Search results for: geographically weighted principal components analysis

Authors: Sujay Kotwale, Ramasubba Reddy M.

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Electrocardiogram (ECG) is a non-invasive technique used to study and analyze various heart diseases. Cardiac arrhythmia is a serious heart disease which leads to death of the patients, when left untreated. An early-time detection of cardiac arrhythmia would help the doctors to do proper treatment of the heart. In the past, various algorithms and machine learning (ML) models were used to early-time detection of cardiac arrhythmia, but few of them have achieved better results. In order to improve the performance, this paper implements principal component analysis (PCA) along with XGBoost model. The PCA was implemented to the raw ECG signals which suppress redundancy information and extracted significant features. The obtained significant ECG features were fed into XGBoost model and the performance of the model was evaluated. In order to valid the proposed technique, raw ECG signals obtained from standard MIT-BIH database were employed for the analysis. The result shows that the performance of proposed method is superior to the several state-of-the-arts techniques.

Keywords: cardiac arrhythmia, electrocardiogram, principal component analysis, XGBoost

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Authors: Fayyaz Rasool, Shakeela Parveen

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The studies on genetic diversity of Labeo rohita by using molecular markers were carried out to investigate the genetic structure by RAPAD marker and the levels of polymorphism and similarity amongst the different groups of five populations of wild and farmed types. The samples were collected from different five locations as representatives of wild and hatchery raised populations. RAPAD data for Jaccard’s coefficient by following the un-weighted Pair Group Method with Arithmetic Mean (UPGMA) for Hierarchical Clustering of the similar groups on the basis of similarity amongst the genotypes and the dendrogram generated divided the randomly selected individuals of the five populations into three classes/clusters. The variance decomposition for the optimal classification values remained as 52.11% for within class variation, while 47.89% for the between class differences. The Principal Component Analysis (PCA) for grouping of the different genotypes from the different environmental conditions was done by Spearman Varimax rotation method for bi-plot generation of the co-occurrence of the same genotypes with similar genetic properties and specificity of different primers indicated clearly that the increase in the number of factors or components was correlated with the decrease in eigenvalues. The Kaiser Criterion based upon the eigenvalues greater than one, first two main factors accounted for 58.177% of cumulative variability.

Keywords: variation, clustering, PCA, wild, hatchery, RAPAD, Labeo rohita

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Authors: Khin Aye Myint, Mohd Rafii Yusop, Mohd Yusoff Abd Samad, Shairul Izan Ramlee, Mohd Din Amiruddin, Zulkifli Yaakub

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The narrow base of genetic is the main obstacle of breeding and genetic improvement in oil palm industry. In order to broaden the genetic bases, the Malaysian Palm Oil Board has been extensively collected wild germplasm from its original area of 11 African countries which are Nigeria, Senegal, Gambia, Guinea, Sierra Leone, Ghana, Cameroon, Zaire, Angola, Madagascar, and Tanzania. The germplasm collections were established and maintained as a field gene bank in Malaysian Palm Oil Board (MPOB) Research Station in Kluang, Johor, Malaysia to conserve a wide range of oil palm genetic resources for genetic improvement of Malaysian oil palm industry. Therefore, assessing the performance and genetic diversity of the wild materials is very important for understanding the genetic structure of natural oil palm population and to explore genetic resources. Principal component analysis (PCA) and Cluster analysis are very efficient multivariate tools in the evaluation of genetic variation of germplasm and have been applied in many crops. In this study, eight populations of MPOB-Senegal oil palm germplasm were studied to explore the genetic variation pattern using PCA and cluster analysis. A total of 20 yield and yield component traits were used to analyze PCA and Ward’s clustering using SAS 9.4 version software. The first four principal components which have eigenvalue >1 accounted for 93% of total variation with the value of 44%, 19%, 18% and 12% respectively for each principal component. PC1 showed highest positive correlation with fresh fruit bunch (0.315), bunch number (0.321), oil yield (0.317), kernel yield (0.326), total economic product (0.324), and total oil (0.324) while PC 2 has the largest positive association with oil to wet mesocarp (0.397) and oil to fruit (0.458). The oil palm population were grouped into four distinct clusters based on 20 evaluated traits, this imply that high genetic variation existed in among the germplasm. Cluster 1 contains two populations which are SEN 12 and SEN 10, while cluster 2 has only one population of SEN 3. Cluster 3 consists of three populations which are SEN 4, SEN 6, and SEN 7 while SEN 2 and SEN 5 were grouped in cluster 4. Cluster 4 showed the highest mean value of fresh fruit bunch, bunch number, oil yield, kernel yield, total economic product, and total oil and Cluster 1 was characterized by high oil to wet mesocarp, and oil to fruit. The desired traits that have the largest positive correlation on extracted PCs could be utilized for the improvement of oil palm breeding program. The populations from different clusters with the highest cluster means could be used for hybridization. The information from this study can be utilized for effective conservation and selection of the MPOB-Senegal oil palm germplasm for the future breeding program.

