Examining pre‐service teachers' Technological Pedagogical Content Knowledge as evolving knowledge domains: A longitudinal approach

The aim of this study is to outline the development and changes in pre‐service teachers' technological pedagogical content knowledge (TPACK) assessments during the first 3 years in teacher education. Specifically, research was conducted at three measurement points over a 3‐year teacher education period. The target group consisted of pre‐service teachers (N = 148) from three Finnish universities. Results indicate a growth in confidence related to all TPACK areas during the research period. The strongest gains were in pedagogical content knowledge. In addition, the gains were larger in other areas related to pedagogical knowledge than areas related to technology or content knowledge. In areas without pedagogical knowledge, the changes were more moderate. In the discussion section, recommendations are provided on the potential of longitudinal use of the TPACK model to study and improve the development of pre‐service teachers' TPACK.     

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki

In this paper we propose a methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants). We demonstrate these methods to data monitored in the urban areas of Thessaloniki and Helsinki in Greece and Finland, respectively. For this purpose, we applied the principal component analysis method in order to inter-compare the patterns of air pollution in the two selected cities. Then, we proceeded with the development of air quality forecasting models for both studied areas. On this basis, we formulated and employed a novel hybrid scheme in the selection process of input variables for the forecasting models, involving a combination of linear regression and artificial neural networks (multi-layer perceptron) models. The latter ones were used for the forecasting of the daily mean concentrations of PM₁₀ and PM_2.5 for the next day. Results demonstrated an index of agreement between measured and modelled daily averaged PM₁₀ concentrations, between 0.80 and 0.85, while the kappa index for the forecasting of the daily averaged PM₁₀ concentrations reached 60% for both cities. Compared with previous corresponding studies, these statistical parameters indicate an improved performance of air quality parameters forecasting. It was also found that the performance of the models for the forecasting of the daily mean concentrations of PM₁₀ was not substantially different for both cities, despite the major differences of the two urban environments under consideration.     

The impacts of the sulphur emission regulation on the sulphur emission abatement innovation system in the Baltic Sea region

Clean Technologies and Environmental Policy - According to the Porter hypothesis, regulations on environmental emissions under certain conditions can promote eco-innovation. This is why the...     

Characteristics of organizational culture at the maintenance units of two Nordic nuclear power plants

This study aims to characterize and assess the organizational cultures of two Nordic nuclear power plant (NPP) maintenance units. The research consisted of NPP maintenance units of Forsmark (Sweden) and Olkiluoto (Finland). The study strives to anticipate the consequences of the current practices, conceptions and assumptions in the given organizations to their ability and willingness to fulfill the organizational core task. The methods utilized in the study were organizational culture and core task questionnaire (CULTURE02) and semi-structured interviews. Similarities and differences in the perceived organizational values, conceptions of one's own work, conceptions of the demands of the maintenance task and organizational practices at the maintenance units were explored. The maintenance units at Olkiluoto and Forsmark had quite different organizational cultures, but they also shared a set of dimensions such as strong personal emphasis placed on safety. The authors propose that different cultural features and organizational practices may be equally effective from the perspective of the core task. The results show that due to the complexity of the maintenance work, the case organizations tend to emphasize some aspects of the maintenance task more than others. The reliability consequences of these cultural solutions to the maintenance task are discussed. The authors propose that the organizational core task, in this case the maintenance task, should be clear for all the workers. The results give implications that this has been a challenge recently as the maintenance work has been changing. The concepts of organizational core task and organizational culture could be useful as management tools to anticipate the consequences of organizational changes.     

Reducing energy consumption by using self-organizing maps to create more personalized electricity use information

Identification of electricity use is one of the key elements to motivate customers to promote activities leading more efficient use of energy. Furthermore, electricity use comparisons with other similar customers give more interesting and concrete point of view to examine own consumption habits. In future, electricity providers and retailers are willing and probably forced by legislation to provide such information by the means of energy conservation and efficiency improvement. On the other hand, high number of customers set challenges to handle electricity use data and to create proper comparison information. In this study we present efficient and highly automated way to create comparison groups based on customers building characteristics. The main advantages of the data-based approach are that customer location is noticed, comparison groups are created using concrete building information, data processing is highly automated and also method is computationally efficient. Additionally, presented method provide tool to target and to create customer specific electricity saving guidance. The performance of suggested approach was tested using data set which contained electricity use and building information concerning almost 8000 customers.     

