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Nanoarchitectured LiMn_2O_4/Graphene/ZnO Composites as Electrodes for Lithium Ion Batteries

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Journal of Materials Science & Technology 2014年第5期30卷 427-433页

作者： saad Aziz Jianqing Zhao Carrington Cain Ying Wang Department of Mechanical & Industrial Engineering Louisiana State University

LiMn2O4 nanoparticles are facilely synthesized using a sol-gel processing method. Graphene is added to LiMn2O4 electrode aiming at increasing specific capacity and improving rate capability. In order to further improve cycling stability of LiMn2O4/graphene electrode, atomic layer deposition （ALD） is used to deposit ultrathin ZnO coating composed of six ZnO ALD layers and modify the surface of either LiMn2O4/graphene electrode or individual LiMn2O4 particles to form nanoarchitectured LiMn2O4/graphene/ZnO electrodes. Both ZnO-ALD-modified LiMn2O4/graphene electrodes demonstrate enhanced cycling performance at 1C, retaining the final discharge capacity above 122 mA h g 1 after 100 electrochemical cycles, which is higher than 115 mA h g-1 of pristine LiMn2O4/graphene electrode and 109 mA h g-1 of bare LiMn2O4 electrode. The improved electrochemical performance of nanoarchitectured LiMn2O4/graphene/ZnO electrodes can be attributed to the cooperative effects from high electronic conductivity of graphene sheets to facilitate electron transportation and effective protection of ZnO ALD coating to restrict Mn dissolution and electrolyte decomposition.

关键词： LiMn2O4/graphene nanocomposite Atomic layer deposition Cathode material Lithium ion battery

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

A Methodology to Reduce Thermal Gradients Due to the Exothermic Reactions in Resin Transfer Molding Applications

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Fluid Dynamics & Materials Processing 2023年第1期19卷 95-103页

作者： Aouatif saad Mohammed EL Ganaoui Advanced Systems Engineering Laboratory National School of Applied SciencesIbn Tofail UniversityKenitra242Morocco LERMAB/IUT Longwy Institut CarnotNancy186France

Resin transfer molding(RTM)is among the most used manufacturing processes for composite ***,the resin cure is initiated by heat supply to the *** supplementary heat generated during the reaction can cause thermal gradients in the composite,potentially leading to undesired residual stresses which can cause shrinkage and *** the present numerical study of these processes,a one-dimensional finite difference method is used to predict the temperature evolution and the degree of cure in the course of the resin polymerization;the effect of some parameters on the thermal gradient is then analyzed,namely:the fiber nature,the use of multiple layers of reinforcement with different thermal properties and also the temperature cycle *** validity of this numerical model is tested by comparison with experimental and numerical results in the existing literature.

关键词： Cure RTM finite difference method thermal gradients residual stresses

Nature-inspired Hybrid Optimization Algorithms for Load Flow Analysis of Islanded Microgrids

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1250-1258页

作者： saad Mohammad Abdullah Ashik Ahmed Quazi Nafees Ul Islam the Department of Electrical and Electronic Engineering Islamic University of TechnologyBoard BazarGazipur 1704Bangladesh

Load flow analysis is a significant tool for proper planning,operation,and dynamic analysis of a power system that provides the steady-state values of voltage magnitudes and angles at the fundamental ***,due to the absence of a slack bus in an islanded microgrid,modified load flow algorithms should be adopted considering the system frequency as one of the solution *** paper proposes the application of nature-inspired hybrid optimization algorithms for solving the load flow problem of islanded *** nature-inspired algorithms such as genetic algorithm(GA),differential evolution(DE),flower pollination algorithm(FPA),and grasshopper optimization algorithm(GOA)are separately merged with imperialistic competitive algorithm(ICA)to form four hybrid algorithms named as ICGA,ICDE,ICFPA,and *** of these algorithms are tested on the 6-bus test system and the modified IEEE 37-bus test system.A comparison among the proposed algorithms is carried out in terms of statistical analysis conducted using SPSS statistics *** the statistical analysis,it is identified that on an average,ICDE takes less number of iterations and consequently needs less execution time compared with other algorithms in solving the load flow problem of islanded microgrids.