Keywords: cluster analysis, genetic variability, germplasm, oil palm, principal component analysis

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Authors: Hanaa Farouk Saleh

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The main purposes of this study are to develop a scale that reflects emerging theoretical understandings of new media credibility, based on the evolution of credibility studies in western researches, identification of the determinants of credibility in the media and its components by comparing traditional and new media credibility scales and building accumulative scale to test new media credibility. This approach was built on western researches using conceptualizations of media credibility, which focuses on four principal components: Source (journalist), message (article), medium (newspaper, radio, TV, web, etc.), and organization (owner of the medium), and adding user and cultural context as key components to assess new media credibility in particular. This study’s value lies in its contribution to the conceptualization and development of new media credibility through the creation of a theoretical measurement tool. Future studies should explore this scale to test new media credibility, which represents a promising new approach in the efforts to define and measure credibility of all media types.

Keywords: credibility scale, media credibility components, new media credibility scale, scale development

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Authors: Aparna Radhakrishnan, Jancy Gupta, R. Dileepkumar

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This paper aims at assessing the Socio Economic Vulnerability (SEV) of dairy farmers to Climate Variability and Change (CVC) in 3 states of Western Ghat region in India. For this purpose, a composite SEV index has been developed on the basis of functional relationships amongst sensitivity, exposure and adaptive capacity using 30 indicators related to dairy farming underlying the principles of Intergovernmental Panel on Climate Change and Fussel framework for nomenclature of vulnerable situation. Household level data were collected through Participatory Rural Appraisal and personal interviews of 540 dairy farmers of nine taluks, three each from a district selected from Kerala, Karnataka and Maharashtra, complemented by thirty years of gridded weather data. The data were normalized and then combined into three indices for sensitivity, exposure and adaptive capacity, which were then averaged with weights given using principal component analysis, to obtain the overall SEV index. Results indicated that the taluks of Western Ghats are vulnerable to CVC. The dairy farmers of Pulpally taluka were most vulnerable having the SEV score +1.24 and 42.66% farmers under high-level vulnerability category. Even though the taluks are geographically closer, there is wide variation in SEV components. Policies for incentivizing the ‘climate risk adaptation’ costs for small and marginal farmers and livelihood infrastructure for mitigating risks and promoting grass root level innovations are necessary to sustain dairy farming of the region.

Keywords: climate change, dairy, vulnerability, livelihoods, adaptation strategies

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Authors: Hazem Al-Mofleh, John Daniels, Joseph McKean

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Within geostatistics research, effective estimation of the variogram points has been examined, particularly in developing robust alternatives. The parametric fit of these variogram points which eventually defines the kriging weights, however, has not received the same attention from a robust perspective. This paper proposes the use of the non-linear Wilcoxon norm over weighted non-linear least squares as a robust variogram fitting alternative. First, we introduce the concept of variogram estimation and fitting. Then, as an alternative to non-linear weighted least squares, we discuss the non-linear Wilcoxon estimator. Next, the robustness properties of the non-linear Wilcoxon are demonstrated using a contaminated spatial data set. Finally, under simulated conditions, increasing levels of contaminated spatial processes have their variograms points estimated and fit. In the fitting of these variogram points, both non-linear Weighted Least Squares and non-linear Wilcoxon fits are examined for efficiency. At all levels of contamination (including 0%), using a robust estimation and robust fitting procedure, the non-weighted Wilcoxon outperforms weighted Least Squares.

Keywords: non-linear wilcoxon, robust estimation, variogram estimation, wilcoxon norm

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Authors: Si Chen, Le Zhang, Xian He

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The research explored the influences of public open space system on housing price through hedonic models, in order to support better open space plans and economic policies. We have three initial hypotheses: 1) public open space system has an overall positive influence on surrounding housing prices. 2) Different public open space types have different levels of influence on motivating surrounding housing prices. 3) Walking and driving accessibilities from property to public open spaces have different statistical relation with housing prices. Cook County, Illinois, was chosen to be a study area since data availability, sufficient open space types, and long-term open space preservation strategies. We considered the housing attributes, driving and walking accessibility scores from houses to nearby public open spaces, and driving accessibility scores to hospitals as influential features and used real housing sales price in 2010 as a dependent variable in the built hedonic model. Through ordinary least squares (OLS) regression analysis, General Moran’s I analysis and geographically weighted regression analysis, we observed the statistical relations between public open spaces and housing sale prices in the three built hedonic models and confirmed all three hypotheses.

Keywords: hedonic model, public open space, housing sale price, regression analysis, accessibility score

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Authors: Tayfun Salihoğlu

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Following the Law No: 6306 on Transformation of Disaster Risk Areas, urban transformation in Turkey found its legal basis. In the best practices all over the World, the urban transformation was shaped as part of comprehensive social programs through the discourses of renewing the economic, social and physical degraded parts of the city, producing spaces resistant to earthquakes and other possible disasters and creating a livable environment. In Turkish practice, a contradictory process is observed. In this study, it is aimed to develop a method for better understanding of the urban space in terms of disaster risks in order to constitute a basis for decisions in Kocaeli Urban Transformation Master Plan, which is being prepared by Kocaeli Metropolitan Municipality. The spatial unit used in the study is the 50x50 meter grids. In order to reflect the multidimensionality of urban transformation, three basic components that have spatial data in Kocaeli were identified. These components were named as 'Problems in Built-up Areas', 'Disaster Risks arising from Geological Conditions of the Ground and Problems of Buildings', and 'Inadequacy of Urban Services'. Each component was weighted and scored for each grid. In order to delimitate urban transformation zones Optimized Outlier Analysis (Local Moran I) in the ArcGIS 10.6.1 was conducted to test the type of distribution (clustered or scattered) and its significance on the grids by assuming the weighted total score of the grid as Input Features. As a result of this analysis, it was found that the weighted total scores were not significantly clustering at all grids in urban space. The grids which the input feature is clustered significantly were exported as the new database to use in further mappings. Total Score Map reflects the significant clusters in terms of weighted total scores of 'Problems in Built-up Areas', 'Disaster Risks arising from Geological Conditions of the Ground and Problems of Buildings' and 'Inadequacy of Urban Services'. Resulting grids with the highest scores are the most likely candidates for urban transformation in this citywide study. To categorize urban space in terms of urban transformation, Grouping Analysis in ArcGIS 10.6.1 was conducted to data that includes each component scores in significantly clustered grids. Due to Pseudo Statistics and Box Plots, 6 groups with the highest F stats were extracted. As a result of the mapping of the groups, it can be said that 6 groups can be interpreted in a more meaningful manner in relation to the urban space. The method presented in this study can be magnified due to the availability of more spatial data. By integrating with other data to be obtained during the planning process, this method can contribute to the continuation of research and decision-making processes of urban transformation master plans on a more consistent basis.

