Search results for: measured data.

Authors: Farhan Ismaeel Dakheel, Raoof Smko, K. Negrat, Abdelsalam Almarimi

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In this paper we used data mining techniques to identify outlier patients who are using large amount of drugs over a long period of time. Any healthcare or health insurance system should deal with the quantities of drugs utilized by chronic diseases patients. In Kingdom of Bahrain, about 20% of health budget is spent on medications. For the managers of healthcare systems, there is no enough information about the ways of drug utilization by chronic diseases patients, is there any misuse or is there outliers patients. In this work, which has been done in cooperation with information department in the Bahrain Defence Force hospital; we select the data for Cardiac patients in the period starting from 1/1/2008 to December 31/12/2008 to be the data for the model in this paper. We used three techniques for finding the drug utilization for cardiac patients. First we applied a clustering technique, followed by measuring of clustering validity, and finally we applied a decision tree as classification algorithm. The clustering results is divided into three clusters according to the drug utilization, for 1603 patients, who received 15,806 prescriptions during this period can be partitioned into three groups, where 23 patients (2.59%) who received 1316 prescriptions (8.32%) are classified to be outliers. The classification algorithm shows that the use of average drug utilization and the age, and the gender of the patient can be considered to be the main predictive factors in the induced model.

Keywords: Data Mining, Clustering, Classification, Drug Utilization..

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Authors: Gen Sakoda, Hideki Takayasu, Misako Takayasu

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Waste reduction is a fundamental problem for sustainability. Methods for waste reduction with point-of-sales (POS) data are proposed, utilizing the knowledge of a recent econophysics study on a statistical property of POS data. Concretely, the non-stationary time series analysis method based on the Particle Filter is developed, which considers abnormal fluctuation scaling known as Taylor's law. This method is extended for handling incomplete sales data because of stock-outs by introducing maximum likelihood estimation for censored data. The way for optimal stock determination with pricing the cost of waste reduction is also proposed. This study focuses on the examination of the methods for large sales numbers where Taylor's law is obvious. Numerical analysis using aggregated POS data shows the effectiveness of the methods to reduce food waste maintaining a high profit for large sales numbers. Moreover, the way of pricing the cost of waste reduction reveals that a small profit loss realizes substantial waste reduction, especially in the case that the proportionality constant  of Taylor’s law is small. Specifically, around 1% profit loss realizes half disposal at =0.12, which is the actual  value of processed food items used in this research. The methods provide practical and effective solutions for waste reduction keeping a high profit, especially with large sales numbers.

Keywords: Food waste reduction, particle filter, point of sales, sustainable development goals, Taylor's Law, time series analysis.

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Authors: Pei Wen Liao, Tsung Hau Jen

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the data of Taiwanese 8th grader in the 4th cycle of Trends in International Mathematics and Science Study (TIMSS) are analyzed to examine the influence of the science teachers- preference in experimental teaching on the relationships between the affective variables ( the perceived usefulness of science, ease of using science and science learning interest) and the academic achievement in science. After dealing with the missing data, 3711 students and 145 science teacher-s data were analyzed through a Hierarchical Linear Modeling technique. The major objective of this study was to determine the role of the experimental teaching moderates the relationship between perceived usefulness and achievement.

Keywords: TIMSS database, Science achievement, Experimental teaching, Perceived Usefulness, Perceived Ease of Use

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Authors: E. Shahbazali, N. Afrasiabi, A. A. Safekordi

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Nowadays, fluidized bed plays an important part in industry. The design of this kind of reactor requires knowing the interfacial area between two phases and this interfacial area leads to calculate the solid holdup in the bed. Consequently achieving interfacial area between gas and solid in the bed experimentally is so significant. On interfacial area measurement in fluidized bed with gas has been worked, but light transmission technique has been used less. Therefore, in the current research the possibility of using of this technique and its accuracy are investigated. Measuring, a fluidized bed was designed and the problems were averted as far as possible. By using fine solid with equal shape and diameter and installing an optical system, the absorption of light during the time of fluidization has been measured. Results indicate that this method that its validity has been proved in the gas-liquid system, by different reasons have less application in gas-solid system. One important reason could be non-uniformity in such systems.

