Hydraulic fracturing of horizontal wells is an essential technology for the exploitation of unconventional resources, but led to environmental concerns. Fracturing fluid upward migration from deep gas reservoirs along abandoned wells may pose contamination threats to shallow groundwater. This study describes the novel application of a nonlinear autoregressive (NAR) neural network to estimate fracturing fluid flow rate to shallow aquifers in the presence of an abandoned well. The NAR network is trained using the Levenberg–Marquardt (LM) and Bayesian Regularization (BR) algorithms and the results were compared to identify the optimal network architecture. For NAR-LM model, the coefficient of determination (R2) between measured and predicted values is 0.923 and the mean squared error (MSE) is 4.2 × 10−4, and the values of R2 = 0.944 and MSE = 2.4 × 10−4 were obtained for the NAR-BR model. The results indicate the robustness and compatibility of NAR-LM and NAR-BR models in predicting fracturing fluid flow rate to shallow aquifers. This study shows that NAR neural networks can be useful and hold considerable potential for assessing the groundwater impacts of unconventional gas development.

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Purpose This paper aims to highlight a model of industry drivers (industries’ environmental reputation and competitive intensity) that affect the sustainability marketing strategy segmentation, targeting and positioning based on customers’ environmental concern and explore the circumstances under which such a strategy affects performance. Design/methodology/approach The authors examined 64 Iranian export companies, which adopted sustainability marketing strategies across seven different industries. Achieved data are analyzed using a structural equation model methodology. Findings The results indicate that industries’ environmental reputation is positively related to the sustainability marketing strategies based on customers’ environmental concern and leads to superior financial and market performance. They also posit that competitive intensity has no significant effect on sustainability marketing strategies. Research limitations/implications This study specifically examines the impact of industry drivers on sustainability marketing strategy and performance. Logically, there might be other factors affecting the sustainability or other value dimensions that are not addressed in this study. Practical implications This paper provides some understanding of how organizations strength their sustainability marketing strategy, and they have to consider what factors to adopt such strategy. This paper also facilitates a better understanding of the customers’ needs and concern as a factor influencing sustainability marketing strategy adoption and implementation. Identifying the customer segmentation and market targeting based on the industry’s environmental can lead to the business will normally tailor the marketing mix (4Ps) with the needs and expectations of the target in mind. Originality/value This paper strengthens the effect of environmental concern of customer to understand what influences the success of the sustainability marketing adoption and implementation by investigating the most influential factors such as industries’ environmental reputation and competitive intensity.

In this paper, we propose an optimization algorithm based on the intelligent behavior of stem cell swarms in reproduction and self-organization. Optimization algorithms, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Ant Colony Optimization (ACO) algorithm and Artificial Bee Colony (ABC) algorithm, can give solutions to linear and non-linear problems near to the optimum for many applications; however, in some case, they can suffer from becoming trapped in local optima. The Stem Cells Algorithm (SCA) is an optimization algorithm inspired by the natural behavior of stem cells in evolving themselves into new and improved cells. The SCA avoids the local optima problem successfully. In this paper, we have made small changes in the implementation of this algorithm to obtain improved performance over previous versions. Using a series of benchmark functions, we assess the performance of the proposed algorithm and compare it with that of the other aforementioned optimization algorithms. The obtained results prove the superiority of the Modified Stem Cells Algorithm (MSCA).

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