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Online since: October 2015
Authors: Olanrewaju Abdul Lateef, Seong Yeow Tan, Lim Tat Lee
Primary data is collected through survey questionnaires.
Data obtained was analysed through descriptive statistics.
Outline of research design Primary data is collected based on convenience sampling.
We use sustainability as a way of marketing to clients Scale Very important important Slightly important Less important Least important Frequency 6 9 8 1 3 Percentage 22.2 33.3 29.6 3.7 11.1 From the data presented above, maintenance organisations have some difficulty with what sustainable building means in practice.
Data obtained was analysed through descriptive statistics.
Outline of research design Primary data is collected based on convenience sampling.
We use sustainability as a way of marketing to clients Scale Very important important Slightly important Less important Least important Frequency 6 9 8 1 3 Percentage 22.2 33.3 29.6 3.7 11.1 From the data presented above, maintenance organisations have some difficulty with what sustainable building means in practice.
Online since: February 2003
Authors: V.A. Khonik
These
regularities can be illustrated by the data shown in Fig. 10 for a Co-based metallic glass.
Figure 12 shows isothermal bending stress relaxation data for a Fe-based metallic glass.
Data taken from Ref
Meanwhile, a number of experimental data indicate that it is just the case.
An increase of testing temperature results in a decrease of the iσ -level, as exemplified by Fig. 19 data.
Figure 12 shows isothermal bending stress relaxation data for a Fe-based metallic glass.
Data taken from Ref
Meanwhile, a number of experimental data indicate that it is just the case.
An increase of testing temperature results in a decrease of the iσ -level, as exemplified by Fig. 19 data.
Online since: September 2024
Authors: Khalid Alzebdeh, Mahmoud Nassar
The examination was conducted utilizing a Cary 630 Fourier Transform Infrared (FTIR) spectrophotometer, which efficiently acquires comprehensive spectral data with high resolution throughout the whole spectrum.
Five replicates of each test of the produced bio-composite have been evaluated to obtain the average values for statistical data including tensile and flexural strengths, tensile and flexural moduli and strain at break.
xk* i= xk0i-minxk0imaxxk0i-minxk0i (12) xk* i= maxxk0i-xk0imaxxk0i-minxk0i (13) Calculate the grey relational coefficient (GRC) The Grey Relational Grade (GRC) is employed to elucidate the connection among normalized data when the optimal result is achieved.
Material preparation, data collection and analysis were performed by Mahmoud Nassar and Khalid Alzebdeh.
Data Availability Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
Five replicates of each test of the produced bio-composite have been evaluated to obtain the average values for statistical data including tensile and flexural strengths, tensile and flexural moduli and strain at break.
xk* i= xk0i-minxk0imaxxk0i-minxk0i (12) xk* i= maxxk0i-xk0imaxxk0i-minxk0i (13) Calculate the grey relational coefficient (GRC) The Grey Relational Grade (GRC) is employed to elucidate the connection among normalized data when the optimal result is achieved.
Material preparation, data collection and analysis were performed by Mahmoud Nassar and Khalid Alzebdeh.
Data Availability Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
Online since: June 2023
Authors: Hiram Ndiritu, Fredrick Njuguna, Benson Gathitu, Meshack Hawi, Jotham Munyalo
Morever, these correlations are generated from experimental data under specific conditions and their validity may fail under different conditions [9].
Particle sizer Analysette 22 NEXT was used for particle size analysis which computes data using laser particle size analysis technique for a wide particle measuring range of between 0.01–3800 μm in order to maximize sensitivity and precision of the smallest particles.
Ps,f= 0 if εs ≥ εs,f,min F εs-εs,f,minrεs,f,min-εss if εs ≥ εs,f,min (21) F, r and s are model empirical data.
This could be attributed to the reduction of cohesive forces between the particles at high temperature [9].
The model was validated using experimental data of Umf, εmf and the pressure drop at the minimum fluidization point and showed good agreement with the experimental results.
Particle sizer Analysette 22 NEXT was used for particle size analysis which computes data using laser particle size analysis technique for a wide particle measuring range of between 0.01–3800 μm in order to maximize sensitivity and precision of the smallest particles.
Ps,f= 0 if εs ≥ εs,f,min F εs-εs,f,minrεs,f,min-εss if εs ≥ εs,f,min (21) F, r and s are model empirical data.
This could be attributed to the reduction of cohesive forces between the particles at high temperature [9].
The model was validated using experimental data of Umf, εmf and the pressure drop at the minimum fluidization point and showed good agreement with the experimental results.
Online since: February 2018
Authors: Yu Wen Cui, Guang Long Xu
It provides reliable thermodynamic data and phase diagram for practical processes and helps to get deeper insights into physical mechanisms of materials behaviors via computer calculation/simulation.
Dever et al. [39] measured the elastic moduli of bcc iron at different temperatures, while Fukuhara et al. [40] obtained data for low carbon steels.
The two sets of experimental data showed a similar temperature dependence, which were approximately linear decrease with temperature.
The data and the linear fitting are illustrated in Fig. 6.
The thermodynamic data were derived from [41].
Dever et al. [39] measured the elastic moduli of bcc iron at different temperatures, while Fukuhara et al. [40] obtained data for low carbon steels.
The two sets of experimental data showed a similar temperature dependence, which were approximately linear decrease with temperature.
The data and the linear fitting are illustrated in Fig. 6.
The thermodynamic data were derived from [41].
