Optimization of surface roughness in end milling Castamide

Castamide is vulnerable to humidity up to 7%; therefore, it is important to know the effect of processing parameters on Castamide with and without humidity during machining. In this study, obtained quality of surface roughness of Castamide block samples prepared in wet and dry conditions, which is processed by using the same cutting parameters, were compared. Moreover, an artificial neural network (ANN) modeling technique was developed with the results obtained from the experiments. For the training of ANN model, material type, cutting speed, cutting rate, and depth of cutting parameters were used. In this way, average surface roughness values could be estimated without performing actual application for those values. Various experimental results for different material types with cutting parameters were evaluated by different ANN training algorithms. So, it aims to define the average surface roughness with minimum error by using the best reliable ANN training algorithm. Parameters as cutting speed (V _c), feed rate (f), diameter of cutting equipment, and depth of cut (a _p) have been used as the input layers; average surface roughness has been also used as output layer. For testing data, root mean squared error, the fraction of variance (R ²), and mean absolute percentage error were found to be 0.0681%, 0.9999%, and 0.1563%, respectively. With these results, we believe that the ANN can be used for prediction of average surface roughness.     

Forecasting Bank Failure: Base Learners,Ensembles and Hybrid Ensembles

The prediction of bankruptcy for financial companies, especially banks, has been extensively researched area and creditors, auditors, stockholders and senior managers are all interested in bank bankruptcy prediction. In this paper, three common machine learning models namely Logistic, J48 and Voted Perceptron are used as the base learners. In addition, an attribute-base ensemble learning method namely Random Subspaces and two instance-base ensemble learning methods namely Bagging and Multi-Boosting are employed to enhance the prediction accuracy of conventional machine learning models for bank failure prediction. The models are grouped in the following families of approaches: (i) conventional machine learning models, (ii) ensemble learning models and (iii) hybrid ensemble learning models. Experimental results indicate a clear outperformance of hybrid ensemble machine learning models over conventional base and ensemble models. These results indicate that hybrid ensemble learning models can be used as a reliable predicting model for bank failures.     

E-learning adoption of academicians: a proposal for an extended model

From the beginning of the e-learning technology era, many concerns arose regarding the use of e-learning in everyday academic processes. Thus, in recent years, a number of studies were conducted on e-learning acceptance, mostly with the use of Unified Theory of Acceptance and Use of Technology (UTAUT) Model. Current study investigates the determinants of acceptance of academic e-learning technologies with the use of proposed modified UTAUT model. The basic UTAUT model was supplemented by the examined new determinants: system interactivity and the area of scientific expertise. Therefore, the paper contributes to UTAUT theory, applied in e-learning, by utilising empirical research with a modified UTAUT model including these two new variables. This study confirmed the hypotheses related to the effects of performance expectancy, effort expectancy, and system interactivity on behavioural intention to use e-learning technologies, and also stated that the area of scientific expertise does not have a direct impact on the intention to use e-learning technologies. The research model has been tested in an academic environment in Turkey.     

Critical information technology issues in Turkish healthcare

While the importance of information technology in reducing soaring healthcare costs and enhancing service quality is increasingly being recognized, significant challenges remain in how it is implemented. Although there are a few studies investigating key IT issues in healthcare in advanced countries, there are virtually none in developing countries. We bridge this gap by investigating the critical information technology issues in healthcare facilities in Turkey. These issues are developed based on the opinions of senior hospital managers. The top ten issues include privacy, quality, security, and the implementation of electronic medical records. Further analyses provide additional insights into the results.     

The synthesis,antimicrobial activity and absorption characteristics of some novel heterocyclic disazo dyes

Aniline derivatives were diazotized and coupled with 3-aminocrotononitrile to give the corresponding 2-arylhydrazono-3-ketiminobutyronitriles. Cyclization of these arylhydrazono derivatives with hydrazine monohydrate afforded 5-amino-4-arylazo-3-methyl-1H-pyrazoles which were subsequently diazotised and coupled with malononitrile to yield a series of pyrazolylhydrazonomalononitriles. These compounds were then reacted with hydrazine monohydrate to provide 10, novel, heterocyclic disazo dyes, which were characterized by elemental analysis and spectral methods. The antimicrobial activity and absorption characteristics of the dyes were also examined in detail.     

Pre-treatment process of B<Subscript>4</Subscript>C particles to improve incorporation into molten AA2014 alloy

The properties of particle-reinforced aluminum alloy composites depend on the microstructure and evolved uniformity distribution of particles in the matrix, wettability of particles by the melt and chemical reactions between particles and matrix. The wettability of B₄C particles by the molten aluminum alloys is generally poor below 1100 °C making their incorporation difficult. In this study, in order to improve incorporation of the B₄C particles by AA2014 alloy melt a novel pre-treatment process was presented and B₄C-2014 Al composites containing B₄C particles up to 12 vol.% were manufactured by using stir casting. SEM observations have shown that uniformity distribution of B₄C in the matrix was satisfactory and there was no reaction at the particle-matrix interface due to low processing temperature. However clustering of B₄C particles was observed in relatively high particle-containing composites, which is commonly associated with the casting route.     

Contextual alignment of ontologies in the eCOIN semantic interoperability framework

The prospect of combining information from diverse sources for superior decision making is plagued by the challenge of semantic heterogeneity, as data sources often adopt different conventions and interpretations when there is no coordination. An emerging solution in information integration is to develop an ontology as a standard data model for a domain of interest, and then to define the correspondences between the data sources and this common model to eliminate their semantic heterogeneity and produce a single integrated view of the data sources. We first claim that this single integrated view approach is unnecessarily restrictive, and instead offer the view that ontologies can simultaneously accommodate multiple integrated views provided the accompaniment of contexts, a set of axioms on the interpretation of data allowing local variations in representation and nuances in meaning, and a conversion function network between contexts to reconcile contextual differences. Then, we illustrate how to achieve semantic interoperability between multiple ontology-based applications. During this process, application ontologies are aligned through the reconciliation of their context models, and a new application with a virtual merged ontology is created. We illustrate this alternative approach with the alignment of air travel and car rental domains, an actual example from our prototype implementation.

Frequency-response-shaped LMS adaptive filter

A new LMS algorithm is introduced for improved performance when a sinusoidal input signal is corrupted by correlated noise. The algorithm is based on shaping the frequency response of the transversal filter. This shaping is performed on-line by the inclusion of an additional term similar to the leakage factor in the adaptation equation of leaky LMS. This new term, which involves the multiplication of the filter coefficient vector by a matrix, is calculated in an efficient manner using the FFT. The proposed adaptive filter is shown analytically to converge in the mean and mean-square sense. The filter is also analyzed in the steady state in order to show the frequency-response-shaping capability. Simulation results illustrate that the performance of the frequency-response-shaped LMS (FRS-LMS) algorithm is very effective even for highly correlated noise.