Search results for: test data compression (TDC)

Authors: Frank Emmert Streib, Matthias Dehmer, Jing Liu, Max Mühlhauser

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In this paper we present a method for gene ranking from DNA microarray data. More precisely, we calculate the correlation networks, which are unweighted and undirected graphs, from microarray data of cervical cancer whereas each network represents a tissue of a certain tumor stage and each node in the network represents a gene. From these networks we extract one tree for each gene by a local decomposition of the correlation network. The interpretation of a tree is that it represents the n-nearest neighbor genes on the n-th level of a tree, measured by the Dijkstra distance, and, hence, gives the local embedding of a gene within the correlation network. For the obtained trees we measure the pairwise similarity between trees rooted by the same gene from normal to cancerous tissues. This evaluates the modification of the tree topology due to progression of the tumor. Finally, we rank the obtained similarity values from all tissue comparisons and select the top ranked genes. For these genes the local neighborhood in the correlation networks changes most between normal and cancerous tissues. As a result we find that the top ranked genes are candidates suspected to be involved in tumor growth and, hence, indicates that our method captures essential information from the underlying DNA microarray data of cervical cancer.

Keywords: Graph similarity, DNA microarray data, cancer.

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Authors: K. Parandhama Gowd

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The aim of this research paper is to conceptualize, discuss, analyze and propose alternate design methodologies for futuristic Black Box for flight safety. The proposal also includes global networking concepts for real time surveillance and monitoring of flight performance parameters including GPS parameters. It is expected that this proposal will serve as a failsafe real time diagnostic tool for accident investigation and location of debris in real time. In this paper, an attempt is made to improve the existing methods of flight data recording techniques and improve upon design considerations for futuristic FDR to overcome the trauma of not able to locate the block box. Since modern day communications and information technologies with large bandwidth are available coupled with faster computer processing techniques, the attempt made in this paper to develop a failsafe recording technique is feasible. Further data fusion/data warehousing technologies are available for exploitation.

Keywords: Flight data recorder (FDR), black box, diagnostic tool, global networking, cockpit voice and data recorder (CVDR), air traffic control (ATC), air traffic, telemetry, tracking and control centers ATTTCC).

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Authors: Youngseok Kim, Chul Park, Jonghwa Yi, Sangsik Choi

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The 2-MHz Side Scan SONAR (SSS) attached to the boat for inspection of underwater structures is affected by shaking. It is difficult to determine the exact scale of damage of structure. In this study, a motion sensor is attached to the inside of the 2-MHz SSS to get roll, pitch, and yaw direction data, and developed the image stabilization tool to correct the sonar image. We checked that reliable data can be obtained with an average error rate of 1.99% between the measured value and the actual distance through experiment. It is possible to get the accurate sonar data to inspect damage in underwater structure.

Keywords: Image stabilization, motion sensor, safety inspection, sonar image, underwater structure.

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Authors: Andrew Cummings, Rhianne Boag, Sarah Bryson, Gordon Turner

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Packages designed for transport of radioactive material must satisfy rigorous safety regulations specified by the International Atomic Energy Agency (IAEA). Higher Activity Waste (HAW) transport packages have to maintain containment of their contents during normal and accident conditions of transport (NCT and ACT). To ensure containment criteria is satisfied these packages are required to be leak-tight in all transport conditions to meet allowable activity release rates. Package design safety reports are the safety cases that provide the claims, evidence and arguments to demonstrate that packages meet the regulations and once approved by the competent authority (in the UK this is the Office for Nuclear Regulation) a licence to transport radioactive material is issued for the package(s). The standard approach to demonstrating containment in the RWM transport safety case is set out in BS EN ISO 12807. In this document a method for measuring a leak rate from the package is explained by way of a small interspace test volume situated between two O-ring seals on the underside of the package lid. The interspace volume is pressurised and a pressure drop measured. A small interspace test volume makes the method more sensitive enabling the measurement of smaller leak rates. By ascertaining the activity of the contents, identifying a releasable fraction of material and by treating that fraction of material as a gas, allowable leak rates for NCT and ACT are calculated. The adherence to basic safety principles in ISO12807 is very pessimistic and current practice in the demonstration of transport safety, which is accepted by the UK regulator. It is UK government policy that management of HAW will be through geological disposal. It is proposed that the intermediate level waste be transported to the geological disposal facility (GDF) in large cuboid packages. This poses a challenge for containment demonstration because such packages will have long seals and therefore large interspace test volumes. There is also uncertainty on the releasable fraction of material within the package ullage space. This is because the waste may be in many different forms which makes it difficult to define the fraction of material released by the waste package. Additionally because of the large interspace test volume, measuring the calculated leak rates may not be achievable. For this reason a justification for a lower releasable fraction of material is sought. This paper considers the use of aerosol processes to reduce the releasable fraction for both NCT and ACT. It reviews the basic coagulation and removal processes and applies the dynamic aerosol balance equation. The proposed solution includes only the most well understood physical processes namely; Brownian coagulation and gravitational settling. Other processes have been eliminated either on the basis that they would serve to reduce the release to the environment further (pessimistically in keeping with the essence of nuclear transport safety cases) or that they are not credible in the conditions of transport considered.

