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A Comparative study of Adomian Decomposition Method with Variational Iteration Method for solving Linear and Nonlinear Differential Equations

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Journal of Applied Mathematics and Physics 2024年第8期12卷 2789-2819页

作者： sarah khaled al Baghdadi N. Ameer Ahammad General studies Department Technical and Vocational Training Corporation Tabuk KSA Department of Mathematics Faculty of Science University of Tabuk Tabuk KSA

This study compares the Adomian Decomposition Method (ADM) and the Variational Iteration Method (VIM) for solving nonlinear differential equations in engineering. Differential equations are essential for modeling dynamic systems in various disciplines, including biological processes, heat transfer, and control systems. This study addresses first, second, and third-order nonlinear differential equations using Mathematica for data generation and graphing. The ADM, developed by George Adomian, uses Adomian polynomials to handle nonlinear terms, which can be computationally intensive. In contrast, VIM, developed by He, directly iterates the correction functional, providing a more straightforward and efficient approach. This study highlights VIM’s rapid convergence and effectiveness of VIM, particularly for nonlinear problems, where it simplifies calculations and offers direct solutions without polynomial derivation. The results demonstrate VIM’s superior efficiency and rapid convergence of VIM compared with ADM. The VIM’s minimal computational requirements make it practical for real-time applications and complex system modeling. Our findings align with those of previous research, confirming VIM’s efficiency of VIM in various engineering applications. This study emphasizes the importance of selecting appropriate methods based on specific problem requirements. While ADM is valuable for certain nonlinearities, VIM’s approach is ideal for many engineering scenarios. Future research should explore broader applications and hybrid methods to enhance the solution’s accuracy and efficiency. This comprehensive comparison provides valuable guidance for selecting effective numerical methods for differential equations in engineering.

关键词： Differential Equations Numerical Analysis Mathematical Computing Engineering Models Nonlinear Dynamics

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A state-Migration Particle swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan khaled sedraoui Yusuf al-Turki MengChu Zhou IEEE the College of Computer science and Technology Chongqing University of Posts and Telecommunications the Department of Computer science Columbia University the College of Computer and Information science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineering and also with K.A.CARE Energy Research and Innovation Center King Abdulaziz University the Center of Research Excellence in Renewable Energy and Power systems the Department of Electrical and Computer Engineering Faculty of Engineering and also with K.A.CARE Energy Research and Innovation Center King Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PsO) to meet scalable ***, conventional PsO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(sPsO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an sPsO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, sPsO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete (HDI) data hyper-parameter adaptation latent factor analysis (LFA) particle swarm optimization (PsO)

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A review of mechanism of action of outdoor exposure in preventing myopia incidence and progression

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International Journal of Ophthalmology(English edition) 2024年第6期17卷 1144-1155页

作者： Chikezie Grand Ihesiulor khaled alzahrani Hema Rhadhakrishnan Division of Pharmacy&Optometry School of Health SciencesFaculty of BiologyMedicine and HealthThe University of ManchesterManchester M139PLUnited Kingdom Department of Ophthalmology King Fahad Armed Forces HospitalJeddah 23311Saudi Arabia

Various studies have suggested several environmental,pharmacological,medical,and optical interventions and some are in use but their efficacy in myopia control may be transient,and the cellular,molecular,and biochemical mechanisms involved *** exposure is currently regarded as an effective and enduring strategy in the control of myopia development and ***,the mechanism behind the effect of outdoor exposure and its association with genetic predisposition and other relatively more significant environmental factors on myopia is still a *** review focuses on survey-based and intervention-based studies carried out to propose a mechanism that accounts for myopia development and important for its control.

关键词： outdoor exposure daylight exposure myopia control myopia

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Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

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Computers, Materials & Continua 2024年第4期79卷 1419-1439页

作者： khaled soliman Mohamed sobh Ayman M.Bahaa-Eldin Department of Computer and systems Engineering Ain Shams UniversityCairo11517Egypt Department of Computer Engineering Technology ElSewedy University of TechnologyCairo44629Egypt

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED *** most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

关键词： Portable executable malware intrusion detection cybersecurity zero-day threats Host IntrusionDetection system(HIDs) machine learning Anomaly-based Intrusion Detection system(AIDs) deep learning

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Defamiliarizing Reality for Mental and Physical subjection:Rereading Harold Pinter's The Room and The Dumb Waiter as Political Comedies

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Fudan Journal of the Humanities and social sciences 2024年第1期17卷 159-191页

作者： Ahmed Abdulkhair Abulmagd khaled Faculty of Languages&Translation Al-Azhar UniversityNasr CityCairoEgypt

While Pinter's earliest plays have been recognized in the modernist history of theatre as comedies of menace and his later plays as political comedies,this article argues that his earliest plays are equally very liable to be interpreted as political *** of their absurdist dramatization of people's helpless exposure to external,unidentifiable threats,a common post-WWII characteristic feature of human experience,I claim that The Room and The Dumb Waiter(both written 1957,staged 1960),two model examples of Pinter's earliest oeuvres,do not simply follow the aesthetic of absurdist theatre to express human *** audience's experience of viewing the theatrical performances of both plays in terms of discursive cyclicality or character normality is subverted into one of changeability,strangeness,and *** foreground the political implications of such revolutionary theatrical experience,Pinter's plays are examined in the light of his unique use of defamiliarization,relying not on Brecht's traditional techniques of singing,dancing,image-projecting,or captioning,but on a simple,dual technique of image destruction and *** consists of divesting characters of their normality and portraying them instead as individuals who identify only with unusual images of place,time,body,and *** this special technique of defamiliarization,both plays are examined to reveal Pinter's central political theme of undermining reality for purposes of mental and physical subjections.