Keywords: Fluidization, Holdup, Light Transmission, Two phase system.

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Authors: Salman Pervaiz, Ibrahim Deiab, Amir Rashid, Mihai Nicolescu, Hossam Kishawy

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Greenhouse gases (GHG) emissions impose major threat to global warming potential (GWP). Unfortunately manufacturing sector is one of the major sources that contribute towards the rapid increase in greenhouse gases (GHG) emissions. In manufacturing sector electric power consumption is the major driver that influences CO2 emission. Titanium alloys are widely utilized in aerospace, automotive and petrochemical sectors because of their high strength to weight ratio and corrosion resistance. Titanium alloys are termed as difficult to cut materials because of their poor machinability rating. The present study analyzes energy consumption during cutting with reference to material removal rate (MRR). Surface roughness was also measured in order to optimize energy consumption.

Keywords: Energy Consumption, CO2 Emission, Ti6Al4V.

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Authors: Rekha Kandwal, Kamal K.Bharadwaj

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Knowledge is indispensable but voluminous knowledge becomes a bottleneck for efficient processing. A great challenge for data mining activity is the generation of large number of potential rules as a result of mining process. In fact sometimes result size is comparable to the original data. Traditional data mining pruning activities such as support do not sufficiently reduce the huge rule space. Moreover, many practical applications are characterized by continual change of data and knowledge, thereby making knowledge voluminous with each change. The most predominant representation of the discovered knowledge is the standard Production Rules (PRs) in the form If P Then D. Michalski & Winston proposed Censored Production Rules (CPRs), as an extension of production rules, that exhibit variable precision and supports an efficient mechanism for handling exceptions. A CPR is an augmented production rule of the form: If P Then D Unless C, where C (Censor) is an exception to the rule. Such rules are employed in situations in which the conditional statement 'If P Then D' holds frequently and the assertion C holds rarely. By using a rule of this type we are free to ignore the exception conditions, when the resources needed to establish its presence, are tight or there is simply no information available as to whether it holds or not. Thus the 'If P Then D' part of the CPR expresses important information while the Unless C part acts only as a switch changes the polarity of D to ~D. In this paper a scheme based on Dempster-Shafer Theory (DST) interpretation of a CPR is suggested for discovering CPRs from the discovered flat PRs. The discovery of CPRs from flat rules would result in considerable reduction of the already discovered rules. The proposed scheme incrementally incorporates new knowledge and also reduces the size of knowledge base considerably with each episode. Examples are given to demonstrate the behaviour of the proposed scheme. The suggested cumulative learning scheme would be useful in mining data streams.

Keywords: Censored production rules, cumulative learning, data mining, machine learning.

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Authors: Hamid A. Jalab, Rabha W. Ibrahim

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This paper introduces an image denoising algorithm based on generalized Srivastava-Owa fractional differential operator for removing Gaussian noise in digital images. The structures of nxn fractional masks are constructed by this algorithm. Experiments show that, the capability of the denoising algorithm by fractional differential-based approach appears efficient to smooth the Gaussian noisy images for different noisy levels. The denoising performance is measured by using peak signal to noise ratio (PSNR) for the denoising images. The results showed an improved performance (higher PSNR values) when compared with standard Gaussian smoothing filter.

Keywords: Fractional calculus, fractional differential operator, fractional mask, fractional filter.

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Authors: Insung Jung, Gi-Nam Wang

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The objective of this paper is to a design of pattern classification model based on the back-propagation (BP) algorithm for decision support system. Standard BP model has done full connection of each node in the layers from input to output layers. Therefore, it takes a lot of computing time and iteration computing for good performance and less accepted error rate when we are doing some pattern generation or training the network. However, this model is using exclusive connection in between hidden layer nodes and output nodes. The advantage of this model is less number of iteration and better performance compare with standard back-propagation model. We simulated some cases of classification data and different setting of network factors (e.g. hidden layer number and nodes, number of classification and iteration). During our simulation, we found that most of simulations cases were satisfied by BP based using exclusive connection network model compared to standard BP. We expect that this algorithm can be available to identification of user face, analysis of data, mapping data in between environment data and information.

