Abstract
Flying Networks (FNs) have emerged as a promising solution to provide on-demand wireless connectivity when network coverage is insufficient or the communications infrastructure is compromised, such as in disaster management scenarios. Despite extensive research on Unmanned Aerial Vehicle (UAV) positioning and radio resource allocation, the challenge of ensuring reliable traffic relay through backhaul links in predictive FNs remains unexplored. This work proposes Simulated Annealing for predictive FNs (SAFnet), an innovative algorithm that optimizes network performance under positioning constraints, limited bandwidth and minimum rate requirements. Our algorithm uniquely leverages prior knowledge of the first-tier node trajectories to assign bandwidth and dynamically adjust the position of the second-tier flying relay. Building upon Simulated Annealing, our approach enhances this well-known AI algorithm with penalty functions, achieving performance levels comparable to exhaustive search while significantly reducing computational complexity.

Abstract
The concept of Industry 4.0 and the introduction of the Internet of Things (IoT) on industrial applications, known as Industrial Internet of Things (IIoT), have been changing the scenario of industrial automation. This new paradigm is expected to optimize industrial processes, increase productivity, lower costs and improve operations integration. For that, structured Machine-to-Machine (M2M) communication is key to ensure agility, interoperability and reliability, with several solutions currently available in the literature and in industry. This paper reviews the state of the art on industrial communication protocols and architectures, providing a classification and comparison of these different solutions based on their most relevant features in the context of Industry 4.0. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Progressively more advanced and complex models are proposed to address problems related to computer vision, forecasting, Internet of Things, Big Data and so on. However, these disciplines require preprocessing steps to obtain meaningful results. One of the most common problems addressed in this stage is the presence of missing values. Understanding the reason why missingness occurs helps to select data imputation methods that are more adequate to complete these missing values. Missing at Random synthetic generation presents challenges such as achieving extreme missingness rates and preserving the consistency of the mechanism. To address these shortcomings, three new methods that generate synthetic missingness under the Missing at Random mechanism are proposed in this work and compared to a baseline model. This comparison considers a benchmark covering 33 data sets and five missingness rates $(10\%, 20\%, 40\%, 60\%, 80\%)$ . Seven data imputation methods are compared to evaluate the proposals, ranging from traditional methods to deep learning methods. The results demonstrate that the proposals are aligned with the baseline method in terms of the performance and ranking of data imputation methods. Thus, three new feasible and consistent alternatives for synthetic missingness generation under Missing at Random are presented.

Abstract
Introdução: Os hábitos alimentares são uma das principais causas modificáveis de doenças crónicas não transmissíveis. A Dieta Mediterrânica é conhecida por ser um padrão alimentar protetor da saúde e sustentável. Nas últimas décadas, Portugal, tem-se distanciado deste padrão alimentar e aumentado o consumo de alimentos ultraprocessados. Objetivos: Avaliar a adesão ao Padrão Alimentar Mediterrânico, o consumo de alimentos ultraprocessados e fatores associados, em crianças e adolescentes. Metodologia: Estudo transversal, observacional descritivo. Aplicou-se um questionário, que incluía a versão portuguesa do Índice KIDMED e um Índice de consumo de alimentos ultraprocessados construído para este projeto, a uma amostra de conveniência de crianças e adolescentes com idades entre os 10 e os 19 anos. Os dados foram tratados e analisados no programa estatístico SPSS® com um nível de confiança de 95%. Resultados: A amostra incluiu 506 participantes, sendo 54,3% do sexo feminino, com uma idade média de 14,1 anos (dp = 2,5). A adesão ao Padrão Alimentar Mediterrânico correlacionou-se com o consumo de alimentos ultraprocessados (r = 0,447; p < 0,001). Verificou-se uma elevada adesão ao Padrão Alimentar Mediterrânico por 47,8% dos participantes, média adesão por 47,2% e baixa adesão por 4,9%, sendo mais elevada nos indivíduos fisicamente ativos (?2p = 0,012; p = 0,021), nos que têm hábitos de sono à semana adequados (?2p = 0,009; p = 0,045) e nos mais novos (r = -0,112; p = 0,003). O consumo de alimentos ultraprocessados revelou uma pontuação média de 21,8 (dp = 4,8), sendo o sexo masculino (?2p = 0,031; p < 0,001) e os que passam mais tempo em frente ao ecrã (?2p = 0,170; p = 0,005) quem mais consome este tipo de alimentos. Conclusões: Observa-se a necessidade de intervir ao nível da promoção e difusão da Dieta Mediterrânica, uma vez que parece ter impacto na diminuição do consumo de alimentos ultraprocessados, de promover a atividade física, estimular hábitos de sono adequados e mitigar o tempo que as crianças e adolescentes passam em frente aos ecrãs.

Abstract
In today's rapidly evolving digital landscape, the substantial advance and rapid growth of data presents companies and their operations with a set of opportunities from different sources that can profoundly impact their competitiveness and success. The literature suggests that data can be considered a hidden weapon that fosters decision-making while determining a company's success in a rapidly changing market. Data are also used to support most organizational activities and decisions. As a result, information, effective data governance, and technology utilization will play a significant role in controlling and maximizing the value of enterprises. This article conducts an extensive methodological and systematic review of the data governance field, covering its key concepts, frameworks, and maturity assessment models. Our goal is to establish the current baseline of knowledge in this field while providing differentiated and unique insights, namely by exploring the relationship between data governance, data assurance, and digital forensics. By analyzing the existing literature, we seek to identify critical practices, challenges, and opportunities for improvement within the data governance discipline while providing organizations, practitioners, and scientists with the necessary knowledge and tools to guide them in the practical definition and application of data governance initiatives. (C) 2024 The Author(s). Published by Elsevier Espana, S.L.U. on behalf of Journal of Innovation & Knowledge.

Abstract
Widespread adoption of distributed energy resources led to changes in low -voltage power grids, turning prosumers into active members of distribution networks. This incentivized the development of consumercentric energy markets. These markets enable trades between peers without third -party involvement. However, violations in network technical constraints during trades challenges integration of market and grid. The methodology used in this work employs batteries to prevent network violations and improve social welfare in communities. The method uses sequential simulations of market optimization and distribution network power flows, installing batteries if violations are identified. Simulation solves nonlinear deterministic optimization for market trades and results are used in power flow analysis. The main contribution is assessing battery participation in energy markets to solve distribution network violations. Case studies use realistic data from distribution grids in Costa Rica neighborhoods. Results indicate potential gains in social welfare when using batteries, and case -by -case analysis for prevention of network violations.