Search results for: multivariate statistical analysis

Authors: Pavlo Selyshchev

Abstract:

We consider fluctuations of defects density taking into account their interaction. Stochastic field of displacement generation rate gives random defect distribution. We determinate statistical characteristics (mean and dispersion) of random field of point defect distribution as function of defect generation parameters, temperature and properties of irradiated crystal.

 

Keywords: Irradiation, Primary Defects, Interaction, Fluctuations.

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Authors: Marzieh Talebzadeh Shoushtari

Abstract:

The present study aimed to determine the effectiveness of Metaphor therapy on depression among female students. The sample included 60 female students with depression symptoms selected by simple sampling and randomly divided into two equal groups (experimental and control groups). Beck Depression Inventory was used to measure the variables. This was an experimental study with a pre-test/post-test design with control group. Eight metaphor therapy sessions were held for the experimental group. A post-test was administered to both groups. Data were analyzed using multivariate analysis of covariance (MANCOVA). Results showed that the Metaphor therapy decreased depression in the experimental group compared to the control group.

Keywords: Metaphor therapy, depression, female, students.

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Authors: S. Soommat, S. Patamatamkul, T. Prempridi, M. Sritulyachot, P. Ineure, S. Yimman

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Currently, slider process of Hard Disk Drive Industry become more complex, defective diagnosis for yield improvement becomes more complicated and time-consumed. Manufacturing data analysis with data mining approach is widely used for solving that problem. The existing mining approach from combining of the KMean clustering, the machine oriented Kruskal-Wallis test and the multivariate chart were applied for defective diagnosis but it is still be a semiautomatic diagnosis system. This article aims to modify an algorithm to support an automatic decision for the existing approach. Based on the research framework, the new approach can do an automatic diagnosis and help engineer to find out the defective factors faster than the existing approach about 50%.

Keywords: Slider process, Defective diagnosis and Data mining.

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Authors: A.K. Tangirala, S. Babji

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In the last few years, three multivariate spectral analysis techniques namely, Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Non-negative Matrix Factorization (NMF) have emerged as effective tools for oscillation detection and isolation. While the first method is used in determining the number of oscillatory sources, the latter two methods are used to identify source signatures by formulating the detection problem as a source identification problem in the spectral domain. In this paper, we present a critical drawback of the underlying linear (mixing) model which strongly limits the ability of the associated source separation methods to determine the number of sources and/or identify the physical source signatures. It is shown that the assumed mixing model is only valid if each unit of the process gives equal weighting (all-pass filter) to all oscillatory components in its inputs. This is in contrast to the fact that each unit, in general, acts as a filter with non-uniform frequency response. Thus, the model can only facilitate correct identification of a source with a single frequency component, which is again unrealistic. To overcome this deficiency, an iterative post-processing algorithm that correctly identifies the physical source(s) is developed. An additional issue with the existing methods is that they lack a procedure to pre-screen non-oscillatory/noisy measurements which obscure the identification of oscillatory sources. In this regard, a pre-screening procedure is prescribed based on the notion of sparseness index to eliminate the noisy and non-oscillatory measurements from the data set used for analysis.

Keywords: non-negative matrix factorization, PCA, source separation, plant-wide diagnosis

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Authors: Teerawach Khamkorn, Laongtip Mathurasa, Savittree Rochanasmita Arnold, Witthaya Mekhum

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The objective of this research seeks to transmit a distance training model to the community in the upper northeastern region. The group sampling consists of 60 community leaders in the municipality of sub-district Kumphawapi, Kumphawapi Disrict, Udonthani Province. The research tools rely on the following instruments, they are : 1) the achievement test of community leaders- training and 2) the satisfaction questionnaires of community leaders. The statistics used in data analysis takes the statistical mean, percentage, standard deviation, and statistical T-test. The resulted findings reveal : 1) the efficiency of the distance training developed by the researcher for the community leaders joining in the training received the average score between in-training and post-training period higher than the setup criterion, 2) the two groups of participants in the training achieved higher knowledge than their pre-training state, 3) the comparison of the achievements between the two group presented no different results, 4) the community leaders obtained the high-to-highest satisfaction.

