Search results for: measured data.

Authors: Khaja Kamaluddin, Muhammed Yousoof

Abstract:

Available Bit Rate Service (ABR) is the lower priority service and the better service for the transmission of data. On wireline ATM networks ABR source is always getting the feedback from switches about increase or decrease of bandwidth according to the changing network conditions and minimum bandwidth is guaranteed. In wireless networks guaranteeing the minimum bandwidth is really a challenging task as the source is always in mobile and traveling from one cell to another cell. Re establishment of virtual circuits from start to end every time causes the delay in transmission. In our proposed solution we proposed the mechanism to provide more available bandwidth to the ABR source by re-usage of part of old Virtual Channels and establishing the new ones. We want the ABR source to transmit the data continuously (non-stop) inorderto avoid the delay. In worst case scenario at least minimum bandwidth is to be allocated. In order to keep the data flow continuously, priority is given to the handoff ABR call against new ABR call.

Keywords: Bandwidth allocation, Virtual Channel (VC), CBR, ABR, MCR and QOS.

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Authors: Herminio Martínez-García, Juan Gámiz, Yolanda Bolea, Antoni Grau

Abstract:

This article presents an interactive system for data acquisition in vehicle testing according to the test process defined in automotive directive 2004/104/EC. The project has been designed and developed by authors for the Spanish company Applus-LGAI. The developed project will result in a new process, which will involve the creation of braking cycle test defined in the aforementioned automotive directive. It will also allow the analysis of new vehicle features that was not feasible, allowing an increasing interaction with the vehicle. Potential users of this system in the short term will be vehicle manufacturers and in a medium term the system can be extended to testing other automotive components and EMC tests.

Keywords: Automotive process, data acquisition system, electromagnetic compatibility (EMC) testing, European Directive 2004/104/EC.

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Authors: Jo-Chi Jao, Yen-Ku Chen, Twei-Shiun Jaw, Po-Chou Chen

Abstract:

This study aimed to investigate the magnetic resonance (MR) signal enhancement ratio (ER) of contrast-enhanced MR angiography (CE-MRA) in normal rats with gadobenate dimeglumine (Gd-BOPTA) using a clinical 3T scanner and an extremity coil. The relaxivities of Gd-BOPTA with saline only and with 4.5% human serum albumin (HSA) were also measured. Compared with Gadolinium diethylenetriaminepentaacetic acid (Gd-DTPA), Gd-BOPTA had higher relaxivities. The maximum ER of aorta (ERa), kidney, liver and muscle with Gd-BOPTA were higher than those with Gd-DTPA. The maximum ERa appeared at 1.2 min and decayed to half at 10 min after Gd-BOPTA injection. This information is helpful for the design of CE-MRA study of rats.

Keywords: Contrast-Enhanced Magnetic Resonance Angiography, Gd-BOPTA, Gd-DTPA, Rat.

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Authors: Amogh Katti, J. Sai Asritha, D. Nivedh, M. Kalyan Srinivas, B. Somnath Chakravarthi

Abstract:

Today's modern education system uses Learning Management System (LMS) portals for the scoring and grading of student performances, to maintain student records, and teachers are instructed to accept assignments through online submissions of .pdf, .doc, .ppt, etc. There is a risk of data tampering in the traditional portals; we will apply the Blockchain model instead of this traditional model to avoid data tampering and also provide a decentralized mechanism for overall fairness. Blockchain technology is a better and also recommended model because of the following features: consensus mechanism, decentralized system, cryptographic encryption, smart contracts, Ethereum blockchain. The proposed system ensures data integrity and tamper-proof assignment submission and grading, which will be helpful for both students and also educators.

Keywords: Education technology, learning management system, decentralized applications, blockchain.

