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.

Abstract

Abstract
Speech recognition aims to convert human speech into text and has applications in security, healthcare, commerce, automobiles, and technology, just to name a few. Inserting residual neural networks before recurrent neural network cells improves accuracy and cuts training time by a good margin. Furthermore, layer normalization instead of batch normalization is more effective in model training and performance enhancement. Also, the size of the datasets presents tremendous influences in achieving the best performance. Leveraging these tricks, this article proposes an automatic speech recognition model with a stacked five layers of customized Residual Convolution Neural Network and seven layers of Bi-Directional Gated Recurrent Units, including a logarithmic so f tmax for the model output. Each of them incorporates a learnable per-element affine parameter-based layer normalization technique. The training and testing of the new model were conducted on the LibriSpeech corpus and LJ Speech dataset. The experimental results demonstrate a character error rate (CER) of 4.7 and 3.61% on the two datasets, respectively, with only 33 million parameters without the requirement of any external language model.