Abstract
The integration of electric vehicles (EVs) into the power grid poses significant challenges and opportunities for energy management systems. This is especially concerning for parking lots or private building condominiums in which refurbishing is not possible or is costly. This paper presents a real-time monitoring approach to EV charging dynamics with battery storage support over a 24 h period. By simulating EV demand, state of charge (SOC), and charging and discharging events, we provide insights into the operational strategies for energy storage systems to ensure maximum charging simultaneity factor through internal power enhancement. The study uses a time-series analysis of EV demand, contrasting it with the battery's SOC, to dynamically adjust charging and discharging actions within the constraints of the upstream infrastructure capacity. The model incorporates parameters such as maximum power capacity, energy storage capacity, and charging efficiencies, to reflect realistic conditions. Results indicate that real-time SOC monitoring, coupled with adaptive charging strategies, can mitigate peak demands and enhance the system's responsiveness to fluctuating loads. This paper emphasizes the critical role of real-time data analysis in the effective management of energy resources in existing parking lots and lays the groundwork for developing intelligent grid-supportive frameworks in the context of growing EV adoption.

Abstract
This paper introduces a mathematical model designed to optimise the operation of natural gas distribution networks, considering the injection of hydrogen in multiple nodes. The model is designed to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe index (WI) and higher heating value (HHV)) within admissible limits. This study also presents the maximum injection allowable of hydrogen correlated with the gas quality index variation. The model has been applied to a case study of a gas network with four distinct scenarios and implemented using Python. The findings of the case study quantify the maximum permitted volume of hydrogen in the network, the total savings in natural gas, and the reduction in carbon dioxide emissions. Lastly, a sensitivity analysis of injected hydrogen as a function of the Wobbe index (WI) and Higher Heating Value (HHV) limits relaxation.

Abstract
Intervention studies have been explored to identify actions to effectively remediate indoor environmental quality (IEQ) problems and to improve people's health, well-being, comfort, and productivity. This study assessed a comprehensive set of IEQ indicators related to ventilation, air pollution, thermal comfort, illuminance, and noise for the first time in Portuguese office buildings. The purpose was to derive evidence-based corrective measures for a further environmental intervention program. The study monitored and surveyed 15 open-space offices from six modern office buildings in Porto (Portugal) during a workday between September and December 2022. Illuminance was of most concern among the assessed IEQ indicators since the measured levels were below the minimum limit required in 27% of the evaluated workplaces. For CO2, although mean concentrations were below 1000 ppm, absolute values exceeding that level were consistently registered in 20% of the offices during the afternoon period. Mean levels of PM2.5, PM10, and ultrafine particles exceeding the WHO guidelines were found in 13%, 7%, and 7% of the offices, respectively. The assessed thermal comfort levels were typically neutral, corresponding to an estimated mean of 6% of dissatisfied people. Based on the findings, an intervention plan was designed to be implemented in the further stages of this work. The priority interventions to test include relocation of printers (PM source removal), optimisation of ventilation rates (using real-time data from CO2 sensors), adjustment of desk positions to improve illuminance, and introduction of indoor plants.

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
This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.