Online since: June 2010
Authors: Marianna Foldvari
The establishment of 'pharmaceutical grade' CNTs may require detailed and
defined structural information; determination of the presence and type of defects; data on electronic
properties, concentration, dispersion state; identification of the type of impurities or contaminating
materials present; level of required purity and limits for impurities.
Analysis of available data can be used to determine which properties of CNTs are particularly important to control in order to achieve both safety and efficacy, to what degree can general requirements be imposed and which will be specific properties that will need to be evaluated case-by-case. 3.1.
Their analysis indicated discrepancies in nanotube dimensions and purity compared with data provided by the companies that synthesized them.
The importance of the availability of detailed and accurate information on the CNT raw materials is key to understanding the toxic effects of nanotubes and is strongly recognized in the field as data essential for their characterization and development as nano-excipients. [25-27]. 3.3.
The use of one ε value for different CNTs in different solvents without a specific calibration curve, or using calibration curves prepared from same CNT dispersion for which concentration is to be determined, introduces unreliable data, and creates confusion in interpretation of different data sets.
Analysis of available data can be used to determine which properties of CNTs are particularly important to control in order to achieve both safety and efficacy, to what degree can general requirements be imposed and which will be specific properties that will need to be evaluated case-by-case. 3.1.
Their analysis indicated discrepancies in nanotube dimensions and purity compared with data provided by the companies that synthesized them.
The importance of the availability of detailed and accurate information on the CNT raw materials is key to understanding the toxic effects of nanotubes and is strongly recognized in the field as data essential for their characterization and development as nano-excipients. [25-27]. 3.3.
The use of one ε value for different CNTs in different solvents without a specific calibration curve, or using calibration curves prepared from same CNT dispersion for which concentration is to be determined, introduces unreliable data, and creates confusion in interpretation of different data sets.
Online since: August 2019
Authors: Mohamed Samuel Moriah Conté, Abdellah Boushaba, Ali Moukadiri
This paper presents the stratigraphic, mineralogical, petrological, geochemical and metallogenic data of the different rock formations sampled during our field surveys throughout the Nimba region.
These data have been processed in different laboratories.
Therefore, the Ce anomaly in seawater and sediments can be used to explain oxidation-reduction conditions [36] (Fig. 52).
Design of a mini-probe, age data for samples from the Central Alps, and comparison to U–Pb (TIMS) data, Chemical Geology, Vol. 191, issues 1-3, 2002, pp. 225-241
Beukes, Fe, C, and O isotope compositions of banded iron formation carbonates demonstrate a major role for dissimilatory iron reduction in ~2.5 Ga marine environments, Earth and Planetary Science Letters, v. 294, 2010, pp. 8–18
These data have been processed in different laboratories.
Therefore, the Ce anomaly in seawater and sediments can be used to explain oxidation-reduction conditions [36] (Fig. 52).
Design of a mini-probe, age data for samples from the Central Alps, and comparison to U–Pb (TIMS) data, Chemical Geology, Vol. 191, issues 1-3, 2002, pp. 225-241
Beukes, Fe, C, and O isotope compositions of banded iron formation carbonates demonstrate a major role for dissimilatory iron reduction in ~2.5 Ga marine environments, Earth and Planetary Science Letters, v. 294, 2010, pp. 8–18
Online since: July 2012
Authors: Heather B. Coan, Thaleia Teli, Christoper Booth, Mark O. Lively, Mark Van Dyke
Data were analyzed for quality control.
STEM analysis data are presented in Supplement 1.
Summaries of Affymetrix GeneChip probe level data.
Java Treeview-extensible visualization of microarray data.
Clustering short time series gene expression data.
STEM analysis data are presented in Supplement 1.
Summaries of Affymetrix GeneChip probe level data.
Java Treeview-extensible visualization of microarray data.
Clustering short time series gene expression data.
Online since: September 2008
Authors: Martin Müller
Fig. 3: Typical two-dimensional cellulose diffraction pattern (raw data without background
subtraction) of a 200 µm-thick tangential section of pine wood, measured with a 250 µm × 250 µm
X-ray beam in 3 s.
A quantitative analysis of wood diffraction diagrams will always start with data reduction in order to yield either radial or azimuthal intensity distributions.
The two-dimensional detector data (a) are the same as in Fig. 3.
There is also some remaining uncertainty about possible artefacts from the sample preparation, and the low total flux (about 6⋅108 photons/s) of the set-up required long data acquisition times (300 s).
All experiments at synchrotron radiation sources presented here and the subsequent data analysis were carried out in collaborations with other scientists (and would not have been possible otherwise!).
A quantitative analysis of wood diffraction diagrams will always start with data reduction in order to yield either radial or azimuthal intensity distributions.
The two-dimensional detector data (a) are the same as in Fig. 3.
There is also some remaining uncertainty about possible artefacts from the sample preparation, and the low total flux (about 6⋅108 photons/s) of the set-up required long data acquisition times (300 s).
All experiments at synchrotron radiation sources presented here and the subsequent data analysis were carried out in collaborations with other scientists (and would not have been possible otherwise!).
Online since: January 2022
Authors: Elyor Berdimurodov, Abduvali Kholikov, Khamdam Akbarov, Lei Guo, Savaş Kaya, Dakeshwar Kumar Verma, Mohamed Rbaa, Omar Dagdag
The resulted data are given in Table 3.
The obtained data are shown in Table 3 [26].
The obtained data are given in Table 4.
The found data are given in Table 3.
The resulted data are shown in Table 7.
The obtained data are shown in Table 3 [26].
The obtained data are given in Table 4.
The found data are given in Table 3.
The resulted data are shown in Table 7.