Abstract
We propose a model that forecasts market correlation structure from link- and node-based financial network features using machine learning. For such, market structure is modeled as a dynamic asset network by quantifying time-dependent co-movement of asset price returns across company constituents of major global market indices. We provide empirical evidence using three different network filtering methods to estimate market structure, namely Dynamic Asset Graph, Dynamic Minimal Spanning Tree and Dynamic Threshold Networks. Experimental results show that the proposed model can forecast market structure with high predictive performance with up to 40%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$40\%$$\end{document} improvement over a time-invariant correlation-based benchmark. Non-pair-wise correlation features showed to be important compared to traditionally used pair-wise correlation measures for all markets studied, particularly in the long-term forecasting of stock market structure. Evidence is provided for stock constituents of the DAX30, EUROSTOXX50, FTSE100, HANGSENG50, NASDAQ100 and NIFTY50 market indices. Findings can be useful to improve portfolio selection and risk management methods, which commonly rely on a backward-looking covariance matrix to estimate portfolio risk.

Abstract

Abstract
This article proposes a methodology for high loss detection in LV network, based on a very small set of commonly available data/metadata from networks connected to an MV/LV substation. The approach is based on a combination of predictors from several distinct categories, including network data, metadata, and measured smart meter data. Several independent groups of unranked real networks were simulated, and it was possible to find the top ten networks with the highest level of losses with a very satisfactory success rate (76% to 98%), depending on selected groupings folds. Due to the impracticability of analyzing all LV networks, the identification of the highest loss ones is essential for the definition of loss reduction planning since, with this list filtering, it is possible to determine with a good degree of certainty which networks require maintenance or upgrade.

Abstract
Electrochemical impedance spectroscopy (EIS) is a reliable technique for gathering information about electrochemical process occurring at the electrode surface and investigating properties of materials. Furthermore, EIS technique can be a very versatile and valuable tool in analytical assays for detection and quantification of several chemically and biologically relevant (bio)molecules. The first part of this Review (Introduction) provides brief insights into (i) theoretical aspects of EIS, (ii) the instrumentation required to perform the EIS studies and (iii) the most relevant representations of impedance experimental data (such as Nyquist and Bode plots). In the end of this section, (iv) theoretical aspects regarding the fitting of the Randles circuit to experimental data are addressed, not only to obtain information about electrochemical processes but also to illustrate its utility for analytical purposes. The second part of the Review (Impedimetric Detection of Disease Biomarkers) focuses on the applications of EIS in the biomedical field, particularly as analytical technique in electrochemical sensors and biosensors for screening disease biomarkers. In the last section (Conclusions and Perspectives), we discuss main achievements of EIS technique in analytical assays and provide some perspectives, challenges and future applications in the biomedical field.

Abstract
Data description is a fundamental step in Research Data Management (RDM). When it comes to images, the challenge is increased, as they have characteristics that differentiate them from other typologies. We conducted a study in which we obtained a set of 27 images described according to their content, by researchers of the projects where they are inserted. After obtaining the ground-truth that would support the analysis, we proceeded to two more stages of description, one through an automatic processing tool (Vision AI) and the other through researchers with no knowledge of the images. We concluded that the human description is more elucidative of the images' content, namely at a semantic level. In turn, the automatic tools enhance a more literal description. This study allowed us to reflect on the description of images in a research context and to discuss the potential of formal analysis and analysis of the semantic expression of images.

Abstract
The member states of the European Union (EU) are actively reassessing their National Energy and Climate Plans (NECPs) [1] to jointly address climate challenges and the impacts of the COVID pandemic and gas supply crisis. This study extends the analyses described in [2] by assessing the impact of the updated NECP drafts for Portugal and Spain [3], [4] on the Iberian Electricity Market (MIBEL). For this, we use CEVESA, a market model for the long-term planning and operation of MIBEL that computes the joint dispatch of energy and secondary reserve of the two interconnected single-price zones. Departing from the expected evolution of the electricity generation technologies and demand available in the NECP drafts, joint scenarios for Portugal and Spain are built with the latest CO2 allowances and fuel prices projections and the latest available historical data of hydro and renewable generation profiles. Simulations provide estimates for the expected market prices, technology generation dispatch, and the usage of the capacity of the interconnection lines between both countries, highlighting potential concerns and knowledge on future NECPs.