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
This study evaluated the impact of a cognitive stimulation program on older adults diagnosed with dementia. The sample comprised 22 participants aged 73-95, randomly assigned to an experimental group (N = 12) and a control group (N = 10). While no statistically significant decreases were observed in the studied dimensions, clinically relevant gains were noted: 41.70% in cognition, 41.60% in quality of life, and 8.3% in functional skills. Although no therapeutic interventions have successfully reversed dementia, healthcare providers reported improvements in participants’ social interactions, behaviors, and engagement in daily institutional routines. These observations suggest tangible benefits from program participation. It’s important to note that staff working closely with these patients daily observed these improvements despite the lack of statistically significant results. This discrepancy highlights the potential limitations of relying solely on quantitative measures to assess such interventions’ effectiveness. In conclusion, the cognitive stimulation program shows promise as a potential tool for slowing dementia-associated degenerative processes, particularly in areas that standard quantitative assessments may not fully capture. Further research with larger sample sizes and more sensitive measures may be warranted to fully understand the program’s impact.

Abstract
To facilitate easy prediction and estimation of Adaptive Optics performance, we have created a fast algorithm named TipTop. This algorithm generates the expected AO Point Spread Function (PSF) for any existing AO observing mode (SCAO, LTAO, MCAO, GLAO) and any set of atmospheric conditions. Developed in Python, TipTop is based on an analytical approach, with simulations performed in the Fourier domain, enabling very fast computation times (less than a second per PSF) and efficient exploration of the extensive parameter space. TipTop can be used for several applications, from assisting in the observation preparation with the Exposure Time Calculator (ETC), to providing PSF models for post-processing. TipTop can also be used to help users in selecting the best NGSs asterism and optimizing their observation. Over the past years, the code has been intensively tested against different other simulation tools, showing very good agreements. TipTop is also currently deployed for VLT instruments, as proof of concepts in preparation of the ELT. The code is available here: https://tiptop.readthedocs.io/en/main/, and we encourage all future observers of the ELT to test it and provide feedback !

Abstract
The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field.

Abstract

Abstract
Computer simulations, which are performed at a single wavelength at a time, have been traditionally used to estimate the optical properties of tissues. The results of these simulations need to be interpolated. For a broadband estimation of tissue optical properties, the use of computer simulations becomes time consuming and computer demanding. When spectral measurements are available for a tissue, the use of the photon diffusion approximation can be done to perform simple and direct calculations to obtain the broadband spectra of some optical properties. The additional estimation of the reduced scattering coefficient at a small number of discrete wavelengths allows to perform further calculations to obtain the spectra of other optical properties. This study used spectral measurements from the heart muscle to explain the calculation pipeline to obtain a complete set of the spectral optical properties and to show its versatility for use with other tissues for various biophotonics applications.