Abstract
In recent years, technologies related to the electrification of transportation have attracted significant attention. Among these, wireless charging stands out, even facing numerous challenges concerning design and parameter optimization. Consequently, this article introduces a novel design methodology to improve the performance of inductive power transfer (IPT) systems for wireless charging applications in electric vehicles. The methodology considers operational limits of switches and passive components. By using a combination of Newton-Raphson and Particle Swarm Optimization (PSO) algorithms, the proposed approach efficiently determines both electrical and physical parameters of converters and coils to achieve maximum efficiency at a chosen operational point. Furthermore, a Maximum Efficiency Point Tracking (MEPT) algorithm is employed for optimal system operation. The proposed methodology is validated through experimental analysis using a 3.6 kW setup. Results demonstrate a power transfer efficiency around 89.4 %, while ensuring that current and voltage levels remain within safe operating areas for the components.

Abstract
This paper proposes to extend the fast-decoupled state estimation formulation to bring its well-known efficiency and benefits to the processing of networks with embedded FACTS devices. The proposed method approaches shunt-, series-, and shunt -series -type devices. The controller parameters are included as new active or reactive state variables, while controlled quantity values are included in the metering scheme of the decoupled approach. From the electrical model adopted for each device, the extended formulation is presented, and a modified fast-decoupled method is devised, seeking to ensure accuracy and impart robustness to the iterative solution. Simulation results conducted throughout the IEEE 30 -bus test system with distinct types of FACTS devices are used to validate and evaluate the performance of the proposed decoupled approaches.

Abstract
This article presents a solution for a work related to the curricular unit Energy Markets and Regulation within the scope of PDEEC-Doctoral Program in Electrical and Computer Engineering. The task consists of evaluating optimal dispatch and market pool results (symmetric and asymmetric) for different periods. To check the technical feasibility of implementing the dispatch recommended by the pool market, a DC power flow is analyzed, by accounting for a network with six busbars. Results show that in some periods of higher demand, there could be an overload in some transmission lines of the considered network for certain results of market dispatch. © 2024, Universidade do Porto - Faculdade de Engenharia. All rights reserved.

Abstract
Methylation is considered one of the proteins' most important post-translational modifications (PTM). Plasticity and cellular dynamics are among the many traits that are regulated by methylation. Currently, methylation sites are identified using experimental approaches. However, these methods are time-consuming and expensive. With the use of computer modelling, methylation sites can be identified quickly and accurately, providing valuable information for further trial and investigation. In this study, we propose a new machine-learning model called MeSEP to predict methylation sites that incorporates both evolutionary and structural-based information. To build this model, we first extract evolutionary and structural features from the PSSM and SPD2 profiles, respectively. We then employ Extreme Gradient Boosting (XGBoost) as the classification model to predict methylation sites. To address the issue of imbalanced data and bias towards negative samples, we use the SMOTETomek-based hybrid sampling method. The MeSEP was validated on an independent test set (ITS) and 10-fold cross-validation (TCV) using lysine methylation sites. The method achieved: an accuracy of 82.9% in ITS and 84.6% in TCV; precision of 0.92 in ITS and 0.94 in TCV; area under the curve values of 0.90 in ITS and 0.92 in TCV; F1 score of 0.81 in ITS and 0.83 in TCV; and MCC of 0.67 in ITS and 0.70 in TCV. MeSEP significantly outperformed previous studies found in the literature. MeSEP as a standalone toolkit and all its source codes are publicly available at https://github.com/arafatro/MeSEP.

Abstract
In 2021, the energy sector was put at risk by extreme weather in many different ways: North America and Spain suffered heavy winter storms that led to the collapse of the electricity network; California speci fi cally experienced heavy droughts and heat -wave conditions, causing the operations of hydropower stations to halt; fl oods caused substantial damage to energy infrastructure in central Europe, Australia, and China throughout the year, and unusual wind drought conditions decreased wind power production in the United Kingdom by almost 40% during summer. The total economic impacts of these extreme weather events are estimated at billions of U.S. dollars. Here we review and assess in some detail the main extreme weather events that impacted the energy sector in 2021 worldwide, discussing some of the most relevant case studies and the meteorological conditions that led to them. We provide a perspective on their impacts on electricity generation, transmission, and consumption, and summarize estimations of economic losses.

Abstract