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Potential role of tubulin glutamylation in neurodegenerative diseases

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Neural Regeneration Research 2024年第6期19卷 1191-1192页

作者： abdullah Md.Sheikh Shatera Tabassum Department of Laboratory Medicine Shimane University Faculty of Medicine

The differentiation of neuronal stem cells into mature neurons is a complex process that involves both structural and functional changes. As cells undergo differentiation, there are notable functional changes, including the expression of various transcription factors, cytokines, and neurotransmitiers. Additionally, structural changes occur as the cells develop various processes from the cell body and establish synaptic contacts with other cells.

关键词： POLARITY

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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Computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah abdullah Al-Johany Sanaa abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

Leveraging User-Generated Comments and Fused BiLSTM Models to Detect and Predict Issues with Mobile Apps

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Computers, Materials & Continua 2024年第4期79卷 735-759页

作者： Wael M.S.Yafooz abdullah Alsaeedi Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.

关键词： Mobile apps issues play store user comments deep learning LSTM bidirectional LSTM

A Novel Intrusion Detection Model of Unknown Attacks Using Convolutional Neural Networks

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Computer Systems Science & Engineering 2024年第2期48卷 431-449页

作者： abdullah Alsaleh Department of Information Engineering Florence UniversityFlorenceItaly Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityMajmaahSaudi Arabia

With the increasing number of connected devices in the Internet of Things(IoT)era,the number of intrusions is also increasing.An intrusion detection system(IDS)is a secondary intelligent system for monitoring,detecting and alerting against malicious activity.IDS is important in developing advanced security models.This study reviews the importance of various techniques,tools,and methods used in IoT detection and/or prevention systems.Specifically,it focuses on machine learning(ML)and deep learning(DL)techniques for IDS.This paper proposes an accurate intrusion detection model to detect traditional and new attacks on the Internet of Vehicles.To speed up the detection of recent attacks,the proposed network architecture developed at the data processing layer is incorporated with a convolutional neural network(CNN),which performs better than a support vector machine(SVM).Processing data are enhanced using the synthetic minority oversampling technique to ensure learning accuracy.The nearest class mean classifier is applied during the testing phase to identify new attacks.Experimental results using the AWID dataset,which is one of the most common open intrusion detection datasets,revealed a higher detection accuracy(94%)compared to SVM and random forest methods.

关键词： Internet of Vehicles intrusion detection machine learning unknown attacks data processing layer

Appropriate Combination of Crossover Operator and Mutation Operator in Genetic Algorithms for the Travelling Salesman Problem

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Computers, Materials & Continua 2024年第5期79卷 2399-2425页

作者： Zakir Hussain Ahmed Habibollah Haron abdullah Al-Tameem Department of Mathematics and Statistics College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Kingdom of Saudi Arabia Faculty of Computing Universiti Teknologi MalaysiaJohor Bahru81310Malaysia College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Kingdom of Saudi Arabia

Genetic algorithms(GAs)are very good metaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems.AsimpleGAbeginswith a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes.It uses a crossover operator to create better offspring chromosomes and thus,converges the population.Also,it uses a mutation operator to explore the unexplored areas by the crossover operator,and thus,diversifies the GA search space.A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution.However,appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem.In this present paper,we aim to study the benchmark traveling salesman problem(TSP).We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances.The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem.The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances.

关键词： Travelling salesman problem genetic algorithms crossover operator mutation operator comprehensive sequential constructive crossover insertion mutation

Unraveling the role of NiSnPH@OOH/CC perovskite hydroxide for efficient electrocatalytic oxidation of methanol to formate

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Nano Research 2024年第4期17卷 2388-2399页

作者： Jing Shao Yusheng Fang Xiaobing Wu Muhammad Imran abdullah Youkun Tao College of Chemistry and Environmental Engineering Shenzhen UniversityShenzhen 518060China School of Science Harbin Institute of TechnologyShenzhen 518055China

