Abstract
This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network.

Abstract
Artificial intelligence (AI) brings great potential but also risks to the electricity industry. Following the EU's current regulatory proposal, the EU Regulation for Artificial Intelligence (AI Act), there will be direct, potentially adverse effects on companies of the electricity industry in Europe and beyond, as well as those active in the development of AI systems. In this paper, we develop a replicable framework for estimating compliance costs for different electricity market agents that will need to comply with the numerous requirements the AI Act imposes. The electricity systems of Austria, Greece and Switzerland are used as case-studies. We estimate annual, aggregated costs for electricity market agents ranging from less than one million to almost 200 million Euros per country, depending on compliance costs scenarios. Results suggest that a profit growth of 10% through AI utilization is needed to offset the highest added compliance cost of the AI Act on electricity market agents. Eventually, we further show how to assess the regional differences of these costs added to system operation, providing spatially disaggregated compliance costs estimates that consider the structural differences of the electricity industry within 26 Swiss cantons.

Abstract
Self-regulation of learning (SRL) plays a decisive role in learning success but characterizing learning environments that facilitate development of SRL skills constitutes a great challenge. Given the growing interest in Immersive Learning Environments (ILE), we sought to understand how ILE are built with attention to SRL, via a literature review of pedagogical uses, practices and strategies with ILE that have an explicit focus on SRL. From a final corpus of 25 papers, we collected 134 extracts attesting use of ILE for SRL. We classified and mapped them using the Beck, Morgado & O’Shea framework and its three dimensions of the immersion phenomenon: system, narrative and challenge. There is a predominance of uses of ILE for SRL aligned with Challenge-based immersion: Skill Training, Collaboration, Engagement, and Interactive Manipulation and Exploration. In contrast, uses aligned with System-based immersion (Emphasis, Accessibility, Seeing the Invisible) were not identified. There were few cases of use of Narrative-based immersion. Uses combining the three dimensions of immersive had residual prevalence. We concluded that there is greater tendency in studies of SRL in ILE to enact active roles (aligned with the Challenge dimension of immersion). The low prevalence of Narrative immersion and System immersion evidence gaps in the diversity of pedagogical uses of ILE to develop SRL, which indicate opportunities for research and creation of innovative educational practices.

Abstract
Reinforced concrete structures are prevalent in infrastructure and are of significant economic and social importance to humanity. However, they are prone to decay from cement paste carbonation. pH sensors have been developed to monitor cement paste carbonation, but their adoption by the industry remains limited. This work introduces two new methods for monitoring cement paste carbonation in real time that have been validated through the accelerated carbonation of cement paste samples. Both configurations depart from traditional pH monitoring. In the first configuration, the carbonation depth of a cement paste sample is measured using two CO2 optical fiber sensors. One sensor is positioned on the surface of the sample, while the other is embedded in the middle. As the carbonation depth progresses and reaches the embedded CO2 sensor, the combined response of the sensors changes. In the second configuration, a multimode fiber is embedded within the paste, and its carbonation is monitored by observing the increase in reflected light intensity (1.6-18%) resulting from the formation of CaCO3. Its applicability in naturally occurring carbonation is tested at concentrations of 3.2% CO2, and the influence of water is positively evaluated; thus, this setup is suitable for real-world testing and applications.

Abstract

Abstract
Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water-energy-social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.