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Authors: Malinee Pongsavee

Abstract:

Formaldehyde is the illegal chemical substance used for food preservation in fish and vegetable. It can promote carcinogenesis. Superoxide dismutases are the important antioxidative enzymes that catalyze the dismutation of superoxide anion into oxygen and hydrogen peroxide. The resultant level of oxidative stress in formaldehyde-treated lymphocytes was investigated. The formaldehyde concentrations of 0, 20, 40, 60, 80 and 120μmol/L were treated in human lymphocytes for 12 hours. After 12 treated hours, the superoxide dismutase activity change was measured in formaldehyde-treated lymphocytes. The results showed that the formaldehyde concentrations of 60, 80 and 120μmol/L significantly decreased superoxide dismutase activities in lymphocytes (P < 0.05). The change of superoxide dismutase activity in formaldehyde-treated lymphocytes may be the biomarker for detect cellular injury, such as damage to DNA, due to formaldehyde exposure.

Keywords: Formaldehyde, lymphocytes, superoxide dismutase activity.

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Authors: S. Wongkoon, M. Pollar, M. Jaroensutasinee, K. Jaroensutasinee

Abstract:

This study aimed at developing a forecasting model on the number of Dengue Haemorrhagic Fever (DHF) incidence in Northern Thailand using time series analysis. We developed Seasonal Autoregressive Integrated Moving Average (SARIMA) models on the data collected between 2003-2006 and then validated the models using the data collected between January-September 2007. The results showed that the regressive forecast curves were consistent with the pattern of actual values. The most suitable model was the SARIMA(2,0,1)(0,2,0)12 model with a Akaike Information Criterion (AIC) of 12.2931 and a Mean Absolute Percent Error (MAPE) of 8.91713. The SARIMA(2,0,1)(0,2,0)12 model fitting was adequate for the data with the Portmanteau statistic Q20 = 8.98644 ( x20,95= 27.5871, P>0.05). This indicated that there was no significant autocorrelation between residuals at different lag times in the SARIMA(2,0,1)(0,2,0)12 model.

Keywords: Dengue, SARIMA, Time Series Analysis, Northern Thailand.

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: Artificial neural networks, fuel consumption, machine learning, regression, statistical tests.

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Authors: Djalal Eddine Khodja, Boukhemis Chetate

Abstract:

In this paper, an automatic system of diagnosis was developed to detect and locate in real time the defects of the wound rotor asynchronous machine associated to electronic converter. For this purpose, we have treated the signals of the measured parameters (current and speed) to use them firstly, as indicating variables of the machine defects under study and, secondly, as inputs to the Artificial Neuron Network (ANN) for their classification in order to detect the defect type in progress. Once a defect is detected, the interpretation system of information will give the type of the defect and its place of appearance.

Keywords: Artificial Neuron Networks (ANN), Effective Value (RMS), Experimental results, Failure detection Indicating values, Motor-converter unit.

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Authors: Alae El Alami, Mohamed Bahaj

Abstract:

This paper proposes an approach for translating an existing relational database (RDB) schema into ORDB. The transition is done with methods that can extract various functions from a RDB which is based on aggregations, associations between the various tables, and the reflexive relationships. These methods can extract even the inheritance knowing that no process of reverse engineering can know that it is an Inheritance; therefore, our approach exceeded all of the previous studies made for ​​the transition from RDB to ORDB. In summation, the creation of the New Data Model (NDM) that stocks the RDB in a form of a structured table, and from the NDM we create our navigational model in order to simplify the implementation object from which we develop our different types. Through these types we precede to the last step, the creation of tables.

The step mentioned above does not require any human interference. All this is done automatically, and a prototype has already been created which proves the effectiveness of this approach.

Keywords: Relational databases, Object-relational databases, Semantic enrichment.

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Authors: K. Thangavel, R. Rathipriya

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For the past one decade, biclustering has become popular data mining technique not only in the field of biological data analysis but also in other applications like text mining, market data analysis with high-dimensional two-way datasets. Biclustering clusters both rows and columns of a dataset simultaneously, as opposed to traditional clustering which clusters either rows or columns of a dataset. It retrieves subgroups of objects that are similar in one subgroup of variables and different in the remaining variables. Firefly Algorithm (FA) is a recently-proposed metaheuristic inspired by the collective behavior of fireflies. This paper provides a preliminary assessment of discrete version of FA (DFA) while coping with the task of mining coherent and large volume bicluster from web usage dataset. The experiments were conducted on two web usage datasets from public dataset repository whereby the performance of FA was compared with that exhibited by other population-based metaheuristic called binary Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of DFA while tackling the biclustering problem.

