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Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area.

Introduction: The PhD program represents the biggest step in the education and the PhD itself is the crown of the work of one scientist ( 1 ). Each PhD thesis represents the contribution of the author to the development of the area in which he/she is active, and must present something new, unknown or unsufficiently explored until then, with direct implication for the practice. Aim: To present the difference between PhD program during 50 years period, with the presentation of the advantages that development and availability of science brought. Results: When different literature was not available in digital form, writing a PhD thesis after a postgraduate study and a long-term specialist work was the crown of the work of medical professional. Unfortunately, this process was sometimes difficult and was dependent on many parameters and subjective opinions, and was subject of numerous manipulations. In the present age, following the reform of education, and the implementation of Bologna Education System, the number of PhD students has increased and the study started to be held at all Universities in Bosnia and Herzegovina ( 2 ). All those who completed the six-year study are able to attend the PhD studies. The main obligation for student and requirement for obtaining PhD degree is article from PhD thesis published in the Current Contents database. This criterion was removed over time and the situation now is that obligation is article that is published in journal thatis in the reference database (without clear definition in which databases). Quantity is achieved but we cannot say that for quality. During the year 2017, in Bosnia and Herzegovina, 11 original articles in the field of clinical medicine were published (indexed in the journals that are in the Current Contents Indexed Publications (journals that belong to CC, SCI and SCI-E base) ( 3 , 4 ). These 11 papers are from the field of gastroenterology, psychiatry and ophthalmology ( 2 , 3 ). If this is the scientific opus of already experienced scientist, the question remains what is expected from young researchers. Conclusion: Which system is better, on this question answer cannot be given, because both systems have shortcommings and advantages. The fact is that there is a gap between generations, which is unlikely to be resolved. It is also a fact that the Bologna system is not ideal, but it is currently our present ( 5 , 6 , 7 ). A lot of students are enrolled, and the system must help them, in the process of successfully completing their PhD programs. This help cannot be achieved through the lowering of criteria (publications in science remain the only weapon in evaluati...

In this position paper, the author proposes the use of social networks of characters as an AI narrative generation mechanism. The first part of the paper offers examples of recent research by literary critics on the relationship between character networks and narrative structure. The second part of the paper offers a simple example of story generation based on a structural balance network model. From Narratives to NetworksFinding realistic but tractable story generation mechanisms is an ongoing challenge for the narrative intelligence community. Most story generators rely on either (1) corpora of pre-existing stories (e.g., MEXICA 1 ), or (2) story grammars (e.g., TALESPIN 2 ). Efforts have been made to broaden generative mechanisms to include games as well as crowdsourcing. 3 In a recent paper, Pablo Gervas argues for the value of chess as a narrative generation mechanism: Chess provides a finite set of characters (pieces), a schematical representation of space (the board), and time (progressive turns), and a very restricted set of possible actions. Yet it also allows very elementary interpretations of game situations in terms of human concepts such as danger, threat, conflict, death, survival, victory or defeat, which can be seen as interesting building blocks for story construction. 4 There is a related body of work that makes use of sports games statistics to generate simple narratives akin to newspaper articles (

Abstract. Simulation techniques used to generate complex biological models are recognized as promising research tools especially in oncology. Here, we present a computer simulation model that uses an agent-based system to mimic the development and progression of solid tumors. The model includes influences of the tumor's own features, the host immune response and level of tumor vascularization. The interactions among those complex systems were modeled using a multi-agent modeling environment provided by Netlogo. The model consists of a hierarchy of active objects including cancer cells, immune cells, and energy availability. The simulations conducted indicate the key importance of the nutrient needs of the tumor cells and of the initial responsiveness of the immune system in the tumor progression. Furthermore, the model strongly suggests that immunotherapy treatment will be efficient in individual with sustained immune responsiveness.

Sentiment analysis is the process of identifying the opinion expressed in text. Recently, it has been used to study behavioral finance, and in particular the effect of opinions and emotions on economic or financial decisions. In this paper, we use a public dataset of labeled tweets that has been labeled by Amazon Mechanical Turk and then we propose a baseline classification model. Then, by using Granger causality of both sentiment datasets with the different stocks, we shows that there is causality between social media and stock market returns (in both directions) for many stocks. Finally, We evaluate this causality analysis by showing that in the event of a specific news on certain dates, there are evidences of trending the same news on Twitter for that stock.

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