Nash bargaining game model for two parallel stages process evaluation with shared inputs

Data envelopment analysis is a non-parametric technique for evaluating peer decision making units (DMUs) with using multiple inputs to produce multiple outputs. In the real world, DMUs usually have complex structures. One of these structures is a two-stage process with intermediate measures. In this structure, there are two stages and each stage uses inputs to produce outputs, separately where the outputs of the first stage are the inputs for the second stage. Cooperative model such as centralized model and non-cooperative model are game theoretic approaches to evaluate two-stage processes. Non-cooperative model supposes that one of the stages is the leader and another stage is the follower, whereas in the centralized model, both stages are evaluated simultaneously. In this paper, we propose a game theoretic model based on the Nash bargaining game to calculate weights when parallel stages with shared inputs compete to reach a high efficiency in the competitive strategy. Two data sets including the bank branches and thermal power plants in Iran are used to show the abilities of proposed model. This model can be applied in other processes such as supply chain, manufacturing and public service units.     

Ohmic‐assisted hydrodistillation of essential oils from Zataria multiflora Boiss (Shirazi thyme)

Ohmic‐assisted hydrodistillation (OAHD) is a combination of ohmic heating and distillation, and could be considered as a novel method for the extraction of essential oils. Major problems with traditional methods are long extraction time and lower purity of the extract. In this study, OAHD was applied as an economic and green technology for the extraction of essential oils from Zataria multiflora Boiss. (Shirazi thyme) aerial parts and the results were compared to those obtained from hydrodistillation (HD) as a conventional method. The results showed that OAHD method had the extraction time of 32.21 ± 2.59 min while this value was about 57.21 ± 2.33 min for hydrodistillation (HD). Scanning electron micrographs of thyme leaves showed a sudden eruption of essential oil glands and their surrounding area for OAHD samples. GC–MS analysis indicated that both methods of OAHD and HD can extract the same compounds.     

Dynamic bitrate allocation of interactive real-time streamed multi-view video with view-switch prediction

In multi-view video (MVV), the real-world scene is usually captured by more than two cameras positioned in an array. A viewer can consume MVV using either a non-interactive or an interactive transmission method. In the context of interactive MVV streaming, view switching may cause a long delay due to the frequent requests by the viewer. In this paper, we consider the use case of real-time interactive MVV (IMVV) streaming, where the view switching delay problem has a significant user experience impact. Our proposed method compress and send all the captured views using a dynamic bitrate allocation method. Also, a novel prediction algorithm has been used to choose possible views that the user might switch to. The predicted view switching is mapped to a hidden Markov model, and the transition probability is solved using Zipf distribution. The experimental results of the proposed method show a superior performance on an objective metric and view-switching delay for better viewing quality over the existing method.     

Usage and impact of technology enabled job learning

Individuals use technology to experiment with new ways of carrying out their tasks and in doing so they learn more about their jobs. The current study examines the role of technology enabled job learning as a key component in the complex relationship between information systems use and technology outcomes. Data from 308 end-users were analyzed to evaluate the relationships between system use and technology enabled job learning, and technology enabled job learning and technology outcomes. Technology enabled job learning was conceptualized in terms of how computer applications helped individuals learn and better perform their jobs. System use was conceptualized in terms of decision support, work integration, and customer service. Technology outcomes were conceptualized in terms of management control, task innovation, task productivity, and customer satisfaction. Results suggest that systems use has a significant, positive effect on job learning, and that job learning has a significant, positive effect on technology outcomes. Post hoc analyses were then conducted to examine the potential mediating role of job learning between systems use and technology outcomes. The findings from this research lead to a greater understanding of how patterns of systems use influence organizationally relevant outcomes through technology-enabled job learning.     

Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.     

DIAL–phoswich hybrid system for remote sensing of radioactive plumes in order to evaluate external dose rate

Radioactive pollutants which are released into the atmosphere are known to present a hazard to human health; therefore they must be identified and well protected. Here, an improved remote sensing system is investigated for monitoring radioactive plumes released into the atmosphere from nuclear sites. It is based on the combination of DIAL technique with a phoswich detector array. The DIAL, using a tunable UV/Vis laser, measures the concentration of the radionuclide and the plume distance, whereas the phoswich detects characteristic hard X/γ ray emissions. Here, we have shown the ability of a hybrid system for the prompt identification and quantification of the effluents containing the uranium radionuclide, using the parameters of uranium such as absorption cross-section, absorption spectrum and density.     

Development of an on-line optical method for assessment of the bubble size and morphology in aerated food products

The quality of whipped products of the food industry is closely linked to the characteristics of the dispersed gas phase, such as the bubble morphology, the mean bubble size and the uniformity of the bubble size distribution. Here, an on-line method based on imaging techniques was developed for measuring these quantities in food foams manufactured in a continuous foaming device. On-line image acquisition was based on a quartz visualization cell that ensured a continuous renewal of the samples. The mean bubble size and the bubble size distribution were obtained using a semi-automated image analysis procedure. The method was validated on four kinds of aerated food emulsions (ice cream, whipped cream, aerated fresh cheese and a foamed sauce), corresponding to a large range of overrun (30–180%). The σ/d₃₂ ratio of bubble size distributions of dairy foamed emulsions was shown to be nearly constant, regardless of the recipe and of operating conditions.     

A hybrid method of LMDI,symmetrical components,and SFA to estimate the distribution of energy-saving potential with consideration of unbalanced components in decomposition analysis

This paper proposes a hybrid method of logarithmic mean divisia index (LMDI), symmetrical components (SC), and stochastic frontier analysis (SFA) to estimate the distribution of energy-saving potential in industrial sectors, while taking into consideration unbalanced components in decomposition analysis. The proposed method uses LMDI to decompose actual energy consumption into activity, structural, and intensity factors, which serve as inputs to SC. In general the results of the decomposition analysis contain some degree of imbalance. The imbalance between these factors may affect economic development of industrial sectors. Since LMDI cannot be used for analyzing the unbalance, SC is used to analyze this. According to this technique, any set of three unbalanced factors could be expressed as the sum of three symmetrical sets of balanced factors. In this research, activity, structural, and intensity factors are selected as unbalanced components. Also, the mean energy consumption is estimated using SC. In this study, SFA is used to measure the technical efficiency of energy conversion in industrial sectors. Therefore, a feasible stochastic frontier production function, which requires only the actual consumption as input and the estimated mean consumption (output from SC) as output, is estimated. Finally, potential savings in these sectors are calculated as technical efficiency. The proposed method is validated by its application to estimate the distribution of energy-saving potential of the selected group of Canadian industrial sectors. The outcome of this study will assist policymakers in reducing industrial energy consumption.