Keywords: Neural network, Back-propagation, classification.

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Authors: Mohammed M. Siddeq, Mohammed H. Rasheed, Omar M. Salih, Marcos A. Rodrigues

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This research proposes a data encoding and decoding method based on the Matrix Minimization algorithm. This algorithm is applied to high-frequency coefficients for compression/encoding. The algorithm starts by converting every three coefficients to a single value; this is accomplished based on three different keys. The decoding/decompression uses a search method called QSS (Quick Sequential Search) Decoding Algorithm presented in this research based on the sequential search to recover the exact coefficients. In the next step, the decoded data are saved in an auxiliary array. The basic idea behind the auxiliary array is to save all possible decoded coefficients; this is because another algorithm, such as conventional sequential search, could retrieve encoded/compressed data independently from the proposed algorithm. The experimental results showed that our proposed decoding algorithm retrieves original data faster than conventional sequential search algorithms.

Keywords: Matrix Minimization Algorithm, Decoding Sequential Search Algorithm, image compression, Discrete Cosine Transform, Discrete Wavelet Transform.

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Authors: Amr A. Fouad, Waleed H. Albuali, Iyad Jresat

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The possible therapeutic effect of cannabidiol, the major non-psychotropic Cannabis constituent, was investigated against acute hepatotoxicity induced by a single oral dose of acetaminophen (500mg/kg) in mice. Cannabidiol (two intraperitoneal injections, 5mg/kg, each) was given 1 hour and 12 hours following acetaminophen administration. Acetaminophen administration caused significant elevations of serum alanine aminotransferase, and hepatic malondialdehyde, and nitric oxide levels, and a significant decrease in hepatic reduced glutathione. Cannabidiol significantly attenuated the deterioration in the measured biochemical parameters resulted from acetaminophen administration. Also, histopathological examination showed that cannabidiol markedly attenuated ameliorated acetaminophen-induced liver tissue damage. These results emphasize that cannabidiol represents a potential therapeutic option to protect against acetaminophen hepartotoxicity which is a common clinical problem.

Keywords: cannabidiol, acetaminophen, liver, mice.

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Authors: F. Garousi, Sz. Veres, É. Bódi, Sz. Várallyay, B. Kovács

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Specific leaf area (SLA; cm2leaf g-1leaf) the ratio of leaf area to leaf dry mass is a key ecophysiological parameter influencing leaf physiology, photosynthesis, and whole plant carbon gain and also can be used as a rapid and diagnostic tool. In this study, two species of soluble inorganic selenium forms, selenite (Se^IV) and selenate (Se^VI) at different concentrations were investigated on maize plants that were growing in nutrient solutions during 2 weeks and at the end of the experiment, amounts of SLA for first and second leaves of maize were measured. In accordance with the results we observed that our regarded Se concentrations in both forms of Se^IV and Se^VI were not effective on maize plants’ SLA significantly although high level of 3 mg.kg-1 Se^IV had negative affect on growth of the samples that had been treated by it but about Se^VI samples we did not observe this state and our different considered Se^VI concentrations were not toxic for maize plants.

Keywords: Maize, Sodium selenate, sodium selenite, specific leaf area.

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Authors: G. Momen, R. Jafari, M. Farzaneh

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Two different superhydrophobic surfaces were elaborated and their oil repellency behavior was evaluated using several liquid with different surface tension. A silicone rubber/SiO2 nanocomposite coated (A) on aluminum substrate by “spin-coating" and the sample B was an anodized aluminum surface covered by Teflon-like coating. A high static contact angle about ∼162° was measured for two prepared surfaces on which the water droplet rolloff. Scanning electron microscopy (SEM) showed the presence of micro/nanostructures for both sample A and B similar to that of lotus leaf. However the sample A presented significantly different behaviour of wettability against the low surface tension liquid. Sample A has been wetted totally by oil (dodecan) droplet while sample B showed oleophobic behaviour. Oleophobic property of Teflon like coating can be contributed to the presence of CF2 and CF3 functional group which was shown by XPS analysis.

