Search results for: data comparison

Authors: H. Hruschka

Abstract:

This paper presents the first comparison of the performance of the restricted Boltzmann machine and the deep belief net on binary market basket data relative to binary factor analysis and the two best-known topic models, namely Dirichlet allocation and the correlated topic model. This comparison shows that the restricted Boltzmann machine and the deep belief net are superior to both binary factor analysis and topic models. Managerial implications that differ between the investigated models are treated as well. The restricted Boltzmann machine is defined as joint Boltzmann distribution of hidden variables and observed variables (purchases). It comprises one layer of observed variables and one layer of hidden variables. Note that variables of the same layer are not connected. The comparison also includes deep belief nets with three layers. The first layer is a restricted Boltzmann machine based on category purchases. Hidden variables of the first layer are used as input variables by the second-layer restricted Boltzmann machine which then generates second-layer hidden variables. Finally, in the third layer hidden variables are related to purchases. A public data set is analyzed which contains one month of real-world point-of-sale transactions in a typical local grocery outlet. It consists of 9,835 market baskets referring to 169 product categories. This data set is randomly split into two halves. One half is used for estimation, the other serves as holdout data. Each model is evaluated by the log likelihood for the holdout data. Performance of the topic models is disappointing as the holdout log likelihood of the correlated topic model – which is better than Dirichlet allocation - is lower by more than 25,000 compared to the best binary factor analysis model. On the other hand, binary factor analysis on its own is clearly surpassed by both the restricted Boltzmann machine and the deep belief net whose holdout log likelihoods are higher by more than 23,000. Overall, the deep belief net performs best. We also interpret hidden variables discovered by binary factor analysis, the restricted Boltzmann machine and the deep belief net. Hidden variables characterized by the product categories to which they are related differ strongly between these three models. To derive managerial implications we assess the effect of promoting each category on total basket size, i.e., the number of purchased product categories, due to each category's interdependence with all the other categories. The investigated models lead to very different implications as they disagree about which categories are associated with higher basket size increases due to a promotion. Of course, recommendations based on better performing models should be preferred. The impressive performance advantages of the restricted Boltzmann machine and the deep belief net suggest continuing research by appropriate extensions. To include predictors, especially marketing variables such as price, seems to be an obvious next step. It might also be feasible to take a more detailed perspective by considering purchases of brands instead of purchases of product categories.

Keywords: binary factor analysis, deep belief net, market basket analysis, restricted Boltzmann machine, topic models

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Authors: Olga Blagova, Yuliya Osipova, Evgeniya Kogan, Alexander Nedostup

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Purpose: to quantify the value of various clinical, laboratory and instrumental signs in the diagnosis of myocarditis in comparison with morphological studies of the myocardium. Methods: in 100 patients (65 men, 44.7±12.5 years) with «idiopathic» arrhythmias (n = 20) and dilated cardiomyopathy (DCM, n = 80) were performed 71 endomyocardial biopsy (EMB), 13 intraoperative biopsy, 5 study of explanted hearts, 11 autopsy with virus investigation (real-time PCR) of the blood and myocardium. Anti-heart antibodies (AHA) were also measured as well as cardiac CT (n = 45), MRI (n = 25), coronary angiography (n = 47). The comparison group included of 50 patients (25 men, 53.7±11.7 years) with non-inflammatory heart diseases who underwent open heart surgery. Results. Active/borderline myocarditis was diagnosed in 76.0% of the study group and in 21.6% of patients of the comparison group (p < 0.001). The myocardial viral genome was observed more frequently in patients of comparison group than in study group (group (65.0% and 40.2%; p < 0.01. Evaluated the diagnostic value of noninvasive markers of myocarditis. The panel of anti-heart antibodies had the greatest importance to identify myocarditis: sensitivity was 81.5%, positive and negative predictive value was 75.0 and 60.5%. It is defined diagnostic value of non-invasive markers of myocarditis and diagnostic algorithm providing an individual assessment of the likelihood of myocarditis is developed. Conclusion. The greatest significance in the diagnosis of latent myocarditis in patients with 'idiopathic' arrhythmias and DCM have AHA. The use of complex of noninvasive criteria allows estimate the probability of myocarditis and determine the indications for EMB.

