Abstract
This study was developed under the scope of a Portuguese project focused on the entrepreneur's perspective and perception on the internationalization process of his company: more specifically, about the factors that enhanced the company entry into foreign markets as well as the constraints found in this process. This work focuses on the importance of using digital transformation to integrate technological tools in international business practice and strategy and the obstacles encountered with introducing these new technologies. This study aims to determine the relationships between technology categories and obstacles. The final goal is to assess the impact of these characteristics of the companies by the sector of economic activity, size, and percentage of profits resulting from international expansion. A questionnaire was designed and sent by email to 8183 companies from the AICEP database, distributed by three main activity sectors. A total of 310 valid answers were gathered from the Portuguese internationalized companies. The research limitations are related to the reduced number of interviews. These interviews showed that managers were not aware of the concept of digital transformation and misunderstood the use of digital technologies in the internationalization process of the business. This limitation can add some bias to the qualitative results. In addition to these limitations, the number of responses per sector was also not homogeneous. The practical implications of this study are that managers and top-level executives can use that to better understand how companies could use digital tools and what obstacles they should avoid when they want to internationalize their business. This paper is one of the first research contributions to analyze the impact of digital transformation in the internalization of Portuguese companies.

Abstract
Nowadays the world faces the challenge to rapidly diminish the use of fossil fuels in order to reduce pollutants and the emission of greenhouse gases and to mitigate the global warming. Renewable energies, such as solar radiation, among others, are playing a relevant role in this context. Namely, the use of thermal energy storage systems in buildings and industry is increasing enabling to reduce operational costs and carbon dioxide emissions. Heat storage systems based in solar thermal panels for heating water in buildings are industrially mature but some improvements can be made to improve their efficiencies. In this work are presented the methods and the results achieved to model the dynamic behavior of the hot water temperature as function of the weather, operating conditions and technical parameters of the thermal solar system. This type of dynamic models will enable to optimize the efficiency of this type of systems regarding the use of auxiliary energy sources to heat the water whenever the temperature in the storage tank falls below a defined threshold level. As future work it is intended to use adaptive control algorithms to reduce the use of backup power sources (electricity, oil, gas) by using the information of the system status as well predictions for hot water consumption profiles and solar radiation.

Abstract

Abstract
Finding the right strategy enabled by a solid digital transformation is a key challenge that Small and Medium Enterprises (SMEs) face. With a high number of transformations that fail, there is a significant investment in research to identify critical factors that ultimately drive a more successful journey, and at the same time, quite a few different models to assess the digital transformation maturity. There is, however, a gap in assessing how ready a SME is to successfully embrace the transformation and an even more significant gap in assessing it from an employee's perspective. This work proposes a model to assess that readiness. The organizational readiness assessment aims to enable SMEs to better understand the foundations that need to be set to successfully deliver the transformation. From the employee's perspective, a user-friendly model assesses the non-technological aspects of an organization, indicating the clear gaps that ultimately might negatively impact the organization's future when digitally transforming. © 2022 Elsevier B.V.. All rights reserved.

Abstract
Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability. Therefore, in this study, we propose an efficient novel hybrid solar irradiance forecasting model based on three steps. In the first step, we employ a powerful variable input selection strategy named as partial mutual information (PMI) to calculate the linear and non-linear correlations of the original solar irradiance data. In the second step, unlike the traditional deep learning models designing their architectures manually, we utilize several deep long short term memory-convolutional neural network (LSTM-CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally, in the third step, we deploy a deep reinforcement learning strategy for selecting the best subset of the combined deep optimized LSTM-CNN models. Through analysing the forecasting results over two real-world datasets gathered from the USA solar irradiance stations, it can be inferred that our proposed algorithm outperforms other powerful benchmarked algorithms in 1-step, 2-step, 12-step, and 24-step ahead forecasting.

Abstract
This work deals with the design and validation of a control strategy for hybrid balancing systems (HBSs), an emerging concept that joins battery equalization and hybridization with supercapacitors (SCs) in the same system. To control this system, we propose a two-layer model predictive control (MPC) framework. The first layer determines the optimal state-of-charge (SoC) reference for the SCs considering long load forecasts and simple pack-level battery models. The second MPC layer tracks this reference and performs charge and temperature equalization, employing more complex module-level battery models and short load forecasts. This division of control tasks into two layers, running at different time scales and model complexities, enables us to reduce computational effort with a small loss of control performance. Experimental validation in a small-scale laboratory prototype demonstrates the effectiveness of the proposed approach in reducing charge, temperature, and stress in the battery pack.