Keywords: Aerosol processes, Brownian coagulation, gravitational settling, transport regulations.

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Authors: Farzad Yavari, Fereydoun Ohadi

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Today, with the development and growth of technology and extreme environmental changes, organizations need to identify opportunities and create creativity and innovation in order to be able to maintain or improve their position in competition with others. In this regard, it is necessary that the resources and assets of the organization are coordinated and reviewed in accordance with the orientation of the strategy. One of the competitive advantages of the present age is knowledge management, which is to equip the organization with the knowledge of the day and disseminate among employees and use it in the development of products and services. Therefore, in the forthcoming research, the impact of dynamic capabilities components (sense, seize, and reconfiguration) has been investigated on knowledge management processes (acquisition, integration and knowledge utilization) in the MAPNA Engineering and Construction Company using a field survey and applied research method. For this purpose, a questionnaire was filled out in the form of 15 questions for dynamic components and 15 questions for measuring knowledge management components and distributed among 46 employees of the knowledge management organization. Validity of the questionnaire was evaluated through content validity and its reliability with Cronbach's coefficient. Pearson correlation test and structural equation technique were used to analyze the data. The results of the research indicate a positive significant correlation between the components of dynamic capabilities and knowledge management.

Keywords: Dynamic capabilities, knowledge management, sense capability, seize capability, reconfigurable capability, knowledge acquisition, knowledge integrity, knowledge utilization.

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Authors: A. U. Haque, W. Asrar, A. A. Omar, E. Sulaeman, J. S. Mohamed Ali

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Due to the interference effects, the intrinsic aerodynamic parameters obtained from the individual component testing are always fundamentally different than those obtained for complete model testing. Consideration and limitation for such testing need to be taken into account in any design work related to the component buildup method. In this paper, the scaled model of a straight rectangular canard of a hybrid buoyant aircraft is tested at 50 m/s in IIUM-LSWT (Low Speed Wind Tunnel). Model and its attachment with the balance are kept rigid to have results free from the aeroelastic distortion. Based on the velocity profile of the test section’s floor; the height of the model is kept equal to the corresponding boundary layer displacement. Balance measurements provide valuable but limited information of overall aerodynamic behavior of the model. Zero lift coefficient is obtained at -2.2o and the corresponding drag coefficient was found to be less than that at zero angle of attack. As a part of the validation of low fidelity tool, plot of lift coefficient plot was verified by the experimental data and except the value of zero lift coefficients, the overall trend has under predicted the lift coefficient. Based on this comparative study, a correction factor of 1.36 is proposed for lift curve slope obtained from the panel method.

Keywords: Wind tunnel testing, boundary layer displacement, lift curve slope, canard, aerodynamics.

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Authors: P. Wlodarczak, J. Soar, M. Ally

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The dramatic rise in the use of Social Media (SM) platforms such as Facebook and Twitter provide access to an unprecedented amount of user data. Users may post reviews on products and services they bought, write about their interests, share ideas or give their opinions and views on political issues. There is a growing interest in the analysis of SM data from organisations for detecting new trends, obtaining user opinions on their products and services or finding out about their online reputations. A recent research trend in SM analysis is making predictions based on sentiment analysis of SM. Often indicators of historic SM data are represented as time series and correlated with a variety of real world phenomena like the outcome of elections, the development of financial indicators, box office revenue and disease outbreaks. This paper examines the current state of research in the area of SM mining and predictive analysis and gives an overview of the analysis methods using opinion mining and machine learning techniques.

