Abstract
Distribution networks are vulnerable to natural hazards which can cause major social and economic consequences. Identifying vulnerable areas and developing operational strategies, such as dispatching mobile energy systems, can help mitigate the effects of extreme events. Conventional approaches, mainly N-1/N-2 contingency security analysis, are efficient but they do not fully provide a comprehensive picture of the stochastic nature of the hazard impact. Stochastic approaches are more accurate but in general they are computationally expensive and hence not practical for the resilient operational decision-making of distribution system operators. Therefore, this paper develops a novel framework based on an adjacency-resource matrix (ARM) and an unsupervised machine learning algorithm to first identify vulnerable nodes. Next, these vulnerable nodes are utilized in the mitigation stage in order to minimize the expected energy not served (EENS) against a natural hazard. The efficiency of the proposed framework is tested on a 125-node Portuguese distribution system.

Abstract
Topics discussed on social media platforms contain a disparate amount of information written in colloquial language, making it difficult to understand the narrative of the topic. In this paper, we take a step forward, towards the resolution of this problem by proposing a framework that performs the automatic extraction of narratives from a document, such as tweet posts. To this regard, we propose a methodology that extracts information from the texts through a pipeline of tasks, such as co-reference resolution and the extraction of entity relations. The result of this process is embedded into an annotation file to be used by subsequent operations, such as visualization schemas. We named this framework Tweet2Story and measured its effectiveness under an evaluation schema that involved three different aspects: (i) as an Open Information extraction (OpenIE) task, (ii) by comparing the narratives of manually annotated news articles linked to tweets about the same topic and (iii) by comparing their knowledge graphs, produced by the narratives, in a qualitative way. The results obtained show a high precision and a moderate recall, on par with other OpenIE state-of-the-art frameworks and confirm that the narratives can be extracted from small texts. Furthermore, we show that the narrative can be visualized in an easily understandable way.

Abstract
By 2050, and in the context of decarbonization and carbon neutrality, many companies worldwide are looking for low-carbon alternatives. Transport companies are probably the most challenging due to the continuing growth in global demand and the high dependency on fossil fuels. Some alternatives are emerging to replace conventional diesel vehicles and thus reduce greenhouse gas emissions and air pollutants. One of these alternatives is the adoption of compressed natural gas (CNG). In this paper, we provide a detailed study of the current emissions from the largest bus fleet company in the metropolitan area of Oporto. For this analysis, we used a top-down and a bottom-up methodology based on EMEP/EEA guidebook to compute the CO2 and air pollution (CO, NMVOC, PM2.5, and NOx) emissions from the fleet. Fuel consumption, energy consumption, vehicle slaughter, electric bus incorporation, and the investments made were taken into consideration in the analyses. From the case study, the overall reduction in CO2 emission was just 6.3%, and the emission factors (air pollutants) from CNG-powered buses and diesel-powered buses are closer and closer. For confirming these results and question the effectiveness of the fleet transitions from diesel to CNG vehicles, we analysed two scenarios. The obtained results reveal the potential and effectiveness of electric buses and other fuel alternatives to reduce CO2 and air pollution.

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. This proposal is devised to optimise the quantity of hydrogen injected to maintain pressure, gas flows, and gas quality indexes (Wobbe Index (WI) and the Higher Heating Value (HHV)) within admissible limits. The model has been applied to a gas network case study with three distinct scenarios and implemented using Python. The findings from the case study show the maximum permissible volume of hydrogen in the network, quantify the total savings in natural gas, and estimate the reduction in carbon dioxide emissions. © 2023 IEEE.

Abstract
Energy poverty vulnerability constitutes a significant concern in Portugal, with 17.5% of the population being unable to keep their home adequately warm. Furthermore, there is evidence that a substantial number of children live in unhealthy homes. This study aims to comprehensively characterise a sample of 101 Portuguese families with children and their homes in order to identify opportunities for actions for promoting long-lasting energy efficiency and environment health-promoting behavioural changes. To accomplish this aim, two tools-a building survey checklist and a questionnaire to participants-were developed and implemented to collect harmonised data on building-specific characteristics and on participants' socioeconomic status and behaviour. The home visits for recruitment and data collection were conducted from July 2021 to April 2022. The results suggest that, for the population under study, the main opportunities for improvement include: (i) replacing low energy-efficient technologies, with high emission rates, namely those used for heating purposes, with cleaner and more efficient alternatives; (ii) providing citizens with detailed information about their home's energy use and indoor air quality and (iii) educating the population on the best-practices for reducing indoor air stuffiness, mitigating the risk of hazardous exposures, improving thermal comfort and saving energy.

Abstract
Many service jobs are carried out in modern offices, with individual offices being increasingly replaced by open-plan settings. The high number of adult people working in office buildings, in most situations sharing the work-place with many others during a considerable part of their daily time, highlights the importance of providing adequate guidance to ensure the quality of office environments. This paper aims to summarize existing data on modern offices' indoor environmental quality (IEQ) conditions in terms of air pollution (volatile organic compounds (VOC), particulate matter and inorganic pollutants), thermal comfort, lighting and acoustics and the respective associations with health and productivity-related outcomes in workers. Evidence shows that al-though many offices present acceptable IEQ, some office settings can have levels of air pollutants, hygrothermal conditions/thermal comfort and illuminance that do not comply with the existing international standards and recommendations. In addition, findings suggest the existence of significant associations between the assessed IEQ indicators and the risk of detrimental effects on health and productivity of office workers. In particular, airborne particles, CO2, O 3 and thermal comfort were linked with the prevalence of sick building syndrome symptoms. Poor lighting and acoustical quality have also been associated with malaise and physiological stress among office workers. Similarly, better productivity levels have been registered for good indoor air quality conditions, in terms of VOC, airborne particles and CO2. Overall, the evidence revised in this work suggests that for promoting health and productivity recommendations for office building managers include actions to ensure that: i) all relevant IEQ indicators are periodically controlled to ensure that levels comply with recommended limit values; ii) declared in-door pollution sources are avoided; iii) adequate ventilation and acclimatization strategies are implemented; and iv) there is the possibility of conduct personalized adjustments to environmental conditions (following workers' preferences).