Keywords: myocarditis, "idiopathic" arrhythmias, dilated cardiomyopathy, endomyocardial biopsy, viral genome, anti-heart antibodies

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Authors: Sungroul Kim, Yeonjin Kim

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This study was conducted by comparing the concentrations of heavy metals analyzed from the same samples with three X-Ray fluorescence (XRF) spectrometer in three different global research institutions, including PAN (A Branch of Malvern Panalytical, Seoul, South Korea), RTI (Research Triangle Institute, NC, U.S.A), and aerosol laboratory in Harvard University, Boston, U.S.A. To achieve our research objectives, the indoor air filter samples were collected at homes (n=24) of adults or child asthmatics then analyzed in PAN followed by Harvard University and RTI consecutively. Descriptive statistics were conducted for data comparison as well as correlation and simple regression analysis using R version 4.0.3. As a result, detection rates of most heavy metals analyzed in three institutions were about 90%. Of the 25 elements commonly analyzed among those institutions, 16 elements showed an R² (coefficient of determination) of 0.7 or higher (10 components were 0.9 or higher). The findings of this study demonstrated that XRF was a useful device ensuring reproducibility and compatibility for measuring heavy metals in PM2.5 collected from indoor air of asthmatics’ home.

Keywords: heavy metals, indoor air quality, PM2.5, X-ray fluorescence

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Authors: Yu-Sheng Lin, Sheng-Teng Huang

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In recent years, the issue of corporate social responsibility has become an enthusiastic discussion and hottest issue around the world. To make the enterprises be sustainable management and sustainable development, more and more enterprises realize that fulfill its corporate social responsibility is the good choice. It is an essential, important issue that the leader needs know how to lead the staff in balance benefit, also emphasize on economic, social and environmental aspects to impact the company, then enhance the consensus. The leader needs to improve cohesion of personnel, and implement the corporate social responsibility in staff behavior, in order to show a performance in the effort of corporate social responsibility of enterprises. The previous literature mostly is committed to comparison of corporate social responsibility in the industry and service industry, regarding to literature of shipping companies were relatively rare. This paper aims to take the domestic and foreign shipping companies of corporate social responsibility reports as the data analysis, and refer to the international convention (GRI) such as association and organization of CSR standard values. Overall comparison with shipping companies of CSR reports, annual reports and other public information, and taking Taiwan shipping companies as the target, respectively, with the international conventions and the world's top ten leading shipping companies to do the comparison and analysis. Shipping companies in Taiwan are bound to the standard that set by the international convention for the first goal diligently and following step is contend with the world's top ten leading shipping companies. There are 3 ~ 5 experts to be involved in interview after the result is completed. They will indicate the superiority and inferiority then provide the opinion, recommendation in the needed action. Through this study, we can explore the importance of corporate social responsibility report for shipping companies, and also provide the clear orientation to external providers to improve corporate social responsibility. In addition, it can provide the academic research and business experts as a reference; finally, serving shipping companies to complete another contribution.

Keywords: Corporate social responsibility (CSR), CSR reports, statistical methods, expert interview method

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Authors: Nikat Parveen, M. Ananthi

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Big data is one of the emerging technology, which collects the data from various sensors and those data will be used in many fields. Data retrieval is one of the major issue where there is a need to extract the exact data as per the need. In this paper, large amount of data set is processed by using the feature selection. Feature selection helps to choose the data which are actually needed to process and execute the task. The key value is the one which helps to point out exact data available in the storage space. Here the available data is streamed and R-Center is proposed to achieve this task.

Keywords: big data, key value, feature selection, retrieval, performance

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Authors: Mark Schudeleit, Christian Sieg, Ferit Küçükay

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This publication focuses on the limits and potentials of 48V hybrid systems, which are especially due to the cost advantages an attractive alternative, compared to established high volt-age HEVs and thus will gain relevant market shares in the future. Firstly, at market overview is given which shows the current known 48V hybrid concepts and demonstrators. These topologies will be analyzed and evaluated regarding the system power and the battery capacity as well as their implemented hybrid functions. The potential in fuel savings and CO2 reduction is calculated followed by the customer-relevant dimensioning of the electric motor and the battery. For both measured data of the real customer operation is used. Subsequently, the CO2 saving potentials of the customer-oriented dimensioned powertrain will be presented for the NEDC and the customer operation. With a comparison of the newly defined drivetrain with existing 48V systems the question can be answered whether current systems are dimensioned optimally for the customer operation or just for legislated driving cycles.

