In this paper we explore the impact of power structure on price, sensitivity of market price, and profits in a two-stage supply chain with a single-product, -supplier and -buyer. We develop and analyze the case where the supplier has dominant bargaining power and the case where the buyer has dominant bargaining power, and consider a pricing scheme for the buyer that involves both a multiplier and a constant mark up. We show that it is optimal for the buyer to set the mark-up to zero and use only a multiplier and that the market price and its sensitivity are higher when operational costs (namely distribution and inventory) exist. We also observe that the sensitivity of the market price increases nonlinearly as the wholesale price increases, and derive a lower bound for it. Through experimental analysis, we show that marginal impact of increasing shipment cost and carrying charge (interest rate) on prices and profits are decreasing in both cases.

While the global production of wind energy is increasing, there exists a significant gap in the academic and practice literature regarding the analysis of wind turbine accidents. Our paper presents the results obtained from the analysis of 240 wind turbine accidents from around the world. The main focus of our paper is revealing the associations between several factors and deaths and injuries in wind turbine accidents. Specifically, the associations of death and injuries with the stage of the wind turbine's life cycle (transportation, construction, operation, and maintenance) and the main cause factor categories (human, system/equipment, and nature) were studied. To this end, we conducted a detailed investigation that integrates exploratory and statistical data analysis and data mining methods. The paper presents a multitude of insights regarding the accidents and discusses implications for wind turbine manufacturers, engineering and insurance companies, and government organizations.

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Abstract-As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.

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