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Amsterdam, Netherlands

Elsevier is an academic publishing company that publishes medical and scientific literature. It is a part of the RELX Group. Based in Amsterdam, the company has operations in the United Kingdom, United States, Mexico, Brazil, Spain, and elsewhere. Wikipedia.

Numerous techniques have been used to identify flow regimes and liquid holdup in horizontal multiphase flow, but often neither perform well nor very accurate. Recently, neuro-fuzzy inference systems learning scheme have been gaining popularity in its capability for solving both prediction and classification problems. It is a hybrid intelligent systems scheme that is able to forecast an output in the uncertainty situations. This paper investigates the capabilities of neuro-fuzzy TypeI in identifying flow regimes and forecasting liquid holdup in horizontal multiphase flow. The performance of neuro-fuzzy modeling scheme is implemented using different real-world industry databases. Comparative studies were carried out to compare neuro-fuzzy systems performance with the most popular existing approaches in identifying flow regimes and predict liquid holdup in horizontal multiphase flow. Results show that neuro-fuzzy is flexible, reliable, outperforms the existing techniques and show bright future capabilities in solving different oil and gas industry problems, namely, rock mechanics properties, water saturation, faceis classification, and distinct bioinformatics applications. © 2010 IMACS. Source

El-Sebakhy E.A.,Elsevier
Expert Systems with Applications | Year: 2011

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models. © 2010 Elsevier Ltd. All rights reserved. Source

Chipperfield L.,Elsevier
Current medical research and opinion | Year: 2010

Biomedical journals and the pharmaceutical industry share the goals of enhancing transparency and expanding access to peer-reviewed research; both industries have recently instituted new policies and guidelines to effect this change. However, while increasing transparency may elevate standards and bring benefits to readers, it will drive a significant increase in manuscript volume, posing challenges to both the journals and industry sponsors. As a result, there is a need to: (1) increase efficiency in the submission process to accommodate the rising manuscript volume and reduce the resource demands on journals, peer reviewers, and authors; and (2) identify suitable venues to publish this research. These shared goals can only be accomplished through close collaboration among stakeholders in the process.In an effort to foster mutual collaboration, members of the pharmaceutical industry and the International Society for Medical Publication Professionals founded a unique collaborative venture in 2008 - the Medical Publishing Insights and Practices initiative (MPIP). At an MPIP roundtable meeting in September 2009,journal editors, publishers and industry representatives identified and prioritized opportunities to streamline the submission process and requirements, and to support prompt publication and dissemination of clinical trial results in the face of increasing manuscript volume. Journal and sponsor participants agreed that more author education on manuscript preparation and submission was needed to increase efficiency and enhance quality and transparency in the publication of industry-sponsored research. They suggested an authors'guide to help bridge the gap between author practices and editor expectations.To address this unmet educational need, MPIP supported development of an Authors' Submission Toolkit to compile best practices in the preparation and submission of manuscripts describing sponsored research.The Toolkit represents a unique collaboration between the pharmaceutical industry and biomedical journals,and reflects both groups' perspectives on how authors can help raise standards and increase efficiency in publishing industry-sponsored studies. The information provided in the toolkit can be useful to help authors navigate the manuscript Source

De Waard A.,Elsevier
IEEE Intelligent Systems | Year: 2010

There has been an ongoing discussion about how to improve the delivery of scientific content using online tools, especially by focusing on content reuse and social media. This column explores how semantic technologies and systems could also enhance the scientific communication process. The author discusses some ongoing initiatives in semantic publishing, which aims to improve how scientists communicate using semantic technologies. The column mentions different types of projects, including efforts focusing on entity enrichment and projects that involve triple markup of documents (subject-predicate-object expressions). However, such approaches are not enough. They help us find information, but they don't help us understand it. The author argues that we need to incorporate a better understanding of how language encodes meaning into our systems, so that we can develop a richer scientific knowledge representation. © 2010 IEEE. Source

Agency: NSF | Branch: Contract | Program: | Phase: SCIENCE & ENGINEERNG INDICATRS | Award Amount: 502.94K | Year: 2015


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