Keywords: Biclustering, Binary Particle Swarm Optimization, Discrete Firefly Algorithm, Firefly Algorithm, Usage profile Web usage mining.

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Authors: B. S. Jatau, Nandom Abu

Abstract:

Structural interpretation of aeromagnetic data and Landsat imagery over the Middle Benue Trough was carried out to determine the depth to basement, delineate the basement morphology and relief, and the structural features within the basin. The aeromagnetic and Landsat data were subjected to various image and data enhancement and transformation routines. Results of the study revealed lineaments with trend directions in the N-S, NE-SW, NWSE and E-W directions, with the NE-SW trends been dominant. The depths to basement within the trough were established to be at 1.8, 0.3 and 0.8km, as shown from the spectral analysis plot. The Source Parameter Imaging (SPI) plot generated showed the centralsouth/ eastern portion of the study area as being deeper in contrast to the western-south-west portion. The basement morphology of the trough was interpreted as having parallel sets of micro-basins which could be considered as grabens and horsts in agreement with the general features interpreted by early workers.

Keywords: Morphology, Middle Benue Trough, Spectral Analysis, Source Parameter Imaging.

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

Abstract:

Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models, on two different real-world electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, Machine Learning, imputation, laboratory variables, algorithmic bias.

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Authors: Varun Singh, Mainak Bandyopadhyay, Maharana Pratap Singh

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The shortest path (SP) problem concerns with finding the shortest path from a specific origin to a specified destination in a given network while minimizing the total cost associated with the path. This problem has widespread applications. Important applications of the SP problem include vehicle routing in transportation systems particularly in the field of in-vehicle Route Guidance System (RGS) and traffic assignment problem (in transportation planning). Well known applications of evolutionary methods like Genetic Algorithms (GA), Ant Colony Optimization, Particle Swarm Optimization (PSO) have come up to solve complex optimization problems to overcome the shortcomings of existing shortest path analysis methods. It has been reported by various researchers that PSO performs better than other evolutionary optimization algorithms in terms of success rate and solution quality. Further Geographic Information Systems (GIS) have emerged as key information systems for geospatial data analysis and visualization. This research paper is focused towards the application of PSO for solving the shortest path problem between multiple points of interest (POI) based on spatial data of Allahabad City and traffic speed data collected using GPS. Geovisualization of results of analysis is carried out in GIS.

Keywords: GIS, Outliers, PSO, Traffic Data.

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Authors: Salima Kouici, Abdelkader Khelladi

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In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied.

Keywords: Binary data, similarity measure, Tθ measures, Agglomerative Hierarchical Clustering.

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Authors: Asem M. Othman

Abstract:

The expansion of the hospitality industry is distinctive in the 21st century. Services, by nature, are intangible. Hence, service quality, in general, is a complicated process to be measured and evaluated. Hotels, as a service sector and part of the hospitality industry, are growing rapidly. This research paper was carried out to identify the quality determinants that may affect hotel guests’ service quality perception. In this research paper, each quality determinant will be discussed, illustrated, and justified thoroughly via a systematic literature review. This paper sets the stage to measure the significant influence of the service quality determinants on guest satisfaction. The knowledge contribution from this study proposes to practitioners and/or hotel service providers, fundamental elements to adopt the implications into their policies.

Keywords: Hotel service, service quality, quality determinants, quality management.

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Authors: Sung Ho Ahn, Jintae Hong, Chang Young Joung, Tae Ho Yang, Sung Ho Heo, Seo Yun Jang

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The performances of nuclear fuels and materials are qualified at an irradiation system in research reactors operating under the commercial nuclear power plant conditions. Fuel centerline temperature, coolant temperature, neutron flux, deformations of fuel stack and swelling are important parameters needed to analyze the nuclear fuel performances. The dimensional stability of nuclear fuels is a key parameter measuring the fuel densification and swelling. In this study, the fuel stack elongation is measured using a LVDT. A mockup LVDT instrumented fuel rod is developed. The performances of mockup LVDT instrumented fuel rod is evaluated by experiments.

