Ranking Fuzzy Cognitive Map based scenarios using ELECTRE III: Applied on housing market

Policy makers often deal with a wide range of alternative probable future states for the entity they work for – a country, for example. To strive for the most desirable state, the policy makers must evaluate and rank all probable future scenarios. To this end, ‘scenario methods’ gained recent popularity are increasingly being employed. Nevertheless, currently, decisions made based on insight gained from scenarios are not made in an integrated systematic process. Current variants of the method help study the role of any research concept individually; thus, they do not provide a complete picture of the research situation. A more suitable variant of the method for today's world should provide a holistic view of the research situation by modelling possible links among research concepts. This paper, introduces a stepwise methodology that can guide the building, developing, and ranking of possible future scenarios, having factored in the possible causal interrelations among research concepts. The method is enriched with a combination of Fuzzy Cognitive Maps, a widely used soft computing method, and ELECTRE III, a popular method of Multi Attribute Decision Making. This paper also presents the results of the application of the proposed methodology for Iran's housing market, highlighting the advantages of the proposed methodology.     

Coopetitive relationships in cross-functional software development teams: How to model and measure?

Understanding simultaneous cooperative and competitive (coopetitive) dynamics in cross-functional software development teams is fundamental to the success of software development process. The recent coopetition research is, however, hampered by a lack of conceptual focus, and the corresponding inconsistent treatment of the constructs associated with cross-functional coopetitive relationships. This study conceptualizes and operationalizes the multi-dimensional construct of cross-functional coopetition, and then presents an instrument for measuring this construct. Cross-functional coopetition is conceptualized with 5 distinct and independent constructs; 3 of them are related to cross-functional cooperation, and 2 are associated with cross-functional competition. The data collected from 115 software development project managers in Australia confirms the applicability of the constructs and their measures. This study contributes to the extant literature by providing a consensus on the conceptualization of cross-functional coopetitive behaviors, particularly in multi-party software development teams. The conceptual basis for cross-functional coopetition and its instrument will aid researchers and project managers interested in understanding coopetition in cross-functional collaborative contexts. Research and practical implications are discussed.     

Developing A Surface Roughness Model for End-Milling of Micro-Channel

Micro-milling operations are one of the common manufacturing processes that are used primarily to produce miniaturized components within the range of less than a millimeter. The surface roughness of the channel plays an important role in the motion of fluid in the channel. Since most traditional finishing operations cannot be performed easily on micro-channels, the study of relationship between micro-milling parameters and surface roughness is of extreme importance. In this work, the geometrical features of cutting edge, along with the concept of minimum chip thickness, are taken into consideration for constructing the micro-channel surface texture using kinematic rules and transformation operators. ACIS, a 3D B-rep geometric kernel, is used as a geometric engine for simulation of surface texture produced in the micro-milling process. In addition, the relationship between cutting conditions and surface roughness is investigated using DOE method and a regression model. All micro-channel experiments were performed on stainless steel 316. Finally, simulation and regression results are compared with measured surface roughness and the validity of these two models is approved. Depending on the available data, one of these two approaches can be used to predict the surface roughness of the channel's floor.     

Application of NN-ARX Model to Predict Groundwater Levels in the Neishaboor Plain, Iran

There is no doubt that groundwater is an important and vital source of water supply in arid and semi-arid areas. Therefore, prediction of groundwater level fluctuations is necessary for planning conjunctive use in these areas. This research was aimed to predict groundwater levels in the Neishaboor plain using Neural Network – AutoRegressive eXtra input (NN-ARX) and Static-NN models. The NN-ARX model determines a nonlinear ARX model of a dynamic system by training a hidden layer neural network with the Levenberg-Marquardt algorithm. In this model the current outputs depend not only on the current inputs, but also on the inputs and outputs at the pervious time periods. The available observation wells in the study area were clustered according to their fluctuation behavior using the “Ward” method, which resulted in six areal zones. Then, for each cluster, an observation well was selected as its representative, and for each zone, values of monthly precipitation, temperature and groundwater extraction were estimated. The best input of the Static-NN model was identified using combination of Gamma Test and Genetic Algorithm. Also, Gamma Test is applied to identify the length of the training dataset. The results showed that the NN-ARX model was suitable and more practical. The performance indicators (R ²?=?0.97, RMSE?=?0.03 m, ME?=?--0.07 m and R ²?=?0.81, RMSE?=?0.35 m, ME?=?0.60 m, respectively for the best and worst performance of model) reveals the effectiveness of this model. Moreover, these results were compared with the results of a static-NN model using t-test, which showed the superiority of the NN-ARX over the static-NN.     