Keywords: Distance Training, Management, Technology, Transmitting.

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: Automotive industry, Industry 4.0, internet of things, IATF 16949:2016, measurement system analysis.

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Authors: Taotao Deng, John D. Nelson

Abstract:

Bus Rapid Transit (BRT) has emerged as a cost-effective transport system for urban mobility. However its ability to stimulate land development remains largely unexplored. The study makes use of qualitative (interview method) and quantitative analysis (questionnaire survey and longitudinal analysis of property data) to investigate land development impact resulting from BRT in Beijing, China. The empirical analysis suggests that BRT has a positive impact on the residential and commercial property attractiveness along the busway corridor. The statistical analysis suggests that accessibility advantage conferred by BRT is capitalized into higher property price. The average price of apartments adjacent to a BRT station has gained a relatively faster increase than those not served by the BRT system. The capitalization effect mostly occurs after the full operation of BRT, and is more evident over time and particularly observed in areas which previously lack alternative mobility opportunity.

Keywords: accessibility, Bus Rapid Transit (BRT), Beijing, property value uplift

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Authors: V. Geetha, K. S. Aprameya

Abstract:

Early diagnosis of dental caries is essential for maintaining dental health. In this paper, method for diagnosis of dental caries is proposed using Laplacian filter, adaptive thresholding, texture analysis and Support Vector Machine (SVM) classifier. Analysis of the proposed method is compared with Otsu thresholding, watershed segmentation and active contouring method. Adaptive thresholding has comparatively better performance with 96.9% accuracy and 96.1% precision. The results are validated using statistical method, two-way ANOVA, at significant level of 5%, that shows the interaction of proposed method on performance parameter measures are significant. Hence the proposed technique could be used for detection of dental caries in automated computer assisted diagnosis system.

Keywords: Computer assisted diagnosis, dental caries, dental radiography, image segmentation.

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Authors: Fariba Esfandiary, Ghafoor Aghaie, Ali Dolati Mehr

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Wheat prediction was carried out using different meteorological variables together with agro meteorological indices in Ardebil district for the years 2004-2005 & 2005–2006. On the basis of correlation coefficients, standard error of estimate as well as relative deviation of predicted yield from actual yield using different statistical models, the best subset of agro meteorological indices were selected including daily minimum temperature (Tmin), accumulated difference of maximum & minimum temperatures (TD), growing degree days (GDD), accumulated water vapor pressure deficit (VPD), sunshine hours (SH) & potential evapotranspiration (PET). Yield prediction was done two months in advance before harvesting time which was coincide with commencement of reproductive stage of wheat (5th of June). It revealed that in the final statistical models, 83% of wheat yield variability was accounted for variation in above agro meteorological indices.

Keywords: Wheat yields prediction, agro meteorological indices, statistical models

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Authors: Urska Cvek, Marjan Trutschl, Randolph Stone II, Zanobia Syed, John L. Clifford, Anita L. Sabichi

Abstract:

Expression data analysis is based mostly on the statistical approaches that are indispensable for the study of biological systems. Large amounts of multidimensional data resulting from the high-throughput technologies are not completely served by biostatistical techniques and are usually complemented with visual, knowledge discovery and other computational tools. In many cases, in biological systems we only speculate on the processes that are causing the changes, and it is the visual explorative analysis of data during which a hypothesis is formed. We would like to show the usability of multidimensional visualization tools and promote their use in life sciences. We survey and show some of the multidimensional visualization tools in the process of data exploration, such as parallel coordinates and radviz and we extend them by combining them with the self-organizing map algorithm. We use a time course data set of transitional cell carcinoma of the bladder in our examples. Analysis of data with these tools has the potential to uncover additional relationships and non-trivial structures.

Keywords: microarrays, visualization, parallel coordinates, radviz, self-organizing maps.

