Interface feature prioritization for web services: Case of online flight reservations

Acceptance, utility, and usability of system designs have become a focal interest in human–computer interaction (HCI) research, yet at present there is a lack detailed understandings of which system design features influence them. The purpose of the study is to examine the effects of five product design features; customization, adaptive behavior, memory load, content density, and speed on user preference through an experimental study by using conjoint analysis. In experimental study, instead of classical conjoint cards, prototypes were generated for products. Besides, desirability and market segments of product prototypes were identified. In line with the results, among the five product design features, speed is the most and customization is the least important features that affect user preference. Contrary to the expectations, customization has a relatively small importance value in this research. Subsequent design features that influence user preference after speed are minimal memory load, adaptive behavior, and content density, respectively. According to findings, interfaces that have high-speed, minimal memory load, adaptive behavior, low content density, and customization features are more preferable than those that do not.     

A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

Biosorption of Acid Red P-2BX by lichens as low-cost biosorbents

The aim of this study is to examine the dye biosorption properties of lichen species called Cladonia convoluta and Evernia prunastri. Since lichens are extensively found in the environment, their suitability as a cheap adsorbent has been investigated in this study. The optimal parameters for textile dye biosorption were also determined. The dried lichen biomass showed better dye biosorption capacity than ash lichen biomass. C. convoluta had better dye biosorption capacity than E. prunastri. Dye biosorption rate was found as 71.41% at optimal conditions. This study concluded that C. convoluta was a successful and cheap biosorbent for treatment of water contaminated by Acid Red P-2BX dye.     

Response Surface Modeling and Evaluation of the Influence of Deposition Parameters on the Electrolytic Cu-Sn Alloy Powders Production

In this article, the electrodeposition process of Cu-Sn alloy powders from tripolyphosphate (TPP)-based electrolytes was investigated as a function of deposition parameters. The effects of deposition parameters such as current density, electrolyte composition (Cu/Sn mole ratio), mechanical stirring speed, and temperature on the Cu content of alloy powder and cathodic current efficiency were evaluated using the response surface methodology (RSM). The empirical models developed in terms of deposition parameters were found to be statistically adequate to describe the process responses. The study revealed that as far as the copper content was concerned in the alloyed powders, all parameters selected had positive correlations. However, a high stirring speed and low current density led to a greater current efficiency. The morphology and chemical composition of the electrodeposited Cu-Sn alloy powders were investigated using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and inductively coupled plasma (ICP) analysis. An SEM analysis showed that the powder morphology was affected considerably by the cathodic current density and stirring speed.     

Arm work interferes with normal ventilation

Arm work, by limiting movement of the chest wall and use of the respiratory muscles, may alter breathing pattern and gas exchange sufficiently to interfere with the ability to perform certain tasks. To determine the effects of arm work on breathing pattern during a well-controlled work task, depth of breathing, breathing frequency and end-expiratory lung volume (EELV) were measured at rest and during cycling exercise using an arm and a leg ergometer. Six subjects performed arm work at light, moderate and heavy intensities (30%, 60% and 90% of maximum arm work capacity respectively) and leg work at three intensities where ventilation was matched for that achieved during the arm work. This matching was necessary since the level of ventilation affects the breathing pattern. Subjects breathed on a mouthpiece and tubing that led to automated equipment for the measurement of respiratory variables. Ventilation during arm work was accomplished with a lower depth of breathing, a higher breathing frequency and a decreased EELV compared to leg work. Arm work places increased demands on the ventilatory system, including the muscles of respiration that are also recruited for task performance. The competition for using these muscles for breathing as opposed to a particular work task may result in a compromise in breathing capacity that ultimately may limit the ability to perform tasks requiring sustained heavy use of the arms. These increased demands on the upper body muscles must be considered when evaluating the ability of individuals to perform tasks that involve heavy arm work.     

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.     

Comparison of thermal comfort properties of single jersey fabrics produced by hollow yarns with different hollowness ratio

In this study, the thermal comfort properties of single jersey fabrics produced by conventional and hollow cotton yarns with different hollowness ratio have been investigated and compared. For this purpose, thermal conductivity, thermal resistance, thermal absorptivity, air permeability and water vapour permeability of core spun, hollow and conventional yarn fabrics were measured and evaluated statistically. It was observed that thermal comfort properties of single jersey fabrics were affected by the yarn structure and the fibre distribution within the yarn. The results showed that hollow yarn fabrics had better thermal comfort properties than that of conventional yarn fabrics. In hollow yarns, as the hollowness ratio increases, air permeability and thermal conductivity of single jersey fabrics decrease but thermal resistance, thermal absorptivity and water vapour permeability increase. Statistical analysis also indicated that the differences between properties of hollow yarn fabrics and conventional yarn fabrics were significant. Furthermore, the yarn hollowness ratio significantly affects thermal comfort properties of single jersey fabrics.     

Effect of soft segment molecular weight on tensile properties of poly(propylene oxide) based polyurethaneureas

Influence of soft segment molecular weight and hard segment content on the morphology, thermomechanical and tensile properties of homologous polyurethaneurea copolymers based on narrow molecular weight poly(propylene oxide)glycol (PPG) oligomers were investigated. A series of polyurethaneureas with hard segment contents of 12–45% by weight and PPG number average molecular weights <M_n> of 2000 to 11,800 g/mol were synthesized and characterized structurally by SAXS and mechanically by DMA and stress strain analysis. Bis(4-isocyanatocyclohexyl)methane and 2-methyl-1,5-diaminopentane were used as the diisocyanate and the chain extender respectively. All copolymers displayed microphase separation by SAXS and DMA. The critical entanglement molecular weight (M_e) of PPG is reported to be around 7700 g/mol. Our mechanical results suggest that when copolymers possess similar hard segment contents and are compared to those based on soft segments with number average molecular weights (M_n) greater than M_e, they generally displayed higher tensile strengths and particularly lower hysteresis and creep than those having soft segment molecular weights below M_e. These results imply that soft segment entanglements in thermoplastic polyurethaneureas may provide a critical contribution to the tensile properties of these copolymers – particularly in the range where the soft segment content is dominant.