Abstract
As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as shoeboxes using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.

Abstract
Accessibility in digital labor is a research line that has been trending over the last few years. The usage of crowdsourcing, especially in the form of microtasks, can become an inclusive solution to support accessible digital work. Integrating cognitive abilities tests and task fingerprinting has proven to be effective mechanisms for microtask personalization when considering neurotypical people. In this article, we report the elaboration of usability tests on microtask personalization with neurodivergent people. The preliminary study recruited six participants with autism, attention deficit hyperactivity disorder, and dyslexia. The results obtained indicate that this solution can be inclusive and increase the accessibility of crowdsourcing tasks and platforms. One limitation of this study is that it is essential to evaluate this solution on a large scale to ensure the identification of errors and/or features of cognitive personalization in microtask crowdsourcing.

Abstract
In recent years, student experience (SX) has drawn the attention of researchers and librarians due to its impact on student engagement and, eventually, academic success. This study aims to explore the intellectual structure of literature focusing on the student experience with academic libraries. Through bibliometric and thematic analyses, we analyzed a sample of 160 articles published between 1995 and 2022. The findings underscore the multifaceted nature of SX research regarding academic libraries. Its intellectual structure unveils six predominant themes: (1) international students' experience; (2) servicescape and service design; (3) impact of technologies; (4) information literacy; (5) psychological and emotional aspects; and (6) engagement and motivation. Additionally, we propose a future research agenda, shedding light on prevalent theories and underexplored topics. This study serves as a valuable resource for researchers and librarians seeking insights into the nuances of SX in academic library settings. In particular, the identification of research clusters and opportunities can assist researchers in better positioning their studies and finding connections across several theoretical lenses and approaches.

Abstract
The literature on the isothermal model gas flow is extensive, but the effect of temperature variation on the hydraulic characteristics has been rarely addressed. Additionally, the impact of hydrogen blending on the thermal condition of NG pipelines is also an emergent topic that requires new approaches to the gas flow problem formulation and resolution. In this paper, a model for the gas flow problem was developed to optimise the operation of natural gas distribution networks with hydrogen injection while maintaining pressure, gas flows, and gas quality indexes within admissible limits. The goal is to maximise the injection of hydrogen and investigate the influences of thermal variations in the gas blending. Also, this model enables the calculation of the maximum permitted volume of hydrogen in the network, quantifying the total savings in natural gas usage and carbon dioxide emissions in different temperature conditions.

Abstract
Renewable energy and electric mobility are crucial in addressing current environmental and energy challenges. As the number of electric vehicles increases, more charging infrastructure connected to the electricity distribution network is required. This paper proposes an approach to sizing a fast charging station for electric vehicles. This challenge is addressed by including a battery energy storage system (BESS) and considering the self-production from a renewable energy source (solar energy) in the system. The aim is to minimise the total energy costs, avoid future infrastructure upgrades, and take advantage of the integration of renewable energy resources. The methodology used is a Biased Random Key Genetic Algorithm (BRKGA) based meta-heuristic. Computational experiments were conducted for the sizing of a charging station under four different scenarios that minimise energy costs. The results show that incorporating BESS can lead to a significant reduction in the costs related to the purchase of energy from the grid. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
While place attachment has been a hot research topic in tourism, place meanings generally have received less attention from researchers. By bridging environmental psychology to the context of tourism, this research employs schema theory to explore how the home environment influences place meanings perceived in foreign destinations by tourists belonging to the same cultural group, i.e., Chinese and Macau outbound tourists in Europe. Semi-structured interviews were conducted, and the findings show that there is much overlap in both groups' place meanings regarding Europe as they are culturally Chinese. Nonetheless, the Portuguese symbolic settings of their home environment are profoundly integrated in the Macau interviewees' autobiographical memories and self-identity, which turns them into "vicarious insiders" of Portugal prior to their actual visits, thus rendering Portugal a specifically meaningful destination. This study makes theoretical contributions to the tourism place literature and provides practical implications regarding meaning marketing for destination management organizations.