Effects of use of higher strength interlayer and external cooling on properties of friction stir welded AA6061-T6 joints

Although sound AA6061-T6 joints can be produced by friction stir welding, a loss in strength takes place in the weld region. In this study, it was demonstrated that the strength of the nugget could be increased by the use of a higher strength interlayer during friction stir welding. This strength recovery cannot, however, be attainable in the heat affected zone. Although an external cooling was applied during welding in order to increase strength in the heat affected zone, it was not sufficient for achieving the required cooling condition for improved strength.     

Evaluating of traumatic brain injuries using artificial neural networks

The purpose of this study is to develop a diagnostic system to detect the severity of traumatic brain injuries using artificial neural networks. Three layered back propagation neural network with an input layer of 10 nodes whose output providing the inputs to a hidden layer was used. Thirty-two patients with traumatic brain injuries in different age and gender were taken in the study. Electroencephalography, Trauma and Glasgow coma scores were used for evaluating the data. The results obtained from the system were compared with the findings of neurologists. We found a significant relationship between the findings of neurologists and systems output for normal, mild, moderate and severe electroencephalography tracing data. Getting this system in routine use will lead to make a rapid decision for the degree of trauma with electroencephalography and revised trauma score.     

Automatic segmentation of corpus collasum using Gaussian mixture modeling and Fuzzy C means methods

This paper presents a comparative study of the success and performance of the Gaussian mixture modeling and Fuzzy C means methods to determine the volume and cross-sectionals areas of the corpus callosum (CC) using simulated and real MR brain images. The Gaussian mixture model (GMM) utilizes weighted sum of Gaussian distributions by applying statistical decision procedures to define image classes. In the Fuzzy C means (FCM), the image classes are represented by certain membership function according to fuzziness information expressing the distance from the cluster centers. In this study, automatic segmentation for midsagittal section of the CC was achieved from simulated and real brain images. The volume of CC was obtained using sagittal sections areas. To compare the success of the methods, segmentation accuracy, Jaccard similarity and time consuming for segmentation were calculated. The results show that the GMM method resulted by a small margin in more accurate segmentation (midsagittal section segmentation accuracy 98.3% and 97.01% for GMM and FCM); however the FCM method resulted in faster segmentation than GMM. With this study, an accurate and automatic segmentation system that allows opportunity for quantitative comparison to doctors in the planning of treatment and the diagnosis of diseases affecting the size of the CC was developed. This study can be adapted to perform segmentation on other regions of the brain, thus, it can be operated as practical use in the clinic.     

The selection of technology forecasting method using a multi-criteria interval-valued intuitionistic fuzzy group decision making approach

Technological forecasting is a tool for organizations to develop their technology strategies. The quality of forecasting is extremely important for the accuracy of the results and in turn company future. Therefore a proper selection methodology of forecasting technique that considers the characteristics of technology and resources needed such as cost, time is essential. On the other hand, although many forecasting techniques are available, there is a high uncertainty in choosing the most appropriate technique among a set of available techniques. In this paper interval valued intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is proposed for the solution of technological forecasting technique selection problem. The proposed method includes seven selection criteria and twelve forecasting technique alternatives. The methodology is applied for 3D TV technology. The results revealed that Fisher Pry method is found as the most appropriate method for forecasting since it has the highest closeness coefficient.     

Development of a decision support system for robot selection

With the availability of more different robot types and models along with their separate specifications, selecting the most appropriate robot is becoming more difficult and complicated for companies. Furthermore, a common set of robot selection criteria is not available for the decision makers. In this study, a two-phase robot selection decision support system, namely ROBSEL, is developed to help the decision makers in their robot selection decisions. In development of ROBSEL, an independent set of criteria is obtained first and arranged in the Fuzzy Analytical Hierarchy Process (FAHP) decision hierarchy. In the first elimination phase of the decision support system, the user obtains the feasible set of robots by providing limited values for the 15 requirements. ROBSEL, then, uses FAHP decision hierarchy to rank the feasible robots in the second phase. ROBSEL is illustrated and tested and several critical issues in its practical usage are explored in the paper. The applications of ROBSEL show that ROBSEL is a useful, practical and easy to use robot selection tool and improves robot selection decisions in the companies.     

