ahmadi Jalal al-Muhtadi Center of Excellence in Information Assurance(CoEIA) King Saud UniversityRiyadh11543Saudi Arabia College of Computer&Information Sciences King Saud UniversityRiyadh11543Saudi Arabia

Electricity theft is a widespread non-technical issue that has a negative impact on both power grids and electricity *** hinders the economic growth of utility companies,poses electrical risks,and impacts the high energy costs borne by *** development of smart grids is crucial for the identification of power theft since these systems create enormous amounts of data,including information on client consumption,which may be used to identify electricity theft using machine learning and deep learning ***,there also exist different solutions such as hardware-based solutions to detect electricity theft that may require human resources and expensive ***-based solutions are presented in the literature to identify electricity theft but due to the dimensionality curse,class imbalance issue and improper hyper-parameter tuning of such models lead to poor *** this research,a hybrid deep learning model abbreviated as RoGRUT is proposed to detect electricity theft as amalicious and non-malicious *** key steps of the RoGRUT are data preprocessing that covers the problem of class imbalance,feature extraction and final theft *** advanced-level models like RoBERTa is used to address the curse of dimensionality issue,the near miss for class imbalance,and transfer learning for *** effectiveness of the RoGRUTis evaluated using the dataset fromactual smartmeters.A significant number of simulations demonstrate that,when compared to its competitors,the RoGRUT achieves the best classification *** performance evaluation of the proposed model revealed exemplary results across *** accuracy achieved was 88%,with precision at an impressive 86%and recall reaching 84%.The f1-Score,a measure of overall performance,stood at 85%.furthermore,themodel exhibited a noteworthyMatthew correlation coefficient of 78%and excelled with an area under the curve of 91%.

关键词： Electricity theft smart grid RoBERTa GRU transfer learning

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Massless Dirac perturbations of black holes in f(Q) gravity: quasinormal modes and a weak deflection angle

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Communications in Theoretical Physics 2024年第9期76卷 75-84页

作者： Ahmad al-Badawi Sohan Kumar Jha Department of Physics Al-Hussein Bin Talal University Department of Physics Chandernagore College

This article considers a static and spherical black hole（BH） in f（Q） gravity. f（Q） gravity is the extension of symmetric teleparallel general relativity, where both curvature and torsion are vanishing and gravity is described by nonmetricity. In this study, we investigate the possible implications of quasinormal mode（QNM） modified Hawking spectra and deflection angles generated by the model. The Wentzel–Kramers–Brillouin method is used to solve the equations of motion for massless Dirac perturbation fields and explore the impact of the nonmetricity parameter（Q0）. Based on the QNM computation, we can ensure that the BH is stable against massless Dirac perturbations and as Q0increases the oscillatory frequency of the mode decreases. We then discuss the weak deflection angle in the weak field limit approximation. We compute the deflection angle up to the fourth order of approximation and show how the nonmetricity parameter affects it. We find that the Q0parameter reduces the deflection angle.

关键词： black holes in f(Q) gravity deflection angle massless Dirac perturbations quasinormal modes

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Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 823-845页

作者： Muaadh A.alsoufi Maheyzah Md Siraj fuad A.Ghaleb Muna al-Razgan Mahfoudh Saeed al-Asaly Taha alfakih faisal Saeed faculty of Computing Universiti Teknologi Malaysia(UTM)Johor Bahru81310Malaysia DAAI Research Group College of Computing and Digital TechnologyBirmingham City UniversityBirminghamB47XGUK Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11345Saudi Arabia

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected ***,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion *** addition,IoT devices generate a high volume of unstructured *** intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data *** the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack *** numerous research domains,deep learning techniques have demonstrated their capability to precisely detect *** study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT ***,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed ***,the Convolutional Neural Network(CNN)technique is employed to create a binary classification *** proposed SAE-CNN approach is validated using the Bot-IoT *** proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,f1 of 99.9%,false Positive Rate(fPR)of 0.0003,and True Positive Rate(TPR)of *** addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

关键词： IoT anomaly intrusion detection deep learning sparse autoencoder convolutional neural network

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A regular MOG black hole's impact on shadows and gravitational weak lensing in the presence of a quintessence field

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Communications in Theoretical Physics 2024年第8期76卷 93-104页

作者： Ahmad al-Badawi Sanjar Shaymatov Mirzabek alloqulov Anzhong Wang Department of Physics Al-Hussein Bin Talal University Institute for Theoretical Physics and Cosmology Zhejiang University of Technology Tashkent State Technical University University of Tashkent for Applied Sciences New Uzbekistan University Institute of fundamental and Applied Research National Research University TIIAME GCAP-CASPER Physics Department Baylor University

We investigate the impact of the modified gravity（MOG） field and the quintessence scalar field on horizon evolution, black hole（BH） shadow and the weak gravitational lensing around a static spherically symmetric BH. We first begin to write the BH metric associated with the MOG parameter and quintessence scalar field. We then determine the BH shadow and obtain numerical solutions for the photon sphere and shadow radius. We show that the MOG（α） and the quintessence（c） parameters have a significant impact on the BH shadow and photon *** on the analysis, we further show that the combined effects of the MOG parameter and quintessence field can increase the values of BH shadow and photon sphere radii. We also obtain constraints on the BH parameters by applying the observational data of Sgr A★and M87★.finally, we consider the weak deflection angle of BH within the context of the Gauss–Bonnet theorem（GBT） and show that the combined effects of the MOG and quintessence parameters do make the value of the deflection angle increase, and find this remarkable property is in good agreement with the physical meaning of both parameters that can maintain the strong gravitational field in the surrounding environment of a BH.

关键词： gravitational weak lensing modified gravity quintessence field shadows

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