Keywords: Axial deformation, elongation measurement, in-pile instrumentation, LVDT.

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Authors: M. Saravanan, M. Madheswaran

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Wireless Sensor Network (WSN) clustering architecture enables features like network scalability, communication overhead reduction, and fault tolerance. After clustering, aggregated data is transferred to data sink and reducing unnecessary, redundant data transfer. It reduces nodes transmitting, and so saves energy consumption. Also, it allows scalability for many nodes, reduces communication overhead, and allows efficient use of WSN resources. Clustering based routing methods manage network energy consumption efficiently. Building spanning trees for data collection rooted at a sink node is a fundamental data aggregation method in sensor networks. The problem of determining Cluster Head (CH) optimal number is an NP-Hard problem. In this paper, we combine cluster based routing features for cluster formation and CH selection and use Minimum Spanning Tree (MST) for intra-cluster communication. The proposed method is based on optimizing MST using Simulated Annealing (SA). In this work, normalized values of mobility, delay, and remaining energy are considered for finding optimal MST. Simulation results demonstrate the effectiveness of the proposed method in improving the packet delivery ratio and reducing the end to end delay.

Keywords: Wireless sensor network, clustering, minimum spanning tree, genetic algorithm, low energy adaptive clustering hierarchy, simulated annealing.

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Authors: Isao Taguchi, Yasuo Sugai

Abstract:

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.

Keywords: data selection, function approximation problem, multistage leaning, neural network, voluntary oscillation.

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Authors: Guy Leshem, Ya'acov Ritov

Abstract:

Traffic Management and Information Systems, which rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections.

Keywords: Machine Learning, Boosting, Classification, TrafficCongestion, Data Collecting, Magnetic Loop Detectors, SignalizedIntersections, Traffic Signal Timing Optimization.

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Authors: Sumiko Anno, Keiji Imaoka, Takeo Tadono, Tamotsu Igarashi, Subramaniam Sivaganesh, Selvam Kannathasan, Vaithehi Kumaran, Sinnathamby Noble Surendran

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Dengue outbreaks are affected by biological, ecological, socio-economic and demographic factors that vary over time and space. These factors have been examined separately and still require systematic clarification. The present study aimed to investigate the spatial-temporal clustering relationships between these factors and dengue outbreaks in the northern region of Sri Lanka. Remote sensing (RS) data gathered from a plurality of satellites were used to develop an index comprising rainfall, humidity and temperature data. RS data gathered by ALOS/AVNIR-2 were used to detect urbanization, and a digital land cover map was used to extract land cover information. Other data on relevant factors and dengue outbreaks were collected through institutions and extant databases. The analyzed RS data and databases were integrated into geographic information systems, enabling temporal analysis, spatial statistical analysis and space-time clustering analysis. Our present results showed that increases in the number of the combination of ecological factor and socio-economic and demographic factors with above the average or the presence contribute to significantly high rates of space-time dengue clusters.

Keywords: ALOS/AVNIR-2, Dengue, Space-time clustering analysis, Sri Lanka.

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Authors: Sri Sumartiningsih, Anies Setiowati

Abstract:

The purpose of this study is to investigate the influence of breaststroke swimming exercise to improving the peak expiratory flow. Methode: This study used 17 students of men aged 19-21 years, APE values measured before and after the study. Style swimming workout done in accordance with a program that has been made. Result: Value of peak expiratory flow in male students obtained on average before exercise (530 ± 15 811) liters / min and after doing the exercises (540.59 ± 17 092) liters / minute. Paired ttest showed t = -6.446 and p = 0.000, which means there are differences in peak expiratory flow values before and after exercise swimming breaststroke. Conclusion: The conclusion is the breaststroke swimming exercise can be improving of peak expiratory flow.

