Adaptive thermal comfort has gained momentum within the scientific community as a cost effective and affordable way of maintaining acceptable levels of comfort in dwellings while abating energy expenditure. At the moment two international standards, namely the European EN16798-1 and the American ASHRAE55-2010 shape the understanding of adaptive comfort around the world. However, in recent years concerns have raised about whether they can accurately represent comfort conditions considering the cultural and societal background of different countries, and whether adaptive thermal comfort will be still feasible in future scenarios of climate change. Considering these challenges, this study presents an algorithm which can model different adaptive comfort models; additionally, it can be implemented into energy simulation engines and therefore used to predict energy consumption under different climates, building typologies, and dynamic comfort conditions. This contribution presents the development of the aforementioned algorithm, called ACCIS (Adaptive-Comfort-Control-Implementation Script), originally written in EnergyPlus Runtime Language (ERL) and later nested in a Python package called ACCIM (Adaptive-Comfort-Control-Implemented Model)”, its main characteristics, and also the implementation into two cases studies: The thermal comfort in social dwellings in Spain and Japan considering present and future climate change scenarios namely Representative Concentration Pathways (RCP) 2.6, 4.5 and 8.5 for years 2050, 2080 and 2100. The results show that the predicted energy consumption of low-income families is strongly influenced by the adaptive comfort model chosen to model their thermal routine and suggest that international standards should be put under revision to consider the local particularities of dwellers in subsidized housing projects. The results of this research can be useful to devise public policies aimed at abating energy cost for low-income dwellers that benefit from social housing programs, particularly in the light of the increment of energy costs for heating and cooling associated with climate change..

Objective: To examine the influence of age, sex and height on the symphysis–ischial spine distance (SID) measured on pelvic Computed tomography (CT)images in subjects of reproductive age, and to determine the interobserver reproducibility. This measurement (SID) is of great importance because the use of intrapartum ultrasound is based on the assumption of a specific value (30 mm) of such a measurement. Methods: This was a cross-sectional descriptive study in which SID was measured in subjects aged 20 to 44 years who had been scheduled for pelvic CT at our centre from January 2018 to May 2021 for different reasons. Radiographic measurements of the pelvis were obtained through the multiplanar reconstruction of the CT image. The images obtained from all of the participants were independently assessed by three senior radiologists, and the SID measurements made by each one were blinded from those of the remaining observers. Correlations between the SID and patient age, height and sex were analyzed by univariate and multivariate linear regression. Results: The mean SID for 87 of the enrolled participants (45 women, 42 men) was 28.2 ± 6.25 mm. Among the observers, the mean difference in this distance was 1 to 2 mm, and was scarcely related to measurement size, with agreement being greater than 70%. The mean SID was significantly related to sex and height (SID = −24.9 − 6.51 × sex (0 or 1) + 0.34 × height (cm); p = 0.01; sex equals 1 for a man and 0 for a woman), such that it was a mean of 2.5 mm greater in women than men (29.50 mm vs. 26.99 mm). Conclusion: Measurements of SID on CT images show good interobserver reproducibility, and are related to sex and height.

The effective asset management of real estate is an area of great interest to the building engineering sector as a whole. The determination and programming of maintenance tasks is essential to allow finance entities to establish market order according to a given budget. The process works slowly, and some optimization is generally required. In this paper, two multilayer perceptrons (MLPs) are developed to determine the economic cost of maintenance works in the two types of real estate asset of more interest to building sector: building sites and dwellings. After training using 76 case studies for building sites and 317 for dwellings, the optimal MLP configurations are shown to have 6 and 12 nodes respectively, and the input variables that most influenced their behavior are also determined. Furthermore, the MLPs showed more optimal behavior than models using multiple linear regression. Finally, the MLPs were tested for 15 new case studies for each model, predicting the budgeted costs of the associated maintenance works with deviations of less than 11% compared with the actual value in most cases. Keywords: multilayer perceptron, budgeted cost, building site, dwelling, real estate asset.