Abstract
Active distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil-fuel-based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two-step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69-bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs. The article investigates factors influencing the flexibility of active distribution networks (ADNs), including joint active and reactive power re-dispatch of DERs, ADN's load model, parallel distribution networks, and upstream network parameters. Numerical results highlight the significance of these factors, emphasizing the need for accurate characterization of transmission and distribution network parameters to facilitate a smooth energy transition and the integration of hybrid renewable energy technologies into ADNs. image

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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
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.