Keywords: urban transformation, GIS, disaster risk assessment, Kocaeli

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Authors: Alaa H. J. Al-Rkaby, A. Chegenizadeh, H. R. Nikraz

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The technique of reinforcing soil is an efficient, reliable and cost-effective alternative way for improving the performance of soil in civil engineering applications. Despite the anisotropic states of stresses induced within soil elements by many geotechnical structures such as footings, highways and offshore, most of the previous studies have been carried out under isotropic conditions. The anisotropic stress state in term of the inclined principal stress and the inequality of the intermediate and minor principal stresses cannot be investigated using conventional devices. Therefore, the advanced hollow cylinder apparatus, used in this work, provides a great opportunity to simulate such anisotropic stress states. To date, very little consideration has been given to how the direction of principal stress α and intermediate principal stress ratio b can affect the performance of the reinforced sand. This study presented that the anisotropic conditions of α and b resulted in significant variations in the deviator stress and volumetric strain of sand reinforced with geosynthetics. Anisotropic effect has been decreased by adding clay content.

Keywords: anisotropy, reinforced sand, direction of principal stress, intermediate principal stress ratio

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Authors: Ali A. Abdollahi

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During the last several decades, scientists have consistently applied Multiple Criteria Decision-Making methods in making decisions about multi-faceted, complicated subjects. While making such decisions and in order to achieve more accurate evaluations, they have regularly used a variety of criteria instead of applying just one Optimum Evaluation Criterion. The method presented here utilizes both ‘quantity’ and ‘quality’ to assess the function of the Multiple-Criteria method. Applying Data envelopment analysis (DEA), weighted aggregated sum product assessment (WASPAS), Weighted Sum Approach (WSA), Analytic Network Process (ANP), and Charnes, Cooper, Rhodes (CCR) methods, we have analyzed thirteen parks in Kerman city. It further indicates that the functions of WASPAS and WSA are compatible with each other, but also that their deviation from DEA is extensive. Finally, the results for the CCR technique do not match the results of the DEA technique. Our study indicates that the ANP method, with the average rate of 1/51, ranks closest to the DEA method, which has an average rate of 1/49.

Keywords: multiple criteria decision making, Data envelopment analysis (DEA), Charnes Cooper Rhodes (CCR), Weighted Sum Approach (WSA)

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Authors: Oguzhan Cepni, Doruk Kucuksarac, M. Hasan Yilmaz

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Changes in the global risk appetite cause co-movement in emerging market risk premiums. However, the sensitivity of the changes in risk premium to the global risk appetite may vary across emerging markets. In this study, how the global risk appetite affects Credit Default Swap (CDS) premiums in emerging markets are analyzed using Principal Component Analysis (PCA) and rolling regressions. The PCA results indicate that the first common component derived by the PCA accounts for almost 76 percent of the common variation in CDS premiums. Additionally, the explanatory power of the first factor seems to be high over the sample period. However, the sensitivity to the global risk factor tends to change over time and across countries. In this regard, fixed effects panel regressions are used to identify the macroeconomic factors driving the heterogeneity across emerging markets. The panel regression results point to the significance of government debt to GDP and international reserves to GDP in explaining sensitivity. Accordingly, countries with lower government debt and higher reserves tend to be less subject to the variations in the global risk appetite.

Keywords: credit default swaps, emerging markets, principal components analysis, sovereign risk

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Authors: Ghada Badr, Arwa Alturki

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The biological function of an RNA molecule depends on its structure. The objective of the alignment is finding the homology between two or more RNA secondary structures. Knowing the common functionalities between two RNA structures allows a better understanding and a discovery of other relationships between them. Besides, identifying non-coding RNAs -that is not translated into a protein- is a popular application in which RNA structural alignment is the first step A few methods for RNA structure-to-structure alignment have been developed. Most of these methods are partial structure-to-structure, sequence-to-structure, or structure-to-sequence alignment. Less attention is given in the literature to the use of efficient RNA structure representation and the structure-to-structure alignment methods are lacking. In this paper, we introduce an O(N2) Component-based Pairwise RNA Structure Alignment (CompPSA) algorithm, where structures are given as a component-based representation and where N is the maximum number of components in the two structures. The proposed algorithm compares the two RNA secondary structures based on their weighted component features rather than on their base-pair details. Extensive experiments are conducted illustrating the efficiency of the CompPSA algorithm when compared to other approaches and on different real and simulated datasets. The CompPSA algorithm shows an accurate similarity measure between components. The algorithm gives the flexibility for the user to align the two RNA structures based on their weighted features (position, full length, and/or stem length). Moreover, the algorithm proves scalability and efficiency in time and memory performance.