关键词： Nature-inspired hybrid algorithm global optimization islanded microgrid load flow analysis

Hybrid-integer algorithm for a multi-objective optimal home energy management system

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Clean Energy 2023年第2期7卷 375-388页

作者： saad Gheouany Hamid Ouadi Saida El Bakali ERERA National School of Arts and CraftsMohammed V UniversityRabatMorocco

Most of the energy produced in the world is consumed by commercial and residential *** the growth in the global economy and world demographics,this energy demand has become increasingly *** has led to higher unit electricity prices,frequent stresses on the main electricity grid and carbon emissions due to inefficient energy *** paper presents an energy-consumption management system based on time-shifting of loads according to the dynamic day-ahead electricity *** simultaneously reduces the electricity bill and the peaks,while maintaining user comfort in terms of the operating waiting time of *** proposed optimization problem is formulated mathematically in terms of multi-objective integer non-linear programming,which involves constraints and consumer *** optimal scheduling,the management problem is solved using the hybridization of the particle swarm optimization algorithm and the branch-and-bound *** techniques are proposed to manage the trade-off between the conflicting *** first technique is the Pareto-optimal solutions classification using supervised learning *** second technique is called the lexicographic *** simulations were performed based on residential building energy consumption,time-of-use pricing(TOU)and critical peak pricing(CPP).The algorithms were implemented in *** results of the current work show that the proposed approach is effective and can reduce the electricity bill and the peak-to-average ratio(PAR)by 28% and 49.32%,respectively,for the TOU tariff rate,and 48.91% and 47.87% for the CPP tariff rate by taking into account the consumer’s comfort level.

关键词： home energy management system smart building coordination of home appliances metaheuristic algorithm day-ahead scheduling multi-objective binary non-linear constraint problem

Reflectometric and interferometric fiber optic sensor's principles and applications

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Frontiers of Optoelectronics 2019年第2期12卷 215-226页

作者： Muhammad Noaman ZAHID Jianliang JIANG saad RIZVI School of Optics and Photonics Beijing Institute of TechnologyBeijing 100081China

Fiber optic sensors have been widely used and studied in recent times. This paper presents operating principles and applications of fiber optic sensors namely reflectometric and interferometric fiber optic sensors. Majority of optical fiber sensors fall under these two broad categories. Both interferometric and reflectometric fiber optic sensors are becoming popular for their ease of use, flexibility, long distance sensing, and potentially noise free detection. Also, these sensors can easily be used in various applications such as structural health monitoring, perimeter intrusion detection, temperature monitoring, and other numerous applications. This paper broadly classifies fiber optic sensors into two subtypes. The paper further highlights different sensors based on their sensing resolution, range, spatial advantages, and applications.

关键词： fiber optic reflectometric interferometric optical fiber sensors sensor applications

Day-ahead seasonal solar radiation prediction,combining VMD and STACK algorithms

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Clean Energy 2023年第4期7卷 911-925页

作者： Saida El Bakali Ouadi Hamid saad Gheouany ERERA National School of Arts and CraftsMohammed V UniversityRabatMorocco

This article proposes a method for accurately predicting solar irradiance over a 24-hour horizon to forecast photovoltaic energy generation in a positive-energy *** order to make this prediction,the input data are divided into seasons and preprocessed using the variational mode decomposition(seasonal-VMD)*** VMD method is used for extracting high-bandwidth features from the input data,decomposing them into a finite number of smooth modes and focusing on specific frequency ***,the accuracy of signal extraction using the VMD method can be improved by selecting particular parameters judiciously,which impacts the smoothing and frequency concentration of the extracted *** this regard,the salp swarm algorithm(SSA)is employed to identify the optimal VMD parameters that can be used to enhance extraction *** addition,the obtained residual between the observed solar irradiation data and their decomposed modes is treated to enhance the prediction process.A stacking algorithm(STACK)is used to predict the following 24-hour solar irradiance modes and the residual,which are finally summed to reconstruct the desired *** performances of the proposed prediction method are evaluated using two quantitative evaluation indices:the normalized root mean square percentage error(NRMSPE)and normalized mean absolute percentage error(NMAPE).The proposed model is trained on data collected for three years in Rabat(2019–22).The performance of the proposed model is evaluated by predicting the 24-hour solar irradiance for a different *** proposed approach seasonal-VMD-STACK is compared with two other methods in the case of using VMD-based STACK without season partition and STACK method ***,the proposed method has exhibited stability and proven good results with an NRMSPE of 3.87%and an NMAPE of 1.58%for cloudy days during the test *** results demonstrate that residual preprocessing,seasonal input data partition and appropriate selec

