3D CFD Simulation of Thermal Hydraulic Performances on Louvered Fin Automotive Heat Exchangers

This study deals with Computational Fluid Dynamics (CFD) studies of the interactions between the air flow and louvered fins which equipped the automotive heat exchangers. 3D numerical simulation results are obtained by using the ANSYS Fluent 13.0 code and compared to experimental data. The paper studies the effect of louver angle and louver pitch geometrical parameters, on overall thermal hydraulic performances of louvered fins. The comparison between CFD simulations and experimental data show that established 3-D CFD model gives a good agreement. The validation agrees, with about 7% of deviation respectively of friction and Colburn factors to experimental results. As first, it is found that the louver angle has a strong influence on the heat transfer rate. Then, louver angle and louver pitch variation of the louvers and their effects on thermal hydraulic performances are studied. In addition to this study, it is shown that the second half of the fin takes has a significant contribution on pressure drop increase without any increase in heat transfer.

Game Skill Measure for Mixed Games

Games can be classified as games of skill, games of chance or otherwise be classified as mixed. This paper deals with the topic of scientifically classifying mixed games as more reliant on elements of chance or elements of skill and ways to scientifically measure the amount of skill involved. This is predominantly useful for classification of games as legal or illegal in deferent jurisdictions based on the local gaming laws. We propose a novel measure of skill to chance ratio called the Game Skill Measure (GSM) and utilize it to calculate the skill component of a popular variant of Poker.

Value of Sharing: Viral Advertisement

Sharing motivations of viral advertisements by consumers and the impacts of these advertisements on the perceptions for brand will be questioned in this study. Three fundamental questions are answered in the study. These are advertisement watching and sharing motivations of individuals, criteria of liking viral advertisement and the impact of individual attitudes for viral advertisement on brand perception respectively. This study will be carried out via a viral advertisement which was practiced in Turkey. The data will be collected by survey method and the sample of the study consists of individuals who experienced the practice of sample advertisement. Data will be collected by online survey method and will be analyzed by using SPSS statistical package program. Recently traditional advertisement mind have been changing. New advertising approaches which have significant impacts on consumers have been argued. Viral advertising is a modernist advertisement mind which offers significant advantages to brands apart from traditional advertising channels such as television, radio and magazines. Viral advertising also known as Electronic Word-of- Mouth (eWOM) consists of free spread of convincing messages sent by brands among interpersonal communication. When compared to the traditional advertising, a more provocative thematic approach is argued. The foundation of this approach is to create advertisements that are worth sharing with others by consumers. When that fact is taken into consideration, in a manner of speaking it can also be stated that viral advertising is media engineering. The content worth sharing makes people being a volunteer spokesman of a brand and strengthens the emotional bonds among brand and consumer. Especially for some sectors in countries which are having traditional advertising channel limitations, viral advertising creates vital advantages.

Comparative Approach of Measuring Price Risk on Romanian and International Wheat Market

This paper aims to present the main instruments used in the economic literature for measuring the price risk, pointing out on the advantages brought by the conditional variance in this respect. The theoretical approach will be exemplified by elaborating an EGARCH model for the price returns of wheat, both on Romanian and on international market. To our knowledge, no previous empirical research, either on price risk measurement for the Romanian markets or studies that use the ARIMA-EGARCH methodology, have been conducted. After estimating the corresponding models, the paper will compare the estimated conditional variance on the two markets.

Operation Assay of an Industrial Single-Source – Single-Detector Gamma CT Using MCNP4C Code Simulation and Experimental Test Comparisons

A 3D industrial computed tomography (CT) manufactured based on a first generation CT systems, single-source – single-detector, was evaluated. Operation accuracy assessment of the manufactured system was achieved using simulation in comparison with experimental tests. 137Cs and 60Co were used as a gamma source. Simulations were achieved using MCNP4C code. Experimental tests of 137Cs were in good agreement with the simulations

Enhancing the Peer-To-Peer Architecture with a Roaming Service and OWL

This paper addresses the problem of building a unified structure to describe a peer-to-peer system. Our approach uses the well-known notations in the P2P area, and provides a global architecture that puts a separation between the platform specific characteristics and the logical ones. In order to enable the navigation of the peer across platforms, a roaming layer is added. The latter provides a capability to define a unique identification of peer and assures the mapping between this identification and those used in each platform. The mapping task is assured by special wrapper. In addition, ontology is proposed to give a clear presentation of the structure of the P2P system without interesting in the content and the resource managed by the peer. The ontology is created according to the web semantic paradigm and using OWL language; so, the structure of the system is considered as a web resource.