Keywords: Social Media, text mining, knowledge discovery, predictive analysis, machine learning.

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Authors: Saad Alkhaldi, Fadi Ghammas, Tariq Alghamdi, Stefano Alexandirs

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The operating conditions and corrosive nature of the process fluid in the Haradh and Hawiyah areas are subjecting facility piping to undesirable corrosion phenomena. Therefore, production headers inside remote headers have been internally cladded with high alloy material to mitigate the corrosion damage mechanism. Corrosion mitigation in the jump-over lines, constructed between the existing flowlines and the newly constructed facilities to provide operational flexibility, is proposed. This corrosion mitigation system includes the application of fusion bond epoxy (FBE) coating on the internal surface of the pipe and depositing corrosion-resistant alloy (CRA) weld layers at pipe and fittings ends to protect the carbon steel material. In addition, high alloy CRA weld material is used to deposit the girth weld between the 90-degree elbows and mating internally coated segments. A rigorous testing and qualification protocol was established prior to actual adoption at the Haradh and Hawiyah Field Gas Compression Program, currently being executed by Saudi Aramco. The proposed mitigation system, aimed at applying the cladding at the ends of the internally FBE coated pipes/elbows, will resolve field joint coating challenges, eliminate the use of approximately 1700 breakout flanges, and prevent the potential hydrocarbon leaks.

Keywords: Corrosion, FBE coated sour service, cost savings.

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Authors: Qimin Yang

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In this paper, 3X3 routing nodes are proposed to provide speedup and parallel processing capability in Data Vortex network architectures. The new design not only significantly improves network throughput and latency, but also eliminates the need for distributive traffic control mechanism originally embedded among nodes and the need for nodal buffering. The cost effectiveness is studied by a comparison study with the previously proposed 2- input buffered networks, and considerable performance enhancement can be achieved with similar or lower cost of hardware. Unlike previous implementation, the network leaves small probability of contention, therefore, the packet drop rate must be kept low for such implementation to be feasible and attractive, and it can be achieved with proper choice of operation conditions.

Keywords: Data Vortex, Packet Switch, Interconnection network, deflection, Network-on-chip

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Authors: Rehab F. Abdel-Kader, Rabab M. Ramadan, Rawya Y. Rizk

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The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the proposed system outperforms other face recognition methods. The overall recognition rate for the rotated test images being 97%, demonstrating that the extracted feature vector is an effective rotation invariant feature set with minimal set of selected features.

Keywords: Discrete Cosine Transform, Face Recognition, Feature Extraction, Log Polar Transform, Particle SwarmOptimization.

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Authors: Wei-Da Toh, Yuan Gao, Minkyu Je

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In this paper, an energy efficient digital baseband circuit for piezoelectric (PE) harvester powered batteryless remote control system is presented. Pulse mode PE harvester, which provides short duration of energy, is adopted to replace conventional chemical battery in wireless remote controller. The transmitter digital baseband repeats the control command transmission once the digital circuit is initiated by the power-on-reset. A power efficient data frame format is proposed to maximize the transmission repetition time. By using the proposed frame format and receiver clock and data recovery method, the receiver baseband is able to decode the command even when the received data has 20% error. The proposed transmitter and receiver baseband are implemented using FPGA and simulation results are presented.

Keywords: Clock and Data Recovery (CDR), Correlator, Digital Baseband, Gold Code, Power-On-Reset.

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Authors: K. Rajasekaran, Kannan Balasubramanian

