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Showing 7 results for Correlation Coefficient
Volume 2, Issue 3 (9-2014)
Abstract
Determining landslide size could be a difficult and expensive task. In this research, size parameters of 142 landslides recognized in Tajan River Basin, northern Iran, have been assessed. The dataset was prepared through the extensive field surveys and using the satellite imagery available via Google Earth. Dependence between landslides area (A-m2), volume (V-m3), and depth (D-m) was appointed by the Pearson correlation coefficient (r) at 0.05 and 0.01 levels. Then, the relation between the area and volume variables has been investigated using 10 curve estimation (CE) models. Coefficient of determination (R2), F statistic, and RMSE were calculated to compare the models with each other. Results showed that the power law fit the data better than other CE models. Although, the quadratic and cubic relationships have represented high R2 and low RMSE, they have resulted negative estimated volumes, and also their F statistic is less than its value in power law. To achieve a better result, the estimated volumes were compared with the observed ones using paired test. Results indicated that the estimated volumes were in conformity with the observed ones and there was no statistically significant difference between them (R2=0.801, sig=0.633). Although, the estimated depths were significantly different from the observed ones. The mean depth was estimated 5.5 m which was close to mean of the actual depths (5.53 m).
Volume 5, Issue 3 (12-2015)
Abstract
The purpose of this study is to examine the correlation between canonical variables (x component) of organizational commitment, including (affective commitment, normative and continuous variable) and quality of services provided to employees (y components), including (responsibility, reliability, trusting, empathy, tangibility). These two sets of change variables are taken to be a suitable model for determining the relationship between Y components. Also it would use to provide a prediction method for y components using canonical correlation. At first, the theoretical model and the theories between the variables were developed and for surveying the research theories structural equation was used. ansar bank counter staves of Tehran city are the statistical population. 320 employees were picked based on the Morgan table using random sampling. A questionnaire was used to collect data, and its reliability and validity assessed by Cronbach's alpha and confirmatory factor analysis. Results of canonical correlation showed a good linear combination between organizational commitment and service quality offered by them. Affective and normative commitment has direct relation with quality components, and continuance commitment correlated inversely with quality components. Normative commitment in empathy have the greatest role in creating the first canonical correlation coefficient as well as a comparison with other canonical variables.
Narges Anoosheh, Afshin Adeli, Abbas Nargesian, Seyed Mehdi Ojagh,
Volume 12, Issue 4 (12-2023)
Volume 12, Issue 4 (12-2023)
Abstract
Aims: The aim of the present study is to evaluate the amount of tilapia fish consumption and the relationship between its consumption amount and some demographic characteristics Such as gender, age, occupation, education, marital status, occupation, number of household members and income.
Methods: The statistical population of this research is the whole country. A questionnaire was designed and sent to people electronically through virtual space. Correlation tests were used to evaluate the number of fish purchases per year as well as the amount of each purchase from the output of SPSS software and to investigate the existence of a significant relationship between consumption and demographic characteristics.
Findings: The results showed that Most of the female respondents are married, aged 20 to 35, employed and have bachelor's and master's degrees. Most of them are employees and earn between 3 and 6 million tomans. It was also found that most households (54%) buy fish up to 20 times a year. The average number of times of buying fish per year by the households of the consumption community was 45 times. Most households in the consumer society buy 2 kg of fish each time they buy. The number of households that consume more than 10 kg of fish per purchase was 3.3%. According to the obtained information, the average amount of fish per purchase was calculated to be 8 kg. The per capita consumption of tilapia in households consuming this fish was 4.2 kg.
Volume 16, Issue 88 (6-2019)
Abstract
The ultrafiltration process as one of the membrane processes based on pressure can replace with the conventional method of liming-carbonation as a new method in sugar industry. In this research, reduction of hardness, increasing the non-sugar rejection components and improving the purity of permeate flow were modeled during the treatment of raw beet syrup with ultrafiltration by artificial neural network. The ultrafiltration process was carried out at three temperatures of 30, 40 and 50 ° C, three pressures in the membrane 1, 2 and 3 times in eight intervals of equal time of 1 to 60 minutes. The best model for reduction of hardness was obtained with a hidden layer, the number of 13 neurons, the tangent transfer function, the momentum learning law, and the percentage of data 40, 35, and 25 for training, evaluation, and test, respectively. The variation of non-sugar rejection compounds with a hidden layer, 15 neurons, tangent transfer function, Levenberg learning law, and assigning 50, 5 and 45 percent of the data to training, evaluating and testing with the least error and the highest correlation coefficient during modeling. Improvement of the purity of permeate flow during ultrafiltration with a hidden layer, 18 neurons, sigmoid transfer function, Levenberg learning law and data percentages 60, 15 and 25 for training, evaluation, and testing created the best network. Also, the highest correlation coefficient between laboratory data and predicted values with the model was obtained for hardness variation, non-sugar rejection compounds and purity, 0.892, 0.985 and 0.985 respectively.
