Abstract
The modern digital era is characterized by a plethora of emerging technologies, methodologies and techniques that are employed in the manufacturing industries with intent to improve productivity, to optimize processes and to reduce operational costs. Yet, algorithms and methodological approaches for improvement of energy consumption and environmental impact are not integrated with the current operational and planning tools used by manufacturing companies. One possible reason for this is the difficulty in bridging the gap between the most advanced energy related ICT tools, developed within the scope of the industry 4.0 era, and the legacy systems that support most manufacturing operational and planning processes. Consequently, this paper proposes a conceptual architecture model for a digital energy management platform, which is comprised of an IIoT-based platform, strongly supported by energy digital twin for interoperability and integrated with AI-based energy data-driven services. This conceptual architecture model enables companies to analyse their energy consumption behaviour, which allows for the understanding of the synergies among the variables that affect the energy demand, and to integrate this energy intelligence with their legacy systems in order to achieve a more sustainable energy demand. © 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the FAIM 2021.

Abstract
Brazil has been failing to offer the most favorable conditions for the implementation of onshore wind farms, due to the presence of multiple barriers. However, the country has observed a fast and expressive wind energy (WE) diffusion (the installed WE capacity grew 37 times in the last decade). Furthermore, its onshore wind farms have reached impressive capacity factors (with productivity levels much higher than the average around the world) and a very low levelized cost of electricity. This study aims at identifying how wind developers plan onshore wind farms to overcome existing barriers. Based on forty-one interviews with relevant stakeholders of the Brazilian WE sector, the study identified efforts targeted at overcoming twenty-four previously identified barriers. Although most barriers may be overcome directly through developer initiatives, addressing higher level barriers, namely an unstable macroeconomic environment, a poor transmission infrastructure, and inadequate access to capital, depends on government actions.

Abstract
Onshore wind energy (WE) has achieved a significant diffusion worldwide, in spite of the existence of multiple barriers to the large-scale implementation of wind farms. These barriers have been reported in a large number of studies, but the literature is lacking a systematized overview of their categories and locations. Based on a framework for the analysis of barriers to the penetration of renewable energy sources proposed by Painuly [363], this systematic literature review contributes to addressing this gap, identifying barriers to the large-scale implementation of onshore wind farms by category (market failures, market distortions, economic and financial, institutional, technical, social and other barriers) and location (countries around the world), and characterizing them by the level of economic development (least developed, developing, in transition, and developed) and stage of diffusion (recent or advanced) in their locations. The framework showed a high level of fit with the case of WE and allowed the identification of 31 barriers in 159 countries. The barriers were found to be mostly present in developing economies with recent diffusion, although some barriers were found to occur broadly across developed economies, regardless of the stage of diffusion. The three most frequently observed barriers were the inadequate consideration of externalities, uncertain and unsupportive governmental policies, and insufficient transmission grids.

Abstract
Industry 4.0 is a result of technological evolution and is intended to promote technological transformations in industry at different levels. The impact in human employment has been perceived as a major threat and is a matter of concern. Some authors argue that automation will bring unimaginable changes as soon as computers get more intelligence and as machines become able to perform complex tasks more efficiently than humans. However, technological progress is also pointed out as a stimulus for human-beings to develop the competencies that differentiate them from the machines. In this context, this study aims to explore the impacts of adopting Industry 4.0 technologies on work. The results of a comprehensive literature review provide an integrated perspective to identify and understand such impacts, analysing them in four categories: evolution of employment and creation of new jobs, human-machine interaction, new competencies creation/ development, and, organizational and professional changes. © 2020 IEEE.

Abstract
Similar to the case of biotechnology industry, companies providing devices in the biomedicine industry face several challenges, and to stand out from competitors need to know how to get to the right customer. Potential customers (i.e., individuals and organizations) may choose to adopt or reject an innovative product and will later confirm that decision or not. Such decision is of utmost importance to the success of innovative products and, therefore, of the company that provides them. The aim of this study is to understand how perceptions formed about a biomedical product can influence its adoption intention and behavior and, hereafter, influence the decision of other potential adopters. Findings from a multiple case study provide a clear definition of the adoption process of a specific biomedical product, combining two existing theories - the Diffusion of Innovations Theory and the Technology Acceptance Model - and including the feedback created by interactions between current users of the product and potential users, to understand what influences potential adopters' decisions. © 2020 IEEE.

Abstract
There are extensive studies in the literature about the reorder point/order quantity policies for inventory management, also known as policies. Over time different algorithms have been proposed to calculate the optimal parameters given the demand characteristics and a fixed cost structure, as well as several heuristics and meta-heuristics that calculate approximations with varying accuracy. This work proposes a new meta-heuristic that evolves closed-form expressions for both policy parameters simultaneously - Cooperative Coevolutionary Genetic Programming. The implementation used for the experimental work is verified with published results from the optimal algorithm, and a well-known hybrid heuristic. The evolved expressions are compared to those algorithms, and to the expressions of previous Genetic Programming approaches available in the literature. The results outperform the previous closed-form expressions and demonstrate competitiveness against numerical methods, reaching an optimality gap of less than , while being two orders of magnitude faster. Moreover, the evolved expressions are compact, have good generalisation capabilities, and present an interesting structure resembling previous heuristics.