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A sensory network consists of multiple detection locations called sensor nodes, each of which is tiny, featherweight and portable. A single path routing protocols in wireless sensor network can lead to holes in the network, since only the nodes present in the single path is used for the data transmission. Apart from the advantages like reduced computation, complexity and resource utilization, there are some drawbacks like throughput, increased traffic load and delay in data delivery. Therefore, multipath routing protocols are preferred for WSN. Distributing the traffic among multiple paths increases the network lifetime. We propose a scheme, for the data to be transmitted through a dominant path to save energy. In order to obtain a high delivery ratio, a basic route reconstruction protocol is utilized to reconstruct the path whenever a failure is detected. A basic reconstruction routing (BRR) algorithm is proposed, in which a node can leap over path failure by using the already existing routing information from its neighbourhood while the composed data is transmitted from the source to the sink. In order to save the energy and attain high data delivery ratio, data is transmitted along a multiple path, which is achieved by BRR algorithm whenever a failure is detected. Further, the analysis of how the proposed protocol overcomes the drawback of the existing protocols is presented. The performance of our protocol is compared to AOMDV and energy efficient node-disjoint multipath routing protocol (EENDMRP). The system is implemented using NS-2.34. The simulation results show that the proposed protocol has high delivery ratio with low energy consumption.

Keywords: Multipath routing, WSN, energy efficient routing, alternate route, assured data delivery.

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Authors: W. Triampo, D. Baowan, I.M. Tang, N. Nuttavut, J. Wong-Ekkabut, G. Doungchawee

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In this work, we consider a deterministic model for the transmission of leptospirosis which is currently spreading in the Thai population. The SIR model which incorporates the features of this disease is applied to the epidemiological data in Thailand. It is seen that the numerical solutions of the SIR equations are in good agreement with real empirical data. Further improvements are discussed.

Keywords: Leptospirosis, SIR Model, Deterministic model, Thailand.

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: A-shaped compact microstrip antenna, Artificial Neural Network (ANN), adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM).

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Authors: Mario Gómez, Won Ho Kim, Ángel Licona, José Carlos Rodríguez

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This article analyzes innovation activity in Mexico and South Korea. It develops an econometric model to test for structural breaks in the number of patent applications filed by residents and nonresidents in these countries during the period of 1965 to 2012. These changes may suggest that firms’ innovative capabilities have changed because of implementing different science, technology and innovation (STI) policies in Mexico and South Korea. Two important features characterize this research from others already developed by these authors. First, the theoretical research framework in this research is the debate between the assimilation view of growth and the accumulation view of growth. This characteristic suggests that trade liberalization should be accompanied by an adequate STI policy to boost competitiveness among indigenous firms. Second, the analysis in this research stresses the importance of key actors (e.g. governments) to successfully develop innovation capabilities among indigenous firms. Therefore, the question conducting this research is how STI policies in Mexico and South Korea contributed to develop firms’ innovation capabilities in these countries during last decades? The results from this research suggests that STI policy in South Korea was more suitable to boost innovation firms to compete in markets. Data to develop this research was released by the World Intellectual Property Organization (WIPO).

Keywords: Econometric methods, innovation, Mexico, South Korea, STI Policy.

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Authors: Tshaudi Motsima

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Class attendance is key at all levels of education. At tertiary level many students develop a tendency of not attending all classes without being aware of the repercussions of not attending all classes. It is important for all students to attend all classes as they can receive first-hand information and they can benefit more. The student who attends classes is likely to perform better academically than the student who does not. The aim of this paper is to assess the relationship between class attendance and academic performance of industrial engineering students. The data for this study were collected through the attendance register of students and the other data were accessed from the Integrated Tertiary Software and the Higher Education Data Analyzer Portal. Data analysis was conducted on a sample of 93 students. The results revealed that students with medium predicate scores (OR = 3.8; p = 0.027) and students with low predicate scores (OR = 21.4, p < 0.001) were significantly likely to attend less than 80% of the classes as compared to students with high predicate scores. Students with examination performance of less than 50% were likely to attend less than 80% of classes than students with examination performance of 50% and above, but the differences were not statistically significant (OR = 1.3; p = 0.750).

Keywords: Class attendance, examination performance, final outcome, logistic regression.

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Authors: Mouataz Zreika, Maria Estela Varua

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Stock investment decisions are often made based on current events of the global economy and the analysis of historical data. Conversely, visual representation could assist investors’ gain deeper understanding and better insight on stock market trends more efficiently. The trend analysis is based on long-term data collection. The study adopts a hybrid method that combines the Clustering algorithm and Force-directed algorithm to overcome the scalability problem when visualizing large data. This method exemplifies the potential relationships between each stock, as well as determining the degree of strength and connectivity, which will provide investors another understanding of the stock relationship for reference. Information derived from visualization will also help them make an informed decision. The results of the experiments show that the proposed method is able to produced visualized data aesthetically by providing clearer views for connectivity and edge weights.