Keywords: 48V hybrid systems, market comparison, requirements and potentials in customer operation, customer-oriented dimensioning, CO2 savings

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Authors: Marco Sewald

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WWSE School Development (Wahrnehmungs- und wertorientierte Schulentwicklung) contains surveys on pupils, teachers and parents and enables schools to align the development to the requirements mentioned by these three stakeholders. WWSE includes a derivative set of questions for Christian schools, meeting their specific needs. The conducted research on WWSE is reflecting contemporary questions on school development, questioning the quality of the implementation of the results of past surveys, delivered by WWSE School Development in Christian schools in Germany. The research focused on questions connected to organizational development, including leadership and change management. This is done contoured to the two other areas of WWSE: human resources development and development of school teaching methods. The chosen research methods are: (1) A quantitative triangulation on three sets of data. Data from a past evaluation taken in 2011, data from a second evaluation covering the same school conducted in 2014 and a structured survey among the teachers, headmasters and members of the school board taken within the research. (2) Interviews with teachers and headmasters have been conducted during the research as a second stage to fortify the result of the quantitative first stage. Results: WWSE is supporting modern school development. While organizational development, leadership, and change management are proofed to be important for modern school development, these areas are widespread underestimated by teachers and headmasters. Especially in comparison to the field of human resource development and to an even bigger extent in comparison to the area of development of school teaching methods. The research concluded, that additional efforts in the area of organizational development are necessary to meet modern demands and the research also shows which areas are the most important ones.

Keywords: school as a social organization, school development, school leadership, WWSE, Wahrnehmungs- und wertorientierte Schulentwicklung

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Authors: Rana I. Abdulghani, Amera I. Melhum

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With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: Sabrina Ladjama, Aicha Rizi, Azzedine Abbaci

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In this work, we use the crossover model to formulate a comprehensive fundamental equation of state for the thermodynamic properties for several n-alkanes in the critical region that extends to the classical region. This equation of state is constructed on the basis of comparison of selected measurements of pressure-density-temperature data, isochoric and isobaric heat capacity. The model can be applied in a wide range of temperatures and densities around the critical point for n-heptane. It is found that the developed model represents most of the reliable experimental data accurately.

Keywords: crossover model, critical region, fundamental equation, n-heptane

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Authors: Matěj Kuc

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This paper compares different types of profitability measures of cooperative banks from two developed regions: the European Union and the United States of America together with Canada. We created balanced dataset of more than 200 cooperative banks covering 2011-2016 period. We made series of tests and run Random Effects estimation on panel data. We found that American and Canadian cooperatives are more profitable in terms of return on assets (ROA) and return on equity (ROE). There is no significant difference in net interest margin (NIM). Our results show that the North American cooperative banks accommodated better to the current market environment.

Keywords: cooperative banking, panel data, profitability measures, random effects

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Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

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Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

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Authors: R. Pääkkönen, L. Korpinen

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We tried statistically to determine the biggest risks for accidents and injuries in Finland compared to other countries. We have a very high incidence of domestic falls and accidental poisoning compared to other European countries. On the other side, we have a relatively low number of accidents in traffic or at work globally, and in European scale, because we have worked hard to diminish these forms of accidents. In Finland, there is work to be done to improve attitudes and actions against domestic accidents.

Keywords: injuries, accident, comparison, Finland

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Authors: Thanh Nguyen, Andrei Doncescu, Pierre Siegel

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Classification is an important data mining technique and could be used as data filtering in artificial intelligence. The broad application of classification for all kind of data leads to be used in nearly every field of our modern life. Classification helps us to put together different items according to the feature items decided as interesting and useful. In this paper, we compare two classification methods Naïve Bayes and ADTree use to detect spam e-mail. This choice is motivated by the fact that Naive Bayes algorithm is based on probability calculus while ADTree algorithm is based on decision tree. The parameter settings of the above classifiers use the maximization of true positive rate and minimization of false positive rate. The experiment results present classification accuracy and cost analysis in view of optimal classifier choice for Spam Detection. It is point out the number of attributes to obtain a tradeoff between number of them and the classification accuracy.