Keywords: Oleophobic, Superhydrophobic, Aluminum surface, Nano-texture.

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Authors: Martin Jelínek, Petr Květon

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The presented study focuses on relations between violent videogames playing and various types of antisocial behavior, namely bullying (verbal, indirect, and physical), physical aggression and delinquency. Relevant relationships were also examined with respect to gender. Violent videogames exposure (VGV) was measured by respondents’ most favored games and self-evaluation of its level of violence and frequency of playing. Antisocial behaviors were assessed by self-report questionnaires. The research sample consisted of 333 (166 males, 167 females) primary and secondary school students at the age between 10 and 19 years (m=14.98, sd=1.77). It was found that violent videogames playing is associated with physical aggression (rho=0.288, 95% CI [0.169;0.400]) and bullying (rho=0.369, 95% CI [0.254;0.476]). By means of gender, these relations were slightly weaker in males (VGV - physical aggression: rho=0.104, 95% CI [-0.061;0.264], VGV – bullying: rho=.200, 95% CI [0.032;0.356]) than in females (VGV - physical aggression: rho=0.257, 95% CI [0.089;0.411], VGV – bullying: rho=0.279, 95% CI [0.110;0.432]).

Keywords: Aggression, bullying, gender, violent videogames.

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Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

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Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: Coastal Erosion, Prognostic Model, DSAS.

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Authors: Najmeh Abedzadeh, Matthew Jacobs

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An intrusion detection system (IDS) is a software application that monitors malicious activities and generates alerts if any are detected. However, most network activities in IDS datasets are normal, and the relatively few numbers of attacks make the available data imbalanced. Consequently, cyber-attacks can hide inside a large number of normal activities, and machine learning algorithms have difficulty learning and classifying the data correctly. In this paper, a comprehensive literature review is conducted on different types of algorithms for both implementing the IDS and methods in correcting the imbalanced IDS dataset. The most famous algorithms are machine learning (ML), deep learning (DL), synthetic minority over-sampling technique (SMOTE), and reinforcement learning (RL). Most of the research use the CSE-CIC-IDS2017, CSE-CIC-IDS2018, and NSL-KDD datasets for evaluating their algorithms.

Keywords: IDS, intrusion detection system, imbalanced datasets, sampling algorithms, big data.

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Authors: Fatma Kürüm Varolgüneş, Fatih Canan

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Mardin is one of the unique cities in Turkey with its rich cultural and historical heritage. Mardin’s traditional dwellings have been affected both by natural data such as climate and topography and by cultural data like lifestyle and belief. However, in the new settlements, housing is formed with modern approaches and unsuitable forms clashing with Mardin’s culture and environment. While the city is expanding, traditional textures are ignored. Thus, traditional settlements are losing their identity and are vanishing because of the rapid change and transformation. The main aim of this paper is to determine the physical and social data needed to define the characteristic features of Mardin’s old and new settlements. In this context, based on social and cultural data, old and new settlement formations of Mardin have been investigated from various aspects. During this research, the following methods have been utilized: observations, interviews, public surveys, literature review, as well as site examination via maps, photographs and questionnaire methodology. In conclusion, this paper focuses on how changes in the physical forms of cities affect the typology and the identity of cities, as in the case of Mardin.

Keywords: Urban and local identity, historical city center, traditional settlements, Mardin, Turkey.

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Authors: Florin Gorunescu

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Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.

Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: Artificial Neural Network, Data Mining, Electroencephalogram, Epilepsy, Feature Extraction, Seizure Detection, Signal Processing.

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Authors: Georgia Pozoukidou

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TELUM software is a land use model designed specifically to help metropolitan planning organizations (MPOs) prepare their transportation improvement programs and fulfill their numerous planning responsibilities. In this context obtaining, preparing, and validating socioeconomic forecasts are becoming fundamental tasks for an MPO in order to ensure that consistent population and employment data are provided to travel demand models. Chittenden County Metropolitan Planning Organization of Vermont State was used as a case study to test the applicability of TELUM land use model. The technical insights and lessons learned from the land use model application have transferable value for all MPOs faced with land use forecasting development and transportation modeling.