Keywords: Clustering, force-directed, graph drawing, stock investment analysis.

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Authors: Yung-Chien Sun, O. Grant Clark

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In this paper, the implementation of a rule-based intuitive reasoner is presented. The implementation included two parts: the rule induction module and the intuitive reasoner. A large weather database was acquired as the data source. Twelve weather variables from those data were chosen as the “target variables" whose values were predicted by the intuitive reasoner. A “complex" situation was simulated by making only subsets of the data available to the rule induction module. As a result, the rules induced were based on incomplete information with variable levels of certainty. The certainty level was modeled by a metric called "Strength of Belief", which was assigned to each rule or datum as ancillary information about the confidence in its accuracy. Two techniques were employed to induce rules from the data subsets: decision tree and multi-polynomial regression, respectively for the discrete and the continuous type of target variables. The intuitive reasoner was tested for its ability to use the induced rules to predict the classes of the discrete target variables and the values of the continuous target variables. The intuitive reasoner implemented two types of reasoning: fast and broad where, by analogy to human thought, the former corresponds to fast decision making and the latter to deeper contemplation. . For reference, a weather data analysis approach which had been applied on similar tasks was adopted to analyze the complete database and create predictive models for the same 12 target variables. The values predicted by the intuitive reasoner and the reference approach were compared with actual data. The intuitive reasoner reached near-100% accuracy for two continuous target variables. For the discrete target variables, the intuitive reasoner predicted at least 70% as accurately as the reference reasoner. Since the intuitive reasoner operated on rules derived from only about 10% of the total data, it demonstrated the potential advantages in dealing with sparse data sets as compared with conventional methods.

Keywords: Artificial intelligence, intuition, knowledge acquisition, limited certainty.

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Authors: Anil Bhandari, Imran Khan Pathan, Peeyush K. Sharma, Rakesh K. Patel, Suresh Purohit

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The purpose of this study was to prepare time and pH dependent release tablets of Ayurvedic Churna formulation and evaluate their advantages as colon targeted drug delivery system. The Vidangadi Churna was selected for this study which contains Embelin and Gallic acid. Embelin is used in Helminthiasis as therapeutic agent. Embelin is insoluble in water and unstable in gastric environment so it was formulated in time and pH dependent tablets coated with combination of two polymers Eudragit L100 and ethyl cellulose. The 150mg of core tablet of dried extract and lactose were prepared by wet granulation method. The compression coating was used in the polymer concentration of 150mg for both the layer as upper and lower coating tablet was investigated. The results showed that no release was found in 0.1 N HCl and pH 6.8 phosphate buffers for initial 5 hours and about 98.97% of the drug was released in pH 7.4 phosphate buffer in total 17 Hours. The in vitro release profiles of drug from the formulation could be best expressed first order kinetics as highest linearity (r²= 0.9943). The results of the present study have demonstrated that the time and pH dependent tablets system is a promising vehicle for preventing rapid hydrolysis in gastric environment and improving oral bioavailability of Embelin and Gallic acid for treatment of Helminthiasis.

Keywords: Embelin, Gallic acid, Vidangadi Churna, Colon targeted drug delivery.

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Authors: G. B. Hong, H. W. Chen

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Liquid-Liquid Equilibrium (LLE) data are measured for the ternary mixtures of water + 1-butanol + butyl acetate and quaternary mixtures of water + 1-butanol + butyl acetate + glycerol at atmospheric pressure at 313.15 K. In addition, isothermal vapor–liquid–liquid equilibrium (VLLE) data are determined experimentally at 333.15 K. The region of heterogeneity is found to increase as the hydrophilic agent (glycerol) is introduced into the aqueous mixtures. The experimental data are correlated with the NRTL model. The predicted results from the solution model with the model parameters determined from the constituent binaries are also compared with the experimental values.

Keywords: LLE, VLLE, hydrophilic agent, NRTL.

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Authors: Kunyanuth Kularbphettong, Cholticha Tongsiri

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This paper aims to create the model for student in choosing an emphasized track of student majoring in computer science at Suan Sunandha Rajabhat University. The objective of this research is to develop the suggested system using data mining technique to analyze knowledge and conduct decision rules. Such relationships can be used to demonstrate the reasonableness of student choosing a track as well as to support his/her decision and the system is verified by experts in the field. The sampling is from student of computer science based on the system and the questionnaire to see the satisfaction. The system result is found to be satisfactory by both experts and student as well. 

