There is a high risk of R & D based innovation being commercialized, especially in the innovation transfer process which is a concern to many entrepreneurs and researchers. The purpose of this research is to develop the criteria of R & D commercialization capability and to propose a combined technique of Structural Equation Modelling (SEM) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for R & D project evaluation. The research utilized a mixed-method approach. The first phase comprised a qualitative study on commercialization criteria development though the survey research of 272 successful entrepreneurs and researchers in all industrial sectors in Thailand. The data was collected with a structured questionnaire and analyzed by SEM. The second phase was involved with SEM-TOPSIS technique development and a case study of 45 R & D projects in research institutes and incubators for technique validation. The research results reveal that there were six criteria for R & D project commercialization capability, these are arranged according to the significance; marketing, technology, finance, non-financial impact, intellectual property, and human resource. The holistic criteria is presented in decreasing order on the ambiguous subjectivity of the fuzzy-expert system, to help with effectively funding R & D and to prevent a resource meltdown. This study applies SEM to the relative weighting of hierarchical criteria. The TOPSIS approach is employed to rank the alternative performance. An integrated SEM-TOPSIS is proposed for the first time and applied to present R & D projects shown to be effective and feasible in evaluating R & D commercialization capacity.

The Purchasing Power Parity (PPP) theory, which serves as a key to the determination of several models of exchange rates, suggests a long-term relationship between exchange rates and relative prices. It states that the price levels in all the countries are the same when measured in terms of a single currency. The purpose of this study is to model the behavior of the exchange rates of five partner countries of Tunisia, namely, (Germany, the United States, France, Italy, the UK, Morocco and Libya) relative to its fundamentals over the period 1990-1999. Beyond the traditional linear cointegration, we use the approaches based on fractional cointegration. We are trying to discriminate between the adjustment dynamics with long memory (but linear) and a dynamics of a short memory (nonlinear). Given the important role of the exchange rates in the successful experience of open economies, we are interested, in this work, in analyzing the dynamics of the exchange rates in the long run. The econometric results obtained through the GPH tests, make us consider the PPP as an event in the long run if significant short-term deviations from the PPP cannot exist. Therefore, the analysis of the fractional cointegration makes the deviations, regarding equilibrium, follow a slightly integrated process and therefore capture a much wider group of research parity or mean-reverting behavior.

The article presents solution procedure of geometric programming to solve the structural model with imprecise coefficients. We have considered a single objective structural optimization model with weight as an objective function. Geometric programming provides a powerful tool for solving a variety of imprecise optimization problems. Here we use nearest interval approximation method to convert a triangular fuzzy number to an interval number. In this paper, we transform this interval number to a parametric interval-valued functional form and then solve the parametric problem by geometric programming technique. The advantage of this technique is that we can find directly optimal solution of the objective function without solving two-level mathematical programs. Numerical example is given to illustrate the model through this approximation method.

A number of factors, e.g. cutting speed and feed rate, affect the surface roughness in machining process. In this paper, an Artificial Neural Network model was used to forecast surface roughness with related inputs, including cutting speed and feed rate. The output of the ANN model input parameters related to the machined surface roughness parameters. In this research, twelve samples of experimental data were used to train the network. Moreover, four other experimental tests were implemented to test the network. The study concludes that ANN was a reliable and accurate method for predicting machining parameters in CNC turning operation of Particulate Reinforced Aluminum Matrix Composites (PAMCs) specimens with 0%, 5%, 10% and 15% filler. The aim of this work is to decrease the production cost and consequently increase the production rate of these materials for industry without any trial and error method procedure.

This paper presents a new search methodology for different sizes of 0-1 Knapsack Problem (KP). The proposed methodology uses a modified scatter search as a meta-heuristic algorithm. Moreover, rough set theory is implemented to improve the initial features of scatter search. Thereby, the preliminary results of applying the proposed approach on some benchmark dataset appear that the proposed method was capable of providing better results in terms of time and quality of solutions.

Capacity waste management is highly essential because under utilization of capacity is often referred to as a major reason for lower productivity among industries around the world. For better estimation of capacity and its utilization and then for its improved management; newer techniques are being devised in industrial sector. The current case of capacity waste problem has been taken up as a Six Sigma project, where we try to analyze critical factors responsible for the capacity waste. Decisions on critical factor selection in analysis phase of Six Sigma are always very crucial. The paper discusses an approach for selection of capacity waste factors at an automotive industry using fuzzy logic based AHP method. The fuzzy AHP is a well recognized tool to undertake the fuzziness of the data involved in choosing the preferences of the different decision variables engaged in the process of capacity waste factors selection. In this context, we have explored six crucial parameters for selection of capacity waste factors. Final ranking is calculated through priority vector thus obtained and it is seen that conveyor malfunction is found to be the key factor for capacity waste among all alternatives at the selected site.

In this work, a low cost glucose and methanol nonenzymatic sensor was prepared using nickel oxide (NiO) nanofilm electrodeposited on a bare Cu electrode. Electrochemical deposition was assisted with cetyl trimethylammonium bromide (CTAB) as a template. Scanning electron microscopy (SEM) was applied to observe the surface morphology of the modified electrode. Cyclic voltammetry (CV) and amperometry techniques were used to study the electrocatalytic behavior of NiO porous film in glucose and methanol detection. For glucose sensing, the electrode showed a linear relationship in the concentration range of 0.01-2.14 mM with a low limit of detection (LOD) 1.7 µM (signal/noise ratio (S/N)=3). Moreover, high sensitivities of 4.02 mA mM?1 cm?2 and 0.38 mA mM?1 cm?2 respectively in glucose and methanol monitoring suggested the modified electrode as an excellent sensor. The NiO-Cu modified electrode was relatively insensitive to common biological interferers. This sensor possessed good poison resistance towards chloride ions, and long term stability and significant selectivity towards glucose and methanol. Finally the proposed sensor was successfully applied for determination of glucose in human blood serum samples.

The bulk room-temperature polymerization of styrene initiated by environmentally friendly catalysts Maghnite-Na+ is investigated. The catalyst removed from the reaction mixture simply by filtration could be regenerated and reused. The effect of the Maghnite-Na+ catalyst loading on degree of polymerization had been studied and state their inverse relation. The catalyst was characterized by X-ray diffraction and FTIR spectroscopy.

The current development of polyurethane self-healing materials has been evaluated and reviewed. Three main ways of self-healing – microcontainers, microvascular networks and reversible polymers - are described, and recent most prominent examples of self-healing materials applications presented.

The adsorption behavior of a potential polynuclear Schiff base, (s)-2-(anthracen-9(10H)-ylideneamino)-3-phenyl propanoic acid (A9Y3PPA) on carbon steel (CS) in 1M hydrochloric acid solution has been investigated using weight loss measurements, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization studies. The surface morphology of the carbon steel specimens in the presence and absence of the inhibitor was evaluated by AFM analysis. The corrosion inhibition efficiencies of parent amine and parent ketone on carbon steel in 1M HCl solution have also been investigated using weight loss studies. The adsorption of A9Y3PPA obeys Langmuir adsorption isotherm. Thermodynamic parameters (Kads, ∆G0ads) were calculated using the adsorption isotherm. Activation parameters of the corrosion process (Ea, ∆H* and ∆S*) were also calculated from the corrosion rates obtained from temperature studies. Tafel plot analysis revealed that A9Y3PPA acts as a mixed type inhibitor. A probable inhibition mechanism was also proposed.