Keywords: classification, data mining, spam filtering, naive bayes, decision tree

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Authors: K. Bensafia, A. Mansour, G. Le Maillot, B. Clement, O. Reynet, P. Ariès, S. Haddab

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This paper presents a system designed for wireless acquisition, the recording of electrocardiogram (ECG) signals and the monitoring of the heart’s health during surgery. This wireless recording system allows us to visualize and monitor the state of the heart’s health during a surgery, even if the patient is moved from the operating theater to post anesthesia care unit. The acquired signal is transmitted via a Bluetooth unit to a PC where the data are displayed, stored and processed. To test the reliability of our system, a comparison between ECG signals processed by a conventional ECG monitoring system (Datex-Ohmeda) and by our wireless system is made. The comparison is based on the shape of the ECG signal, the duration of the QRS complex, the P and T waves, as well as the position of the ST segments with respect to the isoelectric line. The proposed system is presented and discussed. The results have confirmed that the use of Bluetooth during surgery does not affect the devices used and vice versa. Pre- and post-processing steps are briefly discussed. Experimental results are also provided.

Keywords: electrocardiography, monitoring, surgery, wireless system

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Authors: Nebila Lichiheb, LaToya Myles, William Pendergrass, Bruce Hicks, Dawson Cagle

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Atmospheric transport of hazardous materials in urban areas is increasingly under investigation due to the potential impact on human health and the environment. In response to health and safety concerns, several dispersion models have been developed to analyze and predict the dispersion of hazardous contaminants. The models of interest usually rely on meteorological information obtained from the meteorological models of NOAA’s National Weather Service (NWS). However, due to the complexity of the urban environment, NWS forecasts provide an inadequate basis for dispersion computation in urban areas. A dense meteorological network in Washington, DC, called DCNet, has been operated by NOAA since 2003 to support the development of urban monitoring methodologies and provide the driving meteorological observations for atmospheric transport and dispersion models. This study focuses on the comparison of wind observations from the DCNet station on the U.S. Department of Commerce Herbert C. Hoover Building against the North American Mesoscale (NAM) model outputs for the period 2017-2019. The goal is to develop a simple methodology for modifying NAM outputs so that the dispersion requirements of the city and its urban area can be satisfied. This methodology will allow us to quantify the prediction errors of the NAM model and propose adjustments of key variables controlling dispersion model calculation.

Keywords: meteorological data, Washington D.C., DCNet data, NAM model

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Authors: Abera Kotu, Girma Tesema, Mitiku Tadesse

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This study investigated ACE supported instruction and pre-service mathematics teachers’ mental construction in solving equations and inequalities. A mixed approach with concurrent parallel design was employed. It was conducted on two intact groups of regular first-year pre-service mathematics teachers at Fiche College of Teachers’ Education in which one group was assigned as an intervention group and the other group as a comparison group using the lottery method. There were 33 participants in the intervention and 32 participants in the comparison. Six pre-service mathematics teachers were selected for interview using purposive sampling based on pre-test results. An instruction supported with ACE cycle was given to the intervention group for two weeks duration of time. Written tasks, interviews, and observations were used to collect data. Data collected from written tasks were analyzed quantitatively using independent samples t-test and effect size. Data collected from interviews and observations were analyzed narratively. The findings of the study uncovered that ACE-supported instruction has a moderate effect on Pre-service Mathematics Teachers’ levels of conceptualizations of action, process, object, ad schema. Moreover, the ACE supported group out scored and performed better than the usual traditional method supported groups across the levels of conceptualization. The majority of pre-service mathematics teachers’ levels of conceptualizations were at action and process levels and their levels of conceptualization were linked with genetic decomposition more at action and object levels than object and schema. The use of ACE supported instruction is recommended to improve pre-service mathematics teachers’ mental construction.

