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Facies development and sedimentology of the Middle Miocene carbonates of the Raghama Formation, northeastern Saudi Arabia

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Acta Geochimica 2024年第1期43卷 87-96页

作者： Mansour h.al-hashim Abdelbaset S.El-Sorogy Meshal Wadani Department of Geology and Geophysics College of ScienceKing Saud University2455Riyadh 11451Saudi Arabia

Ragahama Formation comprises a siliciclastic continental deposits followed by marine carbonates, representing prograding alluvial fans from adjacent high hinterlands seaward into lagoons and fringing reef environments. The present work aimed to document the facies development and sedimentology of the Raghama carbonates exposed along the eastern coastal plain of the Red Sea, northwestern Saudi Arabia. Four stratigraphic sections were measured and sampled(D1–D4) and thin sections and major and trace element analyses were prepared and applied for petrographic and geochemical approaches. The carbonates were subdivided into three successive fore-reef, reef-core, and back-reef depositional facies. Sandy stromatolitic boundstone, microbial laminites, dolomitic ooidal grainstone, bioclastic coralline algal wackestone, sandy bioclastic wackestone, and coral boundstones were the reported microfacies types. Petrographic analysis reveals that the studied carbonates were affected by dissolution, dolomitization, and aggrading recrystallization, which affects both the original micrite matrix and grains or acts as fracture and veinlet filling leading to widespread vuggy and moldic porosity. No evidence of physical compaction, suggesting rapid lithification and recrystallization during early diagenesis and prior to substantial burial and intensive flushing by meteoric waters. Most of the original microstructure of corals were leached and destructed. This is indicated by the higher depletion in Sr and Ca levels and increase in Mg,Na, Fe, and Mn levels, especially in section D1, in comparison with the worldwide carbonates.

关键词： Raghama Formation Miocene Diagenesis Geochemistry Saudi Arabia

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A hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2497-2517页

作者： Nasser alalwan Ahmed I.Taloba Amr Abozeid Ahmed Ibrahim alzahrani ali h.al-Bayatti Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Information System Department Faculty of Computers and InformationAssiut UniversityAssiutEgypt Mathematics Department Faculty of ScienceAl-Azhar UniversityCairoEgypt Cyber Technology Institute(CTI) De Montfort UniversityLeicesterUK

An illness known as pneumonia causes inflammation in the *** there is so much information available fromvarious X-ray images,diagnosing pneumonia has typically proven *** improve image quality and speed up the diagnosis of pneumonia,numerous approaches have been *** date,several methods have been employed to identify *** Convolutional Neural Network(CNN)has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and ***,these methods are complex,inefficient,and imprecise to analyze a big number of *** this paper,a new hybrid method for the automatic classification and identification of Pneumonia from chest X-ray images is *** proposed method(ABOCNN)utilized theAfrican BuffaloOptimization(ABO)algorithmto enhanceCNNperformance and *** Weinmed filter is employed for pre-processing to eliminate unwanted noises from chest X-ray images,followed by feature extraction using the Grey Level Co-Occurrence Matrix(GLCM)*** features are then selected from the dataset using the ABO algorithm,and ultimately,high-performance deep learning using the CNN approach is introduced for the classification and identification of *** results on various datasets showed that,when contrasted to other approaches,the ABO-CNN outperforms them all for the classification *** proposed method exhibits superior values like 96.95%,88%,86%,and 86%for accuracy,precision,recall,and F1-score,respectively.

关键词： African buffalo optimization convolutional neural network pneumonia X-ray

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Structural Analysis for Qazzaz Metamorphic Core Complex, Northwestern Arabian Shield, Saudi Arabia

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Acta Geologica Sinica(English Edition) 2024年第5期 1187-1201页

作者： Mansour h.al-hAShIM Osama M.K.KASSEM Department of Geology and Geophysics College of Science King Saud University Department of Geology National Research Center

Identifying deformational mechanisms and associated structures at various scales, ranging from regional-scale structures to microscopic fabric, is crucial for the assessment of tectonic development. Thirty-three samples were taken from the Qazzaz metamorphic core complex to estimate the finite strain for felsic and mafic minerals. These samples included gneisses rocks, monzogranite, and metavolcano–sedimentary rocks for both the Thalbah and Bayda groups. Using the Rf/j and Fry methods, the axial ratios(XZ) range about 2.20 to 7.10 and 1.90 to 9.10, respectively. For various rock units, the strain measurements show moderate to highly deformation. Most of the observed samples show shallow WNW dipping along a N to WNW trend of finite strain(X). The short axes(Z) based to be subvertical foliation related with a subhorizontal foliation. The results demonstrate that contacts generated at semi-brittle to ductile deformation and that the strain of magnitude has the same value for different lithologic units. It concluded that nappe generation in orogens results from pure shear deformation.