The sluggish kinetics of oxygen evolution reaction(OER)is the key tailback for hydrogen production from the water electrolysis.Masking OER with thermodynamically auspicious methanol oxidation reaction(MOR)can significantly boost the H_(2) and value-added products production.However,it is currently challenging to achieve a synergistic manipulation of product selectivity and performance for MOR electrocatalyst.Herein,we report NiSnPH@OOH/CC(CC=carbon cloth)perovskite hydroxide nanosphere as an efficient MOR electrocatalyst with high activity,stability,and selectivity towards methanol oxidation to formate.A surface amorphous layer of defect rich NiOOH was generated in operando by selective Sn leaching with stable perovskite hydroxide bulk structure,which mitigates the oxidative power and optimizes the local coordination environment of the active NiOOH sites.In situ Raman combined with electrochemical studies further confirm the key active species,NiOOH,generated in operando enhance the MOR and blocking the over oxidation of methanol to CO_(2).As a result,NiSnPH@OOH/CC effectively masks the OER and attains>99%selectivity with 100%Faradic efficiency for methanol-to-formate.The results of this study show the advances of NiSnPH@OOH/CC as an efficient electrocatalyst for MOR and also suggest its potential applications for various small organic molecules oxidation.

关键词： perovskite hydroxide small organic molecules nickel oxy(hydroxide) value-added products methanol oxidation reaction

Comprehensive analysis of glacier recession(2000-2020)in the Nun-Kun Group of Glaciers,Northwestern Himalaya

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Journal of Mountain Science 2024年第3期21卷 768-785页

作者： Shakil Ahmad ROMSHOO Ummer AMEEN Mustafa Hameed BHAT Tariq abdullah Department of Geoinformatics University of KashmirSrinagarKashmir 190006India Islamic University of Science and Technology AwantiporaKashmir 192122India Centre of Excellence for Glacier Studies in the Western Himalaya University of KashmirSrinagarKashmir 190006India

Himalayan glaciers are shrinking rapidly,especially after 2000.Glacier shrinkage,however,shows a differential pattern in space and time,emphasizing the need to monitor and assess glacier changes at a larger scale.In this study,changes of 48 glaciers situated around the twin peaks of the Nun and Kun mountains in the northwestern Himalaya,hereafter referred to as Nun-Kun Group of Glaciers(NKGG),were investigated using Landsat satellite data during 2000-2020.Changes in glacier area,snout position,Equilibrium Line Altitude(ELA),surface thickness and glacier velocity were assessed using remote sensing data supplemented by field observations.The study revealed that the NKGG glaciers have experienced a recession of 4.5%±3.4%and their snouts have retreated at the rate of 6.4±1.6 m·a^(-1).Additionally,there was a 41%increase observed in the debris cover area during the observation period.Using the geodetic approach,an average glacier elevation change of-1.4±0.4 m·a^(-1)was observed between 2000 and 2012.The observed mass loss of the NKGG has resulted in the deceleration of glacier velocity from 27.0±3.7 m·a^(-1)in 2000 to 21.2±2.2 m·a^(-1)in 2020.The ELA has shifted upwards by 83.0±22 m during the period.Glacier morphological and topographic factors showed a strong influence on glacier recession.Furthermore,a higher recession of 12.9%±3.2%was observed in small glaciers,compared to 2.7%±3.1%in larger glaciers.The debris-covered glaciers showed lower shrinkage(2.8%±1.1%)compared to the clean glaciers(9.3%±5%).The glacier depletion recorded in the NKGG during the last two decades,if continued,would severely diminish glacial volume and capacity to store water,thus jeopardizing the sustainability of water resources in the basin.

关键词： Nun-Kun range Glacier recession Glacier snout Remote Sensing Himalaya Glacier velocity

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization problems.The past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer simulations.Moreover,it is hard to traverse the huge search space within reasonable resource as problem dimension increases.Traditional evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory results.To reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent years.Yet there lacks a thorough review of the state of the art in this specific and important area.This paper provides a comprehensive survey of these evolutionary algorithms for HEPs.We start with a brief introduction to the research status and the basic concepts of HEPs.Then,we present surrogate-assisted evolutionary algorithms for HEPs from four main aspects.We also give comparative results of some representative algorithms and application examples.Finally,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： Farhan Ullah Xuexia Zhang Mansoor Khan Muhammad Abid abdullah Mohamed School of Electrical Engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil Engineering Harbin Engineering UniversityHarbin150001China Research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance grows.Traditional approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering concepts.The approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting