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Investigation of Neutrino-Nucleon Interaction through Intermediate Vector Boson (IVB)

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Journal of Modern Physics 2010年第4期1卷 244-250页

作者： Mohamed Tarek Hussein ahmad islam saad 不详

This work deals with the interaction of neutrino with the nucleon considering data taken from different experiments. It is assumed that the interaction of neutrino with nucleons go through the intermediate vector boson (IVB) which may be the W or Z with effective mass of the order of 80 GeV. The neutrino wave function is obtained via perturbation technique to calculate the weak leptonic current. On the other hand, the quark current is estimated using the measured experimental data of deep inelastic scattering of neutrino-nucleon interaction. Eventually the total interaction transition matrix is calculated as a function of momentum transfer square, q2 and qualitatively compared with the available experimental data. Besides, a comparative study is also done to explore the influence of the target composition during the neutrino weak interactions. In this context an investigation of neutrino-proton and neutrino-neutron interactions are carried out to calculate the deep inelastic cross section in both cases.

关键词： Deep Inelastic Scattering (DIS) Intermediate Vector Boson (IVB) Parton Model Structure Function

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

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Computer Systems Science & Engineering 2024年第2期48卷 363-394页

作者： Nasser Alshammari Shumaila Shahzadi saad Awadh Alanazi Shahid Naseem Muhammad Anwar Madallah Alruwaili Muhammad Rizwan Abid Omar Alruwaili Ahmed Alsayat Fahad ahmad Department of Computer Science College of Computer and Information SciencesJouf UniversitySakakaAl Jouf72341Saudi Arabia Department of Computer Sciences Kinnaird College forWomenLahorePunjab54700Pakistan Department of Information Sciences Division of Sciences and TechnologyUniversity of EducationLahore54770Pakistan Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72341Saudi Arabia Computer Science Florida Polytechnic UniversityLakelandFlorida33805USA Delta3T LahorePunjab54700Pakistan Department of Basic Sciences Common First YearJouf UniversitySakaka72341Saudi Arabia

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies *** vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end *** anomaly detector investigates these identified risks and provides secure network *** enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)*** anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation *** classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

关键词： Software defined network network function virtualization network function virtualization management and orchestration virtual infrastructure manager virtual network function Kubernetes Kubectl artificial intelligence machine learning

Improved Semi-empirical Model of Proton Exchange Membrane Fuel Cell Incorporating Fault Diagnostic Feature

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Journal of Modern Power Systems and Clean Energy 2021年第6期9卷 1566-1573页

作者： saad Saleem Khan Hussain Shareef ahmad Asrul Ibrahim Department of Electrical Engineering College of EngineeringUnited Arab Emirates University IEEE Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan Malaysia

The membrane water content of the proton exchange membrane fuel cell(PEMFC)is the most important feature required for water management of the PEMFC *** improper management of water in the fuel cell may lead to system *** various faults,flooding and drying faults are the most frequent in the PEMFC *** paper presents a new dynamic semi-empirical model which requires only the load current and temperature of the PEMFC system as the input while providing output voltage and membrane water content as its major *** other PEMFC systems,the proposed dynamic model calculates the internal partial pressure of oxygen and hydrogen rather than using special internal ***,the membrane water content and internal resistances of PEMFC are modelled by incorporating the load current condition and temperature of the PEMFC *** model parameters have been extracted by using a quantum lightening search algorithm as an optimization technique,and the performance is validated with experimental data obtained from the NEXA 1.2 k W PEMFC *** further demonstrate the capability of the model in fault detection,the variation in membrane water content has been studied via the *** proposed model could be efficiently used in prognostic and diagnosis systems of PEMFC fault.

关键词： Proton exchange membrane fuel cell(PEMFC)fault membrane water content modelling optimization quan-tum lightening search algorithm

Wormhole solutions and energy conditions in f(R,G)gravity

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Communications in Theoretical Physics 2021年第11期73卷 131-141页

作者： M Farasat Shamir G Mustafa saad Waseem Mushtaq ahmad National University of Computer and Emerging Sciences Lahore CampusPakistan Department of Mathematics Shanghai UniversityShanghai200444China National University of Computer and Emerging Sciences Chiniot-Faisalabad CampusPakistan

In this manuscript,the traversable wormhole solutions have been explored in modified f(R,G)gravity by taking into consideration the model f(R,G)=R+rG2,where R is the Ricci curvature scalar and G is the Gauss–Bonnet *** acceptable form of the redshift function has been incorporated which is a non-constant in nature along with the implementation of those already defined in literature,the two shape functions namely b(r)=r/exp(r-r0)and b(r)=r0log(r+1)/log(r0+1).It is shown by studying the energy condition and through the graphical analysis that the null energy bounds for the effective energy-momentum tensor are generally violated for the presence of the ordinary matter in modified f(R,G)*** conditions associated with the matter content threading the wormhole geometries are evaluated and in general are found to favor the null energy conditions in the vicinity of the wormhole throat,the existence of the nonexotic wormhole geometries threaded by the matter has been confirmed under this gravity.

关键词： f(R,G)gravity energy conditions wormholes shape function

Stress combination:When two negatives may become antagonistic,synergistic or additive for plants?

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Pedosphere 2023年第2期33卷 287-300页

作者： Hera NADEEM Amir KHAN Rishil GUPTA Mohamed HASHEM saad ALAMRI Mansoor ahmad SIDDIQUI Faheem ahmad Department of Botany Aligarh Muslim UniversityAligarh 202002(India) Department of Biology College of ScienceKing Khalid UniversityAbha 61413(Saudi Arabia) Botany and Microbiology Department Faculty of ScienceAssiut UniversityAssiut 71516(Egypt)

Plants in their natural environment are constantly subjected to various abiotic and biotic stressors and,therefore,have developed several defense mechanisms to maintain *** responses are intricate and require various physiological,biochemical,and cellular changes in *** reaction mechanisms in plants subjected to drought,salinity,or heat stress alone have been explained in numerous ***,the field conditions are significantly different from the controlled laboratory *** the field,crops or plants are simultaneously exposed to two or more abiotic and/or biotic stress conditions,such as a combination of salinity and heat,drought and cold,or any of the abiotic stresses combined with pathogen *** have shown that plants’reactions to combinations of more than two stress factors are distinct and cannot be explicitly deduced from their responses to different stresses when applied ***,additional research is needed to understand the complete mechanism of plant responses to stress by analyzing data between single stress and multiple stress *** review aims to provide an overview of current research on plant responses to a combination of various stress conditions and their influence on the metabolic,transcriptional,and physiological characteristics of plants.

关键词： abiotic stress abscisic acid biotic stress defence mechanism multifactorial stress reactive oxygen species stress response

Intelligent Ammunition Detection and Classification System Using Convolutional Neural Network

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Computers, Materials & Continua 2021年第5期67卷 2585-2600页

作者： Gulzar ahmad saad Alanazi Madallah Alruwaili Fahad ahmad Muhammad Adnan Khan Sagheer Abbas Nadia Tabassum School of Computer Science NCBA&ELahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Department of Basic Sciences Jouf UniversitySakakaAljouf72341Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Sciences Kinnaird College for WomenLahore54000Pakistan

Security is a significant issue for everyone due to new and creative ways to commit *** Closed-Circuit Television(CCTV)systems are being installed in offices,houses,shopping malls,and on streets to protect *** monitor CCTV;however,it is difficult for a single person to monitor the actions of multiple people at one ***,there is a dire need for an automated monitoring system that detects a person with ammunition or any other harmful material Based on our research and findings of this study,we have designed a new Intelligent Ammunition Detection and Classification(IADC)system using Convolutional Neural Network(CNN).The proposed system is designed to identify persons carrying weapons and ammunition using CCTV *** weapons are identified,the cameras sound an *** the proposed IADC system,CNN was used to detect firearms and *** CNN model which is a Deep Learning technique consists of neural networks,most commonly applied to analyzing visual imagery has gained popularity for unstructured(images,videos)data ***,this system generates an early warning through detection of ammunition before conditions become *** the faster and earlier the prediction,the lower the response time,loses and potential *** proposed IADC system provides better results than earlier published models like VGGNet,OverFeat-1,OverFeat-2,and OverFeat-3.

关键词： CCTV CNN IADC deep learning intelligent ammunition detection DnCNN

Prediction of Vibration Characteristics in Beam Structure Using Sub-Scale Modeling with Experimental Validation

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Chinese Journal of Mechanical Engineering 2015年第5期28卷 928-934页

作者： ZAI Behzad Ahmed SAMI saad KHAN M Amir ahmad Furqan PARK Myung Kyun Structure Design and Development Directorate Institute of Space Technology Department of Civil and Environmental Engineering KAIST Department of Mechanical Engineering Myongji University

Geometric or sub-scale modeling techniques are used for the evaluation of large and complex dynamic structures to ensure accurate reproduction of load path and thus leading to true dynamic characteristics of such structures. The sub-scale modeling technique is very effective in the prediction of vibration characteristics of original large structure when the experimental testing is not feasible due to the absence of a large testing facility. Previous researches were more focused on free and harmonic vibration case with little or no consideration for readily encountered random vibration. A sub-scale modeling technique is proposed for estimating the vibration characteristics of any large scale structure such as Launch vehicles, Mega structures, etc., under various vibration load cases by utilizing precise scaled-down model of that dynamic structure. In order to establish an analytical correlation between the original structure and its scaled models, different scale models of isotropic cantilever beam are selected and analyzed under various vibration conditions（ i.e. free, harmonic and random） using finite element package ANSYS. The developed correlations are also validated through experimental testing The prediction made from the vibratory response of the scaled-down beam through the established sets of correlation are found similar to the response measured from the testing of original beam structure. The established correlations are equally applicable in the prediction of dynamic characteristics of any complex structure through its scaled-down models. This paper presents modified sub-scale modeling technique that enables accurate prediction of vibration characteristics of large and complex structure under not only sinusoidal but also for random vibrations.

关键词： sub-scale modeling resonance frequency vibration characteristics scale factors power spectral density

Social Distancing and Isolation Management Using Machine-to-Machine Technologies to Prevent Pandemics

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Computers, Materials & Continua 2021年第6期67卷 3545-3562页

作者： Muhammad saad Maaz Bin ahmad Muhammad Asif Khalid Masood Mohammad A.Al Ghamdi College of Computing and Information Sciences PAF KIETKarachiPakistan Department of Computer Science Lahore Garrison UniversityLahorePakistan Department of Computer Science Umm Al-Qura UniversityMakkah CitySaudi Arabia

Social distancing and self-isolation management are crucial preventive measures that can save millions of lives during challenging pandemics of diseases such as the Spanish u,swine u,and coronavirus disease 2019(COVID-19).This study describes the comprehensive and effective implementation of the Industrial Internet of Things and machine-to-machine technologies for social distancing and smart self-isolation *** technologies can help prevent outbreaks of any disease that can disperse widely and develop into a ***,a smart wristband is proposed that incorporates Bluetooth beacon technology to facilitate the tracing and tracking of Bluetooth Low Energy beacon packets for smart contact ***,the connectivity of the device with Android or iOS applications using long-term evolution technology is realized to achieve ***,mathematical formulations are proposed to measure the distance between coordinates in order to detect geo-fencing *** formulations are specically designed for the virtual circular and polygonal boundaries used to restrict suspected or infected persons from trespassing in predetermined areas,e.g.,at home,in a hospital,or in an isolation *** proposed framework outperforms existing solutions,since it is implemented on a wider scale,provides a range of functionalities,and is cost-effective.

关键词： Coronavirus disease 2019 pandemic machine-to-machine industrial internet of things social distance geo-fencing

Cloud-Based Diabetes Decision Support System Using Machine Learning Fusion

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Computers, Materials & Continua 2021年第7期68卷 1341-1357页

作者： Shabib Aftab saad Alanazi Munir ahmad Muhammad Adnan Khan Areej Fatima Nouh Sabri Elmitwally School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan College of Computer and Information Sciences Jouf UniversitySakaka72341Saudi Arabia Riphah School of Computing&Innovation Riphah International UniversityLahore CampusLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Computer Science Faculty of Computers and Articial IntelligenceCairo University12613Egypt

Diabetes mellitus,generally known as diabetes,is one of the most common diseases *** is a metabolic disease characterized by insulin deciency,or glucose(blood sugar)levels that exceed 200 mg/dL(11.1 ml/L)for prolonged periods,and may lead to death if left uncontrolled by medication or insulin *** is categorized into two main types—type 1 and type 2—both of which feature glucose levels above“normal,”dened as 140 mg/*** is triggered by malfunction of the pancreas,which releases insulin,a natural hormone responsible for controlling glucose levels in blood *** and comprehensive analysis of this potentially fatal disease necessitate application of techniques with minimal rates of *** primary purpose of this research study is to assess the potential role of machine learning in predicting a person’s risk of developing ***,research has supported the use of various machine algorithms,such as naïve Bayes,decision trees,and articial neural networks,for early diagnosis of ***,to achieve maximum accuracy and minimal error in diagnostic predictions,there remains an immense need for further research and innovation to improve the machine-learning tools and techniques available to healthcare ***,in this paper,we propose a novel cloud-based machine-learning fusion technique involving synthesis of three machine algorithms and use of fuzzy systems for collective generation of highly accurate nal decisions regarding early diagnosis of diabetes.

关键词： Machine learning fusion articial neural network decision trees naïve Bayes diabetes prediction