Abstract

Abstract
Currently, humanity faces major challenges, namely in terms of energy production, consumption and management, in which electrical engineering should play a relevant role in the search for sustainable solutions. This training area is essential for industrial and economic development, and is therefore associated with economic competitiveness. However, demand for electrical engineering programs is decreasing, despite continuing to be a professional area with high employability. In view of this, it is important to try to understand the motivations and expectations that lead students to apply for programs in the field of electrical engineering. Thus, this study aimed to understand and analyse the motivations and expectations that led students from a Portuguese Polytechnic higher education institution to choose the field of electrical engineering. To this end, a questionnaire survey was used to collect the students' perceptions about why they chose this area, their expectations about their future professional activity, as well as about the role they attribute to engineering. The results show that students highly value the fact that it is a broadband area, with high employability and a wide variety of potential professional activities. Regarding the professional activity that students would like to pursue, the results stand out in the areas of renewable energies, the automobile industry, and electrical installations. The students' perception of the engineering's role reflects a generalized view that its objective is to improve the living conditions of humanity. However, some answers indicate a restricted perception of a technical character (it serves to make projects, calculations). The results obtained help to understand the profile and expectations of students who choose the field of electrical engineering, potentially contributing for the higher education institutions: i) to promote actions with the aim of enhancing the attractiveness of this area of knowledge; ii) to adapt their programs to the expectations of students and thus reduce academic dropouts or failure.

Abstract
Digital transformation has been achieved in one application (user interface – related to workplace excellence and the whole company environment) in a large chemical company in Germany. In connection with variable corporate goals such as fluctuating workload, agile responsiveness to customer inquiries, ecological and economic sustainability which require an intelligent and forward-looking management of the company. Hence, a prototype solution has been created to respond to a very dynamic market. Based on Microsoft PowerPoint (ISpring) and with some add-ons pre-selected operators may interact, including with video content. The current architecture of the IT system has already been done. Adjustments will still be made to become more agile and future-driven to follow all of the company business rules. An artificial intelligence (AI)-based methodical analysis and synthesis approach is followed, for human and other resource input calculation, to follow business KPIs (key performance indicators) and other business goals with an algorithm. This evolution or control system is seen as a natural response to a very complex environment where human effort and error must be minimized (through simplification and a mathematical algorithm and a fast loop e.g., every ten minutes KPIs may be re-calculated according to existing capacity due to availability of equipment and human resources). This holistic approach shortens reaction times to market situations and at the same time minimizes non-value-adding processes. The business roles are determined depending on the culture/size of the company and strategic parameters. The continuously available flexibility in product design and the instability of all resources is of significant importance. After initial research it was found that commercial systems do not have this ability to dynamically and agilely automatically adapt to the given optimum. Instead of isolated partial optimizations, the expected results are compared with the real results in a continuous dynamic simulation and readjusted promptly [1]. This algorithm represents the actual added value, which has a high economic but also humanity advantage, especially in the manufacturing industry. In the future most manufacturing enterprises will have to follow this AI and agile path to competitive advantage vis-à-vis Asian competitors. The last quarter of 2021 experienced a 10% productivity improvement due to this implementation, based on the prototype, and mainly due to one product implementation champion (in an area with 270 employees). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 +/- 0.2140 and 0.8750 +/- 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.

Abstract
The human brain can quickly become overwhelmed by the amounts of data computers can process. Consequently, data abstraction is necessary for a user to grasp information and identify valuable patterns. Data is usually abstracted in a pictorial or graphical format. Nowadays, users demand more personalization from the systems they use. This work proposes a user-centered framework that aims to ease creating visualizations for the developers of a platform while offering the end-user a highly customizable experience. The conceptualized solution was prototyped and tested to ensure the information about the data is transmitted to the user in a quick and effective manner. The results of a user study showed that users are pleased with the usability of the prototype and prove that they desire control over the configuration of their visualizations. This work not only confirmed the usefulness of previously explored personalization options for visual representations, but also explored promising new personalization options. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Antarctic deserts, such as the McMurdo Dry Valleys (MDV), represent extremely cold and dry environments. Consequently, MDV are suitable for studying the environment limits on the cycling of key elements that are necessary for life, like nitrogen. The spatial distribution and biogeochemical drivers of nitrogen-cycling pathways remain elusive in the Antarctic deserts because most studies focus on specific nitrogen-cycling genes and/or organisms. In this study, we analyzed metagenome and relevant environmental data of 32 MDV soils to generate a complete picture of the nitrogen-cycling potential in MDV microbial communities and advance our knowledge of the complexity and distribution of nitrogen biogeochemistry in these harsh environments. We found evidence of nitrogen-cycling genes potentially capable of fully oxidizing and reducing molecular nitrogen, despite the inhospitable conditions of MDV. Strong positive correlations were identified between genes involved in nitrogen cycling. Clear relationships between nitrogen-cycling pathways and environmental parameters also indicate abiotic and biotic variables, like pH, water availability, and biological complexity that collectively impose limits on the distribution of nitrogen-cycling genes. Accordingly, the spatial distribution of nitrogen-cycling genes was more concentrated near the lakes and glaciers. Association rules revealed non-linear correlations between complex combinations of environmental variables and nitrogen-cycling genes. Association rules for the presence of denitrification genes presented a distinct combination of environmental variables from the remaining nitrogen-cycling genes. This study contributes to an integrative picture of the nitrogen-cycling potential in MDV.