New Wavelet-Based Superresolution Algorithm for Speckle Reduction in SAR Images

This paper describes a novel projection algorithm, the Projection Onto Span Algorithm (POSA) for wavelet-based superresolution and removing speckle (in wavelet domain) of unknown variance from Synthetic Aperture Radar (SAR) images. Although the POSA is good as a new superresolution algorithm for image enhancement, image metrology and biometric identification, here one will use it like a tool of despeckling, being the first time that an algorithm of super-resolution is used for despeckling of SAR images. Specifically, the speckled SAR image is decomposed into wavelet subbands; POSA is applied to the high subbands, and reconstruct a SAR image from the modified detail coefficients. Experimental results demonstrate that the new method compares favorably to several other despeckling methods on test SAR images.

Fuzzy Rules Generation and Extraction from Support Vector Machine Based on Kernel Function Firing Signals

Our study proposes an alternative method in building Fuzzy Rule-Based System (FRB) from Support Vector Machine (SVM). The first set of fuzzy IF-THEN rules is obtained through an equivalence of the SVM decision network and the zero-ordered Sugeno FRB type of the Adaptive Network Fuzzy Inference System (ANFIS). The second set of rules is generated by combining the first set based on strength of firing signals of support vectors using Gaussian kernel. The final set of rules is then obtained from the second set through input scatter partitioning. A distinctive advantage of our method is the guarantee that the number of final fuzzy IFTHEN rules is not more than the number of support vectors in the trained SVM. The final FRB system obtained is capable of performing classification with results comparable to its SVM counterpart, but it has an advantage over the black-boxed SVM in that it may reveal human comprehensible patterns.

Instance-Based Ontology Matching Using Different Kinds of Formalism

Ontology Matching is a task needed in various applica-tions, for example for comparison or merging purposes. In literature,many algorithms solving the matching problem can be found, butmost of them do not consider instances at all. Mappings are deter-mined by calculating the string-similarity of labels, by recognizinglinguistic word relations (synonyms, subsumptions etc.) or by ana-lyzing the (graph) structure. Due to the facts that instances are oftenmodeled within the ontology and that the set of instances describesthe meaning of the concepts better than their meta information,instances should definitely be incorporated into the matching process.In this paper several novel instance-based matching algorithms arepresented which enhance the quality of matching results obtainedwith common concept-based methods. Different kinds of formalismsare use to classify concepts on account of their instances and finallyto compare the concepts directly.KeywordsInstances, Ontology Matching, Semantic Web

Inferring Hierarchical Pronunciation Rules from a Phonetic Dictionary

This work presents a new phonetic transcription system based on a tree of hierarchical pronunciation rules expressed as context-specific grapheme-phoneme correspondences. The tree is automatically inferred from a phonetic dictionary by incrementally analyzing deeper context levels, eventually representing a minimum set of exhaustive rules that pronounce without errors all the words in the training dictionary and that can be applied to out-of-vocabulary words. The proposed approach improves upon existing rule-tree-based techniques in that it makes use of graphemes, rather than letters, as elementary orthographic units. A new linear algorithm for the segmentation of a word in graphemes is introduced to enable outof- vocabulary grapheme-based phonetic transcription. Exhaustive rule trees provide a canonical representation of the pronunciation rules of a language that can be used not only to pronounce out-of-vocabulary words, but also to analyze and compare the pronunciation rules inferred from different dictionaries. The proposed approach has been implemented in C and tested on Oxford British English and Basic English. Experimental results show that grapheme-based rule trees represent phonetically sound rules and provide better performance than letter-based rule trees.

Effect of Helium-Argon Mixtures on the Heat Transfer and Fluid Flow in Gas Tungsten Arc Welding

A transient finite element model has been developed to study the heat transfer and fluid flow during spot Gas Tungsten Arc Welding (GTAW) on stainless steel. Temperature field, fluid velocity and electromagnetic fields are computed inside the cathode, arc-plasma and anode using a unified MHD formulation. The developed model is then used to study the influence of different helium-argon gas mixtures on both the energy transferred to the workpiece and the time evolution of the weld pool dimensions. It is found that the addition of helium to argon increases the heat flux density on the weld axis by a factor that can reach 6.5. This induces an increase in the weld pool depth by a factor of 3. It is also found that the addition of only 10% of argon to helium decreases considerably the weld pool depth, which is due to the electrical conductivity of the mixture that increases significantly when argon is added to helium.

Evaluation of Newly Developed Dot-ELISA Test for Identification of Naja-naja sumantrana and Calloselasma rhodostoma Venom Antigens

Snake bite cases in Malaysia most often involve the species Naja-naja and Calloselasma rhodostoma. In keeping with the need for a rapid snake venom detection kit in a clinical setting, plate and dot-ELISA test for the venoms of Naja-naja sumatrana, Calloselasma rhodostoma and the cobra venom fraction V antigen was developed. Polyclonal antibodies were raised and further used to prepare the reagents for the dot-ELISA test kit which was tested in mice, rabbit and virtual human models. The newly developed dot- ELISA kit was able to detect a minimum venom concentration of 244ng/ml with cross reactivity of one antibody type. The dot-ELISA system was sensitive and specific for all three snake venom types in all tested animal models. The lowest minimum venom concentration detectable was in the rabbit model, 244ng/ml of the cobra venom fraction V antigen. The highest minimum venom concentration was in mice, 1953ng/ml against a multitude of venoms. The developed dot-ELISA system for the detection of three snake venom types was successful with a sensitivity of 95.8% and specificity of 97.9%.

