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.

Abstract
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Abstract
In the current competition environment, transportation costs continue to rise, causing a reduction in the profit margins of companies. There are several tools in the literature to support the planning of logistics activities, but individualised solutions are not yet effective. In this study, a linear programming model is proposed to jointly plan the demand fulfilment of two competing companies by encouraging the search for synergies that enhance collaboration in the use of existing resources. To demonstrate the validity of the proposed mode, a case study is carried out and the results obtained with the initiation of the collaboration are evaluated. In conclusion, the proposed model reduces the logistics costs by up to 13%, as well as decreases the carbon footprint by 37%. By focusing on optimising economic and environmental aspects, this approach serves as a guide for companies to promote collaborations and to facilitate decision making at a managerial level.