Celal Bayar Universitesi Fen Bilimleri Dergisi最新文献_第3页

Newly Axial Silicon(IV) Phthalocyanine Photosensitizer: Design, Synthesis and Photochemical Properties 新型轴向硅(IV)酞菁光敏剂:设计、合成及光化学性质

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2021-03-01 DOI: 10.18466/CBAYARFBE.821910

Gülçin Ekineker, Hülya Yanık

引用次数: 2

Attenuation of Docetaxel-induced Oxidative Stress and Apoptosis in HEK 293 Human Embryonic Kidney Cells by Curcumin Treatment 姜黄素对多西他赛诱导的HEK 293人胚胎肾细胞氧化应激和凋亡的抑制作用

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2021-02-20 DOI: 10.18466/CBAYARFBE.752495

S. Ilhan

引用次数: 1

Thermal Behaviours of Flower Shape BSA@Cu(II) Hybrid Nanostructures 花朵形状的热行为BSA@Cu(II)杂化纳米结构

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-12-30 DOI: 10.18466/CBAYARFBE.811643

Burcu Somtürk Yılmaz, Serkan Dayan, C. Altinkaynak, N. Ozpozan, N. Özdemir

引用次数: 1

Polyvinyl Alcohol-Corn Starch-Lemon Peel Biocomposite Films as Potential Food Packaging 聚乙烯醇-玉米淀粉-柠檬皮生物复合膜作为潜在的食品包装

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-12-30 DOI: 10.18466/CBAYARFBE.761144

Pınar Terzioğlu, F. Parın

引用次数: 10

Evaluation of the Bearing Capacity of Sandy Soils with Analytical and Numerical Methods 砂土承载力的解析与数值评价

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-12-29 DOI: 10.18466/CBAYARFBE.705653

Emrah Dagli, Ömer Çapar

{"title":"Evaluation of the Bearing Capacity of Sandy Soils with Analytical and Numerical Methods","authors":"Emrah Dagli, Ömer Çapar","doi":"10.18466/CBAYARFBE.705653","DOIUrl":"https://doi.org/10.18466/CBAYARFBE.705653","url":null,"abstract":"In the present study, the bearing capacities of coarse graded soils beneath the strip foundations were calculated by means of analytical and numerical methods. First all necessary geotechnical properties of the soil were achieved at seven different relative density values of the soil in terms of correlations between friction angle () and dry density (d). Second, 63 bearing capacity analyses of strip foundation systems were conducted by changing the soil parameters and the width of the foundations with analytical and finite element methods (FEM). The Mohr-Coulomb, elastic-plastic, model was chosen for this research. Although explicit analytic solutions were obtained without any difficulty, FEM provided only the load-deformation response at the base of the footing from the models. Because of that, some prediction methods were used to evaluate and find the bearing capacity of the soils beneath the foundation from the load-deformation responses. The results of analytic and numerical analyses of strip foundation laying on loose soil models gave very similar values. Although, very similar bearing capacity values of the foundations laying on dense soil models were calculated from the analytical methods, the results of numerical methods were very divergent and scattered at the same conditions. The reason for this is due to some limitations of the elastic-plastic model and prediction methods.","PeriodicalId":9652,"journal":{"name":"Celal Bayar Universitesi Fen Bilimleri Dergisi","volume":"108 1","pages":"91-100"},"PeriodicalIF":0.0,"publicationDate":"2020-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87648478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Expressions of Small Ubiquitin-like Modifier (SUMO) Related Genes Under Metal (Cu, Zn and Fe) Stress in Arabidopsis thaliana 金属(Cu, Zn和Fe)胁迫下拟南芥SUMO相关基因的表达

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-09-14 DOI: 10.18466/CBAYARFBE.716691

B. Uzilday

引用次数: 0

Artificial Intelligence in Building Information Modeling Research: Country and Document-based Citation and Bibliographic Coupling Analysis 建筑信息模型中的人工智能研究:基于国家和文献的引文与书目耦合分析

