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Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.

In order to adopt specific agile methods and to accommodate lean principles, many organizations need to tailor their processes. Different frameworks supporting and guiding the agile transition and adoption process exist in the literature. However, there is a demand for specific guidelines on the different circumstances influencing agile adoption. This paper provides a Systematic mapping of the available frameworks, issues and factors affecting a successful agile transition and adoption process. Twenty-eight primary studies have been considered. The results are presented according to the three research questions. Firstly, nine systematic approaches and frameworks for agile transition and adoption are analyzed. Secondly, different issues related to the agile transition and adoption process are gathered, compared and synthesized in the following five categories: pre-transformation activities, obstacles and results, stages and activities, agile practices and situational factors. Finally, a list of 154 situational factors affecting the agile transition and adoption process is proposed.

A simulation model based on parallel systems is established, aiming to explore the relation between the number of submissions and the overall standard of academic journals within a similar discipline under peer review. The model can effectively simulate the submission, review, and acceptance behaviors of academic journals in a distributed manner. According to the simulation experiments, it could possibly happen that the overall standard of academic journals deteriorates due to excessive submissions., Comment: This paper is already published in the journal IEEE Access and its copyright is fully open. The title was adjusted according to the review comments