关键词： Defamiliarization Comedy of menace Political comedy subjection Harold Pinter

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Enhancing photovoltaic energy forecasting: a progressive approach using wavelet packet decomposition

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CLEAN ENERGY 2024年第3期8卷 95-108页

作者： Ferkous, khaled Guermoui, Mawloud Bellaour, Abderahmane Boulmaiz, Tayeb Bailek, Nadjem Univ Ghardaia LMTESE Lab Mat Technol Energy Syst & Environm Ghardaia 47000 Algeria Renewable Energy Appl Res Unit URAER Ghardaia 47000 Algeria Ahmed Draia Adrar Univ Fac Sci & Technol Dept Math & Comp Sci Lab Math Modeling & Applicat Adrar 01000 Algeria

Accurate photovoltaic (PV) energy forecasting plays a crucial role in the efficient operation of PV power stations. This study presents a novel hybrid machine-learning (ML) model that combines Gaussian process regression with wavelet packet decomposition to forecast PV power half an hour ahead. The proposed technique was applied to the PV energy database of a station located in algeria and its performance was compared to that of traditional forecasting models. Performance evaluations demonstrate the superiority of the proposed approach over conventional ML methods, including Gaussian process regression, extreme learning machines, artificial neural networks and support vector machines, across all seasons. The proposed model exhibits lower normalized root mean square error (nRMsE) (2.116%) and root mean square error (RMsE) (208.233 kW) values, along with a higher coefficient of determination (R2) of 99.881%. Furthermore, the exceptional performance of the model is maintained even when tested with various prediction horizons. However, as the forecast horizon extends from 1.5 to 5.5 hours, the prediction accuracy decreases, evident by the increase in the RMsE (710.839 kW) and nRMsE (7.276%), and a decrease in R2 (98.462%). Comparative analysis with recent studies reveals that our approach consistently delivers competitive or superior results. This study provides empirical evidence supporting the effectiveness of the proposed hybrid ML model, suggesting its potential as a reliable tool for enhancing PV power forecasting accuracy, thereby contributing to more efficient grid management. This study proposes a new method for forecasting solar power generation 30 minutes ahead. It combines Gaussian process regression with wavelet packet decomposition to capture complex patterns in historical data. This approach outperforms other forecasting models across different seasons and forecast horizons. Graphical Abstract

关键词： short photovoltaic power forecasting wavelet packet decomposition sub-series reconstruction machine learning in energy forecasting sustainable power stations renewable energy DATA-DRIVEN MACHINE

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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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Review of tribological properties of nanoparticle-based lubricants and their hybrids and composites

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Friction 2024年第4期12卷 569-590页

作者： Ye Zar Ni HTWE Aws.s.al-JANABI Yasmin WADZER Hussin MAMAT Ku NOORYAsMIN school of Materials&Mineral Resources Engineering Universiti Sains Malaysia(Engineering Campus)Nibong Tebal 14300Malaysia school of Aerospace Engineering Universiti Sains Malaysia(Engineering Campus)Nibong Tebal 14300Malaysia Universiti sains Malaysia

Due to their encouraging results,nanolubricants have been revolutionary in the field of *** degree,to which the new material may improve the tribology,energy savings,and durability,is a crucial consideration for any new additive to a conventional *** results of the earlier research on carbon,metal,metal oxide,and their composites and hybrid nanolubricants as well as their effects on tribology,are summarized in this review *** most popular measuring methodologies,the tribology results for lubricants with an oil base,biodegradable base,and a water base,as well as the reasons that explain these tribological advancements,are all included in this ***,prospects for more study in this area are emphasized.

关键词： nanolubricants tribology carbon metal metal oxide hybrid

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Concomitant determination of hematological indices supported the application of the albumin-bilirubin score in non-malignant liver diseases

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World Journal of Hepatology 2024年第9期16卷 1308-1311页

作者： Marwan s M al-Nimer Department of Pharmacology College of MedicineUniversity of DiyalaBaqubah 32001Iraq

The albumin-bilirubin(alBI)score is a useful prognostic marker that predicts mortality in patients suffering from terminal ***,it has been reported that alBI score is a predictor of non-malignant liver *** cutoff point of the alBI score that distinguishes hepatocellular carcinoma from non-malignant liver disease is still not ***,the alBI score is a sensi-tive rather than a specific predictor of the poor outcomes of liver *** are many hematological indices and ratios that are utilized as prognostic *** these biomarkers are the neutrophil-to-lymphocyte ratio,platelet-to-lymphocyte ratio(PLR),and platelet-hemoglobin ratio(PHR),which are useful discriminating prognostic biomarkers for liver diseases,e.g.,hepato-cellular carcinoma,hepatitis,liver fibrosis,*** is evidence that PLR and PHR are prognostic biomarkers that predict the poor outcomes of ***,concomitant measurements of alBI score and PHR or alBI score and PLR will improve the predictive value that can differentiate hepatocellular carcinoma from non-malignant diseases.

关键词： albumin-bilirubin score Hepatocellular carcinoma Non-malignant diseases Platelet-lymphocyte ratio Platelet-hemoglobin ratio

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Predicting Energy Consumption Using stacked LsTM snapshot Ensemble

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Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd alghamdi Abdullah s.A.L-Malaise al-Ghamdi Mahmoud Ragab Information systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long short-Term Memory(LsTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean square Error(RMsE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LsTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

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