Evidence-based editing: factors influencing the number of citations in a national journal

Purpose: Citations received by papers published within a journal serve to increase its bibliometric impact. The objective of this paper was to assess the influence of publication language, article type, number of authors, and year of publication on the citations received by papers published in Gaceta Sanitaria, a Spanish-language journal of public health. Methods: The information sources were the journal website and the Web of Knowledge, of the Institute of Scientific Information. The period analyzed was from 2007 to 2010. We included original articles, brief original articles, and reviews published within that period. We extracted manually information regarding the variables analyzed and we also differentiated among total citations and self-citations. We constructed logistic regression models to analyze the probability of a Gaceta Sanitaria paper to be cited or not, taking into account the aforementioned independent variables. We also analyzed the probability of receiving citations from non-Spanish authors. Results: Two hundred forty papers fulfilled the inclusion criteria. The included papers received a total of 287 citations, which became 202 when excluding self-citations. The only variable influencing the probability of being cited was the publication year. After excluding never cited papers, time since publication and review papers had the highest probabilities of being cited. Papers in English and review articles had a higher probability of citation from non-Spanish authors. Conclusions: Publication language has no influence on the citations received by a national, non-English journal. Reviews in English have the highest probability of citation from abroad. Editors should decide how to manage this information when deciding policies to raise the bibliometric impact factor of their journals.

Hybrid simulation-optimization based approach for the optimal design of single-product biotechnological processes

In this work, we present a systematic method for the optimal development of bioprocesses that relies on the combined use of simulation packages and optimization tools. One of the main advantages of our method is that it allows for the simultaneous optimization of all the individual components of a bioprocess, including the main upstream and downstream units. The design task is mathematically formulated as a mixed-integer dynamic optimization (MIDO) problem, which is solved by a decomposition method that iterates between primal and master sub-problems. The primal dynamic optimization problem optimizes the operating conditions, bioreactor kinetics and equipment sizes, whereas the master levels entails the solution of a tailored mixed-integer linear programming (MILP) model that decides on the values of the integer variables (i.e., number of equipments in parallel and topological decisions). The dynamic optimization primal sub-problems are solved via a sequential approach that integrates the process simulator SuperPro Designer® with an external NLP solver implemented in Matlab®. The capabilities of the proposed methodology are illustrated through its application to a typical fermentation process and to the production of the amino acid L-lysine.

Integrating Evidence-Based Practice in the Training Programme Bachelor’s Degree in Nursing, University Jaume I

The Evidence-Based Practice (EBP) aims to combine a form methodological process of professional experience in health with the most current information on the clinical situation. The professional novice can make better decisions despite lacking sufficient years in clinical practice. We then train the student in correct habits within the methodological process by which you can strengthen both their knowledge and their attitude and ability, allowing secure customs, where all of your work is based on PBE.