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Authors: Selvam M, Natarajan. A M, Thangarajan R

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Parsing is important in Linguistics and Natural Language Processing to understand the syntax and semantics of a natural language grammar. Parsing natural language text is challenging because of the problems like ambiguity and inefficiency. Also the interpretation of natural language text depends on context based techniques. A probabilistic component is essential to resolve ambiguity in both syntax and semantics thereby increasing accuracy and efficiency of the parser. Tamil language has some inherent features which are more challenging. In order to obtain the solutions, lexicalized and statistical approach is to be applied in the parsing with the aid of a language model. Statistical models mainly focus on semantics of the language which are suitable for large vocabulary tasks where as structural methods focus on syntax which models small vocabulary tasks. A statistical language model based on Trigram for Tamil language with medium vocabulary of 5000 words has been built. Though statistical parsing gives better performance through tri-gram probabilities and large vocabulary size, it has some disadvantages like focus on semantics rather than syntax, lack of support in free ordering of words and long term relationship. To overcome the disadvantages a structural component is to be incorporated in statistical language models which leads to the implementation of hybrid language models. This paper has attempted to build phrase structured hybrid language model which resolves above mentioned disadvantages. In the development of hybrid language model, new part of speech tag set for Tamil language has been developed with more than 500 tags which have the wider coverage. A phrase structured Treebank has been developed with 326 Tamil sentences which covers more than 5000 words. A hybrid language model has been trained with the phrase structured Treebank using immediate head parsing technique. Lexicalized and statistical parser which employs this hybrid language model and immediate head parsing technique gives better results than pure grammar and trigram based model.

Keywords: Hybrid Language Model, Immediate Head Parsing, Lexicalized and Statistical Parsing, Natural Language Processing, Parts of Speech, Probabilistic Context Free Grammar, Tamil Language, Tree Bank.

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Authors: Liu Zhiyuan, Sun Zongdi, Liu Zhiyuan, Sun Zongdi

Abstract:

Qingdao is a seaside city. Taking into account the characteristics of Qingdao, this article established a multiple linear regression model to analyze the impact of macroeconomic factors on housing prices. We used stepwise regression method to make multiple linear regression analysis, and made statistical analysis of F test values and T test values. According to the analysis results, the model is continuously optimized. Finally, this article obtained the multiple linear regression equation and the influencing factors, and the reliability of the model was verified by F test and T test.

Keywords: Housing prices, multiple linear regression model, macroeconomic factors, Qingdao City.

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Authors: A. Falsafain, S. M. Taheri, M. Mashinchi

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Using a set of confidence intervals, we develop a common approach, to construct a fuzzy set as an estimator for unknown parameters in statistical models. We investigate a method to derive the explicit and unique membership function of such fuzzy estimators. The proposed method has been used to derive the fuzzy estimators of the parameters of a Normal distribution and some functions of parameters of two Normal distributions, as well as the parameters of the Exponential and Poisson distributions.

Keywords: Confidence interval. Fuzzy number. Fuzzy estimation.

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Authors: Abdel-Aziz M. Mohamed, Nermeen Coutry

Abstract:

Lead time is a critical measure of a supply chain's performance. It impacts both the customer satisfactions as well as the total cost of inventory. This paper presents the result of a study on the analysis of the customer order lead-time for a multinational company. In the study, the lead time was divided into three stages respectively: order entry, order fulfillment, and order delivery. A sample of size 2,425 order lines was extracted from the company's records to use for this study. The sample data entails information regarding customer orders from the time of order entry until order delivery. Data regarding the lead time of each stage for different orders were also provided. Summary statistics on lead time data reveals that about 30% of the orders were delivered later than the scheduled due date. The result of the multiple linear regression analysis technique revealed that component type, logistics parameter, order size and the customer type have significant impacts on lead time. Data analysis on the stages of lead time indicates that stage 2 consumed over 50% of the lead time. Pareto analysis was made to study the reasons for the customer order delay in each stage. Recommendation was given to resolve the problem.

Keywords: Lead time reduction, customer satisfaction, service quality, statistical analysis.