Keywords: ACE teaching cycle, APOS theory, mental construction, genetic composition

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Authors: A. Al-Rashidi, A. El-Hamalawi

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In this paper, a performance study was conducted to investigate single and dual-axis PV systems to generate electricity in five different sites in Kuwait. Relevant data were obtained by using two sources for validation purposes. A commercial software, PVsyst, was used to analyse the data, such as metrological data and other input parameters, and compute the performance parameters such as capacity factor (CF) and final yield (YF). The results indicated that single and dual-axis PV systems would be very beneficial to electricity generation in Kuwait as an alternative source to conventional power plants, especially with the increased demand over time. The ranges were also found to be competitive in comparison to leading countries using similar systems. A significant increase in CF and YF values around 24% and 28.8% was achieved related to the use of single and dual systems, respectively.

Keywords: single-axis and dual-axis photovoltaic systems, capacity factor, final yield, Kuwait

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Authors: Kala Bharathan, R. Seshasayanan

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In this paper, a 1-bit Embedded Logic Full Adder (EFA) circuit in transistor level is proposed, which reduces logic complexity, gives low power and high speed. The design is further extended till 64 bits. To evaluate the performance of EFA, a 16, 32, 64-bit both Linear and Square root Carry Select Adder/Subtractor (CSLAS) Structure is also proposed. Realistic testing of proposed circuits is done on 8 X 8 Modified Booth multiplier and comparison in terms of power and delay is done. The EFA is implemented for different multiplier architectures for performance parameter comparison. Overall delay for CSLAS is reduced to 78% when compared to conventional one. The circuit implementations are done on TSMC 28nm CMOS technology using Cadence Virtuoso tool. The EFA has power savings of up to 14% when compared to the conventional adder. The present implementation was found to offer significant improvement in terms of power and speed in comparison to other full adder circuits.

Keywords: embedded logic, full adder, pdp, xor gate

Procedia PDF Downloads 448

Authors: Aabiah Nayeem, Fariha Shafiq, Mustabshra Aftab, Rabia Saman Pirzada, Samia Ghazala

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In this modern era of technology, the concept of Internet of Things is very popular in every domain. It is a widely distributed system of things in which the data collected from sensory devices is transmitted, analyzed locally/collectively then broadcasted to network where action can be taken remotely via mobile/web apps. Today’s mobile computing is also gaining importance as the services are provided during mobility. Through mobile computing, data are transmitted via computer without physically connected to a fixed point. The challenge is to provide services with high speed and security. Also, the data gathered from the mobiles must be processed in a secured way. Mobile computing is strongly influenced by internet of things. In this paper, we have discussed security issues and challenges of internet of things and mobile computing and we have compared both of them on the basis of similarities and dissimilarities.

Keywords: embedded computing, internet of things, mobile computing, wireless technologies

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Authors: Alexiou Dimitra, Fragkaki Maria

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The objective of the paper is the study of geographic, economic and educational variables and their contribution to determine the position of each member-state among the EU-28 countries based on the values of seven variables as given by Eurostat. The Data Analysis methods of Multiple Factorial Correspondence Analysis (MFCA) Principal Component Analysis and Factor Analysis have been used. The cross tabulation tables of data consist of the values of seven variables for the 28 countries for 2014. The data are manipulated using the CHIC Analysis V 1.1 software package. The results of this program using MFCA and Ascending Hierarchical Classification are given in arithmetic and graphical form. For comparison reasons with the same data the Factor procedure of Statistical package IBM SPSS 20 has been used. The numerical and graphical results presented with tables and graphs, demonstrate the agreement between the two methods. The most important result is the study of the relation between the 28 countries and the position of each country in groups or clouds, which are formed according to the values of the corresponding variables.

Keywords: Multiple Factorial Correspondence Analysis, Principal Component Analysis, Factor Analysis, E.U.-28 countries, Statistical package IBM SPSS 20, CHIC Analysis V 1.1 Software, Eurostat.eu Statistics

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Authors: Mesbaholdin Salami, Farzad Movahedi Sobhani, Mohammad Sadegh Ghazizadeh

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Evaluation of organizational performance is among the most important measures that help organizations and entities continuously improve their efficiency. Organizations can use the existing data and results from the comparison of units under investigation to obtain an estimation of their performance. The Malmquist Productivity Index (MPI) is an important index in the evaluation of overall productivity, which considers technological developments and technical efficiency at the same time. This article proposed a model based on the multistage MPI, considering limited data (Grey’s theory). This model can evaluate the performance of units using limited and uncertain data in a multistage process. It was applied by the electricity market manager to Iran’s electric power supply chain (EPSC), which contains uncertain data, to evaluate the performance of its actors. Results from solving the model showed an improvement in the accuracy of future performance of the units under investigation, using the Grey’s system theory. This model can be used in all case studies, in which MPI is used and there are limited or uncertain data.