Keywords: Data mining technique, the decision support system, knowledge and decision rules.

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Authors: R. M. Kashim

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The study investigates the conceptual and procedural knowledge of rational number in primary school teachers, specifically, the primary school teachers level of conceptual knowledge about rational number and the primary school teachers level of procedural knowledge about rational numbers. The study was carried out in Bauchi metropolis in Bauchi state of Nigeria. A Conceptual and Procedural Knowledge Test was used as the instrument for data collection, 54 mathematics teachers in Bauchi primary schools were involved in the study. The collections were analyzed using mean and standard deviation. The findings revealed that the primary school mathematics teachers in Bauchi metropolis posses a low level of conceptual knowledge of rational number and also possess a high level of Procedural knowledge of rational number. It is therefore recommended that to be effective, teachers teaching mathematics most posses a deep understanding of both conceptual and procedural knowledge. That way the most knowledgeable teachers in mathematics deliver highly effective rational number instructions. Teachers should not ignore the mathematical concept aspect of rational number teaching. This is because only the procedural aspect of Rational number is highlighted during instructions; this often leads to rote - learning of procedures without understanding the meanings. It is necessary for teachers to learn rational numbers teaching method that focus on both conceptual knowledge and procedural knowledge teaching.

Keywords: Conceptual knowledge, primary school teachers, procedural knowledge, rational numbers.

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Authors: Pilar Rey-del-Castillo, Jesús Cardeñosa

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There are many situations where input feature vectors are incomplete and methods to tackle the problem have been studied for a long time. A commonly used procedure is to replace each missing value with an imputation. This paper presents a method to perform categorical missing data imputation from numerical and categorical variables. The imputations are based on Simpson-s fuzzy min-max neural networks where the input variables for learning and classification are just numerical. The proposed method extends the input to categorical variables by introducing new fuzzy sets, a new operation and a new architecture. The procedure is tested and compared with others using opinion poll data.

Keywords: Classifier, imputation techniques, fuzzy systems, fuzzy min-max neural networks.

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Authors: Nam Lethanh, Bryan T. Adey

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In this paper, the potential use of an exponential hidden Markov model to model a hidden pavement deterioration process, i.e. one that is not directly measurable, is investigated. It is assumed that the evolution of the physical condition, which is the hidden process, and the evolution of the values of pavement distress indicators, can be adequately described using discrete condition states and modeled as a Markov processes. It is also assumed that condition data can be collected by visual inspections over time and represented continuously using an exponential distribution. The advantage of using such a model in decision making process is illustrated through an empirical study using real world data.

Keywords: Deterioration modeling, Exponential distribution, Hidden Markov model, Pavement management

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Authors: Taruna Katyal Arora, Geeta Kumari, Hari Shankar, Neelima Mishra

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Substandard and counterfeit antimalarials is a major problem in malaria endemic areas. The availability of counterfeit/ substandard medicines is not only decreasing the efficacy in patients, but it is also one of the contributing factors for developing antimalarial drug resistance. Owing to this, a pilot study was conducted to survey quality of drugs collected from different malaria endemic areas of India. Artesunate+Sulphadoxine-Pyrimethamine (AS+SP), Artemether-Lumefantrine (AL), Chloroquine (CQ) tablets were randomly picked from public health facilities in selected states of India. The quality of antimalarial drugs from these areas was assessed by using Global Pharma Health Fund Minilab test kit. This includes physical/visual inspection and disintegration test. Thin-layer chromatography (TLC) was carried out for semi-quantitative assessment of active pharmaceutical ingredients. A total of 45 brands, out of which 21 were for CQ, 14 for AL and 10 for AS+SP were tested from Uttar Pradesh (U.P.), Mizoram, Meghalaya and Gujrat states. One out of 45 samples showed variable disintegration and retension factor. The variable disintegration and retention factor which would have been due to substandard quality or other factors including storage. However, HPLC analysis confirms standard active pharmaceutical ingredient, but may be due to humid temperature and moisture in storage may account for the observed result.