Volume 19, Issue 132 (2-2023)
Abstract
The antifungal activity of Foeniculum Vulgare Mill and Ziziphora clinopodioides Lam. extracts against Aspergillus flavus in tomato paste containing different percentages of the extracts was tested. To this end, Foeniculum Vulgare Mill and Ziziphora clinopodioides Lam. extracts with different concentrations of 0.5, 1 and 2% were prepared and studied during different storage times(35 days).The effect of extracts of Foeniculum Vulgare Mill and Ziziphora Clinopodioides Lam with different concentrations was investigated alone in the environment (in vitro). By injecting 0.1 ml of mold in Sabouraoud dextrose agar broth culture medium, then placing it in an incubator temperature of 25°C ± 0.5, it was kept for 5 weeks (35 days), and one culture was done every week in order for the activity mold to be investigated in different concentrations of extracts.The results of antifungal activity of different levels of the extracts indicated that treatments 3 (containing 2% Foeniculum Vulgare Mill extract) and 4 (containing 3% Foeniculum Vulgare Mill extract) were resistant to the growth of Aspergillus flavus mold mycelium until the end of storage period.Generally, it can be concluded that using 2 or 3% Foeniculum Vulgare Mill extract as a natural preservative in tomato paste has a desirable antifungal activity. Artificial neural network was used to validate and evaluate the results of the experiments in predicting the data of Aspergillus flavus mold growth in tomato paste.In the present study, two hidden layers with 30 neurons were used. The network had two inputs including extract concentration and storage time, and the growth of Aspergillus flavus mold was considered as the target.Evaluation parameters such as correlation coefficient, mean squared error and maximum error showed very good results with values of 0.9993, 0.10934 and 0.13538. The lower the error and the closer the correlation coefficient to 1, the better the prediction is.
Volume 22, Issue 1 (3-2018)
Abstract
Introduction
Entrepreneurship term originates from the French word Enterprender meaning commitment. Entrepreneurship is a good strategy for fighting against poverty and maintaining wealth in low-income communities. The entrepreneurship importance is to the extent that economists consider entrepreneurs as the growth and economic development engine of the society. Entrepreneurship is considered as an essential element for the growth and productivity of rural areas. Ruralism is a dynamic source of entrepreneurship which forms both opportunities and constraints. The entrepreneurship role in rural economic development can be found in areas such as the villager’s access to required goods and services, rural economic growth, reducing the phenomenon of migration to cities, encouraging new businesses creation and promoting social security level and welfare in the village. Rural entrepreneurship is one of the best strategies that can be effective in terms of rural monetizing, job creation and unemployment elimination, preventing immigration, improving the life quality and welfare level in rural community, creating a sense of competition and new businesses and preventing chaos and social unrests. Entrepreneurship development and survive in rural areas is due to factors such as social infrastructure development, doing research and development simultaneously, increasing human resources quality, improving the management quality, education and technology development. Meanwhile, one of the most important and fundamental factors is social capital, because it promotes creativity, innovative behaviors and increases entrepreneur’s thoughtful risk-taking. Social capital is a resource created in in business networks or within individual networks. These resources include information, thoughts, tips, business opportunities, financial capital, leverage, emotional support, good will, trust and collaboration.
