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Modelling Complex Relationships Between Sustainable Competitiveness and Digitalization

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.

AMOS ile Yapısal Eşitlik Modellemesi

Attrition in higher education

In this research, which is part of my PhD research into the influence of the use of social media in higher education, I have lessened the amount of variables, from Tinto’s integration theory. By including only the best-proven predictive variables, based on previous studies, I avoid the capitalization of chance and have built a more easy to use model for teachers and management. The latent variable ‘satisfaction’ is constructed by using just a fraction of the original manifest variables. The simplified model is tested using principal component analysis (PCA), to prove its fit. Furthermore, to better suit students’ contemporary society in the developed world, the model is enriched with the use of social media, in this case Facebook. The purpose of Facebook use (information, education, social and leisure) and the use of different pages amongst students were also measured with PCA. This provided a better insight in the integration/engagement components, which are also included in the new model. According by the measurements of Cronbach’s alpha and Guttman’s lambda-2, the new components showed internal consistency and reliability. In addition, SPSS AMOS was used for testing the fit of the model and showed reasonable values for the normed fit index (NFI), the comparative fit index (CFI), the Tucker-Lewis Index (TLI) and the root mean square error of approximation (RMSEA). This study will compare different background variables within the model to uncover the possible influences upon students’ attrition (and therefor also their success), engagement/satisfaction and social media use. Ultimately this paper will provide jet another piece to the puzzle for a better insight into the factors of students’ attrition and/or success.

The development and validation of a five factor model of sources of self-efficacy in clinical nursing education

Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.

Finite element modelling of carbon fiber - carbon nanostructure - polymer hybrid composite structures

The present study deals with the numerical modelling of hybridlaminated composites, which can be proved especially useful in theengineering and maintenance of advanced aerospace primary structures. Thelamina is comprised of continuous carbon fibers, thermosetting epoxypolymer matrix, as well as carbon nanostructures, such as graphene orcarbon nanotubes, inclusions. Halpin-Tsai equations combined with resultsobtained from nanomechanical analysis are employed in order to evaluatethe elastic properties of the carbon nanostructure/polymer matrix. Then, theobtained elastic properties of the hybrid matrix are used to calculate theorthotropic macro-mechanical properties of the unidirectional compositelamina. A hybrid composite plate is modelled as a 2D structure via theutilization of 4-node, quadrilateral, stress/displacement shell finite elementswith reduced integration formulation. The convergence and analysisaccuracy are tested. The mechanical performance of the hybrid compositesis investigated by considering specific configurations and applyingappropriate loading and boundary conditions. The results are compared withthe corresponding ones found in the open literature, where it is possible.

Structural design and material comparison for aircraft wing box beam panel

This paper aims to present a comprehensive investigation to obtain the structural calculations needed to design a rigid panel of aluminum alloy for the wing box beam of an ATR 72–500 aircraft. For this design process, several types of materials, including composites like CFRP, are considered so it is possible to compare the actual existing part made of aluminum to them, thus checking the advantages these new materials offer. The research presents an introduction to structural design and provides a study of the relevant literature. The aircraft's principal characteristics and performance abilities were collected so that structural loads can be computed. Research used several methods, a design using conventional methods, applying the theory of elasticity is performed using the Theory of Farrar, allowing us to obtain an analytical solution to the problem, followed by checking the obtained results using Ansys FEM software combined with the parts being designed with CATIA. Furthermore, this same panel is calculated using composite materials instead of conventional aluminum, allowing us to compare both solutions. This research shed light on the intricate process of aircraft structural design, materials selection, and calculation methodologies, highlighting the ongoing pursuit of new and advanced materials. This paper makes clear that using composite materials presents several advantages over traditional ones, allowing for lighter, safer, more fuel-efficient, and more sustainable aircraft. The use of composite materials in the construction of airplane structures is driven by many factors. The results show that the chosen composite materials reduce weight, are durable, have low maintenance requirements, reduce noise, enhance fuel economy, and are resistant to corrosion.