Certify or not? An analysis of organic food supply chain with competing suppliers

Customers expect companies to provide clear health-related information for the products they purchase in a big data environment. Organic food is data-enabled with the organic label, but the certification cost discourages small-scale suppliers from certifying their product. This lack of a label means that product that satisfies the organic standard is regarded as conventional product. By considering the trade-off between the profit gained from organic label and additional costs of certification, this paper investigates an organic food supply chain where a leading retailer procures from two suppliers with different brands. Customers care about both the brand-value and quality (more specifically, if food is organic or not) when purchasing the product. We explore the organic certification and wholesale pricing strategies for suppliers, and the supplier selection and retail pricing strategies for the retailer. We find that when two suppliers adopt asymmetric certification strategy, the retailer tends to procure the product with organic label. The supplier without a brand name can compensate with organic certification, which leads to more profits than the branded rival. As the risk of being abandoned by the retailer increases, the supplier without a brand name is more eager than the rival to obtain the organic label. If both suppliers certify the product, however, they will fall into a prisoner’s dilemma under situation with low health utility from organic label and high certification cost

Wind Turbine Accident News (1980-2013)

This data sets includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research paper: Asian, S., Ertek, G., Haksoz, C., Pakter, S., Ulun, S. “Wind Turbine Accidents: A Data Mining Study”. IEEE Systems Journal, vol: PP, issue: 99, Pages: 1 - 12, 2016. DOI: 10.1109/JSYST.2016.2565818. Please refer to the following web page for detailed explanation of the data, together with images: http://ertekprojects.com/wind-turbine-accidents/data

Wind Turbine Accident News (1980-2013)

This data sets includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research paper: Asian, S., Ertek, G., Haksoz, C., Pakter, S. and Ulun, S., 2017. Wind turbine accidents: A data mining study. IEEE Systems Journal, 11(3), pp.1567-1578. As of now, the most extensive data available on the Internet on wind turbines accidents is published by the Caithness Windfarm Information Forum (CWIF), a UK-based grassroots organization opposing wind turbine installations. While the Caithness list is impressive in magnitude, the quality and reliability of the list is open to discussion because of the following reason: * Many of the web links to the news sources are not valid, and some of the accidents appear in multiple lines of the data. In spite of containing much more magnitude of data, the data available in other online sources also exhibit similar deficiencies. So, there are problems when it comes to using the Caithness data or other data in research studies. To this end, we collected data on wind turbine accidents ourselves, also using the data from Caithness and we share our collected data on this page (please click the link at the top of the page to download the data). The data we collected consists of three folders, and a MS Excel file. The folder News.txt contains the accident news, with each news in a separate text file: The folder News.doc contains news, with each news in a separate MS Word file: Finally, the folder News.doc.with.notes contains news, with each news in a separate MS Word file, but with extensive comments, explaining how the database in the MS Excel file was constructed: The MS Excel file News.Database.xlsx contains the structured data created based on the detailed reading of the accident news text: The MS Excel file is the file that was analyzed in our research paper

Berberine: metabolic and cardiovascular effects in preclinical and clinical trials

Berberine is a plant alkaloid with numerous biological activities. A large body of preclinical in vitro and in vivo studies support different pharmacological actions of berberine that could be potentially useful in the management of metabolic diseases associated with high cardiovascular disease risk, such as mixed hyperlipidemia, insulin resistance, metabolic syndrome, and type 2 diabetes. Moreover, it seems that berberine also exerts anti-inflammatory and antiproliferative effects that could play a role in the development of atherosclerosis and its clinical consequences. Recently, the metabolic effects of berberine have been demonstrated in humans, opening new perspectives for the use of this molecule in patient therapy. Larger and longer clinical studies need to be carried out to implement the definition of the therapeutic role of berberine in humans

New knowledge in strategic management through visually mining semantic networks

Today’s highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.</p