51,425 research outputs found

    Business Agglomeration-Based Decision Support Systems to Identify Prospective Locations for New Businesses

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    Selecting the right location when establishing new business firm is one imperative key to a successful growth of an establishment. Additionally, previous studies have also found that business firms form business agglomerations that enable these enterprises to collaborate. However, this agglomeration also produces some latent threats, for instance the intraspecific competition between establishments belongs to the same group. Thus, it is then logical to consider the task of selecting business location for a new establishment as a mission of identifying prospective business agglomeration in which the new establishment would be able to compete with existing business firms. This study develops a decision support system that helps to recognize prospective locations for new businesses by incorporating the competition indices within existing business agglomerations. Results from conducted experiment suggest that the developed system is capable to complete such task with a reasonable degree of acceptance

    Estimating the Potential Gains from Mergers: The Danish Agricultural Extension Services

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    We introduce simple non-parametric models to estimate the potential gains from merging production units. Three effects are distinguished. A merger may affect technical efficiency. It also affects the size of the operation which may or may not be advantageous depending on the return to scale properties of the underlying technologies. Lastly, it affects the mix of inputs available and the mix of outputs demanded. A merged unit face more "balanced" or "harmonic" input and output profiles which is typically advantageous. We use the model to estimate the potential gains from merging agricultural extension offices in Denmark.Teaching/Communication/Extension/Profession,

    Revising the U.S. Vertical Merger Guidelines: Policy Issues and an Interim Guide for Practitioners

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    Mergers and acquisitions are a major component of antitrust law and practice. The U.S. antitrust agencies spend a majority of their time on merger enforcement. The focus of most merger review at the agencies involves horizontal mergers, that is, mergers among firms that compete at the same level of production or distribution. Vertical mergers combine firms at different levels of production or distribution. In the simplest case, a vertical merger joins together a firm that produces an input (and competes in an input market) with a firm that uses that input to produce output (and competes in an output market). Over the years, the agencies have issued Merger Guidelines that outline the type of analysis carried out by the agencies and the agencies’ enforcement intentions in light of state of the law. These Guidelines are used by agency staff in evaluating mergers, as well as by outside counsel and the courts. Guidelines for vertical mergers were issued in 1968 and revised in 1984. However, the Vertical Merger Guidelines have not been revised since 1984. Those Guidelines are now woefully out of date. They do not reflect current economic thinking about vertical mergers. Nor do they reflect current agency practice. Nor do they reflect the analytic approach taken in the 2010 Horizontal Merger Guidelines. As a result, practitioners and firms lack the benefits of up-to-date guidance from the U.S. enforcement agencies

    DUDE-Seq: Fast, Flexible, and Robust Denoising for Targeted Amplicon Sequencing

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    We consider the correction of errors from nucleotide sequences produced by next-generation targeted amplicon sequencing. The next-generation sequencing (NGS) platforms can provide a great deal of sequencing data thanks to their high throughput, but the associated error rates often tend to be high. Denoising in high-throughput sequencing has thus become a crucial process for boosting the reliability of downstream analyses. Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finite-valued source data corrupted by a discrete memoryless channel and effectively corrects substitution and homopolymer indel errors, the two major types of sequencing errors in most high-throughput targeted amplicon sequencing platforms. Our experimental studies with real and simulated datasets suggest that the proposed DUDE-Seq not only outperforms existing alternatives in terms of error-correction capability and time efficiency, but also boosts the reliability of downstream analyses. Further, the flexibility of DUDE-Seq enables its robust application to different sequencing platforms and analysis pipelines by simple updates of the noise model. DUDE-Seq is available at http://data.snu.ac.kr/pub/dude-seq
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