Abstract
The integration of Distributed Energy Resources (DER) imposes challenges to the operation of distribution networks. This paper conducts a systematic assessment of the impact of DER on distribution network overcurrent protection, considering the behavior of Inverter Based Resources (IBR) during faults in the coordination of medium voltage (MV) feeders' overcurrent protection. Through a detailed analysis of various scenarios, we propose adaptive protection solutions that enhance the reliability and resilience of distribution networks in the face of growing renewable energy integration. Results highlight the advantages of using adaptive protection over traditional methods and topology changes, and delve into current protection strategies, identifying limitations and proposing mitigation strategies. © The Institution of Engineering & Technology 2024.

Abstract
The Deep Brain Stimulation of the Anterior Nucleus of the Thalamus (ANT-DBS) is an effective treatment for refractory epilepsy. In order to assess the involvement of the ANT during voluntary hand repetitive movements similar to some seizure-induced ones, we simultaneously collected videoelectroencephalogram ( vEEG) and ANT-Local Field Potential (LFPs) signals from two epilepsy patients implanted with the PerceptTM PC neurostimulator, who stayed at an Epilepsy Monitoring Unit (EMU) for a 5 day period. For this purpose, a repetitive voluntary movement execution protocol was designed and an event-related desynchronisation/synchronisation (ERD/ERS) analysis was performed. We found a power increase in alpha and theta frequency bands during movement execution for both patients. The same pattern was not found when patients were at rest. Furthermore, a similar increase of relative power was found in LFPs from other neighboring basal ganglia. This suggests that the ERS pattern may be associated to upper limb automatisms, indicating that the ANT and other basal ganglia may be involved in the execution of these repetitive movements. These findings may open a new window for the study of seizure-induced movements (semiology) as biomarkers of the beginning of seizures, which can be helpful for the future of adaptive DBS techniques for better control of epileptic seizures of these patients.

Abstract
The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect. Incorporating machine learning (ML) based applications in clinical routine may promote diagnosis reliability. Recently, ML has shown potential for making progress in routine clinical procedures. We aim to systematically review the TILs analysis based on ML in CRC histological images. Deep learning (DL) and non-DL techniques can aid pathologists in identifying TILs, and automated TILs are associated with patient outcomes. However, a large multi-institutional CRC dataset with a diverse and multi-ethnic population is necessary to generalize ML methods. © 2024 Elsevier Ltd

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
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.