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Quantum Mechanical Tunneling of Dislocations: Quantization and Depinning from Peierls Barrier

World Journal of Condensed Matter Physics 2016年第2期6卷 103-108页

作者： Saleem Iqbal Farhana sarwar Syed Mohsin Raza Department of Mathematics University of Balochistan Quetta Pakistan Department of Mathematics F. G. Girls Degree College Quetta Pakistan Department of Physics University of Balochistan Quetta Pakistan

Theories of Mott and Weertmann pertaining to quantum mechanical tunneling of dislocations from Peierls barrier in cubic crystals are revisited. Their mathematical calculations about logarithmic creep rate and lattice vibrations as a manifestation of Debye temperature for quantized thermal energy are found correct but they can not ascertain to choose the mass of phonon or “quanta” of lattice vibrations. The quantum mechanical yielding in metals at relatively low temperatures, where Debye temperatures operate, is resolved and the mathematical formulas are presented. The crystal plasticity is studied with stress relaxation curves instead of logarithmic creep rate. With creep rate formulas of Mott and Weertmann, a new formula based on logarithmic profile of stress relaxation curves is proposed which suggests simultaneous quantization of dislocations with their stress, i.e., and depinning of dislocations, i.e., , where is quantum action, σ is the stress, N is the number of dislocations, A is the area and t is the time. The two different interpretations of “quantum length of Peierls barrier”, one based on curvature of space, i.e., yields quantization of Burgers vector and the other based on the curvature of time, i.e., yields depinning of dislocations from Peierls barrier in cubic crystals, are presented. , i.e., the unitary operator on shear modulus yields the variations in the curvature of time due to which simultaneous quantization, and depinning of dislocations occur from Peierls barrier in cubic crystals.

关键词： Peierls Barrier Quantum Tunneling Dislocations Stress Relaxation Quantum of Stresses Depinning of Dislocations

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Quantum Mechanical Approach for Rutherford Scattering and Nuclear Scattering with Born Approximation

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World Journal of Nuclear Science and Technology 2016年第1期6卷 71-78页

Rutherford classical scattering theory, as its quantum mechanical analogue, is modified for scattering cross-section and the impact parameter by using quantum mechanical momentum, (de Broglie hypothesis), energy relationship for matter oscillator (Einstein’s oscillator) and quantum mechanical wave vectors, and , respectively. It is observed that the quantum mechanical scattering cross-section and the impact parameter depended on inverse square law of quantum action (Planck’s constant). Born approximation is revisited for quantum mechanical scattering. Using Bessel and Neumann asymptotic functions and response of nuclear surface potential barrier, born approximations were modified. The coulombic fields inside the nucleus of the atom are studied for reflection and transmission with corresponding wave vectors, phase shifts and eigenfunctions Bulk quantum mechanical tunneling and reflection scattering, both for ruptured and unruptured nucleus of the atom, are deciphered with corresponding wave vectors, phase shifts and eigenfunction. Similar calculation ware accomplished for quantum surface tunneling and reflection scattering with corresponding wave vectors, phase shifts and eigenfunctions. Such diverse quantum mechanical scattering cross-section with corresponding wave vectors for tunneling and reflection, phase shifts and eigenfunctions will pave a new dimension to understanding the behavior of exchange fields in the nucleus of the atom with insides layers both ruptured and unruptured. Phase shifts, δl for each of the energy profile (partial) will be different and indeed their corresponding wave vectors for exchange energy eigenvalues.

关键词： Rutherford Classical Scattering Theory Scattering Cross Section Impact Parameter Born Approximation Ruptured and Unruptured Nucleus

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Indoor Electromagnetic Radiation Intensity Relationship to Total Energy of Household Appliances

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Computers, Materials & Continua 2022年第3期70卷 5421-5435页

作者： Murad A.A.Almekhlafi Lamia Osman Widaa Fahd N.Al-Wesabi Mohammad Alamgeer Anwer Mustafa Hilal Manar Ahmed Hamza Abu sarwar Zamani Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Electrical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The rapid technological developments in the modern era have led to increased electrical equipment in our daily lives,work,and *** this standpoint,the main objective of this study is to evaluate the potential relationship between the intensity of electromagnetic radiation and the total energy of household appliances in the living environment within the building by measuring and analyzing the strength of the electric field and the entire electromagnetic radiation flux density of electrical devices operating at frequencies(5 Hz to 1 kHz).The living room was chosen as a center for measurement at 15 homes in three different environmental regions(urban,suburbs,and open areas).The three measurement methods are(Mode 1:people in a sitting position with electrical appliances *** 2:People in a standing position with electrical appliances *** 3:People are in the upright positionwhile turning off the electrical devices)in the living *** measurement methods and their results reinforce the importance of this *** results showed that the average electric field strengthmeasured inMode 2 ismuch greater than the two methods,and we also found less electromagnetic radiation in Mode 3 than in the two *** results remain within the recommended overall exposure developed by the International Committee for the Prevention of Non-Ionizing Radiation and the International Electrotechnical Commission.