Keywords: alignment, RNA secondary structure, pairwise, component-based, data mining

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Authors: Hamesh Babu Nanvala, Madhuri Malhal Rao

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The overall development of the Institution is dependent on the approach and attitude of the principal. Influence of principal’s professionalism on overall development of the Institution is the aim of this paper. Professionalism means conducting oneself with responsibility, integrity, accountability and excellence. The predominant characteristic of professionalism is the temperament of oneself to work in the public interest. By summarizing the observations based on authors’ experience regarding professionalism of principals towards the development of their respective institutions and correlating these observations with the findings in the literature and opinion of other principals and staff, the authors conceived a conceptual approach with its attributes by practicing suggested approach principals that can achieve overall development of their institutions.

Keywords: achiever, development, institution, principal, professionalism, student, teacher

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Authors: S. Turkan

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In this paper, the Turkey’s Top 500 Industrial Enterprises data in 2014 were analyzed by data envelopment analysis. Data envelopment analysis is used to detect efficient decision-making units such as universities, hospitals, schools etc. by using inputs and outputs. The decision-making units in this study are enterprises. To detect efficient enterprises, some financial ratios are determined as inputs and outputs. For this reason, financial indicators related to productivity of enterprises are considered. The efficient foreign weighted owned capital enterprises are detected via super efficiency model. According to the results, it is said that Mercedes-Benz is the most efficient foreign weighted owned capital enterprise in Turkey.

Keywords: data envelopment analysis, super efficiency, logistic regression, financial ratios

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Authors: Syed Afrozuddin Ahmed, Junaid Saghir Siddiqi, Sabah Quaiser

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Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. The climatic or environmental structure data and the Dengue Fever (DF) data was processed by coding, editing, tabulating, recoding, restructuring in terms of re-tabulating was carried out, and finally applying different statistical methods, techniques, and procedures for the evaluation. Five climatic variables which we have studied are precipitation (P), Maximum temperature (Mx), Minimum temperature (Mn), Humidity (H) and Wind speed (W) collected from 1980-2012. The dengue cases in Karachi from 2010 to 2012 are reported on weekly basis. Principal component analysis is applied to explore the climatic variables and/or the climatic (structure) which may influence in the increase or decrease in the number of dengue fever cases in Karachi. PC1 for all the period is General atmospheric condition. PC2 for dengue period is contrast between precipitation and wind speed. PC3 is the weighted difference between maximum temperature and wind speed. PC4 for dengue period contrast between maximum and wind speed. Negative binomial and Poisson regression model are used to correlate the dengue fever incidence to climatic variable and principal component score. Relative humidity is estimated to positively influence on the chances of dengue occurrence by 1.71% times. Maximum temperature positively influence on the chances dengue occurrence by 19.48% times. Minimum temperature affects positively on the chances of dengue occurrence by 11.51% times. Wind speed is effecting negatively on the weekly occurrence of dengue fever by 7.41% times.

Keywords: principal component analysis, dengue fever, negative binomial regression model, poisson regression model

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Authors: H. Jung, N. Kim, B. Kang, J. Choe

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History matching is a crucial procedure for predicting reservoir performances and making future decisions. However, it is difficult due to uncertainties of initial reservoir models. Therefore, it is important to have reliable initial models for successful history matching of highly heterogeneous reservoirs such as channel reservoirs. In this paper, we proposed a novel scheme for regenerating geological models using support vector machine (SVM) and principal component analysis (PCA). First, we perform PCA for figuring out main geological characteristics of models. Through the procedure, permeability values of each model are transformed to new parameters by principal components, which have eigenvalues of large magnitude. Secondly, the parameters are projected into two-dimensional plane by multi-dimensional scaling (MDS) based on Euclidean distances. Finally, we train an SVM classifier using 20% models which show the most similar or dissimilar well oil production rates (WOPR) with the true values (10% for each). Then, the other 80% models are classified by trained SVM. We select models on side of low WOPR errors. One hundred channel reservoir models are initially generated by single normal equation simulation. By repeating the classification process, we can select models which have similar geological trend with the true reservoir model. The average field of the selected models is utilized as a probability map for regeneration. Newly generated models can preserve correct channel features and exclude wrong geological properties maintaining suitable uncertainty ranges. History matching with the initial models cannot provide trustworthy results. It fails to find out correct geological features of the true model. However, history matching with the regenerated ensemble offers reliable characterization results by figuring out proper channel trend. Furthermore, it gives dependable prediction of future performances with reduced uncertainties. We propose a novel classification scheme which integrates PCA, MDS, and SVM for regenerating reservoir models. The scheme can easily sort out reliable models which have similar channel trend with the reference in lowered dimension space.