Optimization of the mean radius flow path of a multi-stage steam turbine with evolution algorithms

A prototype model of the mean radius flow path of a four-stage, high speed 1 MWe axial steam turbine was optimized by using evolution algorithms, DE (differential evolution) algorithm in this case. Also the cost-benefits of the optimization were inspected. The optimization was successfully performed but the accuracy of the optimization was slightly less than hoped when compared to the control modeling executed with the CFD (computational fluid dynamics). The mentioned inaccuracy could have been hardly avoided because of problems with an initial presumption involving semi-empiric calculations and of the uncertainty concerning the absolute areas of qualification of the functions. This kind of algebraic modeling was essential for the success of the optimization because e.g. CFD-calculation could not have been done on each step of the optimization. During the optimization some problems occurred with the adequacy of the computer capacity and with finding a suitable solution that would keep the algorithms within mathematically allowable boundaries but would not restrict the progress of the optimization too much. The rest of the problems were due to the novelty of the application and problems with preciseness when handling the areas of qualification of the functions. Although the accuracy of the optimization results was not exactly in accordance with the objective, they did have a favorable effect on the designing of the turbine. The optimization executed with the help of the DE-algorithm got at least about 3.5 % more power out of the turbine which means about 150 000 € cost-benefit per turbine in the form of additional electricity capacity.     

Synthesis of Novel Ag Modified MCM-41 Mesoporous Molecular Sieve and Beta Zeolite Catalysts for Ozone Decomposition at Ambient Temperature

Mesoporous molecular sieve Na-MCM-41 with Si/Al ratio 20 and 50 and Na-beta zeolite with Si/Al ratio 11 were synthesized and characterized using X-ray powder diffraction, scanning electron microscope and nitrogen adsorption. The Ag (5 and 2 wt%) modifications of H-MCM-41-20, H-MCM-41-50 and H-beta-11 catalysts were carried out using impregnation method. The catalysts were tested in the decomposition of ozone at ambient temperature. The 5 wt% Ag-H-MCM-41 catalyst showed a very high decomposition of ozone (~98%). The 5 wt% Ag-H-MCM-41-50 catalyst exhibited higher decomposition rate than 2 wt% Ag-H-MCM-41-50. The Ag modified H-MCM-41 catalyst with higher Si/Al ratio showed higher reaction rate than the catalyst with lower Si/Al ratio. The H-MCM-41 catalyst without Ag exhibited the lowest decomposition of ozone indicating an important role of Ag in the reaction.     

Reductions in particulate and NO(x) emissions by diesel engine parameter adjustments with HVO fuel

Hydrotreated vegetable oil (HVO) diesel fuel is a promising biofuel candidate that can complement or substitute traditional diesel fuel in engines. It has been already reported that by changing the fuel from conventional EN590 diesel to HVO decreases exhaust emissions. However, as the fuels have certain chemical and physical differences, it is clear that the full advantage of HVO cannot be realized unless the engine is optimized for the new fuel. In this article, we studied how much exhaust emissions can be reduced by adjusting engine parameters for HVO. The results indicate that, with all the studied loads (50%, 75%, and 100%), particulate mass and NO(x) can both be reduced over 25% by engine parameter adjustments. Further, the emission reduction was even higher when the target for adjusting engine parameters was to exclusively reduce either particulates or NO(x). In addition to particulate mass, different indicators of particulate emissions were also compared. These indicators included filter smoke number (FSN), total particle number, total particle surface area, and geometric mean diameter of the emitted particle size distribution. As a result of this comparison, a linear correlation between FSN and total particulate surface area at low FSN region was found.     

Steam Dewatering of Filter Cakes in a Vertical Filter Press

Steam dewatering of filter cakes is recognized as a competitive alternative to conventional air drying and thermally assisted mechanical dewatering (TAMD). The main benefit of cake dewatering with high-pressure steam is that mechanical and thermal dewatering can be efficiently performed in a single process step. The target of this study was to determine the potential of a steam-dewatering technique for two industrial mineral suspensions. The first mineral, kaolin, was very difficult to dewater using conventional mechanical dewatering techniques, whereas the second one, ground calcium carbonate (GCC), represented only moderate resistance to filtration. The secondary objective was to compare the filterability of the original kaolin slurry with the same slurry treated with a coagulant (aluminum sulphate, Al₂(SO₄)₃ · 16H₂O). Four different kinds of experiments were performed: tests without any kind of cake dewatering, tests with air drying, tests with steam drying, and tests with both coagulation and steam drying. The obtained results show that steam dewatering is an efficient technique for achieving lower cake-moisture contents. Also, the positive influence of coagulation on the filtration capacity was found to be considerable in the case of kaolin. An approximate energy balance was created for the steam-drying process and the determined energy efficiencies were compared with those obtainable with an ideal thermal drier. The main conclusion from these comparisons is that steam drying can be effectively used instead of traditional thermal drying if the large energy losses can be reduced by proper insulation and heat recovery systems.     

Toward Efficient Execution of RVC-CAL Dataflow Programs on Multicore Platforms

The increasing number of cores in System on Chips (SoC) has introduced challenges in software parallelization. As an answer to this, the dataflow programming model offers a concurrent and reusability promoting approach for describing applications. In this work, a runtime for executing Dataflow Process Networks (DPN) on multicore platforms is proposed. The main difference between this work and existing methods is letting the operating system perform Central processing unit (CPU) load-balancing freely, instead of limiting thread migration between processing cores through CPU affinity. The proposed runtime is benchmarked on desktop and server multicore platforms using five different applications from video coding and telecommunication domains. The results show that the proposed method offers significant improvements over the state-of-art, in terms of performance and reliability.