Abstract
This paper presents a methodology for assessing the long-term economic feasibility of renewable energy-based systems for green hydrogen and ammonia production. A key innovation of this approach is the incorporation of a predictive algorithm that optimizes day-ahead system operation on an hourly basis, aiming to maximize profit. By integrating this feature, the methodology accounts for forecasting errors, leading to a more realistic economic evaluation. The selected case study integrates wind and PV as renewable energy sources, supplying an electrolyser and a Haber-Bosch ammonia production plant. Additionally, all supporting equipment, including an air separation unit for nitrogen production, compressors, and hydrogen / nitrogen / ammonia storage devices, is also considered. Furthermore, an electrochemical battery is included, allowing for an increased electrolyser load factor and smoother operating regimes. The results demonstrate the effectiveness of the proposed methodology, providing valuable insights and performance indicators for this type of energy systems, enabling informed decision-making by investors and stakeholders.

Abstract
Covid-19 is a serious disease caused by the Sars-CoV-2 virus that has been first reported in China at late 2019 and has rapidly spread around the world. As the virus affects mostly the lungs, chest X-rays are one of the safest and most accessible ways of diagnosing the infection. In this paper, we propose the use of an approach for detecting Covid-19 in chest X-ray images through the extraction and classification of local and global percolation-based features. The method was applied in two datasets: one containing 2,002 segmented samples split into two classes (Covid-19 and Healthy); and another containing 1,125 non-segmented samples split into three classes (Covid-19, Healthy and Pneumonia). The 48 obtained percolation features were given as input to six different classifiers and then AUC and accuracy values were evaluated. We employed the 10-fold cross-validation method and evaluated the lesion sub-types with binary and multiclass classification using the Hermite Polynomial classifier, which had never been employed in this context. This classifier provided the best overall results when compared to other five machine learning algorithms. These results based in the association of percolation features and Hermite polynomial can contribute to the detection of the lesions by supporting specialists in clinical practices.

Abstract
The determination of grape quality parameters is intricately linked to the mineral composition of the fruit; this relationship is increasingly affected by the impacts of climate change. The conventional chemical methodologies employed for the mineral quantification of grape tissues are expensive and impracticable for widespread commercial applications. This paper utilized Laser-Induced Breakdown Spectroscopy (LIBS) to analyze the mineral constituents within the skin, pulp, and seeds of two distinct Vitis vinifera cultivars: a white cultivar (Loureiro) and a red cultivar (Vinh & atilde;o). The primary objective was to discriminate the potential variations in the calcium (Ca), magnesium (Mg), and nitrogen (N) concentrations and water content among different grape tissues, explaining their consequential impact on the metabolic constitution of the grapes and, by extension, their influence on various quality parameters. Additionally, the study compared the mineral contents of the white and red grape cultivars across three distinct time points post veraison. Significant differences (p < 0.05) were observed between the Loureiro and Vinh & atilde;o cultivars in Ca concentrations across all the dates and tissues and for Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin and pulp. In the Vinh & atilde;o cultivar, Ca differences were found in the pulp across the dates, N in the seeds, and water content in the skin, pulp, and seeds. Comparing the cultivars within tissues, Ca exhibited differences in the pulp, Mg in the skin and pulp, N in the pulp and seeds, and water content in the skin, pulp, and seeds. These findings provide insights into the relationship between the grape mineral and water content, climatic factors, and viticulture practices within a changing climate.

Abstract
This article is a study introducing a new qualitative research methodology - Intuitive field research or IFRes - involving words and the narrative and relying on the experience and intuition of the [experienced practitioner] researcher (Stein, 2019). Though similar, it is different to autoethnography as the latter’s focus is seen to be on culture (ethnography) whilst IFRes may focus on any aspect – including, also, machine-type interactions. IFRes is a six-step process, described herein, which seeks to take advantage of considerable previous work experience, in the field, to answer a research question posed following a literature review. It is an iterative process which seeks to perfect the knowledge produced (Baldacchino, Ucbasaran & Cabantous, 2023). Intuitive Field Research (IFRes) emerges as a pioneering qualitative research methodology that capitalizes on the nuanced intuition and rich field experiences of researchers to uncover deep insights into complex phenomena (Stein, 2019). Distinct from autoethnography, IFRes introduces a structured six-step process designed to systematically harness and refine these insights for academic and practical application. Originating at the University of Aveiro, this method represents a significant departure from conventional research methodologies by valuing experiential knowledge and intuitive understanding as critical components of the research process. In the context of business and management, IFRes holds particular promise for addressing the intricate challenges of contemporary business environments. These environments demand an agile and nuanced understanding that transcends traditional quantitative analyses, making the case for methodologies that can capture the subtleties of consumer behavior, organizational culture, and innovation dynamics. By enabling researchers and practitioners to integrate their intuitive judgments with rigorous academic inquiry, IFRes offers a unique approach to exploring and solving pressing business and academic issues. This article delineates the foundation of IFRes, its methodological underpinnings, and its potential applications within business and management, illustrating how intuitive insights can drive innovation, strategic decision-making, and transformative organizational practices. Through this expanded lens, IFRes not only contributes to academic discourse but also provides practical frameworks for businesses seeking to navigate the complexities of modern markets and organizational challenges. A practical example of applying Intuitive Field Research (IFRes) in business and management could involve a multinational corporation seeking to enhance its customer experience across diverse markets. By employing IFRes, the corporation's research team could immerse themselves in different cultural contexts, using their intuition and experience to gather nuanced insights into consumer behavior and preferences (Gorry & Westbrook, 2013). This approach would allow them to identify subtle, culturally specific factors influencing customer satisfaction that traditional surveys or data analysis might miss. These insights could then inform tailored strategies for each market, leading to improved customer engagement and loyalty. This example illustrates how IFRes' emphasis on intuitive understanding, combined with rigorous analysis, can address complex challenges in global business environments, leading to innovative solutions and competitive advantages. This article on Intuitive Field Research (IFRes) significantly impacts research by offering a novel method that blends intuitive insights with rigorous academic inquiry. It addresses the need for methodologies that go beyond traditional quantitative analysis to capture the complexities of human behavior and organizational dynamics (Ganzarain, Ruiz & Igartua, 2019). By emphasizing experiential knowledge and intuitive judgment, IFRes empowers researchers and practitioners to uncover deeper understandings of complex issues. This approach fosters innovation, enhances strategic decision-making, and facilitates transformative practices in various fields, thereby enriching academic discourse and offering practical solutions for real-world challenges.

Abstract
Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Liion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.

Abstract
Information systems depend on security mechanisms to detect and respond to cyber-attacks. One of the most frequent attacks is the Distributed Denial of Service (DDoS): it impairs the performance of systems and, in the worst case, leads to prolonged periods of downtime that prevent business processes from running normally. To detect this attack, several supervised Machine Learning (ML) algorithms have been developed and companies use them to protect their servers. A key stage in these algorithms is feature pre-processing, in which, input data features are assessed and selected to obtain the best results in the subsequent stages that are required to implement supervised ML algorithms. In this article, an innovative approach for feature selection is proposed: the use of Visibility Graphs (VGs) to select features for supervised machine learning algorithms used to detect distributed DoS attacks. The results show that VG can be quickly implemented and can compete with other methods to select ML features, as they require low computational resources and they offer satisfactory results, at least in our example based on the early detection of distributed DoS. The size of the processed data appears as the main implementation constraint for this novel feature selection method.