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-09-07 DOI: 10.18466/cbayarfbe.770565

Gozde Basak Ozturk, Mert Tunca

{"title":"Artificial Intelligence in Building Information Modeling Research: Country and Document-based Citation and Bibliographic Coupling Analysis","authors":"Gozde Basak Ozturk, Mert Tunca","doi":"10.18466/cbayarfbe.770565","DOIUrl":"https://doi.org/10.18466/cbayarfbe.770565","url":null,"abstract":"The intense association of the architecture, engineering, construction, operation, and facility management (AECO/FM) industry with cognitive and behavioral technologies leads to the increase in productivity of industry activities. In light of these thoughts, the building information modeling (BIM) platform is included in the AECO/FM industry to further increase efficiency and deliver construction projects economically, timely, and safely. While the BIM platform can work integrated with many programs and systems, concepts that offer innovative and fast solutions such as artificial intelligence (AI) benefit the AECO/FM industry. The main aim of this study is to understand the tendency of AI in BIM research carried out in different countries and by various scholars. This study adopts a bibliometric search, and a scientometric analysis and mapping approach with applying document-based citation analysis, country-based citation analysis, and country-based bibliographic coupling analysis of scientific research of AI and BIM integration. Data on the use of AI and BIM has been collected by reviewing and screening articles selected from the Scopus database. The results reveal that information management, decision support systems, genetic algorithms, neural networks, knowledge-based systems, machine learning, and deep learning effect AI in BIM research. This article contributes to the AECO/FM literature by analyzing and visualizing the current status and relationship between AI and BIM. Therefore, the findings highlight the gaps and trends in AI and BIM studies and provide new recommendations for future studies.","PeriodicalId":9652,"journal":{"name":"Celal Bayar Universitesi Fen Bilimleri Dergisi","volume":"26 1","pages":"269-279"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87668837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Droplet-based Microfluidic Device for the Synthesis of Silica Nanoparticles 基于液滴的二氧化硅纳米颗粒合成微流体装置

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-09-07 DOI: 10.18466/cbayarfbe.729586

E. Erdem

引用次数: 0

Inferences from Bootstrap Method for Ability Parameters in 2-Parameter Logistic Model 二参数Logistic模型中能力参数的自举推断

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-09-04 DOI: 10.18466/cbayarfbe.622868

H. Olmus, Ezgi Nazman

引用次数: 0

Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks 基于预训练卷积神经网络的玉米单倍体和二倍体种子分类

Celal Bayar Universitesi Fen Bilimleri Dergisi Pub Date : 2020-09-01 DOI: 10.18466/cbayarfbe.742889

Emrah Dönmez

{"title":"Classification of Haploid and Diploid Maize Seeds based on Pre-Trained Convolutional Neural Networks","authors":"Emrah Dönmez","doi":"10.18466/cbayarfbe.742889","DOIUrl":"https://doi.org/10.18466/cbayarfbe.742889","url":null,"abstract":"Analysis of agricultural products is an important area that is widely emphasized today. In this context, with the development of technology, computer-aided analysis systems are also being developed. In this study, a system has been proposed for classifying maize seeds as haploid and diploid using pre-trained convolutional neural networks. For this purpose, AlexNet, GoogLeNet, ResNet-18, ResNet-50, and VGG16 pre-trained models have been used as feature extractors for the haploid and diploid seed classification process. In the first stage, the deep features of haploid and diploid maize seeds have been obtained in these models. The features have been taken from different layers of network architecture. Instead of softmax classifier in the last layer of the network, classifiers based on decision tree, k-nearest neighbor, and support vector machine have been used. According to the classification results with these features, the achievements in network architectures and classifier methods have been observed. The experiments have been carried out on a publicly available dataset consisting of 3000 haploid and diploid maize seed images. The experimental results revealed that the developed classification systems demonstrate a remarkable performance.","PeriodicalId":9652,"journal":{"name":"Celal Bayar Universitesi Fen Bilimleri Dergisi","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90769939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4