Abstract
The study of the evacuation of buildings in emergency fire situations has deserved the attention of researchers for decades, particularly regarding the real-time guiding of occupants in their way to exit the building. However, finding solutions to guide the occupants evacuating a building requires a thorough knowledge of that domain. Using ontological models to model the knowledge of a domain allows the understanding of that domain to be shared. This paper presents an ontological model that pretends to reinforce and deepen knowledge of the domain under study and help develop solutions and systems capable of guiding the occupants during a building evacuation. The ontology was developed following the METHONTOLOGY methodology, and for implementation, the Protege tool was used. The ontological model was successfully submitted to a thorough evaluation process and is publicly available on the Web.

Abstract
Purpose The purpose of this study is to explore the potential and growth of big data across several industries between 2016 and 2020. This study aims to analyze the behavior of interest in big data within the community and to identify areas with the greatest potential for future big data adoption. Design/methodology/approach This research uses Google Trends to characterize the community's interest in big data. Community interest is measured on a scale of 0-100 from weekly observations over the past five years. A total of 16 industries were considered to explore the relative interest in big data for each industry. Findings The findings revealed that big data has been of high interest to the community over the past five years, particularly in the manufacturing, computers and electronics industries. However, over the 2020s the interest in the theme decreased by more than 15%, especially in the areas where big data typically had the greatest potential interest. In contrast, areas with less potential interest in big data such as real estate, sport and travel have registered an average growth of less than 10%. Originality/value To the best of the author's knowledge, this study is original in complementing the traditional survey approaches launched among the business communities to discover the potential of big data in specific industries. The knowledge of big data growth potential is relevant for players in the field to identify saturation and emerging opportunities for big data adoption.

Abstract
This chapter aims to combine the contributions scattered in the literature by analyzing the different types of social media marketing actions and their expected outcomes. A systematic literature review was conducted and complemented with interviews with practitioners (n=8) in order to validate the findings. The analysis confirmed that the literature is particularly fragmented, although it approaches a very diversified list of social media marketing actions (n=29). Four types of actions were identified: actions that evoke emotions, actions that foster interaction and involvement, actions of information sharing, and commercial actions. Practitioners involved in the validation process confirmed the adequacy and usefulness of the classification. Social media marketing actions are organized into four blocks according to their objectives and impacts on consumer behavior, hence providing a tool that was recognized by a sample of practitioners as useful to guide their efforts and budgets. © 2023, IGI Global. All rights reserved.

Abstract
Long-term stability and high scalability are significant issues in plasmonic optical fiber sensors. This work presents a highly scalable and low-cost all-chemical approach for production of gold-coated silver thin-films, ensuring high performance and chemical stability. © Optica Publishing Group 2023, © 2023 The Authors.

Abstract
A escola desempenha um papel essencial na promoção de estilos de vida e hábitos alimentares saudáveis. Com o encerramento destes estabelecimentos, devido ao 2.º confinamento imposto pela pandemia COVID-19, tornou-se pertinente avaliar o seu impacto nos estilos de vida das crianças/adolescentes e estudar os respetivos fatores associados. Aplicou-se um questionário online (aos encarregados de educação dos alunos da Educação Pré-Escolar/1.º Ciclo e aos alunos do 2.º/3.º Ciclo) recolhendo informação sociodemográfica, antropométrica, de estado da saúde, estilos de vida e respetivas alterações provocadas pelo 2.º confinamento. Recorreu-se à UniANCOVA para identificar os fatores associados às alterações encontradas. Este estudo realizou-se num Agrupamento de Escolas do Norte de Portugal em 494 alunos (idade média de 11 anos, variando entre os 3 e os 16 anos, 51,0% sexo feminino, 49,2% 3.º Ciclo, 35,2% excesso de peso). Do total, 57,0% cumpre as recomendações das horas de sono, 24,0% do tempo de atividade física e 31,4% de tempo de ecrã. Mais de 70,0% dos alunos incluiu alimentos não recomendáveis aos lanches e ao jantar. Devido ao confinamento, 93,0% relatou pelo menos uma alteração negativa, sendo a mais frequente o aumento do tempo de ecrã diário (63,8%). A alteração positiva mais relatada foi a realização de mais refeições em família (66,5%). Os inquiridos que reportaram uma melhor autoperceção da sua alimentação foram os que tiveram mais alterações positivas dos estilos de vida devido à pandemia. Por outro lado, estar entre o percentil 15 e 85 de Índice de Massa Corporal associou-se a mais alterações negativas.

Abstract
Automatic Root Cause Analysis solutions aid analysts in finding problems' root causes by using automatic data analysis. When trying to locate the root cause of a problem in a manufacturing process, an issue-denominated overlap can occur. Overlap can impede automated diagnosis using algorithms, as the data make it impossible to discern the influence of each machine on the quality of products. This paper proposes a new measure of overlap based on an information theory concept called Positive Mutual Information. This new measure allows for a more detailed analysis. A new approach is developed for automatically finding the root causes of problems when overlap occurs. A visualization that depicts overlapped locations is also proposed to ease practitioners' analysis. The proposed solution is validated in simulated and real case-study data. Compared to previous solutions, the proposed approach improves the capacity to pinpoint a problem's root causes.