An ongoing air quality monitoring program in the Czech Republic has provided nearly continuous data for the concentrations of aerosol and gas-phase pollutants since its inception in February 1992. In addition to PM-2.5 concentrations, the concentrations of sulfate, organic carbon, elemental carbon, trace elements (Al−Pb), and polynuclear aromatic hydrocarbons (PAHs) were also measured. Fine particulate matter (PM-2.5) was composed mainly of organic carbon and sulfate with smaller amounts of trace metals. Coarse particle mass concentrations were typi cally between 10 and 30% of PM-2.5 concentrations. The chemical composition of emissions from power plants, residential space heating, local factories, and motor vehicles was also characterized. The ambient monitoring and source characterization data were then used in receptor modeling calculations, the results of which indicate that residential space heating and power plant emissions ac counted for most of fine particle mass concentrations observed during winter air pollution episodes. Motor vehicles, incinerators, and windblown dust contributed to the balance of the fine particle mass. Peak 24-h average TSP and SO2 concentrations (1100 and 800 μg/m3, respectively) obtained at the main monitoring site at Teplice in northern Bohemia during a severe air pollution episode in 1993 were within a factor of 2 of smoke and SO2 concentrations (1800 and 1600 μg/m3) measured in London during the smog episode of December 5−9, 1952. That pollution episode was thought to have contributed to a substantial increase in mortality.

Tsunamis are unpredictable and infrequent but potentially large impact natural disasters. To prepare, mitigate and prevent losses from tsunamis, probabilistic hazard and risk analysis methods have been developed and have proved useful. However, large gaps and uncertainties still exist and many steps in the assessment methods lack information, theoretical foundation, or commonly accepted methods. Moreover, applied methods have very different levels of maturity, from already advanced probabilistic tsunami hazard analysis for earthquake sources, to less mature probabilistic risk analysis. In this review we give an overview of the current state of probabilistic tsunami hazard and risk analysis. Identifying research gaps, we offer suggestions for future research directions. An extensive literature list allows for branching into diverse aspects of this scientific approach.

The main study objective was to investigate the effect of interactive television-based cognitive training on cognitive performance of 119 healthy older adults, aged 60–87 years. Participants were randomly allocated to a cognitive training group or to an active control group in a single-blind controlled two-group design. Before and after training interactive television cognitive performance was assessed on well validated tests of fluid, higher-order ability, and system usability was evaluated. The participants in the cognitive training group completed a television-based cognitive training programme, while the participants in the active control group completed a TV-based programme of personally benefiting activities. Significant improvements were observed in well validated working memory and executive function tasks in the cognitive training but not in the control group. None of the groups showed statistically significant improvement in life satisfaction score. Participants' reports of “adequate” to “high” system usability testify to the successful development and implementation of the interactive television-based system and compliant cognitive training contents. The study demonstrates that cognitive training delivered by means of an interactive television system can generate genuine cognitive benefits in users and these are measurable using well-validated cognitive tests. Thus, older adults who cannot use or afford a computer can easily use digital interactive television to benefit from advanced software applications designed to train cognition.

Abstract:Growing complexity represents an issue that can be identified in various disciplines. In system dynamics, causal loop diagrams are used for capturing dynamic nature of modelled systems. Increasing complexity of developed diagrams is associated with the tendency to include more variables into a model and thus make it more realistic and improve its value. This is even multiplied during group modelling workshops where several perspectives are articulated, shared and complex diagrams developed. This process easily generates complex diagrams that are difficult or even impossible to be comprehended by individuals. As there is a lack of available methods that would help users to cope with growing complexity, this manuscript suggests an original method. The proposed method systematically helps to simplify the complex causal loop diagrams. It is based on three activities iteratively applied during particular steps: endogenisation, encapsulation and order-oriented reduction. Two case studies are used to explain method details, prove its applicability and highlight added value. Case studies include the simplification of both original group causal loop diagram, and group diagram adapted from a study already published in a prestigious journal. Although the presented method has its own limitations, meaningfulness of its application in practice is verified. The method can help to cope with the complexity in any domain, in which causal loop diagrams are used.

BackgroundThere 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.MethodsCognitively 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.ResultsA 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.DiscussionThe 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.

Introduction: Energy return on energy invested (EROEI) of fossil fuels has been declining sharply, while modern renewable energy sources generally have even lower EROEI than fossil fuels. It has been repeatedly proven that economic growth expressed in the form of growth of real Gross Domestic Product (GDP) is closely related to intensified energy consumption and escalated usage of natural resources in general. This problem remains scarcely explored in pure economic modelling. Objectives: This study presents a novel model titled Energy Extended Neoclassical Growth Model (EENGM), which focuses on the consequences of declining quantity and quality of extractable fossil fuels and lower quality of the succeeding renewable energy technology for economic growth. Method: The Neoclassical growth model is translated into a system dynamics format and is extended by important feedback mechanisms, which are identified as important from the literature and mostly missing from the analyzed system dynamics models with a similar scope. Two scenarios assess the EENGM performance: business as usual (BAU) and the sustainability strategy (SUS). Results: Sensitivity analysis is performed for the Energy Return on Energy Invested (EROEI) parameter and results in the investment share in GDP varying between 27 and 40%, while the energy sector investment largely displaces investment in other economic sectors. The EENGM is associated with new behavior whereby the underperforming energy sector limits GDP growth and seizes most of the available investment. The adoption of the SUS strategy causes 28% lower cumulative fossil fuel aggregate consumption which still corresponds to higher than 1.5 °C global warming compared to the preindustrial levels. Conclusion: The share of consumption in the GDP of an economy undergoing energy transition can approach levels previously seen only in totally war-oriented economies. Even omitting other negative environmental feedback, the feasibility of the successful energy transition of the system in its contemporary form, with the currently available renewable energy technology, seems to be highly uncertain.

E-Learning as an advanced form of learning supported by various technological principles and tools became a point of interest of both academicians and practitioners several years ago. Current literature contains mostly successful stories of its utilization in the business environment highlighting processes and decisions which should be done and make. Rarely are mentioned negative experiences related processes and decision that should not be realized. Hence, it is difficult to fully and explicitly define related bottlenecks or pitfalls. The aim of this paper is to assemble particular fragments and provide readers with a summary of general barriers of e-Learning implementation in small and medium-sized organizations together with recommendations how these barriers can be overcome. Therefore, this position paper provides basic literature overview of conditions and barriers to successful e-Learning implementation in the business settings. The investigation of published case studies reveals that there are several barriers, which need to be taken into the consideration to support fruitful usage of e-Learning technologies in the workplace. Furthermore, suggestions of potential activities that may contribute to coping with these barriers are depicted.

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