Keywords: Malmquist Index, Grey's Theory, CCR Model, network data envelopment analysis, Iran electricity power chain

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Authors: S. H. Borghei, E. Teymourian, M. Mobin, G. M. Komaki, S. Sheikh

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Imperialist competitive algorithm (ICA) is a recent meta-heuristic method that is inspired by the social evolutions for solving NP-Hard problems. The ICA is a population based algorithm which has achieved a great performance in comparison to other meta-heuristics. This study is about developing enhanced ICA approach to solve the cell formation problem (CFP) using sequence data. In addition to the conventional ICA, an enhanced version of ICA, namely EICA, applies local search techniques to add more intensification aptitude and embed the features of exploration and intensification more successfully. Suitable performance measures are used to compare the proposed algorithms with some other powerful solution approaches in the literature. In the same way, for checking the proficiency of algorithms, forty test problems are presented. Five benchmark problems have sequence data, and other ones are based on 0-1 matrices modified to sequence based problems. Computational results elucidate the efficiency of the EICA in solving CFP problems.

Keywords: cell formation problem, group technology, imperialist competitive algorithm, sequence data

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Authors: Han-Taw Chen, Chung-Hou Lai, Tzu-Hsiang Lin, Ge-Jang He

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This study applies the inverse method and three-dimensional CFD commercial software in conjunction with the experimental temperature data to investigate the heat transfer and fluid flow characteristics of the plate-fin heat sink in a closed rectangular enclosure for various values of fin height. The inverse method with the finite difference method and the experimental temperature data is applied to determine the heat transfer coefficient. The k-ε turbulence model is used to obtain the heat transfer and fluid flow characteristics within the fins. To validate the accuracy of the results obtained, the comparison of the average heat transfer coefficient is made. The calculated temperature at selected measurement locations on the plate-fin is also compared with experimental data.

Keywords: inverse method, FLUENT, k-ε model, heat transfer characteristics, plate-fin heat sink

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Authors: Tisciane Perpetuo e Oliveira, Dante Inga Narvaez, Marcelo Gradella Villalva

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Installations of solar photovoltaic systems have increased considerably in the last decade. Therefore, it has been noticed that monitoring of meteorological data (solar irradiance, air temperature, wind velocity, etc.) is important to predict the potential of a given geographical area in solar energy production. In this sense, the present work compares two computational tools that are capable of estimating the energy generation of a photovoltaic system through correlation analyzes of solar radiation data: PVsyst software and an algorithm based on the PVlib package implemented in MATLAB. In order to achieve the objective, it was necessary to obtain solar radiation data (measured and from a solarimetric database), analyze the decomposition of global solar irradiance in direct normal and horizontal diffuse components, as well as analyze the modeling of the devices of a photovoltaic system (solar modules and inverters) for energy production calculations. Simulated results were compared with experimental data in order to evaluate the performance of the studied methods. Errors in estimation of energy production were less than 30% for the MATLAB algorithm and less than 20% for the PVsyst software.

Keywords: energy production, meteorological data, irradiance decomposition, solar photovoltaic system

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Authors: Yasemin Kaya, Ahmet N. Eraslan

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In this study, the thermoelastic behavior of a long tube is studied by taking into account the temperature dependency of all mechanical and thermal properties. As the tube is heated slowly, an uncoupled solution procedure is adopted under free and radially constrained boundary conditions. The nonlinear heat conduction equation is solved by a finite element collocation procedure and the corresponding distributions of stress and strain are computed by shooting iterations. The computational model is verified in comparison to the analytical solution by shutting down the temperature dependency of physical properties. In the analysis, experimental data available in the literature is used to describe the coefficient of thermal expansion $\alpha$, the thermal conductivity $k$, the modulus of rigidity $G$, the yield strength $\sigma_{0}$, and the Poisson's ratio $\nu$ of Nickel. Results of the analysis are presented in comparison to those having constant physical properties. As a result of the calculations, the temperature dependency of the material properties should be taken into account at higher temperature ranges.

