Abstract
Companies focus on achieving high service levels and need to combine short service times with the dynamics between cost and quality. Their transportation systems are therefore a fundamental part; they must be reliable and efficient. This study was implemented in a company of the marine industry, and its final product has special characteristics that require special transportation, i.e., they need a truck with a special structure to be able to transport the boats. This situation causes the vehicle to return empty to the company, a route that the company must support economically. The company has already approached several options with logistic service providers (3PL) without obtaining positive solutions. It is in this sense that the present project arises, which aims to develop a tool for the creation of round-trip circuits, given that in the current context the company intends to acquire a vehicle with reduced environmental impact. In a first phase we analyze the company’s needs based on the unique characteristics of the final product, then we study the existing options on the market. Culminating in the proposal of a vehicle that allows performing a circuit in round trip (distribute the final product and return with raw material and not empty) powered by renewable energy. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Nowadays, social media are inevitably part of people's daily lives. Thus, political communication should also go through digital communication channels, particularly on social media. In such channels, it is important to define a digital marketing and communication strategy to attract new voters and consecutively more votes. As in offline communication channels and also in digital communication, one of the indispensable points in political communication is the candidate’s image. This image must show its own style and differentiate the candidate from his opponents. The main objective of this study is to understand the influence of social media on Portuguese voters’ decision-making process. Throughout the study, different research questions were also analyzed to access which social media are the most used to follow the online political campaign and which criteria influence the voting decision-making process. To achieve this purpose, exploratory research was carried out through questionnaire surveys. Three surveys were conducted based on the Portuguese presidential elections of January 24, 2021. The surveys were distributed before, during, and after the end of the electoral campaign, and 106 people were questioned and answered all 3 surveys. With the results of this study, it was possible to conclude that only 11% of respondents changed their voting intention due to the political communication made by political parties on social media during this electoral campaign. The social media most used by respondents was Facebook, which is also the one they consider the safest and most trustworthy to follow political communication in online media.

Abstract
Ferramentas de e-learning no contexto ensino-aprendizagem

Abstract
Recently, deep learning has proven to be a successful technique especially in medical image analysis. This paper aims to highlight the importance of deep learning architectures in lung disease diagnosis using CXR images. Related articles were identified through searches of electronic resources, including IEEE, Springer, Elsevier, PubMed, Nature and, Hindawi digital library. The inclusion of articles was based on high-performance artificial intelligence models, developed for the classification of possible findings in CXR images published from 2018 to 2023.After the quality assessment of papers, 129 articles were included according to PRISMA guidelines. Papers were studied by types of lung disease, data source, algorithm type, and outcome metrics. Three main categories of computer-aided lung disease detection were covered: traditional machine learning, deep learning-based methods, and combination of aforementioned methods for all lung diseases.The results showed that various pre-trained networks including ResNet, VGG, and DenseNet, are the most frequently used CNN architectures and would result in a notable increase in sensitivity and accuracy. Recent research suggests that utilizing a combination of deep networks with a robust machine learning classifier can outperform deep learning approaches that rely solely on fully connected neural networks as their classifier. Finally, the limitations of the existing literature and potential future research opportunities in possible findings in CXR images using deep learning architectures are discussed in this systematic review.

Abstract
Research into the role of digital influencers in marketing strategies is a rapidly developing area that has attracted the interest of researchers and organizations. In recent years, organizations have become increasingly interested in using digital influencers to promote their brands and disseminate advertising messages with a high impact on their target audience. Digital influencers are beginning to be used as models for sustainable consumption behavior (for example in the fashion, food, and health sectors) by promoting environmental and sustainable values. By promoting sustainable content and disseminating messages of environmental awareness, digital influencers can help achieve the Sustainable Development Goals (SDGs). This study aims to identify the attributes (attitude homophily, physical attractiveness, and social attractiveness) and perceived characterizations (trustworthiness, perceived expertise, and parasocial relationship) of digital influencers and their impact on purchase intention among a sample of Portuguese consumers. It also aims to identify the most relevant types of digital influencers according to their areas of influence (fashion, sports, beauty, and cinema/TV/music) and their impact on purchase intention. For data collection, an online questionnaire was developed and administered to a non-probabilistic convenience sample. Only respondents who had experience purchasing a product or service after watching a YouTuber's advertisement (screening question) or following or searching for a digital influencer could complete the questionnaire. A total of 243 valid questionnaires were received. The main findings are that the attributes and perceived characterizations of digital influencers have a positive and significant impact on purchase intention. It was also found that digital influencers can enhance shopping experience and credibility, which has a strong impact on consumers' purchase intentions. In terms of sector, the data show that the most important influencer in the 'Fashion' sector is Helena Coelho, in the 'Sports' sector is Cristiano Ronaldo, in the 'Beauty' sector is Sara Sampaio, and in the 'Music, TV, Cinema' sector is Ricardo Araujo Pereira. This study can help companies use digital influencers more effectively in their digital marketing strategies, as credibility, experience, and parasocial relationships have a strong impact on consumers' purchase intention.