Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in Turkey: A case of hybrids

This paper aims to determine the factors that have an impact on the consumers’ willingness to pay a premium for hybrid automobiles in Turkey. A web-based random survey was conducted in different regions of Turkey. A questionnaire was administered to 1983 participants in January–March of 2009. The questionnaire was prepared by taking the issues raised in various sources into account. An ordered Probit model was used to meet the objective. Results show that variables such as income, gender, education, concerns about global warming, number of automobiles, importance of automobile performance, risk preference, attitude toward the alternative energy sources have an impact on the consumers’ willingness to pay a premium for hybrids. Findings suggest that consumers who have high income, higher educational level, and concerns about the global warming are more likely to pay a premium for hybrids. This study is expected to make important contributions to the current literature related to the consumers’ willingness to pay for hybrids by providing a research study from a developing country’s perspective. Results of this study also make important contributions to the policy and decision makers, environmental groups and automotive industry.     

Properties of pumice aggregate concretes at elevated temperatures and comparison with ANN models

The mechanical properties and thermal conductivity of concretes including pumice aggregate (PA) exposed to elevated temperature were analyzed by thermal conductivity, compressive strength, flexure strength, dynamic elasticity modulus (DEM) and dry unit weight tests. PA concrete specimens were cast by replacing a varying part of the normal aggregate (0–2 mm) with the PA. All concrete samples were prepared and cured at 23 ± 10C lime saturated water for 28 days. Compressive strength of concretes including PA decreased that reductions were 14, 19, 25 and 34% for 25, 50, 75 and 100% PA, respectively. The maximum thermal conductivity of 1.9382 W/mK was observed with the control samples containing normal aggregate. The tests were carried out by subjecting the samples to a temperature of 0, 100, 200, 300, 400 500, 600 and 700 °C for 3 h, then cooling by air cooling or in water method. The results indicated that all concretes exposed to a temperature of 500 and 700 °C occurred a significant decrease in thermal conductivity, compressive strength, flexure strength and DEM. An artificial neural network (ANN) approach was used to model the thermal and mechanical properties of PA concretes. The predicted values of the ANN were in accordance with the experimental data. The results indicate that the model can predict the concrete properties after elevated temperatures with adequate accuracy. Copyright © 2016 John Wiley & Sons, Ltd.     

Thermodynamic analysis of the interaction between 3-aminophenylboronic acid and monosaccharides for development of biosensor

The special interaction between 3-aminophenylboronic acid and monosaccharides, particularly glucose, fructose and galactose, has been investigated and used for the development of electrochemical and surface plasmon resonance (SPR) based saccharide sensors. The binding constants and thermodynamic parameters of interaction such as free energy of binding (ΔG), enthalpy (ΔH) and entropy (ΔS) were determined for the first time using isothermal titration calorimetry revealed the enthalpic and entropic contributions to the free energies of binding. Effects of pH and buffer on the binding constants (k) between 3-aminophenylboronic acid and monosaccharides were also examined. The binding constant of fructose was found to be six times higher than glucose and four times higher than galactose at pH 11, leading the specific detection of fructose. SPR and potentiometric calibration for fructose were linear in the concentration range of 10–120 mM and 8–84 mM, respectively. Limit of detection of the SPR and potentiometric method were found to be as 8.9 mM and 1.4 mM, respectively.     

Anaerobic digestion of Aegean olive mill effluents with and without pretreatment

BACKGROUND: Olive oil production is an important economical activity in the Aegean region of Turkey. However, the effluents of the olive oil producing mills with their high organic loads and toxic compounds are causing serious environmental problems. The anaerobic biological treatment of olive mill wastewater (OMWW) using the treatment plants of the regional industries could be a method of choice and within the scope of this study floccular and granular sludges were investigated in batch mode for their success in the treatment of OMWW while producing biogas. The major limitation of this treatment is the inhibition of methanogenic bacteria by the phenolic compounds in OMWW. Thus an integrated solution was suggested in which a pre‐treatment step (dephenolization) was also introduced before biological step. RESULTS: The effluents of 27 olive mills out of 47 were found to have total phenolics (TP) less than 3 g L^?1 and could be treated anaerobically after simple dilution. The biogas production for the untreated OMWW was higher for floccular sludge than for the granular sludge (68.5 mL and 45.7 mL respectively). Combined pre‐treatment experiments, first coagulation with polyaluminum chloride, followed by flocculation with cationic polyelectrolyte and finally Fenton's oxidation, could remove 80% of TP and 95% of the total suspended solids. CONCLUSION: OMWW having TP values less than 3 g L^?1 can be treated anaerobically using floccular sludge after simple dilution and biogas can be produced. For OMWW samples having higher TP values pre‐treatment is necessary and the pre‐treatment given in this study may be used effectively. Copyright © 2010 Society of Chemical Industry     

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.