Keywords: thermoelasticity, long tube, temperature-dependent properties, internal heating

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Authors: Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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A database that records average traffic speeds measured at five-minute intervals for all the links in the traffic network of a metropolitan city. While learning from this data the models that can predict future traffic speed would be beneficial for the applications such as the car navigation system, building predictive models for every link becomes a nontrivial job if the number of links in a given network is huge. An advantage of adopting k-nearest neighbor (k-NN) as predictive models is that it does not require any explicit model building. Instead, k-NN takes a long time to make a prediction because it needs to search for the k-nearest neighbors in the database at prediction time. In this paper, we investigate how much we can speed up k-NN in making traffic speed predictions by reducing the amount of data to be searched for without a significant sacrifice of prediction accuracy. The rationale behind this is that we had a better look at only the recent data because the traffic patterns not only repeat daily or weekly but also change over time. In our experiments, we build several different k-NN models employing different sets of features which are the current and past traffic speeds of the target link and the neighbor links in its up/down-stream. The performances of these models are compared by measuring the average prediction accuracy and the average time taken to make a prediction using various amounts of data.

Keywords: big data, k-NN, machine learning, traffic speed prediction

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Authors: Ahmed R. Abdelaziz, Zaki M. I. Osha

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Solar radiation data are of great significance for solar energy system design. This study aims at developing and calibrating new empirical models for estimating monthly mean daily global solar radiation on a horizontal surface in Alexandria, Egypt. Day length hours, sun height, day number, and declination angle calculated data are used for this purpose. A comparison between measured and calculated values of solar radiation is carried out. It is shown that all the proposed correlations are able to predict the global solar radiation with excellent accuracy in Alexandria.

Keywords: solar energy, global solar radiation, model, regression coefficient

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Authors: Gabrielle Holt

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This paper is looking to prove the efficacy of the Schulte Holt IOL Toric Calculator Processor (Schulte Holt ITCP). It has been completed using manually inputted data into the Barrett Toric Calculator and comparing the number of minutes taken to complete the Toric calculations, the number of errors identified during completion, and distractions during completion. It will then compare that data to the number of minutes taken for the Schulte Holt ITCP to complete also, using the Barrett method, as well as the number of errors identified in the Schulte Holt ITCP. The data clearly demonstrate a momentous advantage to the Schulte Holt ITCP and notably reduces time spent doing Toric Calculations, as well as reducing the number of errors. With the ever-growing number of cataract surgeries taking place around the world and the waitlists increasing -the Schulte Holt IOL Toric Calculator Processor may well demonstrate a way forward to increase the availability of ophthalmologists and ophthalmic staff while maintaining patient safety.

Keywords: Toric, toric lenses, ophthalmology, cataract surgery, toric calculations, Barrett

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Authors: Faisal Kalota

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Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: big data, learning analytics, analytics, big data in education, Hadoop

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Authors: Tetsuro Saeki, Yuichi Kato, Shoutarou Mizuno

Abstract:

STRIM (Statistical Test Rule Induction Method) has been proposed as a method to effectively induct if-then rules from the decision table which is considered as a sample set obtained from the population of interest. Its usefulness has been confirmed by simulation experiments specifying rules in advance, and by comparison with conventional methods. However, scope for future development remains before STRIM can be applied to the analysis of real-world data sets. The first requirement is to determine the size of the dataset needed for inducting true rules, since finding statistically significant rules is the core of the method. The second is to examine the capacity of rule induction from datasets with contaminated attribute values created by missing data and noise, since real-world datasets usually contain such contaminated data. This paper examines the first problem theoretically, in connection with the rule length. The second problem is then examined in a simulation experiment, utilizing the critical size of dataset derived from the first step. The experimental results show that STRIM is highly robust in the analysis of datasets with contaminated attribute values, and hence is applicable to realworld data.

Keywords: rule induction, decision table, missing data, noise

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