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Background Sepsis, a dysregulated host response following infection, is associated with massive immune activation and high mortality rates. There is still a need to define further risk factors and laboratory parameters predicting the clinical course. Iron metabolism is regulated by both, the body’s iron status and the immune response. Iron itself is required for erythropoiesis but also for many cellular and metabolic functions. Moreover, iron availability is a critical determinant in infections because it is an essential nutrient for most microbes but also impacts on immune function and intravascular oxidative stress. Herein, we used a prospective study design to investigate the putative impact of serum iron parameters on the outcome of sepsis. Methods Serum markers of iron metabolism were measured in a prospective cohort of 61 patients (37 males, 24 females) with sepsis defined by Sepsis-3 criteria in a medical intensive care unit (ICU) and compared between survivors and non-survivors. Regulation of iron parameters in patients stratified by focus of infection and co-medication as well as association of the markers with sepsis severity scores and survival were investigated with linear and logistic regression corrected for sex and age effects. Results Positive correlations of increased serum iron and ferritin concentrations upon ICU admission with the severity of organ failure (SOFA score) and with mortality were observed. Moreover, high TF-Sat, elevated ferritin and serum iron levels and low transferrin concentrations were associated with reduced survival. A logistic regression model consisting of SOFA and transferrin saturation (SOFA–TF-Sat) had the best predictive power for survival in septic ICU patients. Of note, administration of blood transfusions prior to ICU admission resulted in increased TF-Sat and reduced survival of septic patients. Conclusions Our study could show an important impact of serum iron parameters on the outcome of sepsis. Furthermore, we identified transferrin saturation as a stand-alone predictor of sepsis survival and as a parameter of iron metabolism which may in a combined model improve the prediction power of the SOFA score. Trial registration The study was carried out in accordance with the recommendations of the Declaration of Helsinki on biomedical research. The study was approved by the institutional ethics review board of the Medical University Innsbruck (study AN2013-0006 ).

Object-oriented programming is the dominant paradigm for general-purpose programming languages. While several attempts have been made to introduce object models into constraint modelling languages, these often have restrictions in terms of their expressivity, are not available in mainstream modelling languages, or are incompatible with modern solving technology. To address these challenges, this paper identifies essential requirements for expressive and elegant object-oriented constraint modelling. We propose an object model that supports decision variables of object type, objects referring to other objects, and, crucially, variable sets of objects, whose cardinality is decided by the solver. The object model is presented as an extension of the MiniZinc modelling language that can be translated into standard MiniZinc. A number of examples and a case study demonstrate the viability of the approach.

State‐of‐the‐art recommender systems support users in the selection of items from a predefined assortment (for example, movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are represented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to nonconfigurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products. In addition to existing approaches we discuss relevant issues for future research.

The development of problem solvers for configuration tasks is one of the most successful and mature application areas of artificial intelligence. The provision of tailored products, services, and systems requires efficient engineering and design processes where configurators play a crucial role. Because one of the core competencies of Siemens is to provide such highly engineered and customized systems, ranging from solutions for medium‐sized and small businesses up to huge industrial plants, the efficient implementation and maintenance of configurators are important goals for the success of many departments. For more than 25 years the application of constraint‐based methods has proven to be a key technology in order to realize configurators at Siemens. This article summarizes the main aspects and insights we have gained looking back over this period. In particular, we highlight the main technology factors regarding knowledge representation, reasoning, and integration that were important for our achievement. Finally we describe selected key application areas where the business success vitally depends on the high productivity of configuration processes.

This paper describes and evaluates approaches to model and solve technical product configuration problems using different artificial intelligence methodologies. By means of a typical example, the benefits and limitations of different artificial intelligence methods are discussed and a flexible software architecture for integrating different solvers in a product configurator is proposed.

Whereas the configuration of consumer products such as PCs, cars, insurances, and such is well understood and supported by commercial tools, large-scale industrial systems still raise considerable challenges concerning modeling, solving, and performance. After a short explanation of the importance of this topic to Siemens, this chapter presents the domain of railway interlocking systems as an example of such complex industrial systems and discusses detailed requirements for their configuration. It then reports on techniques used for solving those requirements at Siemens as well as on results of their application.

Most of contemporary software systems are implemented using an object-oriented approach. Modeling phases -- during which software engineers analyze requirements to the future system using some modeling language -- are an important part of the development process, since modeling errors are often hard to recognize and correct. In this paper we present a framework which allows the integration of Answer Set Programming into the object-oriented software development process. OOASP supports reasoning about object-oriented software models and their instantiations. Preliminary results of the OOASP application in CSL Studio, which is a Siemens internal modeling environment for product configurators, show that it can be used as a lightweight approach to verify, create and transform instantiations of object models at runtime and to support the software development process during design and testing.

Reconfiguration is an important activity for companies selling configurable products or services which have a long life time. However, identification of a set of required changes in a legacy configuration is a hard problem, since even small changes in the requirements might imply significant modifications. In this paper we show a solution based on answer set programming, which is a logic-based knowledge representation formalism well suited for a compact description of (re)configuration problems. Its applicability is demonstrated on simple abstractions of several real-world scenarios. The evaluation of our solution on a set of benchmark instances derived from commercial (re)configuration problems shows its practical applicability.

Background: Requirement engineering is often considered a critical activity in system development projects. The increasing complexity of software, as well as number and heterogeneity of stakeholders, motivate the development of methods and tools for improving large-scale requirement engineering. Aims: The empirical study presented in this paper aims to identify and understand the characteristics and challenges of a platform, as desired by experts, to support requirement engineering for individual stakeholders, based on the current pain-points of their organizations when dealing with a large number requirements. Method: We conducted a multiple case study with three companies in different domains. We collected data through ten semi-structured interviews with experts from these companies. Results: The main pain-point for stakeholders is handling the vast amount of data from different sources. The foreseen platform should leverage such data to manage changes in requirements according to customers' and users' preferences. It should also offer stakeholders an estimation of how long a requirements engineering task will take to complete, along with an easier requirements dependency identification and requirements reuse strategy. Conclusions: The findings provide empirical evidence about how practitioners wish to improve their requirement engineering processes and tools. The insights are a starting point for in-depth investigations into the problems and solutions presented. Practitioners can use the results to improve existing or design new practices and tools.