Idiopathic Constipation can be Subdivided in Clinical Subtypes: Data Mining by Cluster Analysis on a Population based Study

The prevalence of non organic constipation differs from country to country and the reliability of the estimate rates is uncertain. Moreover, the clinical relevance of subdividing the heterogeneous functional constipation disorders into pre-defined subgroups is largely unknown.. Aim: to estimate the prevalence of constipation in a population-based sample and determine whether clinical subgroups can be identified. An age and gender stratified sample population from 5 Italian cities was evaluated using a previously validated questionnaire. Data mining by cluster analysis was used to determine constipation subgroups. Results: 1,500 complete interviews were obtained from 2,083 contacted households (72%). Self-reported constipation correlated poorly with symptombased constipation found in 496 subjects (33.1%). Cluster analysis identified four constipation subgroups which correlated to subgroups identified according to pre-defined symptom criteria. Significant differences in socio-demographics and lifestyle were observed among subgroups.

Hybridizing Genetic Algorithm with Biased Chance Local Search

This paper explores university course timetabling problem. There are several characteristics that make scheduling and timetabling problems particularly difficult to solve: they have huge search spaces, they are often highly constrained, they require sophisticated solution representation schemes, and they usually require very time-consuming fitness evaluation routines. Thus standard evolutionary algorithms lack of efficiency to deal with them. In this paper we have proposed a memetic algorithm that incorporates the problem specific knowledge such that most of chromosomes generated are decoded into feasible solutions. Generating vast amount of feasible chromosomes makes the progress of search process possible in a time efficient manner. Experimental results exhibit the advantages of the developed Hybrid Genetic Algorithm than the standard Genetic Algorithm.

Transcutaneous Inductive Powering Links Based on ASK Modulation Techniques

This paper presented a modified efficient inductive powering link based on ASK modulator and proposed efficient class- E power amplifier. The design presents the external part which is located outside the body to transfer power and data to the implanted devices such as implanted Microsystems to stimulate and monitoring the nerves and muscles. The system operated with low band frequency 10MHZ according to industrial- scientific – medical (ISM) band to avoid the tissue heating. For external part, the modulation index is 11.1% and the modulation rate 7.2% with data rate 1 Mbit/s assuming Tbit = 1us. The system has been designed using 0.35-μm fabricated CMOS technology. The mathematical model is given and the design is simulated using OrCAD P Spice 16.2 software tool and for real-time simulation, the electronic workbench MULISIM 11 has been used.

Design of Thermal Control Subsystem for TUSAT Telecommunication Satellite

TUSAT is a prospective Turkish Communication Satellite designed for providing mainly data communication and broadcasting services through Ku-Band and C-Band channels. Thermal control is a vital issue in satellite design process. Therefore, all satellite subsystems and equipments should be maintained in the desired temperature range from launch to end of maneuvering life. The main function of the thermal control is to keep the equipments and the satellite structures in a given temperature range for various phases and operating modes of spacecraft during its lifetime. This paper describes the thermal control design which uses passive and active thermal control concepts. The active thermal control is based on heaters regulated by software via thermistors. Alternatively passive thermal control composes of heat pipes, multilayer insulation (MLI) blankets, radiators, paints and surface finishes maintaining temperature level of the overall carrier components within an acceptable value. Thermal control design is supported by thermal analysis using thermal mathematical models (TMM).

Changes in the Research of Crisis

Thanks to the interdisciplinary nature of crises, the position of researchers in that field is rather difficult. Very often the traditional methods of research cannot be applied there. The article is aimed at the changes in crises research. It describes the substance of individual changes and emphasizes the shift in research approaches to the crisis.

Cloud Computing Initiative using Modified Ant Colony Framework

Scheduling of diversified service requests in distributed computing is a critical design issue. Cloud is a type of parallel and distributed system consisting of a collection of interconnected and virtual computers. It is not only the clusters and grid but also it comprises of next generation data centers. The paper proposes an initial heuristic algorithm to apply modified ant colony optimization approach for the diversified service allocation and scheduling mechanism in cloud paradigm. The proposed optimization method is aimed to minimize the scheduling throughput to service all the diversified requests according to the different resource allocator available under cloud computing environment.

A Low-Voltage Current-Mode Wheatstone Bridge using CMOS Transistors

This paper presents a new circuit arrangement for a current-mode Wheatstone bridge that is suitable for low-voltage integrated circuits implementation. Compared to the other proposed circuits, this circuit features severe reduction of the elements number, low supply voltage (1V) and low power consumption (

Combining Bagging and Boosting

Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.