关键词： Exposure assessment electromagnetic field household appliances indoor various environments epidemiology

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Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

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Computer Systems Science & Engineering 2023年第10期47卷 1267-1281页

作者： Abdelwahed Motwakel MarwaObayya Nadhem Nemri Khaled Tarmissi Heba Mohsen Mohammed Rizwanulla Ishfaq Yaseen Abu sarwar Zamani Department of Information Systems College of Business Administration in Hawtat bani TamimPrince Sattam bin Abdulaziz UniversityHowtat Bani Tamim16622Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and *** RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy *** energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power *** energy consumption of RE and NRE sources using effective models becomes *** this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power *** data may influence the prediction performance of the results in prediction *** proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter *** addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL *** design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

关键词： Sustainability renewable energy power source energy prediction deep learning fusion model metaheuristics

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Potassium hydroxide based biorefinery concepts for non-wood bioresources

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Journal of Bioresources and Bioproducts 2018年第1期3卷 1-2页

作者： M sarwar Jahan Zhibin He Pulp and Paper Research Division BCSIR LaboratoriesDhakaDr.Qudrat-i-Khuda RoadDhaka 1205Bangladesh Department of Chemical Engineering University of New BrunswickFrederictonNBE3B5A3 Canada

The paper and paperboard production in 2016 in the world was 110.07 million tones.1 Bangladesh consumes only about 3.5~4 kg paper and board products per capita,while the developed countries consume about 300 kg/***’s consumption is also much lower than the world’s average(@50 kg/capita)and the Asia’s average(@50 kg/capita).2 To reach the world’s or Asia’s level,Bangladesh’s consumption of paper and board products needs to increase 10 *** forest resources are very limited in Bangladesh,alternative fibrous raw materials are required to achieve this *** many studies have been carried out on alternative raw materials available in Bangladesh.

关键词： Non-wood Biorefinery Potassium hydroxide Pulping Soil amendment

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Malware Detection Using Decision Tree Based SVM Classifier for IoT

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Computers, Materials & Continua 2022年第7期72卷 713-726页

作者： Anwer Mustafa Hilal Siwar Ben Haj Hassine Souad Larabi-Marie-Sainte Nadhem Nemri Mohamed K.Nour Abdelwahed Motwakel Abu sarwar Zamani Mesfer Al Duhayyim Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Computer Science College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

The development in Information and Communication Technology has led to the evolution of new computing and communication *** revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart *** of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through *** enables secure wireless communication over the Internet to allow efficient analysis of medical *** these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical *** work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in *** proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and *** extraction is performed by calculating the weight of each feature and reduces the features with less *** Vector Machine classifier is employed in the second step to classify the malware and benign ***,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and ***-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

关键词： Blockchain malware detection classification feature selection internet of medical things

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埃塞俄比亚塔纳湖鲤科鱼类体内亚美尼亚许氏绦虫Khawia armeniaca(Cholodkovsky,1915)Shulman,1958(绦虫纲:鲤蠢目)的形态鉴定

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水生生物学报 2021年第4期45卷 917-924页

作者： KIBET Caroline Jepkorir 赵文婷 sarwar Huda 聂品中国科学院水生生物研究所淡水生态与生物技术国家重点实验室农业部淡水养殖病害防治重点实验室武汉430072 中国科学院大学北京100049 肯尼亚国家博物馆鱼类学科组动物学系肯尼亚内罗毕P.O Box 40568-00100 青岛农业大学海洋科学与工程学院青岛266109

在埃塞俄比亚境内塔纳湖中发现了1种寄生于鲤科鱼类:间魮(Labeobarbus intermedius)和Labeobarbus tsanensis体内的许氏绦虫,经形态学鉴定其为亚美尼亚许氏绦虫Khawia armeniaca(Cholodkovsky,1915)(绦虫纲:鲤蠢目)。该绦虫鉴别特征为... 详细信息