The fast pyrotechnic reaction of silicon with lead oxides: Differential scanning calorimetry and hot-stage microscopy studies

Differential scanning calorimetry has been applied in the study of the fast pyrotechnic reaction of silicon with (a) PbO; and (b) Pb₃O₄ in powder form. Samples were heated up to 725 °C at heating rates from 0.3 to 160 °C min^?1 under a nitrogen atmosphere. Hot-stage microscopy was used to observe phase changes up to 1000 °C at heating rates of 0.1–100 °C min^?1, and X-ray analysis to identify products. The DSC studies of both compositions indicate two exothermic peaks at 548–667 °C and 667–726 °C, corresponding to surface and bulk reaction. The use of PbO with a smaller particle size results in a larger first and smaller second peak indicating enhanced surface activity. For Pb₃O₄/Si compositions, reaction is principally between Si and PbO, resulting from the decomposition of PbO. At a heating rate of 100 °C min ^?1 both compositions ignite above 700 °C. Hot-stage microscopy correlates phase and colour changes with temperature of occurrence of peaks in the DSC studies.     

Audiovisual emotion recognition using ANOVA feature selection method and multi-classifier neural networks

To make human–computer interaction more naturally and friendly, computers must enjoy the ability to understand human’s affective states the same way as human does. There are many modals such as face, body gesture and speech that people use to express their feelings. In this study, we simulate human perception of emotion through combining emotion-related information using facial expression and speech. Speech emotion recognition system is based on prosody features, mel-frequency cepstral coefficients (a representation of the short-term power spectrum of a sound) and facial expression recognition based on integrated time motion image and quantized image matrix, which can be seen as an extension to temporal templates. Experimental results showed that using the hybrid features and decision-level fusion improves the outcome of unimodal systems. This method can improve the recognition rate by about 15 % with respect to the speech unimodal system and by about 30 % with respect to the facial expression system. By using the proposed multi-classifier system that is an improved hybrid system, recognition rate would increase up to 7.5 % over the hybrid features and decision-level fusion with RBF, up to 22.7 % over the speech-based system and up to 38 % over the facial expression-based system.     

Thermal decomposition of potassium permanganate: Thermogravimetry,differential scanning calorimetry and hot-stage microscopy studies

Thermogravimetry (TG), differential scanning calorimetry (d.s.c.), and hot-stage microscopy (HSM) techniques have been applied in a study of the thermal decomposition of potassium permanganate. Single crystals of potassium permanganate and ground samples are studied at heating rates from 0.62 to 10 °C min^?1, and results compared with previous isothermal studies. Comparison of theoretical and experimental weight loss is made, for both forms of potassium permangante, and a possible mechanism based on diffusion chain theory and nucleation suggested.     

Performance optimization of current focusing and virtual electrode strategies in retinal implants

The electrode configuration in an implanted visual prosthesis array affects the spatial electric field distribution within the retina, contributing to current focusing and virtual electrode (VE) stimulation strategies. In this paper, a finite element model incorporating various electrode configurations was used to study the interaction between electrode size and electrode-to-cell distance in current focusing and VE stimulation paradigms. The electrode array unit comprises an active electrode, six flanking return electrodes and a distant monopolar return. A quasi-monopolar (QMP) fraction is defined as the proportion of current which can be preferentially returned through the distant return, in comparison with the more adjacent flanking electrodes. The simulation results indicate that current focusing and VE strategies can be optimized by tuning the QMP fraction. The QMP fraction is adjusted to optimize the electric field spread based on retinal ganglion cell (RGC) density in the degenerate retina, thereby offsetting the effect of inhomogeneous distribution of surviving RGCs and leading to a uniform stimulation paradigm across electrodes. Importantly, there is negligible difference in functional performance across electrode configurations for distances less than the electrode diameter, implying that the stimulation mode does not significantly affect activation threshold or activated retinal area for electrode diameters greater than the retinal thickness. Furthermore, the QMP fraction has a significant effect on VE performance, defined by activation threshold and activated retinal area, when threshold current is evenly divided between two adjacent active electrodes.     

Enlarging the guaranteed region of attraction in nonlinear systems with bounded parametric uncertainty

A novel approach to enlarge the guaranteed region of attraction in nonlinear systems with bounded parametric uncertainties based on the design of a nonlinear controller is proposed.The robust domain of attraction(RDA) is estimated using the parameter-dependent quadratic Lyapunov function and enlarged by the optimal controlling parameters.The problem of extending the RDA is indicated in a form of three-layer optimization problem.Some examples illustrate the efficiency of the proposed strategy in enlarging RDA.