Keywords: history matching, principal component analysis, reservoir modelling, support vector machine

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Authors: Masoud Sheidai

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Biologists with a special field of population genetics and phylogeny have different research tasks such as populations’ genetic variability and divergence, species relatedness, the evolution of genetic and morphological characters, and identification of DNA SNPs with adaptive potential. To tackle these problems and reach a concise conclusion, they must use the proper and efficient statistical and bioinformatic methods as well as suitable genetic and morphological characteristics. In recent years application of different bioinformatic and statistical methods, which are based on various well-documented assumptions, are the proper analytical tools in the hands of researchers. The species delineation is usually carried out with the use of different clustering methods like K-means clustering based on proper distance measures according to the studied features of organisms. A well-defined species are assumed to be separated from the other taxa by molecular barcodes. The species relationships are studied by using molecular markers, which are analyzed by different analytical methods like multidimensional scaling (MDS) and principal coordinate analysis (PCoA). The species population structuring and genetic divergence are usually investigated by PCoA and PCA methods and a network diagram. These are based on bootstrapping of data. The Association of different genes and DNA sequences to ecological and geographical variables is determined by LFMM (Latent factor mixed model) and redundancy analysis (RDA), which are based on Bayesian and distance methods. Molecular and morphological differentiating characters in the studied species may be identified by linear discriminant analysis (DA) and discriminant analysis of principal components (DAPC). We shall illustrate these methods and related conclusions by giving examples from different edible and medicinal plant species.

Keywords: GWAS analysis, K-Means clustering, LFMM, multidimensional scaling, redundancy analysis

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Authors: Junjun Chen, Yingxiu Li

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The occupational well-being of school principals has played a vital role in the pursuit of individual and school wellness and success. However, principals’ well-being worldwide is under increasing threat because of the challenging and complex nature of their work and growing demands for school standardisation and accountability. Pressure is particularly acute in the post-pandemicfuture as principals attempt to deal with the impact of the pandemic on top of more regular demands. This is particularly true in Hong Kong, as school principals are increasingly wedged between unparalleled political, social, and academic responsibilities. Recognizing the semantic breadth of well-being, scholars have not determined a single, mutually agreeable definition but agreed that the concept of well-being has multiple dimensions across various disciplines. The multidimensional approach promises more precise assessments of the relationships between well-being and other concepts than the ‘affect-only’ approach or other single domains for capturing the essence of principal well-being. The multiple-dimension well-being concept is adopted in this project to understand principal well-being in this study. This study aimed to understand the situation of principal well-being and its influential drivers with a sample of 670 principals from Hong Kong and Mainland China. An online survey was sent to the participants after the breakout of COVID-19 by the researchers. All participants were well informed about the purposes and procedure of the project and the confidentiality of the data prior to filling in the questionnaire. Confirmatory factor analysis and structural equation modelling performed with Mplus were employed to deal with the dataset. The data analysis procedure involved the following three steps. First, the descriptive statistics (e.g., mean and standard deviation) were calculated. Second, confirmatory factor analysis (CFA) was used to trim principal well-being measurement performed with maximum likelihood estimation. Third, structural equation modelling (SEM) was employed to test the influential factors of principal well-being. The results of this study indicated that the overall of principal well-being were above the average mean score. The highest ranking in this study given by the principals was to their psychological and social well-being (M = 5.21). This was followed by spiritual (M = 5.14; SD = .77), cognitive (M = 5.14; SD = .77), emotional (M = 4.96; SD = .79), and physical well-being (M = 3.15; SD = .73). Participants ranked their physical well-being the lowest. Moreover, professional autonomy, supervisor and collegial support, school physical conditions, professional networking, and social media have showed a significant impact on principal well-being. The findings of this study will potentially enhance not only principal well-being, but also the functioning of an individual principal and a school without sacrificing principal well-being for quality education in the process. This will eventually move one step forward for a new future - a wellness society advocated by OECD. Importantly, well-being is an inside job that begins with choosing to have wellness, whilst supports to become a wellness principal are also imperative.

Keywords: well-being, school principals, quantitative, influential factors

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Authors: Abbas Hani, Maryam Jassasizadeh

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The concentrations of Cd, Pb, and Ni were determined from 40 soil samples collected in surface soils of Khark Island. Geostatistic methods and GIS were used to identify heavy metal sources and their spatial pattern. Principal component analysis coupled with correlation between heavy metals showed that level of mentioned heavy metal was lower than the standard level. Then the data obtained from the soil analyzing were studied for the purposes of normal distribution. The best way of interior finding for cadmium and nickel was ordinary kriging and the best way of interpolation of lead was inverse distance weighted. The result of this study help us to understand heavy metals distribution and make decision for remediation of soil pollution.

Keywords: geostatistics, ordinary kriging, heavy metals, GIS, Khark

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Authors: Oguz Ugurkan Akkaya, Alpaslan Sipahi, Ozgur Firat Pamukcu, Murat Yasar, Tolga Guler, Arif Ulu, Ferit Cakir

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With the increase in human population, expansion, and renovation of cities, infrastructure systems today need to be manufactured to be more durable and long-lasting. The most cost-effective and durable manufacturing of components is a general problem of all engineering disciplines. Therefore, it is important to determine the most optimal components. This study mainly focuses on the most optimal component design of the polymer concrete. For this purpose, the lower and upper limits of the three main components of the polymer concrete are determined. The effects of these three principal components on the compressive strength, tensile strength, and unit price of polymer concrete are estimated using the response surface method. Box-Behnken Design is used in designing the experiments. Compressive strength, tensile strength, and unit prices are successfully estimated with variance ratios (R²) of 0.82, 0.92, and 0.90, respectively, and the optimum mixture quantity is determined.

