The problems related to construction production are multi-faceted and complex. This has promoted the search for different methods/approaches for analizing the data which supports the decision-making process in the construction industry. In the article the authors focus their attention on well-known methods and tools, and on some new approaches to solving decision-making problems. The aim of the article is to analyze the methods used to analyse data in a construction company, convey their advantages and disadvantages, and specify the degree of efficiency in the discussed area.

In the paper an approach to decision making in situations with non-point-like characterisation and subjective evaluation of the actions is considered. The decision situation is represented mathematically as fuzzy multiobjective linear programming (fMOLP) model, where we apply the reduced fuzzy matrices instead of fuzzy classical numbers. The fMOLP model with reduced parameters is decomposable into the set of point-like models and the point-like models enable effective construction of an optimisation procedure – fBIP, see Wojewnik (2006ab), extending the bireference procedure by Michalowski and Szapiro (1992). The approach is applied to a fuzzy optimization problem in the area of telecommunication services.

In the field experiments performed in 1999–2001 the profitability of late blight control in accordance with three decision support systems: NegFry, Simphyt and Stephan with routine fungicide program was compared. Potato protection carried out according to the recommendations of the decision support systems guaranteed higher profitability of late blight control than when potato was protected routinely. The highest profitability was recorded for susceptible variety Bekas protected according to NegFr.

A significant part of the knowledge used in the production processes is represented with natural language. Yet, the use of that knowledge

in computer-assisted decision-making requires the application of appropriate formal and development tools. An interesting possibility is

created by the use of an ontology that is understandable both for humans and for the computer. This paper presents a proposal for

structuring the information about the foundry processes, based on the definition of ontology adapted to the physical structure of the

ongoing technological operations that make up the process of producing castings.

The suitability of a land plot in a real estate market could be identified as a good investment because the land plot is deemed as popular. This activity is important for economic growth, who is one of the sustainable development goals. Mostly, all research in this field is focused on sustainability as well as the opinions of professionals. However, this field should be explored from another side which is based on real geodata. Criteria and its weight are very important in decision support systems. The correct criteria can help in selection of the best real estate object for an investment, but it is not only useful but also and a challenging task that has not yet been solved. The methods of research are data graphical analysis, correlation, decision supporting systems, etc. The research aims at determining the significance of the connections and using them as the criteria in the selected decision supporting method. In addition, it will be determined which decision supporting method defines the most suitable object for investment. These new criteria are proposed for operation in the land use models. Furthermore, it has been identified as one criterion, which is significant in the urban and agrarian territories. Also it turned out, that the land plot is the most active when it is as far from a densely built-up residential territory as possible and as close to a school, and when the land plot is as large as possible.

Malignant melanomas are the most deadly type of skin cancer, yet detected early have high chances of successful treatment. In the last twenty years, the interest in automatic recognition and classification of melanoma dynamically increased, partly because of appearing public datasets with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task due to uneven sizes of datasets, huge intra-class variation with small interclass variation, and the existence of many artifacts in the images. One of the most recognized methods of melanoma diagnosis is the ABCD method. In the paper, we propose an extended version of this method and an intelligent decision support system based on neural networks that uses its results in the form of hand-crafted features. Automatic determination of the skin features with the ABCD method is difficult due to the large diversity of images of various quality, the existence of hair, different markers and other obstacles. Therefore, it was necessary to apply advanced methods of pre-processing the images. The proposed system is an ensemble of ten neural networks working in parallel, and one network using their results to generate a final decision. This system structure enables to increase the efficiency of its operation by several percentage points compared with a single neural network. The proposed system is trained on over 5000 and tested afterwards on 200 skin moles. The presented system can be used as a decision support system for primary care physicians, as a system capable of self-examination of the skin with a dermatoscope and also as an important tool to improve biopsy decision making.

In this paper the authors propose a decision support system for automatic blood smear analysis based on microscopic images. The images are pre-processed in order to remove irrelevant elements and to enhance the most important ones – the healthy blood cells (erythrocytes) and the pathologic ones (echinocytes). The separated blood cells are analysed in terms of their most important features by the eigenfaces method. The features are the basis for designing the neural network classifier, learned to distinguish between erythrocytes and echinocytes. As the result, the proposed system is able to analyse the smear blood images in a fully automatic way and to deliver information on the number and statistics of the red blood cells, both healthy and pathologic. The system was examined in two case studies, involving the canine and human blood, and then consulted with the experienced medicine specialists. The accuracy of classification of red blood cells into erythrocytes and echinocytes reaches 96%.