Keywords: Breaststroke, peak expiratory flow.

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

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Minimization methods for training feed-forward networks with Backpropagation are compared. Feedforward network training is a special case of functional minimization, where no explicit model of the data is assumed. Therefore due to the high dimensionality of the data, linearization of the training problem through use of orthogonal basis functions is not desirable. The focus is functional minimization on any basis. A number of methods based on local gradient and Hessian matrices are discussed. Modifications of many methods of first and second order training methods are considered. Using share rates data, experimentally it is proved that Conjugate gradient and Quasi Newton?s methods outperformed the Gradient Descent methods. In case of the Levenberg-Marquardt algorithm is of special interest in financial forecasting.

Keywords: Backpropagation algorithm, conjugacy condition, line search, matrix perturbation

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Authors: Z. Pavlík, A. Trník, M. Pavlíková, M. Keppert, R. Černý

Abstract:

A new type of lightweight plaster with the thermal capacity enhanced by PCM (Phase Change Material) addition is analyzed. The basic physical characteristics, namely the bulk density, matrix density, total open porosity, and pore size distribution are measured at first. For description of mechanical properties, compressive strength measurements are done. The thermal properties are characterized by transient impulse techniques as well as by DSC analysis that enables determination of the specific heat capacity as a function of temperature. The resistivity against the liquid water ingress is described by water absorption coefficient measurement. The experimental results indicate a good capability of the designed plaster to moderate effectively the interior climate of buildings.

Keywords: Lime-pozzolan plaster, PCM addition, enhanced thermal capacity, DSC analysis.

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Authors: Herti Utami, Sutijan, Roto, Wahyudi Budi Sediawan

Abstract:

α-Pinene is the main component of the most turpentine oils. The hydration of α-pinene with acid catalysts leads to a complex mixture of monoterpenes. In order to obtain more valuable products, the α-pinene in the turpentine can be hydrated in dilute mineral acid solutions to produce α-terpineol. The design of separation processes requires information on phase equilibrium and related thermodynamic properties. This paper reports the results of study on liquid-liquid equilibrium (LLE) of system containing α- pinene + water and α-terpineol + water. Binary LLE for α-pinene + water system, and α-terpineol + water systems were determined by experiment at 301K and atmospheric pressure. The two component mixture was stirred for about 30min, then the mixture was left for about 2h for complete phase separation. The composition of both phases was analyzed by using a Gas Chromatograph. The experimental data were correlated by considering both NRTL and UNIQUAC activity coefficient models. The LLE data for the system of α-pinene + water and α-terpineol + water were correlated successfully by the NRTL model. The experimental data were not satisfactorily fitted by the UNIQUAC model. The NRTL model (α =0.3) correlates the LLE data for the system of α-pinene + water at 301K with RMSD of 0.0404%. And the NRTL model (α =0.61) at 301K with RMSD of 0.0058 %. The NRTL model (α =0.3) correlates the LLE data for the system of α- terpineol + water at 301K with RMSD of 0.1487% and the NRTL model (α =0.6) at 301K with RMSD of 0.0032%, between the experimental and calculated mole fractions.

Keywords: α-Pinene, α-Terpineol, Liquid-liquid Equilibrium, NRTL model, UNIQUAC model

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Authors: Suboohi Shervani, Jingjing Ling, Jiabin Liu, Tahir Husain

Abstract:

Offshore oil-spill has become the most emerging problem in the world. In the current paper, a graphene/ZnO/polymer nanocomposite thin film is coated on stainless steel mesh via layer by layer deposition method. The structural characterization of materials is determined by Scanning Electron Microscopy (SEM) and X-ray diffraction (XRD). The total petroleum hydrocarbons (TPHs) and separation efficiency have been measured via gas chromatography – flame ionization detector (GC-FID). TPHs are reduced to 2 ppm and separation efficiency of the nanocomposite coated mesh is reached ≥ 99% for the final sample. The nanocomposite coated mesh acts as a promising candidate for the separation of oil- water mixture.

