Abstract
Achieving a sustainable energy future requires a clean, affordable energy supply and active consumer engagement in the energy market. This study proposes to evaluate and simulate energy consumers' willingness to participate in demand-side management programs using an agent-based modelling approach to address the social learning effect as a key factor influencing energy consumer behaviour. The proposed agent-based model simulates households' electricity consumer interactions examining how the willingness to shift electricity usage is encouraged through the social environment, while accounting for the diversity among consumers. Data from a survey conducted in Portugal, including questions about the influence of recommendations from friends or family members on individuals' willingness to engage in demand response activities, are used to test the proposed simulation. The findings reveal that social learning significantly impacts demand response acceptance, yet the extent of this influence varies depending on the socio-economic characteristics of households' electricity consumers. The study confirms agent-based model as an effective approach for capturing social dynamics and supporting electricity market decision making, providing valuable insights for devising consumers engagement strategies.

Abstract
Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data. Consequently, the latter crowdsourcing model is exposed to manipulation. This work contributes with an explainable and online classification method to recognize fake news in real-time. The proposed method combines both unsupervised and supervised Machine Learning approaches with online created lexica. The profiling is built using creator-, content- and context-based features using Natural Language Processing techniques. The explainable classification mechanism displays in a dashboard the features selected for classification and the prediction confidence. The performance of the proposed solution has been validated with real data sets from Twitter and the results attain 80% accuracy and macro F-measure. This proposal is the first to jointly provide data stream processing, profiling, classification and explainability. Ultimately, the proposed early detection, isolation and explanation of fake news contribute to increase the quality and trustworthiness of social media contents.

Abstract
Agile methodologies, initially designed for the project level, face challenges when applied at enterprise levels where complex projects and diverse stakeholders are involved. To meet this challenge, several large-scale agile methodologies have been proposed. However, these approaches are not flexible enough or tailored to the needs of organizations, projects, and their teams. It is in this context that hybrid methodologies have emerged. This study aims to conduct a systematic literature review to trace the evolution of hybrid scaling of agile and characterize different approaches to implement it. This study starts by assessing 1509 studies through the use of the PRISMA 2020 framework and identifies 38 relevant studies in this field. The findings indicate that the majority of studies are from 2021 onwards and that qualitative methodologies supported by case studies predominate, making it possible to characterize tailoring processes in these organizations. Moreover, the implementation of hybrid scaling of agile is supported by the paradigm of ambidextrous strategy, a combination of agile with traditional project management methodologies, and continuous improvements. This study contributes insights into navigating the complexities of agile scaling, offering practical guidance for organizations seeking to optimize their project management practices.

Abstract
Structural health monitoring (SHM) of reinforced concrete structures (RCS) is crucial for mitigating the consequences of their deterioration. By identifying and addressing the issues early, SHM helps reduce environmental impact, safeguard lives, and enhance economic resilience. Rebar corrosion is a leading cause of early RCS decay and optical fibre sensors (OFS) have been employed for its monitoring. Reflection optrodes using optical fibres where the tip is coated with iron (Fe) thin films offer a robust, long-lasting and straightforward solution. This study investigates the tracking of spectral changes during the Fe thin film corrosion, which has been neglected in the literature, in favour of tracking reflection changes from thin film spalling. A multimode fibre tip, coated with a thin Fe layer embedded in concrete, allows spectral changes to be observed during corrosion. A 100 nm thick Fe film was deposited using radio frequency magnetron sputtering on polished fibre tips. Corrosion was induced by applying salted water drops and allowing the fibre tip to dry. Corrosion monitoring was successful for both air-exposed and cement-embedded tips, with results compared to reflection simulations of Fe, Fe2O3, and Fe2O3 thin films. This study supports monitoring at different wavelengths, enhancing robustness, cost-effectiveness and earlier detection. © The Authors.

Abstract
Active distribution networks (ADNs) are consistently being developed as a result of increasing penetration of distributed energy resources (DERs) and energy transition from fossil-fuel-based to zero carbon era. This penetration poses technical challenges for the operation of both transmission and distribution networks. The determination of the active/reactive power capability of ADNs will provide useful information at the transmission and distribution systems interface. For instance, the transmission system operator (TSO) can benefit from reactive power and reserve services which are readily available by the DERs embedded within the downstream ADNs, which are managed by the distribution system operator (DSO). This article investigates the important factors affecting the active/reactive power flexibility area of ADNs such as the joint active and reactive power dispatch of DERs, dependency of the ADN's load to voltage, parallel distribution networks, and upstream network parameters. A two-step optimization model is developed which can capture the P/Q flexibility area, by considering the above factors and grid technical constraints such as its detailed power flow model. The numerical results from the IEEE 69-bus standard distribution feeder underscore the critical importance of considering various factors to characterize the ADN's P/Q flexibility area. Ignoring these factors can significantly impact the shape and size of Active Distribution Networks (ADN) P/Q flexibility maps. Specifically, the Constant Power load model exhibits the smallest flexibility area; connecting to a weak upstream network diminishes P/Q flexibility, and reactive power redispatch improves active power flexibility margins. Furthermore, the collaborative support of reactive power from a neighboring distribution feeder, connected in parallel with the studied ADN, expands the achievable P/Q flexibility. These observations highlight the significance of accurately characterizing transmission and distribution network parameters. Such precision is fundamental for ensuring a smooth energy transition and successful integration of hybrid renewable energy technologies into ADNs. The article investigates factors influencing the flexibility of active distribution networks (ADNs), including joint active and reactive power re-dispatch of DERs, ADN's load model, parallel distribution networks, and upstream network parameters. Numerical results highlight the significance of these factors, emphasizing the need for accurate characterization of transmission and distribution network parameters to facilitate a smooth energy transition and the integration of hybrid renewable energy technologies into ADNs. image

Abstract
The integration of Distributed Energy Resources (DER) imposes challenges to the operation of distribution networks. This paper conducts a systematic assessment of the impact of DER on distribution network overcurrent protection, considering the behavior of Inverter Based Resources (IBR) during faults in the coordination of medium voltage (MV) feeders' overcurrent protection. Through a detailed analysis of various scenarios, we propose adaptive protection solutions that enhance the reliability and resilience of distribution networks in the face of growing renewable energy integration. Results highlight the advantages of using adaptive protection over traditional methods and topology changes, and delve into current protection strategies, identifying limitations and proposing mitigation strategies. © The Institution of Engineering & Technology 2024.