Abstract
The aim of this work is to determine, based on a market research, the level of passenger satisfaction with public transport services, in order to support better marketing decisions. This survey involves dimensions such as the level of satisfaction with timetables and frequency, vehicle conditions, driver attitudes and behavior, fares and information made available to passengers. The study was applied to the case of public transport in the Porto Metropolitan Area, Portugal, and aims to help define recommendations to improve the quality of service and define more effective marketing strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
Renewable energy generation technologies, heat pumps or electric vehicle (EV) charging stations use active power electronics such as IGBT or MOSFET for AC to DC conversion with the consequence of emissions in the higher frequency range above 2 kHz (non-intentional supraharmonic emissions) and with an impact to the higher frequency grid impedance. In this study the impact of active power electronics on the higher frequency grid impedance in the range up to 150 kHz is analyzed. As existing grid modelling solutions do not consider these technologies sufficiently, this study analyzes the impact of a vehicle to grid (V2G) chargers to a representative distribution grid considering different grid topologies and different types of V2G chargers. The study shows that the additional capacitance and inductance (LCL filter, DC link capacitor) introduced in the electrical grid causes parallel and series resonances in a wide frequency range starting from 500 Hz up to 50 kHz. The grid topology and the number of V2G chargers connected determines the frequency range and characteristics of resonances. Finally, the major contribution of this study is outlining the importance of considering the higher frequency grid impedance for characterization of supraharmonic emissions (primary vs. secondary emissions) and their propagation. © 2024 The Authors

Abstract
In this paper, the performance analysis of Machine Learning (ML) algorithms for fault analysis in photovoltaic (PV) plants, is given for different algorithms. To make the comparison more relevant, this study is made based on a real dataset. The goal was to use electric and environmental data from a PV system to provide a framework for analysing, comparing, and discussing five ML algorithms, such as: Multilayer Perceptron (MLP), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM). The research findings suggest that an algorithm from the Gradient Boosting family called LightGBM can offer comparable or better performance in fault diagnosis for PV system. © 2024 P. Monteiro et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0. All Rights Reserved.

Abstract

Abstract
Wikipedia is the world's largest online encyclopedia, but maintaining article quality through collaboration is challenging. Wikipedia designed a quality scale, but with such a manual assessment process, many articles remain unassessed. We review existing methods for automatically measuring the quality of Wikipedia articles, identifying and comparing machine learning algorithms, article features, quality metrics, and used datasets, examining 149 distinct studies, and exploring commonalities and gaps in them. The literature is extensive, and the approaches follow past technological trends. However, machine learning is still not widely used by Wikipedia, and we hope that our analysis helps future researchers change that reality.

Abstract
In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action.Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts.A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.