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Lattice distortion generates bound states in the continuum

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Science China(Physics,Mechanics & Astronomy) 2024年第3期67卷 166-166页

作者： almas Sadreev Kirensky Institute of Physics Federal Research Center KSC Siberian BranchRussian Academy of SciencesKrasnoyarsk 660036Russia

High-Q optical cavity is an indispensable component of many photonic devices, such as lasers, sensors, harmonic generation and photon emission, and chiral *** way of realizing ultrahigh Q-factor relies on Fabry-Pérot... 详细信息

关键词： Lattice distortion cavity

An integrated framework for assessing renewable-energy supply chains using multicriteria decision-making:a study on Bangladesh

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Clean Energy 2024年第3期8卷 1-19页

作者： A.M.almas Shahriyar Azad Zarin Tasnim Oishi Md.Ariful Haque Pronob Das Sabbir Ahmed Udoy Khaled Mohammad Shifullah Bhuiya Department of Industrial&Production Engineering Rajshahi University of Engineering&TechnologyRajshahi-6204Bangladesh Department of Mechanical Engineering Rajshahi University of Engineering&TechnologyRajshahi-6204Bangladesh

The depletion in non-renewable energy sources and a fast-growing population in Bangladesh are exacerbating the already existing energy scarcity,highlighting the need for an efficient and robust renewable-energy supply *** primary goal of this study is to evaluate the most optimized renewable-energy supply chain based on natural resource availability and government policies of *** the present study,four renewable energy resources,including solar,biomass,wind and hydropower,are studied and nine subcriteria are defined under four primary criteria for each supply *** for Order Preference by Similarity to Ideal Solution(TOPSIS)and VIseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR)are multicriteria decision-making approaches used in this study to compare and choose the best renewable-energy supply *** relative significance of four supply-chain criteria for primary renewable energy in this study,namely energy procurement,production,operations and maintenance costs,and social and environmental impact,is gathered via a *** results of this research,supported by a comprehensive sensitivity analysis,indicate that hydropower is the best renewable-energy supply chain,followed by wind as a compromise solution,biomass and *** study also demonstrates that no energy source can satisfy all supply-chain criteria alone;each resource is better for a specific criterion-solar is better for procurement,hydropower is significant for production and wind is remarkable for operations and social ***,to maximize output,renewable energy sources must be *** Bangladesh’s perspective,for the first time,by using TOPSIS and VIKOR together,this study offers significant insights to establish an efficient and sustainable renewable-energy supply chain for practitioners,academics and policymakers.

关键词： renewable-energy supply chain Bangladesh multicriteria decision-making TOPSIS(Technique for Order Preference by Similarity to Ideal Solution) VIKOR(VIseKriterijumska Optimizacija I Kompromisno Resenje)

The Impact of Peer Instruction on Students’ Achievement in Mathematics Analysis

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Psychology Research 2020年第12期10卷 497-506页

作者： Serkan Kaymak Nuri Balta abdullah almas Bibigul Kazmagambet Omar Mbala Suleyman Demirel University AlmatyKazakhstan

In this research,we investigated the effect of peer instruction method on the first course mathematics education students’academic achievement and the attitudes of students by using peer instruction *** implementation was carried out seven weeks with 60 participants in an introduction to mathematics analysis lesson in Suleyman Demirel University in *** first course groups were randomly assigned to treatment and control *** final results were obtained to measure students’academic achievement and questionnaires used to indicate attitudes towards peer *** were analyzed,using analysis of t-test(p=0.029)with an alpha significance level of *** results of final questions indicated,peer instruction has a significant positive effect on students’achievement in the introduction to mathematics analysis course and the results of questionnaires demonstrated peer instruction(PI)has a positive impact on peers’understanding by making learning interesting and interactive,and it also creates conducive atmosphere in the class.

关键词： peer instruction mathematics achievement mathematics analysis attitudes towards peer instruction method

Optimal cost and feasible design for gridconnected microgrid on campus area using the robust-intelligence method

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Clean Energy 2022年第1期6卷 59-76页

作者： Mohamad almas Prakasa Subiyanto Subiyanto Department of Electrical Engineering Faculty of EngineeringUniversitas Negeri SemarangSemarang 50229Indonesia

In this paper,a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum *** campus microgrid needs to be optimized with further investigation,especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply.A modified combination of genetic algorithm and particle swarm optimization(MGAPSO)is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel ***,a sustainable energy-management system is also defined to analyse the characteristics of the *** optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional *** MGAPSO optimization reduces the system cost by up to 11.99%compared with the conventional *** the rest of the paper,the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation *** investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.