A complex model of mechanically ventilated ARDS lungs is proposed in the paper. This analogue is based on a combination of four components that describe breathing mechanics: morphology, mechanical properties of surfactant, tissue and chest wall characteristics. Physical-mathematical formulas attained from experimental data have been translated into their electrical equivalents and implemented in MultiSim software. To examine the adequacy of the forward model to the properties and behaviour of mechanically ventilated lungs in patients with ARDS symptoms, several computer simulations have been performed and reported in the paper. Inhomogeneous characteristics observed in the physical properties of ARDS lungs were mapped in a multi-lobe model and the measured outputs were compared with the data from physiological reports. In this way clinicians and scientists can obtain the knowledge on the moment of airway zone reopening/closure expressed as a function of pressure, volume or even time. In the paper, these trends were assessed for inhomogeneous distributions (proper for ARDS) of surfactant properties and airway geometry in consecutive lung lobes. The proposed model enables monitoring of temporal alveolar dynamics in successive lobes as well as those occurring at a higher level of lung structure organization, i.e. in a point P0 which can be used for collection of respiratory data during indirect management of recruitment/de-recruitment processes in ARDS lungs. The complex model and synthetic data generated for various parametrization scenarios make possible prospective studies on designing an indirect mode of alveolar zone management, i.e. with

Cross-docking is a strategy that distributes products directly from a supplier or manufacturing plant to a customer or retail chain, reducing handling or storage time. This study focuses on the truck scheduling problem, which consists of assigning each truck to a door at the dock and determining the sequences for the trucks at each door considering the time-window aspect. The study presents a mathematical model for door assignment and truck scheduling with time windows at multi-door cross-docking centers. The objective of the model is to minimize the overall earliness and tardiness for outbound trucks. Simulated annealing (SA) and tabu search (TS) algorithms are proposed to solve large-sized problems. The results of the mathematical model and of meta-heuristic algorithms are compared by generating test problems for different sizes. A decision support system (DSS) is also designed for the truck scheduling problem for multi-door cross-docking centers. Computational results show that TS and SA algorithms are efficient in solving large-sized problems in a reasonable time.

The paper features a comprehensive approach to risk management worked out during the ValueSec project (EU 7th Framework Programme). The motivation for research was presented, along with the course of the research, achieved project results and validation results. The methodology of risk management and a supporting tool were developed as a result of the project. They help decision makers to make complex strategic decisions about security measures. These complex decision-related problems were the reason to launch the research. The elaborated methodology is based on three pillars: assessment of the considered security measure ability to reduce risk, costs and benefits analysis with respect to the security measure application, and analysis of legal, social, cultural, and other restrictions that might impair or even destroy the efficiency of the functioning measures. In the project these restrictions are called qualitative criteria. The main added value of the ValueSec project is the elaboration of a special software module to analyse impacts of qualitative criteria on the considered measure. Based on the methodology, a ValueSec Toolset prototype was developed. The prototype was then validated in the following application domains: mass event, railway transport security, airport and air transport security, protection against flood, and protection of smart grids against cyber-attacks.

This article discusses the results of studies using the developed artificial neural networks in the analysis of the occurrence of the four main mechanisms destroying the selected forging tools subjected to five different surface treatment variants (nitrided layer, pad welded layer and three hybrid layers, i.e. AlCrTiSiN, Cr/CrN and Cr/AlCrTiN). Knowledge of the forging tool durability, needed in the process of artificial neural network training, was included in the set of training data (about 800 records) derived from long-term comprehensive research carried out under industrial conditions. Based on this set, neural networks with different architectures were developed and the results concerning the intensity of the occurrence of thermal-mechanical fatigue, abrasive wear, mechanical fatigue and plastic deformation were generated for each type of the applied treatment relative to the number of forgings, pressure, friction path and temperature.

The article herein presents the method and algorithms for forming the feature space for the base of intellectualized system knowledge for the support system in the cyber threats and anomalies tasks. The system being elaborated might be used both autonomously by cyber threat services analysts and jointly with information protection complex systems. It is shown, that advised algorithms allow supplementing dynamically the knowledge base upon appearing the new threats, which permits to cut the time of their recognition and analysis, in particular, for cases of hard-to-explain features and reduce the false responses in threat recognizing systems, anomalies and attacks at informatization objects. It is stated herein, that collectively with the outcomes of previous authors investigations, the offered algorithms of forming the feature space for identifying cyber threats within decisions making support system are more effective. It is reached at the expense of the fact, that, comparing to existing decisions, the described decisions in the article, allow separate considering the task of threat recognition in the frame of the known classes, and if necessary supplementing feature space for the new threat types. It is demonstrated, that new threats features often initially are not identified within the frame of existing base of threat classes knowledge in the decision support system. As well the methods and advised algorithms allow fulfilling the time-efficient cyber threats classification for a definite informatization object.