Keywords: Box-Behnken Design, compressive strength, mechanical tests, polymer concrete, tensile strength

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Authors: Salisu ibrahim, Abdalla Rababah

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In this research, we use Weighted G2-Multi-degree reduction of Bezier curve of degree n to a Bezier curve of degree m, m < n. The degree reduction of Bezier curves is used to represent a given Bezier curve of n by a Bezier curve of degree m, m < n. Exact degree reduction is not possible, and degree reduction is approximate process in nature. We derive a weighted degree reducing method that is geometrically continuous at the end points. Different norms will be considered, several error minimizations will be given. The proposed methods produce error function that are less than the errors of existing methods.

Keywords: Bezier curves, multiple degree reduction, geometric continuity, error function

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Authors: K. A. Bindu, T. V. Raja, P. M. Rojan, A. Siby

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The multivariate path coefficient approach was employed to study the effects of various production and reproduction traits on the slaughter body weight of rabbits. Information on 562 rabbits maintained at the university rabbit farm attached to the Centre for Advanced Studies in Animal Genetics, and Breeding, Kerala Veterinary and Animal Sciences University, Kerala State, India was utilized. The manifest variables used in the study were age and weight of dam, birth weight, litter size at birth and weaning, weight at first, second and third months. The linear multiple regression analysis was performed by keeping the slaughter weight as the dependent variable and the remaining as independent variables. The model explained 48.60 percentage of the total variation present in the market weight of the rabbits. Even though the model used was significant, the standardized beta coefficients for the independent variables viz., age and weight of the dam, birth weight and litter sizes at birth and weaning were less than one indicating their negligible influence on the slaughter weight. However, the standardized beta coefficient of the second-month body weight was maximum followed by the first-month weight indicating their major role on the market weight. All the other factors influence indirectly only through these two variables. Hence it was concluded that the slaughter body weight can be predicted using the first and second-month body weights. The principal components were also developed so as to achieve more accuracy in the prediction of market weight of rabbits.

Keywords: component analysis, multivariate, slaughter, regression

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Authors: Noor F. Ilias, Mazlifah Omar, Hashbullah Ismail

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Exercise training has been shown to improve functional capacity and is recommended as a therapy for management of blood pressure. Our purpose was to establish whether different exercise capacity produces different effect size for Cardiorespiratory Fitness (CRF), systolic (SBP) and diastolic (DBP) blood pressure in patients with hypertension. Exercise characteristic is required in order to have optimal benefit from the training, but optimal exercise capacity is still unwarranted. A MEDLINE search (1985 to 2015) was conducted for exercise based rehabilitation trials in hypertensive patients. Thirty-seven studies met the selection criteria. Of these, 31 (83.7%) were aerobic exercise and 6 (16.3%) aerobic with additional resistance exercise, providing a total of 1318 exercise subjects and 819 control, the total of subjects was 2137. We calculated exercise volume and energy expenditure through the description of exercise characteristics. 4 studies (18.2%) were 451kcal - 900 kcal, 12 (54.5%) were 900 kcal – 1350 kcal and 6 (27.3%) >1351kcal per week. Peak oxygen consumption (peak VO2) increased by mean difference of 1.44 ml/kg/min (95% confidence interval [CI]: 1.08 to 1.79 ml/kg/min; p = 0.00001) with weighted mean 21.2% for aerobic exercise compare to aerobic with additional resistance exercise 4.50 ml/kg/min (95% confidence interval [CI]: 3.57 to 5.42 ml/kg/min; p = 0.00001) with weighted mean 14.5%. SBP was clinically reduce for both aerobic and aerobic with resistance training by mean difference of -4.66 mmHg (95% confidence interval [CI]: -5.68 to -3.63 mmHg; p = 0.00001) weighted mean 6% reduction and -5.06 mmHg (95% confidence interval [CI]: -7.32 to -2.8 mmHg; p = 0.0001) weighted mean 5% reduction respectively. Result for DBP was clinically reduce for aerobic by mean difference of -1.62 mmHg (95% confidence interval [CI]: -2.09 to -1.15 mmHg; p = 0.00001) weighted mean 4% reduction and aerobic with resistance training reduce by mean difference of -3.26 mmHg (95% confidence interval [CI]: -4.87 to -1.65 mmHg; p = 0.0001) weighted mean 6% reduction. Optimum exercise capacity for 451 kcal – 900 kcal showed greater improvement in peak VO2 and SBP by 2.76 ml/kg/min (95% confidence interval [CI]: 1.47 to 4.05 ml/kg/min; p = 0.0001) with weighted mean 40.6% and -16.66 mmHg (95% confidence interval [CI]: -21.72 to -11.60 mmHg; p = 0.00001) weighted mean 9.8% respectively. Our data demonstrated that aerobic exercise with total volume of 451 kcal – 900 kcal/ week energy expenditure may elicit greater changes in cardiorespiratory fitness and blood pressure in hypertensive patients. Higher exercise capacity weekly does not seem better result in management hypertensive patients.