Keywords: Antimalarial medicines, counterfeit, substandard, thin layer chromatography.

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Authors: S. Kotsiantis, A. Kostoulas, S. Lykoudis, A. Argiriou, K. Menagias

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Estimates of temperature values at a specific time of day, from daytime and daily profiles, are needed for a number of environmental, ecological, agricultural and technical applications, ranging from natural hazards assessments, crop growth forecasting to design of solar energy systems. The scope of this research is to investigate the efficiency of data mining techniques in estimating minimum, maximum and mean temperature values. For this reason, a number of experiments have been conducted with well-known regression algorithms using temperature data from the city of Patras in Greece. The performance of these algorithms has been evaluated using standard statistical indicators, such as Correlation Coefficient, Root Mean Squared Error, etc.

Keywords: regression algorithms, supervised machine learning.

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Authors: Farshad Arvin, Shyamala Doraisamy

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This paper presents a new hardware interface using a microcontroller which processes audio music signals to standard MIDI data. A technique for processing music signals by extracting note parameters from music signals is described. An algorithm to convert the voice samples for real-time processing without complex calculations is proposed. A high frequency microcontroller as the main processor is deployed to execute the outlined algorithm. The MIDI data generated is transmitted using the EIA-232 protocol. The analyses of data generated show the feasibility of using microcontrollers for real-time MIDI generation hardware interface.

Keywords: Signal processing, MIDI, Microcontroller, EIA-232.

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Authors: B. Elshafei, X. Mao

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The demand for renewable energy is significantly increasing, major investments are being supplied to the wind power generation industry as a leading source of clean energy. The wind energy sector is entirely dependable and driven by the prediction of wind speed, which by the nature of wind is very stochastic and widely random. This s0tudy employs deep multi-fidelity Gaussian process regression, used to predict wind speeds for medium term time horizons. Data of the RUNE experiment in the west coast of Denmark were provided by the Technical University of Denmark, which represent the wind speed across the study area from the period between December 2015 and March 2016. The study aims to investigate the effect of pre-processing the data by denoising the signal using empirical wavelet transform (EWT) and engaging the vector components of wind speed to increase the number of input data layers for data fusion using deep multi-fidelity Gaussian process regression (GPR). The outcomes were compared using root mean square error (RMSE) and the results demonstrated a significant increase in the accuracy of predictions which demonstrated that using vector components of the wind speed as additional predictors exhibits more accurate predictions than strategies that ignore them, reflecting the importance of the inclusion of all sub data and pre-processing signals for wind speed forecasting models.

Keywords: Data fusion, Gaussian process regression, signal denoise, temporal extrapolation.

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Authors: M. Santhalakshmi, P Suganthi

Abstract:

Wireless Sensor Networks (WSNs) have many advantages. Their deployment is easier and faster than wired sensor networks or other wireless networks, as they do not need fixed infrastructure. Nodes are partitioned into many small groups named clusters to aggregate data through network organization. WSN clustering guarantees performance achievement of sensor nodes. Sensor nodes energy consumption is reduced by eliminating redundant energy use and balancing energy sensor nodes use over a network. The aim of such clustering protocols is to prolong network life. Low Energy Adaptive Clustering Hierarchy (LEACH) is a popular protocol in WSN. LEACH is a clustering protocol in which the random rotations of local cluster heads are utilized in order to distribute energy load among all sensor nodes in the network. This paper proposes Connected Dominant Set (CDS) based cluster formation. CDS aggregates data in a promising approach for reducing routing overhead since messages are transmitted only within virtual backbone by means of CDS and also data aggregating lowers the ratio of responding hosts to the hosts existing in virtual backbones. CDS tries to increase networks lifetime considering such parameters as sensors lifetime, remaining and consumption energies in order to have an almost optimal data aggregation within networks. Experimental results proved CDS outperformed LEACH regarding number of cluster formations, average packet loss rate, average end to end delay, life computation, and remaining energy computation.

Keywords: Wireless sensor network, connected dominant set, clustering, data aggregation.

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Authors: Edwaldo R. B. Monteiro, Patricia D. M. Plentz, Edson R. De Pieri

Abstract:

Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e, meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia.

Keywords: Deadline missing, historical data, mobile robots, prediction mechanism.

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