Abstract
Innovation plays a key role in meeting the challenges of the future, but despite the unprecedented investment in innovation, Portugal has seen a decline in the various indicators that assess the country's performance. This study aims to answer questions about the state of innovation in Portugal, based on the relevant global and European innovation indicators, comparing the country's performance with that of Ireland, Belgium, and the Czech Republic. Using secondary data collected from the reports of the last four years, explanatory research was conducted based on statistical and graphical methods in order to establish causal relationships. The areas where the main changes have taken place are presented, highlighting the aspects in which Portugal stands out for superior or poor performance, providing a benchmark for the definition of policies to foster innovation in Portugal. The results demonstrate that institutions, business sophistication, and knowledge and technology score negatively, while creativity stands out as a strength. Environmental sustainability, firms' investment in innovation, and the impact of innovation on sales are aspects that Portugal needs to improve; human capital and the attractiveness of the R & D system deserve positive remarks. It is fundamental to understand how Portugal is preparing for the future and what the country can learn from others. This study is limited by the specific period in analysis, which could affect causal relationships, and the historical perspective could provide guidelines to the understanding of the relative position of the country. This study contributes new perspectives and knowledge about the state of innovation in Portugal, providing clues to entrepreneurs, policy makers, and the scientific community.

Abstract

Abstract
In the evolving landscape of global education, Artificial Intelligence's (AI) integration into Learning Management Systems (LMS) promises a transformative shift. This paper presents Mozambique's journey in this domain, comparing it with global advancements. While the Mozambican higher education sector stands at the cusp of a digital revolution, its engagement with AI in LMS remains foundational. This is juxtaposed against the global trend where AI tools, such as ChatGPT, are rapidly becoming standard in many educational platforms, enhancing personalization, efficiency, and data-driven insights. The benefits of AI integration, such as tailored learning experiences and administrative automation, are counterbalanced by challenges, including data privacy concerns and over-reliance on technology. Drawing from real-world case studies, the paper highlights pioneering endeavours that showcase AI's potential in reshaping educational paradigms. As Mozambique navigates its unique challenges, insights from global best practices offer a roadmap for harnessing the transformative potential of AI in LMS, aiming to elevate its higher education sector to new heights.;Na evolução da educação global, a integração da Inteligência Artificial (IA) nos Sistemas de Gestão de Aprendizagem (LMS) promete uma transformação significativa. Este artigo investiga a jornada de Moçambique neste domínio, comparando-a com os avanços globais. Enquanto o setor de ensino superior moçambicano está à beira de uma revolução digital, seu envolvimento com a IA em LMS ainda está em uma fase inicial. Isso é contrastado com a tendência global, onde ferramentas de IA, como o ChatGPT, estão rapidamente se a se tornar padrão em muitas plataformas educativas, aprimorando a personalização, eficiência e insights baseados em dados. Os benefícios da integração da IA, como experiências de aprendizagem adaptadas e automação administrativa, são equilibrados por desafios, incluindo preocupações com a privacidade dos dados e excesso de dependência da tecnologia. Através de estudos de caso do mundo real, o artigo destaca esforços pioneiros que mostram o potencial da IA em remodelar os paradigmas educacionais. Enquanto Moçambique navega pelos seus desafios únicos, os insights das melhores práticas globais oferecem um roteiro para aproveitar o potencial transformador da IA em SGA, com o objetivo de elevar seu setor de ensino superior a novos patamares.

Abstract
Global warming presents a significant threat to the sustainability of agricultural systems, demanding increased irrigation to mitigate the impacts of prolonged dry seasons. Efficient water management strategies, including deficit irrigation, have thus become essential, requiring continuous crop monitoring. However, conventional monitoring methods are laborious and time-consuming. This study investigates the potential of aerial imagery captured by unmanned aerial vehicles (UAVs) to predict critical water stress indicators-relative water content (RWC), midday leaf water potential (psi MD), stomatal conductance (gs)-as well as the pigment content (chlorophyll ab, chlorophyll a, chlorophyll b and carotenoids) of trees in an olive orchard. Both thermal and spectral vegetation indices are calculated and correlated using linear and exponential regression models. The results reveal that the thermal vegetation indices contrast in estimating the water stress indicators, with the Crop Water Stress Index (CWSI) demonstrating higher precision in predicting the RWC (R2 = 0.80), psi MD (R2 = 0.61) and gs (R2 = 0.72). Additionally, the Triangular Vegetation Index (TVI) shows superior accuracy in predicting the chlorophyll ab (R2 = 0.64) and chlorophyll a (R2 = 0.61), while the Modified Chlorophyll Absorption in Reflectance Index (MCARI) proves most effective for estimating the chlorophyll b (R2 = 0.52). This study emphasizes the potential of UAV-based multispectral and thermal infrared imagery in precision agriculture, enabling assessments of the water status and pigment content. Moreover, these results highlight the vital importance of this technology in optimising resource allocation and enhancing olive production, critical steps towards sustainable agriculture in the face of global warming.

Abstract
This paper presents a new strategy for recharging electric vehicles in residential buildings. The proposed approach minimizes the difference between desired and final state of charge (SOC) by the end of the charging period, by adjusting the charging power for each vehicle in real-time. A non-linear optimization problem is formulated, considering the initial and final SOC, as well as available charging time, and total available power. Results were compared to a baseline and show that the proposed solution outperforms the currently most used nonoptimized method, particularly in high demand scenarios, where we achieve values of 9.3% of curtailed range when compared with the non-optimized methodology.

Abstract
Over the past decades, underwater robotics has enjoyed growing popularity and relevance. While performing a mission, one crucial task for Autonomous Underwater Vehicles (AUVs) is bottom tracking, which should keep a constant distance from the seabed. Since static obstacles like walls, rocks, or shipwrecks can lie on the sea bottom, bottom tracking needs to be extended with obstacle avoidance. As AUVs face a wide range of uncertainties, implementing these essential operations is still challenging. A simple rule-based control method has been proposed in [7] to realize obstacle avoidance. In this work, we propose an alternative AI-based control method using a Long Short-Term Memory network. We compare the performance of both methods using real-world data as well as via a simulator. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.