Keywords: blood Pressure, exercise, hypertension, peak VO2

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Authors: Getachew Weldemichael, Sentayehu Alamerew, Leta Tulu, Gezahegn Berecha

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Coffee quality improvement program is becoming the focus of coffee research, as the world coffee consumption pattern shifted to high-quality coffee. However, there is limited information on the genetic variation of C. Arabica for quality improvement in potential specialty coffee growing areas of Ethiopia. Therefore, this experiment was conducted with the objectives of determining the magnitude of variation among 105 coffee accessions collected from east Wollega coffee growing areas and assessing correlations between the different coffee qualities attributes. It was conducted in RCRD with three replications. Data on green bean physical characters (shape and make, bean color and odor) and organoleptic cup quality traits (aromatic intensity, aromatic quality, acidity, astringency, bitterness, body, flavor, and overall standard of the liquor) were recorded. Analysis of variance, clustering, genetic divergence, principal component and correlation analysis was performed using SAS software. The result revealed that there were highly significant differences (P<0.01) among the accessions for all quality attributes except for odor and bitterness. Among the tested accessions, EW104 /09, EW101 /09, EW58/09, EW77/09, EW35/09, EW71/09, EW68/09, EW96 /09, EW83/09 and EW72/09 had the highest total coffee quality values (the sum of bean physical and cup quality attributes). These genotypes could serve as a source of genes for green bean physical characters and cup quality improvement in Arabica coffee. Furthermore, cluster analysis grouped the coffee accessions into five clusters with significant inter-cluster distances implying that there is moderate diversity among the accessions and crossing accessions from these divergent inter-clusters would result in hetrosis and recombinants in segregating generations. The principal component analysis revealed that the first three principal components with eigenvalues greater than unity accounted for 83.1% of the total variability due to the variation of nine quality attributes considered for PC analysis, indicating that all quality attributes equally contribute to a grouping of the accessions in different clusters. Organoleptic cup quality attributes showed positive and significant correlations both at the genotypic and phenotypic levels, demonstrating the possibility of simultaneous improvement of the traits. Path coefficient analysis revealed that acidity, flavor, and body had a high positive direct effect on overall cup quality, implying that these traits can be used as indirect criteria to improve overall coffee quality. Therefore, it was concluded that there is considerable variation among the accessions, which need to be properly conserved for future improvement of the coffee quality. However, the variability observed for quality attributes must be further verified using biochemical and molecular analysis.

Keywords: accessions, Coffea arabica, cluster analysis, correlation, principal component

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Authors: Manpreet Kaur, Badaruddoza, Sandeep Kaur Brar

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The increasing prevalence of adolescent obesity is one of the major causes to be hypertensive in adulthood. Various statistical methods have been applied to examine the performance of anthropometric indices for the identification of adverse cardiovascular risk profile. The present work was undertaken to determine the significant traditional risk factors through principal component factor analysis (PCFA) among population based Punjabi adolescents aged 10-18 years. Data was collected among adolescent children from different schools situated in urban areas of Punjab, India. Principal component factor analysis (PCFA) was applied to extract orthogonal components from anthropometric and physiometric variables. Association between components were explained by factor loadings. The PCFA extracted four factors, which explained 84.21%, 84.06% and 83.15% of the total variance of the 14 original quantitative traits among boys, girls and combined subjects respectively. Factor 1 has high loading of the traits that reflect adiposity such as waist circumference, BMI and skinfolds among both sexes. However, waist circumference and body mass index are the indicator of abdominal obesity which increases the risk of cardiovascular diseases. The loadings of these two traits have found maximum in girls adolescents (WC=0.924; BMI=0.905). Therefore, factor 1 is the strong indicator of atherosclerosis in adolescents. Factor 2 is predominantly loaded with blood pressures and related traits (SBP, DBP, MBP and pulse rate) which reflect the risk of essential hypertension in adolescent girls and combined subjects, whereas, factor 2 loaded with obesity related traits in boys (weight and hip circumferences). Comparably, factor 3 is loaded with blood pressures in boys and with height and WHR in girls, while factor 4 contains high loading of pulse pressure among boys, girls and combined group of adolescents.

Keywords: adolescent obesity, cvd, hypertension, punjabi population

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Authors: Lucia Billeci, Gennaro Tartarisco, Maurizio Varanini

