Abstract
Falls are one of the causes of severe hilliness among elders, and the COVID-19 pandemic increased the number of unattended cases because of the social distancing measures. This study aims to create a dataset that collects the data from a 3-axis acceleration sensor fixed on a hinged board apparatus that mimics a human fall event. The datalogging system uses off-the-shelf devices to measure, collect and store the data. The resulting dataset includes data from different angle positions and heights, corresponding to joints of the lower limbs of the human body (ankle, knee, and hip). We use the dataset with a threshold-based fall detection algorithm. The result from the Receiver Operating Characteristic curve shows a good behavior with a mean Area Under the Curve of 0.77 and allow to compute a best threshold value with False Positive Rate of 14.8% and True Positive rate of 89.1%. The optimal threshold value may vary depending on the specific population, activity patterns, and environmental conditions, which may require further customization and validation in real-world settings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
The advantages of Virtual Reality (VR) over traditional training, together with the development of VR technology, have contributed to an increase in the body of literature on training professionals with VR. However, there is a gap in the literature concerning the comparison of training in a Virtual Environment (VE) with the same training in a Real Environment (RE), which would contribute to a better understanding of the capabilities of VR in training. This paper presents a study with firefighters (N = 12) where the effect of a firefighter training exercise in a VE was evaluated and compared to that of the same exercise in a RE. The effect of environments was evaluated using psychophysiological measures by evaluating the perception of stress and fatigue, transfer of knowledge, sense of presence, cybersickness, and the actual stress measured through participants' Heart Rate Variability (HRV). The results showed a similar perception of stress and fatigue between the two environments; a positive, although not significant, effect of the VE on the transfer of knowledge; the display of moderately high presence values in the VE; the ability of the VE not to cause symptoms of cybersickness; and finally, obtaining signs of stress in participants' HRV in the RE and, to a lesser extent, signs of stress in the VE. Although the effect of the VE was shown to be non-comparable to that of the RE, the authors consider the results encouraging and discuss some key factors that should be addressed in the future to improve the results of the training VE.

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
Local electricity markets are emerging as a viable solution to overcome the challenges brought by the large penetration of distributed renewable generation and the need to put consumers as central players in the system, with an active and dynamic role. Although there is significant literature addressing the topic of local electricity markets, this is still a rather new and emerging topic. Hence, this study provides an overall view of this domain and a perspective on future needs and challenges that must be addressed. This review introduces the most important concepts in the local electricity market domain, provides an analysis on the different policy and regulatory framework, exposes the most relevant worldwide initiatives related to the field implementation, and scrutinizes the alternative local market models proposed in the literature. The discussion puts forth the main benefits and barriers of the currently proposed local market models, and the expected impact of their widespread implementation. The review is concluded with the wrap-up and discussion on the most relevant paths for future research and development in this field of study.

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.

Abstract
The energy sector transition to more decentralized and renewable structures requires greater participation by local consumers, which may be enabled by innovative models such as the setup of renewable energy communities (RECs). To maximize the self-consumption of local renewable energy generated by assets normally connected to the low voltage distribution grid, these RECs typically involve jointly owned assets such as collective photovoltaic solar panels (CPVs) and collective energy storage systems (CESS). This work proposes a novel mathematical model for a REC, accounting for three distinct economic approaches to the redistribution of collective benefits among community members. The main objective of this study is to understand how the participation of community members in collective assets (CAs) can help increase the fairness and equity of RECs. An illustrative REC case comprising members with individual and collective ownership of the assets is used to assess the proposed economic approaches. Extracting several answers, among them that the most advantageous configuration comes from agents with quotas in the CESS and CPV. An important conclusion is that depending on the selected economic approach, the social welfare and agent's revenue vary significantly. In any case, CESSs increase equity among REC members.