Liquefaction/Solubilization of Low-Rank Turkish Coals by White-Rot Fungus (Phanerochaete chrysosporium)

Microbial coal liquefaction/solubilization of three low-rank Turkish coals (Bursa-Kestelek, Kütahya-Seyitömer and Mu?la-Yata?an lignite) was attempted by using a white-rot fungus (Phanerochaete chrysosporium DSM No. 6909); chemical compositions of the products were investigated. The lignite samples were oxidized by nitric acid under moderate conditions and then oxidized samples were placed on the agar medium of Phanerochaete chrysosporium. FTIR spectra of raw lignites, oxidized lignites and liquid products were recorded, and the acetone-soluble fractions of these samples were identified by GC-MS technique. Results show that the fungus affects the nitro and carboxyl/carbonyl groups in oxidized lignite sample, the liquid products obtained by microbial effects are the mixture of water-soluble compounds, and show limited organic solubility.     

New conjugated azomethine oligomers obtained from bis-(hydroxyphenyl)methylenediamine via oxidative polycondensation and their complexes with metals

A new conjugated aromatic oligo(azomethine) derivative was synthesized by oxidative polycondensation of 1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine (HPMPDA) by air, H₂O₂ and NaOCl oxidants in an aqueous alkaline medium. The structures of 1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine and oligo-1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine (OHPMPDA) were confirmed by FT-IR, UV–vis, ¹H NMR, ¹³C NMR and elemental analysis. The characterization was made by TGA–DTA, size exclusion chromatography (SEC), magnetic moment and solubility tests. The ¹H NMR and ¹³C NMR data showed that the polymerization was proceeded by C–C coupling according to ortho and para positions of –OH group of 1,4-bis[(2-hydroxyphenyl)methylene]phenylenediamine. Metal complex compounds of OHPMPDA were synthesized with metal salts of Fe, Co, Ni, Cu, Zn, Cd, Mn, Cr, Pb and Hg. Elemental analyses of oligomer–metal complexes suggested that the ratio of metal to oligomer is 1:1. Thermal stabilities of the oligomer–metal complexes were determined by thermogravimetric analyses (TGA). According to TG analyses, oligomer–metal complexes were fairly stable against temperature and thermal decomposition. Also, electrical conductivities of OHPMPDA and oligomer–metal complexes were measured by four-point technique. The results of this study showed that aromatic oligoazomethine and its metal complexes were an interesting class of conjugated compounds of which electronic structure and the other properties can be regulated over a wide range by using different oxidation reagents.     

An investigation of some Schiff bases as corrosion inhibitors for austenitic chromium–nickel steel in H2SO4

The corrosion inhibition of austenitic chromium–nickel steel by two Schiff bases, N-(1-toluidine)salicylaldimine and N-(2-hydroxyphenyl)salicylaldimine, was investigated in sulphuric acid medium. The effect of concentration and temperature on inhibition properties was determined. It was found that when the concentrations of inhibitor were increased the inhibition efficiencies () and surface coverage () increased. Some thermodynamic parameters such as free energy of adsorption, G _ads, and enthalpy, H, were determined for the Schiff bases. Experimental results agree with the Temkin isotherm for N-(1-toluidine)salicylaldimine, but the Langmuir isotherm is more appropriate for N-(2-hydroxyphenyl)salicylaldimine.     

Estimating Construction Material Indices with ARIMA and Optimized NARNETs

Construction Industry operates relying on various key economic indicators. One of these indicators is material prices. On the other hand, cost is a key concern in all operations of the construction industry. In the uncertain conditions, reliable cost forecasts become an important source of information. Material cost is one of the key components of the overall cost of construction. In addition, cost overrun is a common problem in the construction industry, where nine out of ten construction projects face cost overrun. In order to carry out a successful cost management strategy and prevent cost overruns, it is very important to find reliable methods for the estimation of construction material prices. Material prices have a time dependent nature. In order to increase the foreseeability of the costs of construction materials, this study focuses on estimation of construction material indices through time series analysis. Two different types of analysis are implemented for estimation of the future values of construction material indices. The first method implemented was Autoregressive Integrated Moving Average (ARIMA), which is known to be successful in estimation of time series having a linear nature. The second method implemented was Non-Linear Autoregressive Neural Network (NARNET) which is known to be successful in modeling and estimating of series with non-linear components. The results have shown that depending on the nature of the series, both these methods can successfully and accurately estimate the future values of the indices. In addition, we found out that Optimal NARNET architectures which provide better accuracy in estimation of the series can be identified/discovered as result of grid search on NARNET hyperparameters.