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Fetal cardiac monitoring by fetal electrocardiogram (fECG) can provide significant clinical information about the healthy condition of the fetus. Despite this potentiality till now the use of fECG in clinical practice has been quite limited due to the difficulties in its measuring. The recovery of fECG from the signals acquired non-invasively by using electrodes placed on the maternal abdomen is a challenging task because abdominal signals are a mixture of several components and the fetal one is very weak. This paper presents an approach for fECG extraction from abdominal maternal recordings, which exploits the characteristics of pseudo-periodicity of fetal ECG. It consists of devising a quality index (fQI) for fECG and of finding the linear combinations of preprocessed abdominal signals, which maximize these fQI (quality index optimization - QIO). It aims at improving the performances of the most commonly adopted methods for fECG extraction, usually based on maternal ECG (mECG) estimating and canceling. The procedure for the fECG extraction and fetal QRS (fQRS) detection is completely unsupervised and based on the following steps: signal pre-processing; maternal ECG (mECG) extraction and maternal QRS detection; mECG component approximation and canceling by weighted principal component analysis; fECG extraction by fQI maximization and fetal QRS detection. The proposed method was compared with our previously developed procedure, which obtained the highest at the Physionet/Computing in Cardiology Challenge 2013. That procedure was based on removing the mECG from abdominal signals estimated by a principal component analysis (PCA) and applying the Independent component Analysis (ICA) on the residual signals. Both methods were developed and tuned using 69, 1 min long, abdominal measurements with fetal QRS annotation of the dataset A provided by PhysioNet/Computing in Cardiology Challenge 2013. The QIO-based and the ICA-based methods were compared in analyzing two databases of abdominal maternal ECG available on the Physionet site. The first is the Abdominal and Direct Fetal Electrocardiogram Database (ADdb) which contains the fetal QRS annotations thus allowing a quantitative performance comparison, the second is the Non-Invasive Fetal Electrocardiogram Database (NIdb), which does not contain the fetal QRS annotations so that the comparison between the two methods can be only qualitative. In particular, the comparison on NIdb was performed defining an index of quality for the fetal RR series. On the annotated database ADdb the QIO method, provided the performance indexes Sens=0.9988, PPA=0.9991, F1=0.9989 overcoming the ICA-based one, which provided Sens=0.9966, PPA=0.9972, F1=0.9969. The comparison on NIdb was performed defining an index of quality for the fetal RR series. The index of quality resulted higher for the QIO-based method compared to the ICA-based one in 35 records out 55 cases of the NIdb. The QIO-based method gave very high performances with both the databases. The results of this study foresees the application of the algorithm in a fully unsupervised way for the implementation in wearable devices for self-monitoring of fetal health.

Keywords: fetal electrocardiography, fetal QRS detection, independent component analysis (ICA), optimization, wearable

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Authors: Halil Ibrahim Demir, Abdullah Hulusi Kokcam, Fuat Simsir, Özer Uygun

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Traditionally, three important manufacturing functions which are process planning, scheduling and due-date assignment are performed sequentially and separately. Although there are numerous works on the integration of process planning and scheduling and plenty of works focusing on scheduling with due date assignment, there are only a few works on integrated process planning, scheduling and due-date assignment. Although due-dates are determined without taking into account of weights of the customers in the literature, here weighted due-date assignment is employed to get better performance. Jobs are scheduled according to weighted earliest due date dispatching rule and due dates are determined according to some popular due date assignment methods by taking into account of the weights of each job. Simulated Annealing, Evolutionary Strategies, Random Search, hybrid of Random Search and Simulated Annealing, and hybrid of Random Search and Evolutionary Strategies, are applied as solution techniques. Three important manufacturing functions are integrated step-by-step and higher integration levels are found better. Search meta-heuristics are found to be very useful while improving performance measure.

Keywords: process planning, weighted scheduling, weighted due-date assignment, simulated annealing, evolutionary strategies, hybrid searches

Procedia PDF Downloads 434

Authors: M. Jahanandish, M. B. Zeydabadinejad

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In this paper, von Mises and Drucker-Prager yield criteria, as typical ones that consider the effect of intermediate principal stress σ2, have been selected and employed for investigating the influence of σ2 on the solution of a typical stability problem. The bearing capacity factors have been calculated under plane strain condition (strip footing) and axisymmetric condition (circular footing) using the method of stress characteristics together with the criteria mentioned. Different levels of σ2 relative to the other two principal stresses have been considered. While a higher σ2 entry in yield criterion gives a higher bearing capacity; its entry in equilibrium equations (axisymmetric) causes substantial reduction.

Keywords: intermediate principal stress, plane strain, axisymmetric, yield criteria

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Authors: T. Mersmann, P. Nyhuis

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In the current economic climate, for many businesses it is generally no longer sufficient to pursue exclusively economic interests. Instead, integrating ecological and social goals into the corporate targets is becoming ever more important. However, the holistic integration of these new goals is missing from current factory planning approaches. This article describes the conceptual framework for a planning methodology for sustainable factories. To this end, the description of the key areas for action is followed by a description of the principal components for the systematization of sustainability for factories and their stakeholders. Finally, a conceptual framework is presented which integrates the components formulated into an established factory planning procedure.

Keywords: factory planning, stakeholder, systematization, sustainability

Procedia PDF Downloads 427

Authors: Yashi Ye

Abstract:

Principles play vital roles in shaping the school culture and promoting teachers' professional learning by exerting their leadership. In the changing time of the 21st century, the creative leadership of school leaders is increasingly important in cultivating the professional learning communities of teachers for eventually improving student performance in every continent. This study examines under what conditions and how principal creative leadership contributes to teachers’ professional learning and school culture. Data collected from 632 teachers in 30 primary and middle schools in the cities of Chengdu and Chongqing in mainland China are analyzed using structural equation modeling and bootstrapping tests. A moderated mediation model of principle creative leadership effects is used to analyze professional teacher learning and school culture in which the mediator will be school culture and the moderator will be power distance orientation. The results indicate that principal creative leadership has significant direct and indirect effects on teachers' professional learning. A positive correlation between principal creative leadership, professional teacher learning, and school culture is observed. Further model testing found that teacher power distance orientation moderated the significant effect of principal creative leadership on school culture. When teachers perceived higher power distance in teacher-principal relations, the effects of principal creative leadership were stronger than for those who perceived low power distance. The results indicate the “culture change” in the young generation of teachers in China, and further implications to understanding the cultural context in the field of educational leadership are discussed.

Keywords: power distance orientation, principal creative leadership, school culture, teacher professional learning

Procedia PDF Downloads 118