Keywords: Calibration data requirements, land use models, land use planning, Metropolitan Planning Organizations.

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Authors: C. C. Su, S. H. Chang

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The carbon based coils with the nanometer scale have the 3 dimension helix geometry. We synthesized the carbon nano-coils by the use of chemical vapor deposition technique with iron and tin as the catalysts. The fabricated coils have the external diameter of ranging few hundred nm to few thousand nm. The Scanning Electro-Microscope (SEM) and Tunneling Electro-Microscope has shown detail images of the coil-s structure. The fabrication of the carbon nano-coils can be grown on the metal and non-metal substrates, such as the stainless steel and silicon substrates. Besides growth on the flat substrate; they also can be grown on the stainless steel wires. After the synthesis of the coils, the mechanical and electro-mechanical property is measured. The experimental results were reported.

Keywords: Carbon nanocoils, chemical vapor deposition, nano-materials

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Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: Data mining, K-means, road traffic accidents, Waze, Weka.

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Authors: Yoonsuh Jung

Abstract:

As DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an ‘optimal’ value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.

Keywords: Cross Validation, Parameter Averaging, Parameter Selection, Regularization Parameter Search.

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Authors: Xu LiYun, Briand Florent, Fan GuoLiang

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The loop-layout design problem (LLDP) aims at optimizing the sequence of positioning of the machines around the cyclic production line. Traffic congestion is the usual criteria to minimize in this type of problem, i.e. the number of additional cycles spent by each part in the network until the completion of its required routing sequence of machines. This paper aims at applying several improvements mechanisms such as a positioned-based crossover operator for the Genetic Algorithm (GA) called a Two Points Crossover (TPC) and an offspring selection process. The performance of the improved GA is measured using well-known examples from literature and compared to other evolutionary algorithms. Good results show that GA can still be competitive for this type of problem against more recent evolutionary algorithms.

Keywords: Crossover, genetic algorithm, layout design problem, loop-layout, manufacturing optimization.

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Authors: W. S. Hsu, S. W. Chen, Y. T. Ku, Y. Chiang, J. R. Wang , J. H. Yang, C. Shih

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ALOHA code was used to calculate the concentration under the CO₂ storage burst condition for Maanshan nuclear power plant (NPP) in this study. Five main data are input into ALOHA code including location, building, chemical, atmospheric, and source data. The data from Final Safety Analysis Report (FSAR) and some reports were used in this study. The ALOHA results are compared with the failure criteria of R.G. 1.78 to confirm the habitability of control room. The result of comparison presents that the ALOHA result is below the R.G. 1.78 criteria. This implies that the habitability of control room can be maintained in this case. The sensitivity study for atmospheric parameters was performed in this study. The results show that the wind speed has the larger effect in the concentration calculation.

Keywords: PWR, ALOHA, habitability, Maanshan.

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Authors: Suzan Wedyan

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Associative classification (AC) is a data mining approach that combines association rule and classification to build classification models (classifiers). AC has attracted a significant attention from several researchers mainly because it derives accurate classifiers that contain simple yet effective rules. In the last decade, a number of associative classification algorithms have been proposed such as Classification based Association (CBA), Classification based on Multiple Association Rules (CMAR), Class based Associative Classification (CACA), and Classification based on Predicted Association Rule (CPAR). This paper surveys major AC algorithms and compares the steps and methods performed in each algorithm including: rule learning, rule sorting, rule pruning, classifier building, and class prediction.

Keywords: Associative Classification, Classification, Data Mining, Learning, Rule Ranking, Rule Pruning, Prediction.

