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The utilization of management control systems in university management poses a considerable challenge because university’s strategic goals are not identical to those applied in profit-oriented management. A university’s management control system should take into account the processing of management information for management purposes, allowing for the relationships between different groups of stakeholders. The specificity of the university operation assumes conducting long-term scientific research and educational programmes. Therefore, the controlling approach to university management should considerat long-term performance measurement as well as management in key areas such as research, provision of education to students, and interaction with the tertiary institution’s socio-economic environment. This paper aims to develop a conceptual framework of the Intelligent Management Control System for Higher Education (IMCSHE) based on cognitive agents. The main findings are related to developing the assumption, model, and technological basis including the artificial intelligence method.

The paper presents a case study of modelling real-case industrial scenario of a grain-trading Polish company with Unified Process Metamodel (UPM). UPM is a novel approach for continuous process modelling. Many industrial processes, such as grain storage, are hard to describe by the use of conventional tools. With this example the authors emphasize the benefits of continuous process modeling paradigm over the token-based methodology and show the facilities of storage modeling and behavioral annotation in the UPM.

Background: This paper describes the use of fluorescence induced by UV radiation to evaluate the share of tracer in feed mixture. Methods: For the purpose of this study, three substances were used. They are as follows: Tinopal, Rhodamine B, and Uranine. Tracer in the form of maize or kardi was added to chicken feed before the mixing process. Grains used in the process were grinded in the mill sieve with a mesh size of 4 and 6 mm. The drawn samples of the mixture were illuminated with UV radiation to make grain tracer light, and then the photo was taken with a digital camera. The acquired images were analyzed with the use of a computer program running on the RGB color model, which was the way to obtain essential information about the percentage share of tracer. Results: It was observed that, in the case of kardi grains, the proposed method gives results significantly deviating from the verification method. Conclusions: Only the tests with the use of maize having an average particle diameter of 2.4 mm and tinted with the solution of Rhodamine B led to acceptable results (consensual with the predetermined verification level).

The paper presents test results for the assessment of the tracer content in a three-component (green peas, sorghum, maize) feed mixture that is based on the fluorescent method. The homogeneity of mixtures was determined on the basis of the maize content (as the key component), which was treated with fluorescent substance: tinopal, rhodamine B, uranine and eosin. The key components were wet-treated with fluorescent substances with different concentrations. Feed components were mixed in a vertical funnel-flow mixer. 10 samples were collected from each mixed batch. Samples were placed in a chamber equipped with UV light and, then, an image recorded as BMP file was generated. The image was analysed by means of the software programme Patan. On the basis of the analyses conducted, data on the maize content marked with a fluorescent marker were obtained. Additionally, the content of the key component was determined in a conventional manner – using an analytical scale. Results indicate the possibility of using this method for homogeneity assessment of the three-component grain mixture. From these tests, fluorescent substances that can be applied in the case of maize as a key component, together with their minimum concentrations, were identified: tinopal 0.3%, rhodamine B 0.001%.

Research in the field of collective intelligence is currently focused mainly on determining ways to provide a more and more accurate prediction. However, the development of collective intelligence requires a more formal approach. Thus the natural next step is to introduce the formal model of collective. Many scientists seem to see this need, but available solutions usually focus on narrow specialization. The problems within the scope of collective intelligence field typically require complex models. Sometimes more than one model has to be used. This paper addresses both issues. Authors introduce graph-based meta-model of collective that intend to describe all collective’s properties based on psychological knowledge, especially on Surowiecki’s work. Moreover, we introduced the taxonomy of metrics that allow assessing the qualitative aspects of crowd’s structure and dynamics.

Automated reasoning is becoming crucial for information systems. Building one uniform decision support system has become too complicated. The natural approach is to divide the task and combine the results from different subsystems into one uniform answer. It is the basic idea behind the system approach, where one solution is a composition of multiple subsystems. In this paper, the main emphasis is on establishing the theoretical framework that combines various reasoning methods into a collective system. The system’s formal abstraction uses graph theory and provides a discussion on possible aggregation function definitions. The proposed framework is a tool for building and testing specific approaches rather than the solution itself.

The paper is a wide survey over one of the knowledge representation and processing solutions, namely knowledge bases. Due to current terminological inconsistency authors propose the complex definition of knowledge base in the field of knowledge representation. The overview of the most common reality description methods is provided in order to discuss its usefulness in knowledge base design. Authors not only give the definition of the knowledge base but also prove its completeness on the example of Semantic Knowledge Base project. The project aims at developing the general domain knowledge base using ontology base and semantic networks as basic knowledge representation methods.

Developing a knowledge-based system is a demanding task. One of the basic issues is to build a communication interface with a human. The basis for such an interface is mostly natural language. In this article, the process of building and supplementing data with one of the modules of the Semantic Knowledge Base, which is responsible for user interaction, is presented. For this purpose, the process of data acquisition from the WordNet database was carried out by remodeling it by the use of Association-Oriented Database Metamodel.

Semantic networks are nowadays one of the most frequently used knowledge representation method. In this paper authors present a novel approach towards the definition and semantics of semantic networks with the use of predefined primitives as key structure elements. The presented solution is a part of Semantic Knowledge Base project, in which it is used to store complex information such as facts and rules. This approach aims at increased expressiveness of knowledge representation with the higher level of clarity of message. It introduces not only a unique duality of nodes types, namely operators and operands, but also provides mechanisms such as multiplicity, quantifiers or modifiers possible to apply to each and every of network elements.

Logic, language and information integration is one of areas broadly explored nowadays and at the same time promising. Authors use that approach in their 8 years long research into Structured Semantic Knowledge Base System. The aim of this paper is to present authors idea of system capable of generating synergy effect while storing various type of information. The key assumption, which has been adopted, is the thesis that the attempt to find universal way of the reality description is very inefficient solution. Combination of several solutions into one system must be based on the principle of supporting rather than on redundancy, ambiguity or mutual antagonism areas of logic, language and information. Natural language processing in the context of logics or information processing is ineffective, not to say pointless in the means of certainty. It is simply caused by the fact that natural languages have been formed long time ago as the main communication channel, rather than for automatic information processing. It was one of the reasons that logic has been founded as the universal language of mathematics and formalized communication in more extensive sense. Information processing however brought more sophisticated problems that mathematics could not smoothly solve, what made computer science appear. Studies over artificial intelligence revealed even more complex issues to solve. The unity of those three areas: logics, language and information is a necessity to acquire complementarity and synergy.