关键词： distributed energy and smart grid renewable-energy system optimization MGAPSO algorithm

Application of the Fictitious Domain Method for Navier-Stokes Equations

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Computers, Materials & Continua 2022年第10期73卷 2035-2055页

作者： almas Temirbekov Zhadra Zhaksylykova Yerzhan Malgazhdarov Syrym Kasenov Al-Farabi Kazakh National University AlmatyKazakhstan Abai Kazakh National Pedagogical University AlmatyKazakhstan Sarsen Amanzholov East Kazakhstan University Ust-KamenogorskKazakhstan

To apply the fictitious domain method and conduct numericalexperiments, a boundary value problem for an ordinary differential equation is considered. The results of numerical calculations for different valuesof the iterative parameter τ and the small parameter ε are presented. Astudy of the auxiliary problem of the fictitious domain method for NavierStokes equations with continuation into a fictitious subdomain by highercoefficients with a small parameter is carried out. A generalized solutionof the auxiliary problem of the fictitious domain method with continuationby higher coefficients with a small parameter is determined. After all theabove mathematical studies, a computational algorithm has been developedfor the numerical solution of the problem. Two methods were used to solvethe problem numerically. The first variant is the fictitious domain methodassociated with the modification of nonlinear terms in a fictitious *** model problem shows the effectiveness of using such a modification. Theproposed version of the method is used to solve two problems at once that arisewhile numerically solving systems of Navier-Stokes equations: the problem ofa curved boundary of an arbitrary domain and the problem of absence of aboundary condition for pressure in physical formulation of the internal flowproblem. The main advantage of this method is its universality in developmentof computer programs. The second method used calculation on a uniform gridinside the area. When numerically implementing the solution on a uniformgrid inside the domain, using this method it’s possible to accurately take intoaccount the boundaries of the curved domain and ensure the accuracy of thevalue of the function at the boundaries of the domain. Methodical calculationswere carried out, the results of numerical calculations were obtained. Whenconducting numerical experiments in both cases, quantitative and qualitativeindicators of numerical results coincide.

关键词： Fictitious domain method Navier-Stokes equations difference schemes approximation computational algorithm numerical experiment

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

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Computers, Materials & Continua 2022年第1期70卷 619-635页

作者： Javaria Amin Muhammad almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

CNN Based Features Extraction and Selection Using EPO Optimizer for Cotton Leaf Diseases Classification

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Computers, Materials & Continua 2023年第9期76卷 2779-2793页

作者： Mehwish Zafar JaveriaAmin Muhammad Sharif Muhammad almas Anjum Seifedine Kadry Jungeun Kim Department of Computer Science COMSATS University IslamabadWah CampusWah Cantt47040Pakistan Department of Computer Science University ofWahWah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Worldwide cotton is the most profitable cash *** year the production of this crop suffers because of several *** an early stage,computerized methods are used for disease detection that may reduce the loss in the production of *** several methods are proposed for the detection of cotton diseases,however,still there are limitations because of low-quality images,size,shape,variations in orientation,and complex *** to these factors,there is a need for novel methods for features extraction/selection for the accurate cotton disease *** in this research,an optimized features fusion-based model is proposed,in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features,each model extracts the feature vector of length N×*** that,the extracted features are serially concatenated having a feature vector lengthN×*** prominent features are selected usingEmperor PenguinOptimizer(EPO)*** method is evaluated on two publically available datasets,such as Kaggle cotton disease dataset-I,and Kaggle *** EPO method returns the feature vector of length 1×755,and 1×824 using dataset-I,and dataset-II,*** classification is performed using 5,7,and 10 folds *** Quadratic Discriminant Analysis(QDA)classifier provides an accuracy of 98.9%on 5 fold,98.96%on 7 fold,and 99.07%on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor(KNN)provides 99.16%on 5 fold,98.99%on 7 fold,and 99.27%on 10 fold using Kaggle cotton-leaf-infection dataset-II.

关键词： Deep learning cotton disease detection features selection classification efficientnet-b0 inception-v3 quadratic discriminant analysis subspace KNN

Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

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Computers, Materials & Continua 2021年第8期68卷 2451-2467页

作者： Javaria Amin Muhammad Sharif Muhammad almas Anjum Yunyoung Nam Seifedine Kadry David Taniar Department of Computer Science University of Wah47040Pakistan Department of Computer Science Comsats University IslamabadWah Campus47040Pakistan College of Electrical and Mechanical Engineering National University of Sciences&Technology(NUST)Islamabad44000Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab University115020Lebanon Faculty of Information Technology Monash UniversityClaytonVictoria3800Australia

Coronavirus 19(COVID-19)can cause severe pneumonia that may be *** diagnosis is *** tomography(CT)usefully detects symptoms of COVID-19 *** this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and *** the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung *** the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification ***3 is an effective decoder that helps to refine segmentation of lesion *** model was trained using fine-tuned hyperparameters selected after extensive ***,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented *** computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for *** performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital *** results were better than those of existing methods.

关键词： Deeplabv3 fusion genetic algorithm gabor watershed

Malaria Parasite Detection Using a Quantum-Convolutional Network

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Computers, Materials & Continua 2022年第3期70卷 6023-6039页

作者： Javaria Amin Muhammad almas Anjum Abida Sharif Mudassar Raza Seifedine Kadry Yunyoung Nam University of Wah Wah CanttPakistan National University of Technology(NUTECH) IslamabadPakistan COMSATS University Islamabad Vehari CampusVehariPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Malaria is a severe illness triggered by parasites that spreads via mosquito *** underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through ***-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected ***,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria *** methods comprise three core steps:localization,segmentation,and *** the first core step,an improved FRCNN model is proposed for the localization of the infected malaria ***,the RGB localized images were converted into YCbCr channels to normalize the image intensity ***,the actual lesion region was segmented using a histogram-based color thresholding *** segmented images were employed for classification in two different *** the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria ***,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for ***,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to *** proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.

关键词： Quantum convolutional RGB YCbCr histogram Malaria