Methodology
In terms of purpose, the present study is practical and in terms of the method, it is descriptive-analytic. In the research process, the theoretical foundation of entrepreneurship, social capital and literature review were studied first; and components, indexes and items have been determined accordingly based on the available information about the study region. The statistical population has been selected from three groups of occupation, agriculture, industry and services; and a sample size of 250 people was selected randomly between them. 72 individual in the industry, 124 in agriculture and 54 people work in the services field. In the present study, the formal method has been used to increase the credit rating. For this purpose, the questionnaire was distributed to experts, specialists and professors. After the compilation and gathering their comments, the necessary reforms were made. Cronbach's alpha coefficient was used to determine the research stability. The calculated alpha value is 0.791 which indicates that the number is desirable and the necessary accuracy for the structures stability is used in the questionnaire and the designed items to measure the indices have an internal correlation. In this research, factor analysis, Bartlett test, Pearson coefficient and regression analysis were used to analyze quantitative data.
Findings and Discussion
In order to measure the relationship between social capital and entrepreneurship, we first examine the relationships between variables. Here, we use Pearson, Spearman and Kendall correlation coefficients for analyzing according to the variables scale type. According to Pearson correlation coefficients and social capital and entrepreneurship significant levels in the studied population, a significant and positive relationship exists at 99% level. Therefore, the null hypothesis is rejected and a significant relationship exists between social capital and entrepreneurship. Five factors with eigenvalues greater than 5.0 are extracted by using factor analysis technique in the main components method; the dimension variables affecting the rural entrepreneurs are packed based on the load factor after varimax rotation method. The five factors determined 64.12% of the total variance related to plan effects. In other words, 64% of the relationship between social capital and rural entrepreneurs is related to these factors from the perspective of rural entrepreneurs. Regression analysis and Fisher statistic have been used to consider the social capital prediction on rural entrepreneurship. Accordingly, the determination coefficient of 0.934 indicates that the independent variable explains 93% of dependent variable variations.
Conclusion
There is a positive and meaningful relation between rural entrepreneurship and social capital in examining the findings. People who have high social capital spends a little time protecting themselves from being exploited in economic interactions and monitoring the other’s performances; this makes more time for thinking, creativity, innovation and ultimately entrepreneurship. Any improvement in social capital and related factors leads to a betterment in entrepreneurship. The factor analysis of entrepreneurship shows that facilities availability with the highest loading factor has been the most effective factor in the entrepreneurship process; and participation in village development had the highest loading factor in social capital formation. In regression analysis, psychological characteristics have the most impact on rural entrepreneurship prediction.
Entrepreneurship term originates from the French word Enterprender meaning commitment. Entrepreneurship is a good strategy for fighting against poverty and maintaining wealth in low-income communities. The entrepreneurship importance is to the extent that economists consider entrepreneurs as the growth and economic development engine of the society. Entrepreneurship is considered as an essential element for the growth and productivity of rural areas. Ruralism is a dynamic source of entrepreneurship which forms both opportunities and constraints. The entrepreneurship role in rural economic development can be found in areas such as the villager’s access to required goods and services, rural economic growth, reducing the phenomenon of migration to cities, encouraging new businesses creation and promoting social security level and welfare in the village. Rural entrepreneurship is one of the best strategies that can be effective in terms of rural monetizing, job creation and unemployment elimination, preventing immigration, improving the life quality and welfare level in rural community, creating a sense of competition and new businesses and preventing chaos and social unrests. Entrepreneurship development and survive in rural areas is due to factors such as social infrastructure development, doing research and development simultaneously, increasing human resources quality, improving the management quality, education and technology development. Meanwhile, one of the most important and fundamental factors is social capital, because it promotes creativity, innovative behaviors and increases entrepreneur’s thoughtful risk-taking. Social capital is a resource created in in business networks or within individual networks. These resources include information, thoughts, tips, business opportunities, financial capital, leverage, emotional support, good will, trust and collaboration.
Methodology
In terms of purpose, the present study is practical and in terms of the method, it is descriptive-analytic. In the research process, the theoretical foundation of entrepreneurship, social capital and literature review were studied first; and components, indexes and items have been determined accordingly based on the available information about the study region. The statistical population has been selected from three groups of occupation, agriculture, industry and services; and a sample size of 250 people was selected randomly between them. 72 individual in the industry, 124 in agriculture and 54 people work in the services field. In the present study, the formal method has been used to increase the credit rating. For this purpose, the questionnaire was distributed to experts, specialists and professors. After the compilation and gathering their comments, the necessary reforms were made. Cronbach's alpha coefficient was used to determine the research stability. The calculated alpha value is 0.791 which indicates that the number is desirable and the necessary accuracy for the structures stability is used in the questionnaire and the designed items to measure the indices have an internal correlation. In this research, factor analysis, Bartlett test, Pearson coefficient and regression analysis were used to analyze quantitative data.
