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.

Abstract
Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

Abstract
The Internet of Things (IoT) has revolutionized how objects and devices interact, creating new possibilities for seamless connectivity and data exchange. This paper presents a unique and effective method for transferring images via the Message Queuing Telemetry Transport (MQTT) protocol in an encrypted manner. The image is split into multiple messages, with each carrying a segment of the image, and employ top-notch encryption techniques to ensure secure communication. Applying this process, the message payload is split into smaller segments, and consequently, it minimizes the network bandwidth impact while mitigating potential of packet loss or latency issues. Furthermore, by applying encryption techniques, we guarantee the confidentiality and integrity of the image data during transmission, safeguarding against unauthorized access or tampering. Our experiments in a real-world scenario involving remote indicator panels with LEDs verify the effectiveness of our approach. By using our proposed method, we successfully transmit images over MQTT, achieving secure and reliable data transfer while ensuring the integrity of the image content. Our results demonstrate the feasibility and effectiveness of the proposed approach for image transfer in IoT applications. The combination of message segmentation, MQTT protocol, and encryption techniques offers a practical solution for transmitting images in resource-constrained IoT networks while maintaining data security. This approach can be applied in different applications.

Abstract
Incidental visualizations are meant to be perceived at-a-glance, on-the-go, and during short exposure times, but are not seen on demand. Instead, they appear in people's fields of view during an ongoing primary task. They differ from glanceable visualizations because the information is not received on demand, and they differ from ambient visualizations because the information is not continuously embedded in the environment. However, current graphical perception guidelines do not consider situations where information is presented at specific moments during brief exposure times without being the user's primary focus. Therefore, we conducted a crowdsourced user study with 99 participants to understand how accurate people's incidental graphical perception is. Each participant was tested on one of the three conditions: position of dots, length of lines, and angle of lines. We varied the number of elements for each combination and the display time. During the study, participants were asked to perform reproduction tasks, where they had to recreate a previously shown stimulus in each. Our results indicate that incidental graphical perception can be accurate when using position, length, and angles. Furthermore, we argue that incidental visualizations should be designed for low exposure times (between 300 and 1000 ms).