The subject of this textbook is a methodical approach on the complex problem-solving process of conceptual structural design, leading to a controlled build-up of insight into the behaviour of the structure and supporting the actual successive design decisions during the conceptual design phase on the basis of a coherent set of solution components.
Digitalization is the core component of future development in the 4.0 industrial era. It represents a powerful mechanism for enhancing the sustainable competitiveness of economies worldwide. Diverse triggering effects shape future digitalization trends. Thus, the main research goal in this study is to use sustainable competitiveness pillars (such as social, economic, environmental and energy) to evaluate international digitalization development. The proposed empirical model generates comprehensive knowledge of the sustainable competitiveness-digitalization nexus. For that purpose, a nonlinear regression has been applied on gathered annual data that consist of 33 European countries, ranging from 2010 to 2016. The dataset has been deployed using Bernoulli’s binominal distribution to derive training and testing samples and the entire analysis has been adjusted in that context. The empirical findings of artificial neural networks (ANN) suggest strong effects of the economic and energy use indicators on the digitalization progress. Nonlinear regression and ANN model summary report valuable results with a high degree of coefficient of determination (R2>0.9 for all models). Research findings state that the digitalization process is multidimensional and cannot be evaluated as an isolated phenomenon without incorporating other relevant factors that emerge in the environment. Indicators report the consumption of electrical energy in industry and households and GDP per capita to achieve the strongest effect.
MULTIFILE
First year undergraduate students at the Amsterdam University of Applied Sciences of two different departments, were asked to join self-reported surveys in two succeeding years. Along with background variables, effort and commitment, the surveys asked elements of engagement. The later was analysed with factor analysis. The data of the surveys together with the results of the exams from the first year, were investigated to find out if the mandatory study choice test (SCT) taken before entering the faculty, had any predictive effect on their success. Not only basic statistical analysis like correlation was performed, but also more advanced analysis such as structural equation modelling (SEM) was used to uncover the value of the SCT. After a model was built, the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA), were used to determine the fit of the model. By comparing the influence of the variables on the SCT, the benefits of the latter will be determined and ultimately enhance the knowledge about influences upon student success in higher education.
