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Abstract
The literature on the impact of research output (RO) on economic growth (EG) has been rapidly expanding. However, the single growth processes of technological laggard countries and the mediating roles of human capital (HC) and structural change have been overlooked. Based on cointegration analyses and Granger causality tests over 40 years (1980-2019) for Portugal, five results are worth highlighting: (1) in the short run, RO is critical to promote EG; (2) the long run relation between RO and EG is more complex, being positive and significant in the case of global and research fields that resemble capital goods (Life, Physical, Engineering & Technology, and Social Sciences), and negative in the case of research fields that resemble final goods (Clinical & Pre-Clinical Health, and Arts & Humanities); (3) existence of important short run mismatches between HC and scientific production, with the former mitigating the positive impact of the latter on EG; (4) in the long run, such mismatches are only apparent for 'general' HC (years of schooling of the population 25 + years), with the positive association between RO and EG being enhanced by increases in 'specialized' HC (number of R&D researchers); (5) structural change processes favouring industry amplify the positive (long-run) association and (short-run) impact of RO on EG. Such results robustly suggest that even in technologically laggard contexts, scientific production is critical for economic growth, especially when aligned with changes in sectoral composition that favour industry.

Abstract
Due to the current focus on offshore renewable energies worldwide, more capacity of them is expected in the future. The electrical layout design considerably affects overall implementation cost of these offshore power plants as well as the losses of energy inside the farms. Considering the increasing size of offshore wind farms, it is necessary to develop more robust and computationally efficient methods to design the electrical layout of these farms. In this work, a two-phase approach is proposed for the optimization of the electrical layout of the offshore wind farms; the proposed framework aims at the minimization of the ohmic losses and the cost of the cables. To solve the optimization problem, Simulated Annealing (SA) is applied in this study. A tool is also developed using Python programming language to implement the framework for the optimization of the electrical layout of the offshore farms. The proposed method is then applied to a farm with 100 turbines and an overall rated capacity of 1GW. The results approved the accuracy of the two-phase approach in finding the optimal electrical layout as well as the high efficiency in terms of the computational burden.

Abstract
The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, which is a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FMs), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FMs in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FMs into clinical practice for the prevention/management of GC cases, thereby improving patient outcomes.

Abstract
Memory safety issues in C are the origin of various vulnerabilities that can compromise a program’s correctness or safety from attacks. We propose an approach to tackle memory safety by replicating Rust’s Mid-level Intermediate Representation (MIR) Borrow Checker. Our solution uses static analysis and successive source-to-source code transformations to be composed upstream of the compiler, ensuring maximal compatibility with existing build systems. This allows us to apply the memory safety guarantees of the rustc compiler to C code with fewer changes than a rewrite in Rust. In this work, we present a comprehensive study of Rust’s efforts towards ensuring memory safety, and describe the theoretical basis for a C borrow checker, alongside a proof-of-concept that was developed to demonstrate its potential. We have evaluated the prototype on the CHStone and bzip2 benchmarks. This prototype correctly identified violations of the ownership and aliasing rules, and exposed incompatibilities between such rules and common C patterns, which can be addressed in future work.

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