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Authors: Said Hadjerrouit

Abstract:

Wikis are considered to be part of Web 2.0 technologies that potentially support collaborative learning and writing. Wikis provide opportunities for multiple users to work on the same document simultaneously. Most wikis have also a page for written group discussion. Nevertheless, wikis may be used in different ways depending on the pedagogy being used, and the constraints imposed by the course design. This work explores students- uses of wiki in teacher education. The analysis is based on a taxonomy for classifying students- activities and actions carried out on the wiki. The article also discusses the implications for using wikis as collaborative writing tools in teacher education.

Keywords: Behaviorism, collaborative writing, socioconstructivism, taxonomy, web 2.0 technology, wiki

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Authors: Xiaoshu Lu, Päivi Leino-Arjas, Kustaa Piha, Akseli Aittomäki, Peppiina Saastamoinen, Ossi Rahkonen, Eero Lahelma

Abstract:

Sickness absence represents a major economic and social issue. Analysis of sick leave data is a recurrent challenge to analysts because of the complexity of the data structure which is often time dependent, highly skewed and clumped at zero. Ignoring these features to make statistical inference is likely to be inefficient and misguided. Traditional approaches do not address these problems. In this study, we discuss model methodologies in terms of statistical techniques for addressing the difficulties with sick leave data. We also introduce and demonstrate a new method by performing a longitudinal assessment of long-term absenteeism using a large registration dataset as a working example available from the Helsinki Health Study for municipal employees from Finland during the period of 1990-1999. We present a comparative study on model selection and a critical analysis of the temporal trends, the occurrence and degree of long-term sickness absences among municipal employees. The strengths of this working example include the large sample size over a long follow-up period providing strong evidence in supporting of the new model. Our main goal is to propose a way to select an appropriate model and to introduce a new methodology for analysing sickness absence data as well as to demonstrate model applicability to complicated longitudinal data.

Keywords: Sickness absence, longitudinal data, methodologies, mix-distribution model.

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Authors: Rusudan Kinkladze, Guguli Kurashvili, Ketevan Chitaladze

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Foreign direct investment is a driving force in the development of the interdependent national economies, and the study and analysis of investments is an urgent problem. It is particularly important for transitional economies, such as Georgia, and the study and analysis of investments is an urgent problem. Consequently, the goal of the research is the study and analysis of direct foreign investments in Georgia, and identification and forecasting of modern trends, and covers the period of 2006-2015. The study uses the methods of statistical observation, grouping and analysis, the methods of analytical indicators of time series, trend identification and the predicted values are calculated, as well as various literary and Internet sources relevant to the research. The findings showed that modern investment policy In Georgia is favorable for domestic as well as foreign investors. Georgia is still a net importer of investments. In 2015, the top 10 investing countries was led by Azerbaijan, United Kingdom and Netherlands, and the largest share of FDIs were allocated in the transport and communication sector; the financial sector was the second, followed by the health and social work sector, and the same trend will continue in the future. 

Keywords: Foreign Direct Investments, methods, statistics, analysis.

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Authors: M. Rahmati, S.M. Mirzababaei

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With the advance of information technology in the new era the applications of Internet to access data resources has steadily increased and huge amount of data have become accessible in various forms. Obviously, the network providers and agencies, look after to prevent electronic attacks that may be harmful or may be related to terrorist applications. Thus, these have facilitated the authorities to under take a variety of methods to protect the special regions from harmful data. One of the most important approaches is to use firewall in the network facilities. The main objectives of firewalls are to stop the transfer of suspicious packets in several ways. However because of its blind packet stopping, high process power requirements and expensive prices some of the providers are reluctant to use the firewall. In this paper we proposed a method to find a discriminate function to distinguish between usual packets and harmful ones by the statistical processing on the network router logs. By discriminating these data, an administrator may take an approach action against the user. This method is very fast and can be used simply in adjacent with the Internet routers.

Keywords: Data Mining, Firewall, Optimization, Packetclassification, Statistical Pattern Recognition.

