Pavel Čech

A proposal for a computer-based framework of support for public health in the management of biological incidents: the Czech Republic experience

Aims: Biological incidents jeopardising public health require decision-making that consists of one dominant feature: complexity. Therefore, public health decision-makers necessitate appropriate support. Methods: Based on the analogy with business intelligence (BI) principles, the contextual analysis of the environment and available data resources, and conceptual modelling within systems and knowledge engineering, this paper proposes a general framework for computer-based decision support in the case of a biological incident. At the outset, the analysis of potential inputs to the framework is conducted and several resources such as demographic information, strategic documents, environmental characteristics, agent descriptors and surveillance systems are considered. Results: Consequently, three prototypes were developed, tested and evaluated by a group of experts. Their selection was based on the overall framework scheme. Subsequently, an ontology prototype linked with an inference engine, multi-agent-based model focusing on the simulation of an environment, and expert-system prototypes were created. Conclusions: All prototypes proved to be utilisable support tools for decision-making in the field of public health. Nevertheless, the research revealed further issues and challenges that might be investigated by both public health focused researchers and practitioners.

Complexity of ambient intelligence in managerial work

Recently, ambient intelligence a vision of information society of the future has become the subject of attention of many theorists and practitioners. The achievement of ambient intelligence postulates an adequate shift in thinking. The shift in thinking concerns also managerial work. The paper presents on field experience on how to test the meaningfulness of teaching systems thinking for managers.

The effect of cognitive training on the subjective perception of well-being in older adults

Background There is a growing number of studies indicating the major consequences of the subjective perception of well-being on mental health and healthcare use. However, most of the cognitive training research focuses more on the preservation of cognitive function than on the implications of the state of well-being. This secondary analysis of data from a randomised controlled trial investigated the effects of individualised television-based cognitive training on self-rated well-being using the WHO-5 index while considering gender and education as influencing factors. The effects of cognitive training were compared with leisure activities that the elderly could be engaged in to pass time. Methods Cognitively healthy participants aged 60 years or above screened using the Mini-Mental State Examination (MMSE) and Major Depression Inventory (MDI) were randomly allocated to a cognitive training group or to an active control group in a single-blind controlled two-group design and underwent 24 training sessions. Data acquired from the WHO-5 questionnaire administered before and after intervention were statistically analysed using a mixed design model for repeated measures. The effect of individualised cognitive training was compared with leisure activities while the impact of gender and education was explored using estimated marginal means. Results A total of 81 participants aged 67.9 ± 5.59 [60–84] without cognitive impairments and absent of depression symptoms underwent the study. Participants with leisure time activities declared significantly higher scores compared to participants with cognitive training M = 73.48 ± 2.88, 95% CI [67.74–79.22] vs M = 64.13 ± 3.034, 95% CI [58.09–70.17] WHO-5 score. Gender and education were found to moderate the effect of cognitive training on well-being when compared to leisure activities. Females engaged in leisure activities in the control group reported higher by M = 9.77 ± 5.4, 95% CI [−0.99–20.54] WHO-5 scores than females with the cognitive training regimen. Participants with high school education declared leisure activities to increase WHO-5 scores by M = 14.59 ± 5.39, 95% CI [3.85–25.34] compared to individualised cognitive training. Discussion The findings revealed that individualised cognitive training was not directly associated with improvements in well-being. Changes in the control group indicated that involvement in leisure time activities, in which participants were partly free to choose from, represented more favourable stimulation to a self-perceived sense of well-being than individualised cognitive training. Results also supported the fact that gender and education moderated the effect of cognitive training on well-being. Females and participants with high school education were found to be negatively impacted in well-being when performance connected with cognitive training was expected.

Self-organizational Aspects and Adaptation of Agent Based Simulation Based on Economic Principles

The agent-oriented approach is one of most frequently used techniques for complex system simulation today. This paper is investigating application of multi-agent system consisting of four basic types of agents for creating virtual economy environment for further testing and research in areas of multi-agent coordination and self-organization. Although the proposed system is in several aspects simplified, for example banking sector and government are not included into model, it provides useful basis for research of adaptation mechanisms, manufacturing management, supply chain management, and customer behaviour modelling. Individual goals and strategies are forming collective effort of pursue of given goals, respecting constraints and limitations set on level of the whole agent community. Our goal is to design a system consisting of agents capable of self-organization into structures allowing processing of resources in the given environment and creating production and supply chains with maximum efficiency possible.

Comparative analysis of selected path-planning approaches in large-scale multi-agent-based environments

Abstract Large-scale models are currently used for the simulation, analysis and control of real systems, whether technical, biological, social or economic. In multi-agent simulations of virtual economies, it is important to schedule a large number of agents across the cities involved, in order to establish a functional supply chain network for industrial production. This study describes an experimental evaluation of path-planning approaches in the field of multi-agent modelling and simulation, applied to a large-scale setting. The experimental comparison is based on a model in which agents represent economic entities and can participate in mutual interactions. For the purposes of experiment, the model is scaled to various degrees of complexity in terms of the numbers of agents and transportation nodes. Various numbers of agents are used to explore the way in which the models complexity influences the runtime of the path-planning task. The results indicate that there are significant differences between the runtime performances associated with single approaches, for differing levels of system complexity and model sizes. The study reveals that the appropriate sharing of shortest path information can significantly improve path-planning activities. Hence, this work extends current research in the field of path-planning for multi-agent simulations by conducting an experimental performance analysis of five distinct path-planning approaches and a statistical evaluation of the results. This statistical evaluation contrasts with performance analyses conducted on the basis of ‘Big O’ notation for algorithmic complexity, which describes the limiting behaviour of the algorithm and gives only a rough performance estimate.

The Emergence of Public Intelligence: Penetration of Business Intelligence into the Public Administration Realm

The current organisations face the challenge to gain and sustain the competitive advantage. The Business Intelligence tools support the organisational processes and enhance their efficiency. Nevertheless, these still have not penetrated to satisfactorily extent to the public administration realm. Therefore, this paper introduces the framework focused on the Business Intelligence implementation in the public administration. Firstly, the basic concepts linked with Big Data and Business Intelligence are described. Afterwards, the specific characteristics of public administration are considered. Finally, the practical model of Business Intelligence in context of the discussed issues is provided.