关键词： weather forecasting parametric optimization time series data preprocessing machine learning

Current experimental models, assessment and dietary modulations of intestinal permeability in broiler chickens

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Animal Nutrition 2021年第3期7卷 801-811页

作者： saad Gilani Peter V.Chrystal Reza Barekatain Danisco Animal Nutrition(IFF) Oegstgeestthe Netherlands Baiada Poultry Pty Ltd Pendle HillNSWAustralia South Australian Research and Development Institute Roseworthy CampusRoseworthySAAustralia School of Animal and Veterinary Sciences Roseworthy CampusUniversity of AdelaideRoseworthySAAustralia

Maintaining and optimising the intestinal barrier(IB)function in poultry has important implications for the health and performance of the *** a key aspect of the IB,intestinal permeability(IP)is mainly controlled by complex junctional proteins called tight junction proteins(TJ)that link enterocytes *** disruption of TJ is associated with increased gut leakage with possible subsequent impli-cations for bacterial translocation,intestinal inflammation,compromised health and performance of the *** considerable data being available for other species,research on IP in broiler chickens and in general avian species is still an understudied *** paper reviews the available literature with a specific focus on IP in broiler chickens with consideration given to practical factors affecting the IP,current assessment methods,markers and nutritional modulation of *** experimental models to induce gut leakage are discussed including pathogens,rye-based diets,feed deprivation and stress-inducing agents such as exogenous glucocorticoids and heat *** various markers including fluorescein isothiocyanate dextran,expression of TJ and bacterial translocation have been widely utilized to study IP,recent studies have identified a number of excreta biomarkers to evaluate intestinal integrity,in particular non-invasive *** the research on various nutrients and feed additives to potentially modulate IP is still at an early stage,the most promising outcomes are anticipated for probiotics,prebiotics,amino acids and those feed ingredients,nutrients and additives with anti-inflammatory *** research gaps are identified for the mechanistic mode of action of various nutrients to influence IP under different experimental *** modulation of IP through various strategies(*** manipulation of diet)may be regarded as a new frontier for disease prevention and improving the health and performance of poultry particul

关键词： Intestinal barrier function Permeability Tight junction protein Gut leakage model

Reinforcement Learning-Empowered Graph Convolutional Network Framework for Data Integrity Attack Detection in Cyber-Physical Systems

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 797-806页

作者： Edeh Vincent Mehdi Korki Mehdi Seyedmahmoudian Alex Stojcevski saad Mekhilef School of Science Computingand Engineering TechnologiesSwinburne University of TechnologyMelbourneAustralia

The massive integration of communication and information technology with the large-scale power grid has enhanced the efficiency, safety, and economical operation of cyber-physical systems. However, the open and diversified communication environment of the smart grid is exposed to cyber-attacks. Data integrity attacks that can bypass conventional security techniques have been considered critical threats to the operation of the grid. Current detection techniques cannot learn the dynamic and heterogeneous characteristics of the smart grid and are unable to deal with non-euclidean data types. To address the issue, we propose a novel Deep-Q-Network scheme empowered with a graph convolutional network (GCN) framework to detect data integrity attacks in cyber-physical systems. The simulation results show that the proposed framework is scalable and achieves higher detection accuracy, unlike other benchmark techniques.

关键词： Deep reinforcement learning graph convolutional network heterogeneous smart grid network