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.
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Machine learning models have proven to be reliable methods in classification tasks. However, little research has been done on classifying dwelling characteristics based on smart meter & weather data before. Gaining insights into dwelling characteristics can be helpful to create/improve the policies for creating new dwellings at NZEB standard. This paper compares the different machine learning algorithms and the methods used to correctly implement the models. These methods include the data pre-processing, model validation and evaluation. Smart meter data was provided by Groene Mient, which was used to train several machine learning algorithms. The models that were generated by the algorithms were compared on their performance. The results showed that Recurrent Neural Network (RNN) 2performed the best with 96% of accuracy. Cross Validation was used to validate the models, where 80% of the data was used for training purposes and 20% was used for testing purposes. Evaluation metrices were used to produce classification reports, which can indicate which of the models work the best for this specific problem. The models were programmed in Python.
Societal one-way directed approaches of sustainable primary school building design cause persistent physical building problems. It affects the performances of the societal challenge of designing real sustainable school buildings, as well as the educational and social processes, and its end-user performances. Conventional building construction approaches build traditionally their designs on a syntheses of dialogues and consensus during decision-making processes, due to a variety of different interests. Principals define their ambitions and requirements into a team of mainly technical domain related disciplines. There are no design methods available that connect human systems and ecosystems integrated and balance the dynamic multi-level scaled mechanisms of human needs and sustainability development factors. The presented analytic framework recognizes similarity patterns between these multi-level scaled social systems, ecosystems and sustainable development entities, qualitatively as well as quantitatively. It delivers a new polarity based dynamic system that contributes to the client briefs and physical building morphological factors from a more sustainable development base. This theoretical approach establishes Sustainability-Centered Guidelines for primary schools (SCGs) design and building.