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Authors: Shuguo Yang, Xiaoyan Yang

Abstract:

It is practically significant to research the traffic flow of intersection because the capacity of intersection affects the efficiency of highway network directly. This paper analyzes the traffic conditions of an intersection in certain urban by the methods of queuing theory and statistical experiment, sets up a corresponding mathematical model and compares it with the actual values. The result shows that queuing theory is applied in the study of intersection traffic flow and it can provide references for the other similar designs.

Keywords: Intersection, Queuing theory, Statistical experiment, System metrics.

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Authors: David Calderón Villegas, Thomas Kalitzky

Abstract:

Run-of-river (RoR) hydropower projects represent a viable, clean, and cost-effective alternative to dam-based plants and provide decentralized power production. However, RoR schemes’ cost-effectiveness depends on the proper selection of site and design flow, which is a challenging task because it requires multivariate analysis. In this respect, this study presents the development of an investment decision support tool for assessing the optimal size of an RoR scheme considering the technical, environmental, and cost constraints. The net present value (NPV) from a project perspective is used as an objective function for supporting the investment decision. The tool has been tested by applying it to an actual RoR project recently proposed in Colombia. The obtained results show that the optimum point in financial terms does not match the flow that maximizes energy generation from exploiting the river's available flow. For the case study, the flow that maximizes energy corresponds to a value of 5.1 m3/s. In comparison, an amount of 2.1 m3/s maximizes the investors NPV. Finally, a sensitivity analysis is performed to determine the NPV as a function of the debt rate changes and the electricity prices and the CapEx. Even for the worst-case scenario, the optimal size represents a positive business case with an NPV of 2.2 USD million and an internal rate of return (IRR) 1.5 times higher than the discount rate. 

Keywords: small hydropower, renewable energy, RoR schemes, optimal sizing, financial analysis

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Authors: Jalal Haghighat Monfared, Zahra Akbari

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Today, business executives need to have useful information to make better decisions. Banks have also been using information tools so that they can direct the decision-making process in order to achieve their desired goals by rapidly extracting information from sources with the help of business intelligence. The research seeks to investigate whether there is a relationship between the quality of decision making and the business intelligence capabilities of Mellat Bank. Each of the factors studied is divided into several components, and these and their relationships are measured by a questionnaire. The statistical population of this study consists of all managers and experts of Mellat Bank's General Departments (including 190 people) who use commercial intelligence reports. The sample size of this study was 123 randomly determined by statistical method. In this research, relevant statistical inference has been used for data analysis and hypothesis testing. In the first stage, using the Kolmogorov-Smirnov test, the normalization of the data was investigated and in the next stage, the construct validity of both variables and their resulting indexes were verified using confirmatory factor analysis. Finally, using the structural equation modeling and Pearson's correlation coefficient, the research hypotheses were tested. The results confirmed the existence of a positive relationship between decision quality and business intelligence capabilities in Mellat Bank. Among the various capabilities, including data quality, correlation with other systems, user access, flexibility and risk management support, the flexibility of the business intelligence system was the most correlated with the dependent variable of the present research. This shows that it is necessary for Mellat Bank to pay more attention to choose the required business intelligence systems with high flexibility in terms of the ability to submit custom formatted reports. Subsequently, the quality of data on business intelligence systems showed the strongest relationship with quality of decision making. Therefore, improving the quality of data, including the source of data internally or externally, the type of data in quantitative or qualitative terms, the credibility of the data and perceptions of who uses the business intelligence system, improves the quality of decision making in Mellat Bank.

Keywords: Business intelligence, business intelligence capability, decision making, decision quality.

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis.

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Authors: F. Lazzali, M. Farsi

Abstract:

Several models of vulnerability assessment have been proposed. The selection of one of these models depends on the objectives of the study. The classical methodologies for seismic vulnerability analysis, as a part of seismic risk analysis, have been formulated with statistical criteria based on a rapid observation. The information relating to the buildings performance is statistically elaborated. In this paper, we use the European Macroseismic Scale EMS-98 to define the relationship between damage and macroseismic intensity to assess the seismic vulnerability. Applying to Algiers area, the first step is to identify building typologies and to assign vulnerability classes. In the second step, damages are investigated according to EMS-98.