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Flow Regimes in Bubble Columns with and without Internals: A Review

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Fluid Dynamics & Materials Processing 2024年第2期20卷 239-256页

作者： Ayat N.Mahmood Amer A.Abdulrahman Laith S.Sabri Abbas J.Sultan hasan Shakir Majdi Muthanna h.al-Dahhan Department of Chemical Engineering University of Technology-IraqBaghdadIraq Chemical and Petroleum Industries Engineering Department Al-Mustaqbal University CollegeBabylonIraq Department of Chemical and Biochemical Engineering Missouri University of Science and TechnologyRollaUSA

hydrodynamics characterization in terms ofﬂow regime behavior is a crucial task to enhance the design of bubble column reactors and scaling up related *** review presents recent studies on the typicalﬂow regimes established in bubble *** effort is also provided to introduce relevant deﬁnitions pertaining to thisﬁeld,namely,that of“void fraction”and related(local,chordal,cross-sectional and volumetric)*** studies involving different parameters that affect design and operating conditions are also discussed in *** the second part of the review,the attention is shifted to cases with internals of various types(perfo-rated plates,bafﬂes,vibrating helical springs,mixers,and heat exchanger tubes)immersed in the bubble *** is shown that the presence of these elements has a limited inﬂuence on the global column ***,they can make the homogeneousﬂow regime more stable in terms of transition gas velocity and transi-tion holdup *** last section is used to highlight gaps which have not beenﬁlled yet and future directions of investigation.

关键词： hydrodynamics ﬂow regime bubble column heat-exchanging internals

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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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Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

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Digital Communications and Networks 2024年第2期10卷 369-379页

作者： héber h.Arcolezi Jean-François Couchot Bechara al Bouna Xiaokui Xiao Inria andÉcole Polytechnique(IPP) PalaiseauFrance Femto-ST Institute Univ.Bourg.Franche-ComtéUBFCCNRSBelfortFrance TICKET Lab. Antonine University Hadat-BaabdaBaabdaLebanon School of Computing National University of SingaporeSingaporeSingapore

This paper investigates the problem of collecting multidimensional data throughout time(i.e.,longitudinal studies)for the fundamental task of frequency estimation under Local Differential Privacy(LDP)*** to frequency estimation of a single attribute,the multidimensional aspect demands particular attention to the privacy ***,when collecting user statistics longitudinally,privacy progressively ***,the“multiple”settings in combination(i.e.,many attributes and several collections throughout time)impose several challenges,for which this paper proposes the first solution for frequency estimates under *** tackle these issues,we extend the analysis of three state-of-the-art LDP protocols(Generalized Randomized Response–GRR,Optimized Unary Encoding–OUE,and Symmetric Unary Encoding–SUE)for both longitudinal and multidimensional data *** the known literature uses OUE and SUE for two rounds of sanitization(***),i.e.,L-OUE and L-SUE,respectively,we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility(i.e.,L-OSUE).also,for attributes with small domain sizes,we propose Longitudinal GRR(L-GRR),which provides higher utility than the other protocols based on unary ***,we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates(alLOMFREE),which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol,i.e.,either L-GRR or *** shown in the results,alLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.

