Myopic Versus Farsighted Behaviors in a Low-Carbon Supply Chain with Reference Emission Effects

The increased carbon emissions cause relatively climate deterioration and attract more attention of governments, consumers, and enterprises to the low-carbon manufacturing. This paper considers a dynamic supply chain, which is composed of a manufacturer and a retailer, in the presence of the cap-and-trade regulation and the consumers’ reference emission effects. To investigate the manufacturer’s behavior choice and its impacts on the emission reduction and pricing strategies together with the profits of both the channel members, we develop a Stackelberg differential game model in which the manufacturer acts in both myopic and farsighted manners. By comparing the equilibrium strategies, it can be found that the farsighted manufacturer always prefers to keep a lower level of emission reduction. When the emission permit price is relatively high, the wholesale/retail price is lower if the manufacturer is myopic and hence benefits consumers. In addition, there exists a dilemma that the manufacturer is willing to act in a farsighted manner but the retailer looks forward to a partnership with the myopic manufacturer. For a relatively high price of emission permit, adopting myopic strategies results in a better performance of the whole supply chain

Low Carbon Distribution Channel Coordination with a Capital-Constrained Retailer

Capital constraints exist in many supply chains. We examine a low carbon distribution channel that consists of a manufacturer and a retailer, in which the retailer is constrained by capital. The retailer can be financed by bank credit from a competitive bank market. A Stackelberg model is developed to analyze the integrated decision-making process of ordering, financing, and emission reduction. By comparing the decentralized and centralized channels, we obtain that the manufacturer’s green technology investment should be linearly proportional to the retailer’s order quantity in both channels. Thus, a large order quantity leads to increased efforts to reduce emissions. Results further show that the centralized channel in some cases has fewer emissions and can generate more profits for the whole supply chain compared with the decentralized channel. We therefore propose a revenue sharing contract with a function form to coordinate the distribution channel. When the government allocates appropriate quotas to the supply chain, high carbon price can benefit the environment and supply chain efficiency

Machine learning for bioinformatics and neuroimaging

Machine Learning (ML) is a well-known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience. It involves computational methodologies to address complex real-world problems and promises to enable computers to assist humans in the analysis of large, complex data sets. ML approaches have been widely applied to biomedical fields and a great body of research is devoted to this topic. The purpose of this article is to present the state-of-the art in ML applications to bioinformatics and neuroimaging and motivate research in new trendsetting directions. We show how ML techniques such as clustering, classification, embedding techniques and network-based approaches can be successfully employed to tackle various problems such as gene expression clustering, patient classification, brain networks analysis, and identification of biomarkers. We also present a short description of deep learning and multiview learning methodologies applied in these contexts. We discuss some representative methods to provide inspiring examples to illustrate how ML can be used to address these problems and how biomedical data can be characterized through ML. Challenges to be addressed and directions for future research are presented and an extensive bibliography is included