Findings and Discussion
In order to measure the relationship between social capital and entrepreneurship, we first examine the relationships between variables. Here, we use Pearson, Spearman and Kendall correlation coefficients for analyzing according to the variables scale type. According to Pearson correlation coefficients and social capital and entrepreneurship significant levels in the studied population, a significant and positive relationship exists at 99% level. Therefore, the null hypothesis is rejected and a significant relationship exists between social capital and entrepreneurship. Five factors with eigenvalues greater than 5.0 are extracted by using factor analysis technique in the main components method; the dimension variables affecting the rural entrepreneurs are packed based on the load factor after varimax rotation method. The five factors determined 64.12% of the total variance related to plan effects. In other words, 64% of the relationship between social capital and rural entrepreneurs is related to these factors from the perspective of rural entrepreneurs. Regression analysis and Fisher statistic have been used to consider the social capital prediction on rural entrepreneurship. Accordingly, the determination coefficient of 0.934 indicates that the independent variable explains 93% of dependent variable variations.
Conclusion
There is a positive and meaningful relation between rural entrepreneurship and social capital in examining the findings. People who have high social capital spends a little time protecting themselves from being exploited in economic interactions and monitoring the other’s performances; this makes more time for thinking, creativity, innovation and ultimately entrepreneurship. Any improvement in social capital and related factors leads to a betterment in entrepreneurship. The factor analysis of entrepreneurship shows that facilities availability with the highest loading factor has been the most effective factor in the entrepreneurship process; and participation in village development had the highest loading factor in social capital formation. In regression analysis, psychological characteristics have the most impact on rural entrepreneurship prediction.
Volume 24, Issue 4 (10-2024)
Abstract
Piers are vital design elements for a bridge under seismic loading; ensuring their stability and health is crucial for the overall safety of the bridge. The most common methods available for detecting damage, primarily used in the bridge deck, are modal methods. These methods have errors in detecting damage in the structure for various reasons. On the other hand, these methods require healthy pier information. In addition to modal methods, other methods based on energy and data analysis using wavelet transform also exist, the shortcomings of which are mentioned in this article. The fundamental problem that most methods face is the existence of healthy pier information, and furthermore, parameters that should be used to detect damage must be computable or measurable. In this study, a new method is presented, using the concept of correlation, to detect the presence and location of damage with minimal error and without having healthy pier information. To this end, the tallest pier of Ramp A of the Shahid Bakri Bridge complex in Tehran was selected as the case study. Before modeling, the accuracy of the modeling method was validated, and then a precise nonlinear model was built in the OpenSees software using as-built sheets. For the first time in this article, damage in the model was created in different scenarios: reduction of stiffness in concrete cover materials, reduction of cross-sectional area of rebars at various points of the section. The location of the damage was also separately applied at three heights of the pier: 15, 25, and 35 percent of the height. To generate data on the pier, it was considered to load it with an impact load on the top of the pier. This load was applied as an impact on the deck of the pier for a very short time, and the data was collected using accelerometers at the pier height, and the correlation between each pair of consecutive sensors was calculated. Due to the high velocity of the compressive wave in concrete and the overlap that occurs in the return of the wave at both ends of the pier, data collection was performed only for about 0.001 seconds. Finally, due to the weakness of the correlation coefficient in magnifying the location of damage, using the concept of correlation, a damage index based on this concept was presented, and the capability of the presented index in detecting the presence and location of damage in various damage scenarios was evaluated. The results obtained indicate the proper performance of this index in detecting damage in various scenarios and damage intensities between 10 and 30 percent. The presented index only made an error in detecting the location of damage in cases where damage occurred in the concrete cover and rebars in small areas, but even in these cases, the presence of damage was well demonstrated. Due to limitations in sensor placement at the pier height, increasing the distance between sensors and reducing their number was also investigated, and it was observed that even with greater distances between sensors, the presented index has the capability to detect the presence and location of damage, and after finding the approximate location of damage, it is possible to identify the exact location of damage by re-sensoring in probable areas.
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