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Qualitative Fourier Transform Infrared Spectroscopic Analysis of Polyether-Based Polymer Electrolytes
Published in Chin Hua Chia, Chin Han Chan, Sabu Thomas, Functional Polymeric Composites, 2017
Siti Rozana Bt. Abdul Karim, Chin Han Chan
The general procedure of performing deconvolution starts with any commercial FTIR software, for example SPECTRUM software. First, open new file by clicking File, then Open file (*.sp for spectra). From here, one can select more than one file to open the spectra. Usually, the spectrum is in transmittance mode. In order to perform deconvolution, the spectrum shall be changed to absorbance mode. To change it, go to Process, then Absorbance. In order to get the data for the selected region or band to deconvolute, each spectrum will be exported to a new Excel file. The file will be in Comma Separated Values (.csv) format. To export the data, go to “File → Export → Export data dialog box will appear → Change the file name and default directory to one’s own folder → Apply to all → Export” (Fig. 8.26). Open the file from one’s folder. An Excel file (in .csv format) containing the wavenumber (cm−1) and absorbance (A) will appeared. Next, open the Excel file and copy the wavenumber and absorbance of selected region and paste it to the new book in origin software. Figure 8.27 shows the sample of data for PEO/PMMA blend in ClO4− (wavenumber: 650−600 cm−1) region. Plot the graph using line. Then, reverse the scale of the graph to make it in the same way as the original FTIR spectra as shown in Figure 8.28.
Python and Its Libraries for Machine Learning
Published in Shrirang Ambaji Kulkarni, Varadraj P. Gurupur, Steven L. Fernandes, Introduction to IoT with Machine Learning and Image Processing using Raspberry Pi, 2020
Shrirang Ambaji Kulkarni, Varadraj P. Gurupur, Steven L. Fernandes
Let us first consider how to read data from a CSV file. CSV stands for comma separated values. It stores data that is delimited by commas; CSV files can be imported or exported to popular spreadsheets like Excel and officeorg.
The dynamics of ecological sustainability in housing delivery: developers’ perspectives
Published in Architectural Engineering and Design Management, 2023
J. A. Bamgbade, Evena Shallonia Fung, Taofeeq D. Moshood, Ahmed Mohammed Kamaruddeen, SanChuin Liew
In this study, random sampling from the respondents’ population was taken. The respondents are Sarawak developers registered under the Sarawak Housing and Real Estate Developers’ Association (SHEDA). There are six major SHEDA branches: Kuching, Serian, Sri Aman, Miri, Bintulu, and Sibu. The study's population consisted of real estate developers and other allied professionals from these branches, numbering 221. Therefore, we took a random sample from this population for the survey instrument administration. An online survey method was used in this study due to its convenience in exporting the responses to Microsoft Excel in CSV format. The power analysis was conducted using the software programme G*Power 3.1.9.2 to determine the sample size of this report. This analysis used six (6) predictor variables to evaluate sample size using this G*Power model (Faul et al., 2007). As depicted in Figures 1 and 2 below, the G*Power result indicated that a sample size of 146 is required for this study's statistical analysis.
Quantitative prediction of fracture toughness (K Ic ) of polymer by fractography using deep neural networks
Published in Science and Technology of Advanced Materials: Methods, 2022
Y. Mototake, K. Ito, M. Demura
The database includes comma separated values (CSV) files listing information for all tests, PDF files containing fracture surface photos for each set of test conditions, and tab separated values (TSV) files containing load–displacement curve data. Each test was assigned an identification code. Since the fracture surface images were recorded in the lossy compressed JPEG format in the PDF file of each test and bitmap data without degradation could not be obtained, the image data were extracted, as it is in the JPEG format, using the pdfimages [15] command. Note that the extracted image file had its left and right sides reversed from when the PDF was displayed, so the left and right sides were reversed using the convert command of Imagemagick [16]. From among all the test data, 770 data records were obtained by extracting the test data in which was recorded and which included fracture surface data. These 770 data records were used as the dataset for the analysis in this study.
50 Years of international journal of systems science: a review of the past and trends for the future
Published in International Journal of Systems Science, 2021
Xinxin Wang, Yurui Chang, Zeshui Xu, Zidong Wang, Visakan Kadirkamanathan
From the first document written by B. Porter (Porter, 1970) was published in IJSS in 1970, all papers published in the journal up to the end of 2019 are included in our comprehensive overview of the journal. We collected data sources from Web of Science (WoS), owned by the company Thomson & Reuters Corporation. WoS is one of the widely used databases in academia (Falagas et al., 2008), and offers information on leading journals suitable for bibliometric analysis through its details on publications (Cortés-Sánchez, 2019). WoS contains multiple databases, including WoS Core Collection, Derwent Innovations Index, Inspec, KCI-Korean Journal Database, Medline, Russian Science Citation Index and SciELO Citation Index. Through the search function in WoS, and selecting: Publication name = International Journal of Systems Science; Timespan=1970-2019; data; Database = All databases, 7,528 documents were retrieved and selected for the bibliometric analysis. To analyse these documents, we exported them in plain text and Comma-Separated Values (CSV) file formats. This permits detailed and representative perspectives information of documents to be derived, such as title, abstract, keywords, and references.