关键词： Local differential privacy Discrete distribution estimation Frequency estimation Multidimensional data Longitudinal studies

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Vanillylacetone attenuates cadmium chloride-induced hippocampal damage and memory loss through upregulation of nuclear factor erythroid 2-related factor 2 gene and protein expression

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Neural Regeneration Research 2024年第12期19卷 2750-2759页

作者： Fahaid h.A.L-hashem Salah O.Bashir Amal F.Dawood Moutasem S.Aboonq Ismaeel Bin-Jaliah Abdulaiziz M.al-Garni Mohamed D.Morsy Department of Physiology College of MedicineKing Khalid UniversityAbhaSaudi Arabia Department of Basic Medical Sciences College of MedicinePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Physiology College of MedicineTaibah UniversityAl-Madinah Al-MunawwarahSaudi Arabia Department of Medicine College of MedicineKing Khalid UniversityAbhaSaudi Arabia

Memory loss and dementia are major public h.alth concerns with a substantial economic *** stress has been shown to play a crucial role in the pathophysiology of hippocampal damage-induced memory *** investigate whether the antioxidant and anti-inflammatory compound vanillyla cetone(zingerone) can protect against hippocampal damage and memory loss induced by cadmium chloride(CdCl_(2)) administration in rats,we explo red the potential involvement of the nuclear factor erythroid 2-related factor 2(Nrf2) signaling pathway,which is known to modulate oxidative stress and *** h.alt hy male Wistar rats were divided into five groups:vehicle-treated(control),vanillylacetone,CdCl_(2),vanillylacetone+ CdCl_(2),vanillylacetone+ CdCl_(2)+ brusatol(a selective pharmacological N rf2inhibitor) *** effectively attenuated CdCl_(2)-induced damage in the dental gyrus of the hippocampus and improved the memory function assessed by the Morris Water Maze ***,vanillylacetone markedly decreased the hippocampal tissue levels of inflammatory biomarkers(interleukin-6,tumor necrosis factor-α,intracellular cell adhesive molecules) and apoptosis biomarkers(Bax and cleaved caspase-3).The control and CdCl_(2)-treated groups treated with va nillylacetone showed reduced generation of reactive oxygen species,decreased malondialdehyde levels,and increased superoxide dismutase and glutathione activities,along with significant elevation of nuclear Nrf2 mRNA and protein expression in hippocampal *** the protective effects of vanillylacetone we re substantially blocked by the co-administration of brusatol(a selective N rf2 inhibitor).Va nillylacetone mitigated hippocampal damage and memory loss induced by CdCl_(2),at least in part, by activating the nuclear transcription factor ***,vanillylacetone exerted its potent antioxidant and antiinflammatory actions.

关键词： hippocampus neuroprotective Nrf2 gene oxidative stress vanillylacetone

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Single image super-resolution:a comprehensive review and recent insight

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Frontiers of Computer Science 2024年第1期18卷 139-156页

作者： hanadi al-MEKhLAFI Shiguang LIU College of Intelligence and Computing Tianjin UniversityTianjin 300350China

Super-resolution(SR)is a long-standing problem in image processing and computer vision and has attracted great attention from researchers over the *** main concept of SR is to reconstruct images from low-resolution(LR)to high-resolution(hR).It is an ongoing process in image technology,through up-sampling,de-blurring,and *** neural network(CNN)has been widely used to enhance the resolution of images in recent *** alternative methods use deep learning to improve the progress of image super-resolution based on ***,we review the recent findings of single image super-resolution using deep learning with an emphasis on distillation knowledge used to enhance image super-resolution.,it is also to highlight the potential applications of image super-resolution in security monitoring,medical diagnosis,microscopy image processing,satellite remote sensing,communication transmission,the digital multimedia industry and video ***,we present the challenges and assess future trends in super-resolution based on deep learning.

关键词： super-resolution deep learning single-image interpolation-based learning-based reconstruction-based

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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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Developing a Model for Parkinson’s Disease Detection Using Machine Learning algorithms

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Computers, Materials & Continua 2024年第6期79卷 4945-4962页

作者： Naif al Mudawi Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia

Parkinson’s disease(PD)is a chronic neurological condition that progresses over *** start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or *** patient’s symptoms become more severe due to the worsening of their signs over *** this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk *** research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of *** aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD *** research methodology involves developing a model using a machine learning *** the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).In addition,GridSearchCV was utilised to measure each.algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning *** 98%accuracy,LightGBM had the highest accuracy in this ***,KNN,and SVM came in second with 96%***,the performance scores of NB and LR were recorded to be 76%and 83%,*** is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory ***,we extracted meaningful insights and figured out potential gaps on top of *** extracting meaningful in

关键词： Light GBM cross-validation ROC-AUC Parkinson’s disease(PD) SVM and XGBoost

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