Keywords: Oil-spill, graphene, oil-water separation, nanocomposite.

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Authors: M. G. C. A. Cimino, G. Frosini, B. Lazzerini, F. Marcelloni

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In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition data sets affected by noise and outliers. Robust fuzzy C-means (robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise is modeled as a separate cluster and is characterized by a prototype that has a constant distance δ from all data points. Distance δ determines the boundary of the noise cluster and therefore is a critical parameter of the algorithm. Though some approaches have been proposed to automatically determine the most suitable δ for the specific application, up to today an efficient and fully satisfactory solution does not exist. The aim of this paper is to propose a novel method to compute the optimal δ based on the analysis of the distribution of the percentage of objects assigned to the noise cluster in repeated executions of the robust-FCM with decreasing values of δ . The extremely encouraging results obtained on some data sets found in the literature are shown and discussed.

Keywords: noise prototype, robust fuzzy clustering, robustfuzzy C-means

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Authors: Raif Parlakkaya, Ahmet Diken, Erkan Kara

Abstract:

This paper examines the relationship between corporate governance rating and stock prices of 26 Turkish firms listed in Turkish stock exchange (Borsa Istanbul) by using panel data analysis over five-year period. The paper also investigates the stock performance of firms with governance rating with regards to the market portfolio (i.e. BIST 100 Index) both prior and after governance scoring began. The empirical results show that there is no relation between corporate governance rating and stock prices when using panel data for annual variation in both rating score and stock prices. Further analysis indicates surprising results that while the selected firms outperform the market significantly prior to rating, the same performance does not continue afterwards.

Keywords: Corporate governance, stock price, performance, panel data analysis.

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Authors: Dmitry V. Fomichev, Vladimir I. Solonin

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This paper presents the findings from a numerical simulation of the flow in 37-rod fuel assembly models spaced by a double-wire trapezoidal wrapping as applied to the BREST-OD-300 experimental nuclear reactor. Data on a high static pressure distribution within the models, and equations for determining the fuel bundle flow friction factors have been obtained. Recommendations are provided on using the closing turbulence models available in the ANSYS Fluent. A comparative analysis has been performed against the existing empirical equations for determining the flow friction factors. The calculated and experimental data fit has been shown.

An analysis into the experimental data and results of the numerical simulation of the BREST-OD-300 fuel rod assembly hydrodynamic performance are presented.

Keywords: BREST-OD-300, ware-spaces, fuel assembly, computation fluid dynamics.

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Authors: Sándor Juhász, Renáta Iváncsy

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This paper describes an enhanced cookie-based method for counting the visitors of web sites by using a web log processing system that aims to cope with the ambitious goal of creating countrywide statistics about the browsing practices of real human individuals. The focus is put on describing a new more efficient way of detecting human beings behind web users by placing different identifiers on the client computers. We briefly introduce our processing system designed to handle the massive amount of data records continuously gathered from the most important content providers of the Hungary. We conclude by showing statistics of different time spans comparing the efficiency of multiple visitor counting methods to the one presented here, and some interesting charts about content providers and web usage based on real data recorded in 2007 will also be presented.

Keywords: Cookie based identification, real data, user activitytracking, web auditing, web log processing

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Authors: Amit Sachan

Abstract:

The network designing and data modeling developments which are the two significant research tasks in direction to tolerate power control of Microgrid concluded using IEC 61850 data models and facilities. The current casing areas of IEC 61580 include infrastructures in substation automation systems, among substations and to DERs. So, for LV microgrid power control, previously using the IEC 61850 amenities to control the smart electrical devices, we have to model those devices as IEC 61850 data models and design a network topology to maintenance all-in-one communiqué amid those devices. In adding, though IEC 61850 assists modeling a portion by open-handed several object models for common functions similar measurement, metering, monitoring…etc., there are motionless certain missing smithereens for building a multiplicity of functions for household appliances like tuning the temperature of an electric heater or refrigerator.

Keywords: IEC 61850, RCMC, HCMC, DER Unit Controller.

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