Keywords: Damage, EMS-98, inventory building, vulnerability classes

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

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A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian Automotive Sector, Stock Market Decisions, Equity Portfolio Analysis, Decision Tree Classifiers, Statistical Data Analysis.

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Authors: George E. Tsekouras, Evi Sampanikou

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We compare three categorical data clustering algorithms with respect to the problem of classifying cultural data related to the aesthetic judgment of comics artists. Such a classification is very important in Comics Art theory since the determination of any classes of similarities in such kind of data will provide to art-historians very fruitful information of Comics Art-s evolution. To establish this, we use a categorical data set and we study it by employing three categorical data clustering algorithms. The performances of these algorithms are compared each other, while interpretations of the clustering results are also given.

Keywords: Aesthetic judgment, comics artists, cluster analysis, categorical data.

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Authors: Chaiwat Waree

Abstract:

This research aims at development of the Multiple Intelligences Measurement of Elementary Students. The structural accuracy test and normality establishment are based on the Multiple Intelligences Theory of Gardner. This theory consists of eight aspects namely linguistics, logic and mathematics, visual-spatial relations, body and movement, music, human relations, self-realization/selfunderstanding and nature. The sample used in this research consists of elementary school students (aged between 5-11 years). The size of the sample group was determined by Yamane Table. The group has 2,504 students. Multistage Sampling was used. Basic statistical analysis and construct validity testing were done using confirmatory factor analysis. The research can be summarized as follows; 1. Multiple Intelligences Measurement consisting of 120 items is content-accurate. Internal consistent reliability according to the method of Kuder-Richardson of the whole Multiple Intelligences Measurement equals .91. The difficulty of the measurement test is between .39-.83. Discrimination is between .21-.85. 2). The Multiple Intelligences Measurement has construct validity in a good range, that is 8 components and all 120 test items have statistical significance level at .01. Chi-square value equals 4357.7; p=.00 at the degree of freedom of 244 and Goodness of Fit Index equals 1.00. Adjusted Goodness of Fit Index equals .92. Comparative Fit Index (CFI) equals .68. Root Mean Squared Residual (RMR) equals 0.064 and Root Mean Square Error of Approximation equals 0.82. 3). The normality of the Multiple Intelligences Measurement is categorized into 3 levels. Those with high intelligence are those with percentiles of more than 78. Those with moderate/medium intelligence are those with percentiles between 24 and 77.9. Those with low intelligence are those with percentiles from 23.9 downwards.

Keywords: Multiple Intelligences, Measurement, Elementary Students.

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Authors: Z. Bottyán, F. Wantuch, A. Z. Gyöngyösi, Z. Tuba, K. Hadobács, P. Kardos, R. Kurunczi

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The sensitivity of UAVs to the atmospheric effects are apparent. All the same the meteorological support for the UAVs missions is often non-adequate or partly missing. In our paper we show a new complex meteorological support system for different types of UAVs pilots, specialists and decision makers, too. The mentioned system has two important parts with different forecasts approach such as the statistical and dynamical ones. The statistical prediction approach is based on a large climatological data base and the special analog method which is able to select similar weather situations from the mentioned data base to apply them during the forecasting procedure. The applied dynamic approach uses the specific WRF model runs twice a day and produces 96 hours, high resolution weather forecast for the UAV users over the Hungary. An easy to use web-based system can give important weather information over the Carpathian basin in Central-Europe. The mentioned products can be reached via internet connection.

Keywords: Aviation meteorology, statistical weather prediction, unmanned aerial systems, WRF.

