Purpose Decoupling of pressures ranging from regulatory compliance and stakeholders expectations to business competitiveness and sustainability, companies need to align their environmental strategies with a broader consideration of these influences. This paper aims at developing a dynamic alignment model to enhance the environmental performance that considers the influential pressures based on a multi-criteria decision-making process. Design/methodology/approach Authors have proposed a dynamic model for the alignment of the environmental performance based on a hybrid multi-criteria decision-making approach combining the analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). This model considers contemporary strategic dynamism of the environmental performance and provides a methodology to assist companies prioritizing the environmental aspects based on the influential pressures and deciding on the enhancement pathways. Findings The proposed model based on a hybrid multi-criteria decision-making process allows prioritizing the environmental aspects considering the allocated weights to the alignment-triggered pressures and draw the way to develop different pathways to improve the alignment. Practical implications The proposed dynamic alignment model presents an instrument for the continuous alignment of the environmental performance and an effective management of changes and contributes to minimize gaps and divergences. Originality/value In this paper, the environmental performance has been approached through the contemporary strategic dynamism with the deployment of the multi-criteria decision-making techniques to yield an alignment framework for the environmental decision that combines the internal and external approaches for an effective and sustainable improvement of the environmental performance.
: The aim of this study was to investigate that nickel chloride (NiCl2) induced reproductive toxicity in pre- implanted Wistar Rats and examined the possible protective effect of zinc chloride and selenium on plasma concentration of the hormones of 17 b etradiol (E2) and progesterone (prog); on the reproductive organ’s histology and on development. Experimental results showed the subcutaneous (s.c) administration of Nicl2 to Wistar albino Rats induced a decrease in plasma concentration of E2 and prog in addition, disturbance in development parameters and structural damages to the histology of the reproductive organs. Conversely, Se and ZnCl2 dues to the antioxidants property, regulate the secretion of E2 and Prog hormones, prevent alterations in the reproductive organs and in development in preimplanted rates receiving NiCl2.
This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.
This work presents the prediction of the rate of progression in oil drilling based on random forest algorithm, which is part of the family of ensemble machine learning. The ROP parameter plays a very important role in oil drilling, which has a great impact on drilling costs, and its prediction allows drilling engineers to choose the best combination of input parameters for better progress in drilling operations. To resolve this problem, several works have been realized with the different modeling techniques as machine learning: RNAs, Bayesian networks, SVM etc. The random forest algorithm chosen for our model is better than the other MLS techniques. in speed or precision, following what we found in the literature and tests done with the open source machine learning tool on historical oil drilling logs from fields of Hassi Terfa located in southern Algeria.
In this work a new data-driven approach for Remaining Useful Life estimation of aircraft engines is developed. The proposed approach is a regularized Single Hidden Layer Feedforward Neural network (SLFN) with incremental constructive enhancements. The training rules of this algorithm are inspired form different Extreme Learning Machine (ELM) variants. Particle Swarm Optimization (PSO) algorithm is integrated to enhance tracking ability of the best regularization parameter to reduce the norm of the tuned weights. The proposed approach is evaluated using C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset and compared to its other derivatives and proved its accuracy. C-MAPSS software has revisions in military and civil applications. In this paper, the military version of its application is the used one.
The main objective of this works is to study and improve the performances of the Single hidden Layer Feedforward Neural network (SLFN) for the application of Remaining Useful Life (RUL) prediction of aircraft engines. The most common problems in SLFNs based old training algorithms such as backpropagation are time consuming, over-fitting and the appropriate network architecture identification. In this paper a new incremental constructive learning algorithm based on Extreme Learning Machine algorithm is proposed for founding the appropriate architecture of a neural network under less computational costs. The aim of the proposed training approach is to study its maximum capabilities during RUL prediction by reducing over-fitting and human intervention. The performances of the proposed approach which are evaluated on C-MAPPS dataset and compared with its original variant from the literature. Experimental results proved that the new algorithm outperforms the old one in many metrics evaluations.