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Authors: Fawzi Irshaid, Kamal Mansi

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Thyroid dysfunction is one of the most frequently reported complications of chronic blood transfusion therapy in patients with beta-thalassemia major (BTM). However, the occurrence of thyroid dysfunction and its possible association with iron overload in BTM patients is still under debate. Therefore, this study aimed to investigate the status of thyroid functions and iron overload in adolescent and young adult patients with BTM in Jordan population. Thirty six BTM patients aged 12-28 years and matched controls were included in this study. All patients have been receiving frequent blood transfusion to maintain pretransfusion hemoglobin concentration above 10 g dl-1 and deferoxamine at a dose of 45 mg kg-1 day-1 (8 h, 5-7 days/week) by subcutaneous infusion. Blood samples were drawn from patients and controls. The status of thyroid functions and iron overload was evaluated by measurements of serum free thyroxine (FT4), triiodothyronine (FT3), thyrotropin (TSH) and serum ferritin level. A number of some hematological and biochemical parameters were also measured. It was found that hematocrit, serum ferritin, hemoglobin, FT3 and zinc, copper mean values were significantly higher in the patients than in the controls (P< 0.05). On other hand, leukocyte, FT4 and TSH mean values were similar to that of the controls. In addition, our data also indicated that all of the above examined parameters were not significantly affected by the patient-s age and gender. Deferoxamine approach for removing excess iron from our BTM patient did not normalize the values of serum ferritin, copper and zinc, suggesting poor compliance with deferoxamine chelation therapy. Thus, we recommend the use of a combination of deferoxamine and deferiprone to reduce the risk of excess of iron in our patients. Furthermore, thyroid dysfunction appears to be a rare complication, because our patients showed normal mean levels for serum TSH and FT4. However, high mean levels of serum ferritin, zinc, copper might be seen as potential risk factors for initiation and development of thyroid dysfunctions and other diseases. Therefore, further studies must be carried out at yearly intervals with large sample number, to detect subclinical thyroid dysfunction cases.

Keywords: beta-thalassemia major, deferoxamine, iron overload, triiodothyronine, zinc.

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Authors: Tu Kun, Gu Nai-jie, Bi Kun, Liu Gang, Dong Wan-li

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As a structure for processing string problem, suffix array is certainly widely-known and extensively-studied. But if the string access pattern follows the “90/10" rule, suffix array can not take advantage of the fact that we often find something that we have just found. Although the splay tree is an efficient data structure for small documents when the access pattern follows the “90/10" rule, it requires many structures and an excessive amount of pointer manipulations for efficiently processing and searching large documents. In this paper, we propose a new and conceptually powerful data structure, called splay suffix arrays (SSA), for string search. This data structure combines the features of splay tree and suffix arrays into a new approach which is suitable to implementation on both conventional and clustered computers.

Keywords: suffix arrays, splay tree, string search, distributedalgorithm

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Authors: Jianqin Qu

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This paper proposes a rigid point set matching algorithm in arbitrary dimensions based on the idea of symmetric covariant function. A group of functions of the points in the set are formulated using rigid invariants. Each of these functions computes a pair of correspondence from the given point set. Then the computed correspondences are used to recover the unknown rigid transform parameters. Each computed point can be geometrically interpreted as the weighted mean center of the point set. The algorithm is compact, fast, and dimension free without any optimization process. It either computes the desired transform for noiseless data in linear time, or fails quickly in exceptional cases. Experimental results for synthetic data and 2D/3D real data are provided, which demonstrate potential applications of the algorithm to a wide range of problems.

Keywords: Covariant point, point matching, dimension free, rigid registration.

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Authors: Laurent Thiry, Michel Hassenforder

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This paper presents a general approach to implement efficient queries’ interpreters in a functional programming language. Indeed, most of the standard tools actually available use an imperative and/or object-oriented language for the implementation (e.g. Java for Jena-Fuseki) but other paradigms are possible with, maybe, better performances. To proceed, the paper first explains how to model data structures and queries in a functional point of view. Then, it proposes a general methodology to get performances (i.e. number of computation steps to answer a query) then it explains how to integrate some optimization techniques (short-cut fusion and, more important, data transformations). It then compares the functional server proposed to a standard tool (Fuseki) demonstrating that the first one can be twice to ten times faster to answer queries.

Keywords: Data transformation, functional programming, information server, optimization.

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Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker

Abstract:

Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.

Keywords: CUSUM, evidence theory, KL divergence, quickest change detection, time series data.

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