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Authors: Jenan Shakoor

Abstract:

Obesity prevalence is increasing worldwide. University life is a challenging period especially for students who have to leave their familiar surroundings and settle in a new environment. The current study aimed to assess the diet and exercise habits and their association with body mass index (BMI) among nursing college students living at Kirkuk University residence. This was a descriptive study. A non-probability (purposive) sample of 101 students living in Kirkuk University residence was recruited during the period from the 15^th November 2015 to the 5^th May 2016. A questionnaire was constructed for the purpose of the study which consisted of four parts: the demographic characteristics of the study sample, eating habits, eating at college and healthy habits. The data were collected by interviewing the study sample and the weight and height were measured by a trained researcher at the college. Descriptive statistical analysis was undertaken. Data were prepared, organized and entered into the computer file; the Statistical Package for Social Science (SPSS 20) was used for data analysis. A p value≤ 0.05 was accepted as statistical significant. A total of 63 (62.4%) of the sample were aged20-21with a mean age of 22.1 (SD±0.653). A third of the sample 38 (37.6%) were from level four at college, 67 (66.3%) were female and 46 45.5% of participants were from a middle socio-economic status. 14 (13.9%) of the study sample were overweight (BMI =25-29.9kg/m²) and 6 (5.9%) were obese (BMI≥30kg/m²) compared to 73 (72.3%) were of normal weight (BMI =18.5-24.9kg/m²). With regard to eating habits and exercise, 42 (41.6%) of the students rarely ate breakfast, 79 (78.2%) eat lunch at university residence, 77 (78.2%) of the students reported rarely doing exercise and 62 (61.4%) of them were sleeping for less than eight hours. No significant association was found between the variables age, sex, level of college and socio-economic status and BMI, while there was a significant association between eating lunch at university and BMI (p =0.03). No significant association was found between eating habits, healthy habits and BMI. The prevalence of overweight and obesity among the study sample was 19.8% with female students being more obese than males. Further studies are needed to identify BMI among residence students in other colleges and increasing the awareness of undergraduate students to healthy food habits.

Keywords: Body mass index, diet, obesity, university residence.

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Authors: Sambit Prasad Kar, P.Palanisamy

Abstract:

In this paper a novel method for multiple one dimensional real valued sinusoidal signal frequency estimation in the presence of additive Gaussian noise is postulated. A computationally simple frequency estimation method with efficient statistical performance is attractive in many array signal processing applications. The prime focus of this paper is to combine the subspace-based technique and a simple peak search approach. This paper presents a variant of the Propagator Method (PM), where a collaborative approach of SUMWE and Propagator method is applied in order to estimate the multiple real valued sine wave frequencies. A new data model is proposed, which gives the dimension of the signal subspace is equal to the number of frequencies present in the observation. But, the signal subspace dimension is twice the number of frequencies in the conventional MUSIC method for estimating frequencies of real-valued sinusoidal signal. The statistical analysis of the proposed method is studied, and the explicit expression of asymptotic (large-sample) mean-squared-error (MSE) or variance of the estimation error is derived. The performance of the method is demonstrated, and the theoretical analysis is substantiated through numerical examples. The proposed method can achieve sustainable high estimation accuracy and frequency resolution at a lower SNR, which is verified by simulation by comparing with conventional MUSIC, ESPRIT and Propagator Method.

Keywords: Frequency estimation, peak search, subspace-based method without eigen decomposition, quadratic convex function.

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Authors: Abdel-Rahman M. Jaradat, , Mansour I. Irshid, Talha T. Nassar

Abstract:

A novel file splitting technique for the reduction of the nth-order entropy of text files is proposed. The technique is based on mapping the original text file into a non-ASCII binary file using a new codeword assignment method and then the resulting binary file is split into several subfiles each contains one or more bits from each codeword of the mapped binary file. The statistical properties of the subfiles are studied and it is found that they reflect the statistical properties of the original text file which is not the case when the ASCII code is used as a mapper. The nth-order entropy of these subfiles are determined and it is found that the sum of their entropies is less than that of the original text file for the same values of extensions. These interesting statistical properties of the resulting subfiles can be used to achieve better compression ratios when conventional compression techniques are applied to these subfiles individually and on a bit-wise basis rather than on character-wise basis.

Keywords: Bit-wise compression, entropy, file splitting, source mapping.

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