在埃塞俄比亚境内塔纳湖中发现了1种寄生于鲤科鱼类:间魮(Labeobarbus intermedius)和Labeobarbus tsanensis体内的许氏绦虫,经形态学鉴定其为亚美尼亚许氏绦虫Khawia armeniaca(Cholodkovsky,1915)(绦虫纲:鲤蠢目)。该绦虫鉴别特征为头节呈半球状,边缘光滑,无皱褶;睾丸分布区域从虫体中部至阴茎囊之前;卵巢前卵黄腺分布于虫体中后部至阴茎囊前部区域,少数排列在阴茎囊之后;子宫起始于阴茎囊后方弯曲环绕直至卵巢后部区域,有少许卵黄腺排列在子宫和卵巢的两侧。此外,***卵巢呈滤泡状或蝴蝶状,雌雄生殖孔分离但彼此距离很近,开口于体表并形成同一生殖腔,且雄性生殖孔位于雌性生殖孔前方。卵巢后卵黄腺数目少于100个,分布于虫体末端,卵黄腺距离卵巢后翼较远,部分样品内卵黄腺接近卵巢后翼。

关键词：纽带科亚美尼亚许氏绦虫形态学形态测量学塔纳湖埃塞俄比亚

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Feature Selection with Stacked Autoencoder Based Intrusion Detection in Drones Environment

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Computers, Materials & Continua 2022年第12期73卷 5441-5458页

作者： Heba G.Mohamed Saud S.Alotaibi Majdy M.Eltahir Heba Mohsen Manar Ahmed Hamza Abu sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

The Internet of Drones(IoD)offers synchronized access to organized airspace for Unmanned Aerial Vehicles(known as drones).The availability of inexpensive sensors,processors,and wireless communication makes it possible in real time *** several applications comprise IoD in real time environment,significant interest has been received by research *** IoD operates in wireless environment,it is needed to design effective intrusion detection system(IDS)to resolve security issues in the IoD *** article introduces ametaheuristics feature selection with optimal stacked autoencoder based intrusion detection(MFSOSAEID)in the IoD *** major intention of the MFSOSAE-ID technique is to identify the occurrence of intrusions in the IoD *** do so,the proposed MFSOSAE-ID technique firstly pre-processes the input data into a compatible *** addition,the presented MFSOSAEID technique designs a moth flame optimization based feature selection(MFOFS)technique to elect appropriate ***,firefly algorithm(FFA)with stacked autoencoder(SAE)model is employed for the recognition and classification of intrusions in which the SAE parameters are optimally tuned with utilize of *** performance validation of the MFSOSAE-ID model was tested utilizing benchmark dataset and the outcomes implied the promising performance of the MFSOSAE-ID model over other techniques with maximum accuracy of 99.72%.

关键词： Internet of drones unmanned aerial vehicles security intrusion detection machine learning

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Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

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Computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： Saud S.Alotaibi Amal Al-Rasheed Sami Althahabi Manar Ahmed Hamza Abdullah Mohamed Abu sarwar Zamani Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,Symbiotic Organism Search(SOS)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COVID-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

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Intelligent Optimization-Based Clustering with Encryption Technique for Internet of Drones Environment

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Computers, Materials & Continua 2022年第12期73卷 6617-6634页

作者： Dalia H.Elkamchouchi Jaber S.Alzahrani Hany Mahgoub Amal S.Mehanna Anwer Mustafa Hilal Abdelwahed Motwakel Abu sarwar Zamani Ishfaq Yaseen Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo 11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recent technological developments have revolutionized the functioning of Wireless Sensor Network(WSN)-based industries with the development of Internet of Things(IoT).Internet of Drones(IoD)is a division under IoT and is utilized for communication amongst *** drones are naturally mobile,it undergoes frequent topological *** alterations in the topology cause route election,stability,and scalability problems in *** is considered as an effective method to transmit the images in IoD *** current study introduces an Atom Search Optimization basedClusteringwith Encryption Technique for Secure Internet of Drones(ASOCE-SIoD)*** key objective of the presented ASOCE-SIoD technique is to group the drones into clusters and encrypt the images captured by *** presented ASOCE-SIoD technique follows ASO-based Cluster Head(CH)and cluster construction *** addition,signcryption technique is also applied to effectually encrypt the images captured by drones in IoD *** process enables the secure transmission of images to the ground *** order to validate the efficiency of the proposed ASOCE-SIoD technique,several experimental analyses were conducted and the outcomes were inspected under different *** comprehensive comparative analysis results established the superiority of the proposed ASOCE-SIoD model over recent approaches.

关键词： Internet of drones atom search algorithm clustering encryption signcryption

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