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
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
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
In this work, the implementation of an efficient multi-threading algorithm for calculating the power flow in electricity distribution networks is carried out using recursion and parallel programming. With the integration of renewable energy, energy storage systems and distributed generation, the ability of power flow simulations becomes a crucial factor in finding the best solution in the shortest possible time. We propose the direct use of graph theory to represent distribution network topologies. In this data structure, the traversal algorithms are inherently recursive, thus enabling the development of algorithms with parallel programming to obtain the power flow calculation faster and more efficiently. Results under a 809 buses test system show that the implementation provides additional computation efficiency of 32% with recursion techniques and 27% with parallel programming, due the expense of threads' allocation the combined gain reaches 50%.

Abstract
The intensification of environmental impacts and the increased economic risks are triggering a technological race towards a low-carbon economy. In this socioeconomic scenario of increasing changes and environmental concerns, microgrids (MGs) play an important role in integrating distributed energy resources. Thus, a planning strategy for grid-connected MGs with distributed energy resources and electric vehicle (EV) charging stations is proposed in this paper. The developedmathematical model aims to defineMGexpansion decisions that satisfy the growing electricity demand (including EV charging demand) at the lowest possible cost; such decisions include investments in PV units, wind turbines, energy storage systems, and EV charging stations. The objective function is based on the interests of the MG owner, considering constraints associated with the main distribution grid. A mixed-integer linear programming model is used to formulate the problem, ensuring the solution's optimality. The applicability of the proposed model is evaluated in the 69-bus distribution grid. Promising results concerning grid-connected MGs were obtained, including the enhancement of energy exchange with the grid according to their needs.

Abstract
Digital images from orbital platforms are the main source of information for mapping and decision-making. Their use has become increasingly popular over the years and has expanded into various areas. Feature extraction in digital images has been widely researched in Image Analysis, Photogrammetry, and Computer Vision. Works related to feature extraction for the generation and updating of GISs are generally divided into anthropic features such as buildings and/or highways and natural features such as vegetation areas or bodies of water. One attractive methodology for feature extraction, especially for rivers and bodies of water, is based on active contours, formulated based on the evolution of curves, which can have parametric models (Snakes) or geometric models (Level set). In this context, this work intends to identify and compare some characteristics of parametric and geometric active contour methods and apply them to orbital images from the OLI and PAN sensors of the LANDSAT 8 and CBERS 4 satellites for feature extraction, correlating these characteristics with the parameters required in the mathematical models of active contours. The present work makes use of Digital Image Processing (DIP) methods, with the first processing stage known as pre-processing, consisting of interconnected tasks that can be used to extract some information about the objects present in the scene. Subsequently, in the processing stage, the features of interest are extracted with the help of the Fiji and Icy software using Level Set and Snake, respectively. Regardless of the method used, the results presented in this work show an extraction time compatible with application needs, as they are developed semi-automatically.