An attempt has been made to develop linear regression models and Artificial Neural Networks (ANN) to predict the heating and cooling energy demands, energy consumptions and CO2 emissions of office ...buildings in Chile. The calculation of dependent variables to calibrate and evaluate the models has been determined starting from the ISO 13790:2008 standard, assigning constructive characteristics to each of the geometries studied based on the Chilean standards, studying 77,000 cases. A total of 8 fundamental variables have been considered to cover the design parameters. In energy consumption and CO2 emissions cases, the linear regression models that offer a better performance are those where the predictive variables have been transformed. Whereas, the multilayer perceptron adjusted over the variables without being transformed, provides greater accuracy in the determination of the demand, consumption and CO2 emissions both for heating and cooling, offering ECM values closer to 0, with an R2 coefficient above 99%. It is foreseen that the models developed can be used to estimate the energy saving between different design outlines during the project phases when the construction standards, systems and internal loads are defined.
•Comparison of linear regression and ANN models.•Predictive methods for energy demand, consumption and CO2 emissions.•Multilayer perceptron accuracy with an R2 coefficient above 99%.•Multiple variable iterations on the first stages of building design.
Fuel poverty is a pertinent issue for vulnerable households both in industrialized and developing countries, which is related to energy prices and accessibility of energy services. This research ...explores the feasibility of predictive models to prevent fuel poverty through the Fuel Poverty Potential Risk Index (FPPRI). Two statistical models, multiple linear regression (MLR) and artificial neural networks (ANN), have been developed and applied to predict the probability of low-income households falling into fuel poverty when being allocated a social dwelling. The case study used to validate the model is located in the Bio-Bio Region of Chile and the households considered belong to the most vulnerable social strata. The models have considered the design and constructive features of common typologies of Chilean social dwellings, family income levels, changes in energy usage patterns and energy prices. Through extensive simulation and testing, ANNs have been found to be more accurate than MLRs for all situations, with a R2 coefficient above 99.6% and 80.7% respectively, despite their greater complexity. The result of this research can be useful in providing tools to fairly and accurately assign social dwellings to vulnerable households to prevent them from falling into fuel poverty.
•Comparison of multiple linear regression and artificial neural networks models.•Multiple linear regression models with an R2 coefficient above 80.7%.•Multilayer perceptron accuracy with an R2 coefficient above 99.6%.•Fuel Poverty Potential Risk Index for social dwellings allocation.•Integration of adaptive comfort algorithms to assess the risk of fuel poverty.
Fuel poverty is a pressing issue in several European countries, and Spain is no exception. Traditionally, it has been associated with cold conditions, but recent studies in the field have stressed ...its prevalence in warm countries too, during summer. Further, forecasts of climate change for these territories predict more severe summers. This envisages a scenario where low-income families might suffer from fuel poverty due to their inability to afford the energy bill to cool their homes, for tackling which the European Union and its member states are devising strategies. Adaptive comfort models have emerged as a sustainable and resilient approach in this regard. This study aims at clarifying how a change in the behavioural patterns of users, following the adaptive model might reduce the incidence of fuel poverty, compared to the static model based solely on active cooling. For this purpose, a common typology of social dwelling has been simulated in 10 cities representative of the diverse climates of Spain; both the current and future climate change scenarios have been considered. Results indicate that the mixed-mode is effective in alleviating fuel poverty not only in the present scenario, but also in 2050 and 2100, except for the most underprivileged households earning less than 500 € per month. The outcomes of this study will be of use to policy makers, designers, and stakeholders in targeting families in need for specific subsidies to afford a comfortable environment during summer.
•Evaluation of mixed-mode effectiveness to reduce the risk of fuel poverty in summer.•Assessment of social housing in 10 Spanish cities considering various income levels.•The number of fuel poverty cases would be reduced considering future climate scenarios.
Numerous studies about climate change have emerged in recent years because of their potential impact on many activities of human life, amongst which, the building sector is no exception. Changes in ...climate conditions have a direct influence on the external conditions for buildings and, thus, on their energy demand. In this context, computer aided simulation provides handy tools that help in assessing this impact. This paper investigates climate data for future scenarios and the effect on energy demand in office buildings in Chile. This data has been generated in the 9 climatic zones that are representative of the main inhabited areas, for the years 2020, 2050 and 2080. Predictions have been produced for the acknowledged A2 ‘medium-high’ Greenhouse Gases emissions GHG scenario, pursuant the Intergovernmental Panel on Climate Change (IPCC). The effect of climate change on the energy demand for office buildings is optimized by implementing the calculation procedure of ISO-13790:2008, based on iterations of its envelope and form. As a result, this research clarifies how future climate scenarios will affect the energy demand for different types of office buildings in Chile, and how their shape and enclosure can be optimized.
•Forecast of 9 Chilean climate zones under Greenhouse Gases Scenario A2.•Influence of envelope and form on future energy demand in office buildings.•Multiple iterations on Form Ratio (FR) and Window-to-Wall Ratio (WWR).•Optimization in early stages of design considering global warming.
Public housing policies face a challenge in order to meet not only the right to housing, but also an affordable and comfortable use for them. In this context, most of the studies related to fuel ...poverty are based on a diagnosis of existing conditions, but there is a lack of information focusing on how to predict the risk of fuel poverty in future dwellings considering a context of climate change. This research develops an index to assist policymakers in the decision-making process during the early stages of social housing allocation. The analysis is based on the applicability of adaptive comfort, the influence of climate change, the urban context and the building features. Energy use patterns and the ability to pay utility bills have been also considered for social housing in the Central-South of Chile. After that, several future scenarios are discussed considering the probable income and energy inflation rates. The potential risk variables that influence the early stages of design are also discussed. The results reveal that the Fuel Poverty Potential Risk Index is an effective tool to select appropriate housing for the most disadvantaged and vulnerable segments of society, considering the future climate, income and energy price trends.
•Development of Fuel Poverty Potential Risk Index for building allocation.•Influence of future income and energy inflation rates to minimize the risk of FP.•Integration of adaptive comfort algorithms to determine Fuel Poverty.•Discussion considering climate change during the early stages of building design.
Summary
In addition to their detection in typical X‐linked severe combined immunodeficiency, hypomorphic mutations in the interleukin (IL)‐2 receptor common gamma chain gene (IL2RG) have been ...described in patients with atypical clinical and immunological phenotypes. In this leaky clinical phenotype the diagnosis is often delayed, limiting prompt therapy in these patients. Here, we report the biochemical and functional characterization of a nonsense mutation in exon 8 (p.R328X) of IL2RG in two siblings: a 4‐year‐old boy with lethal Epstein–Barr virus‐related lymphoma and his asymptomatic 8‐month‐old brother with a TlowB+natural killer (NK)+ immunophenotype, dysgammaglobulinemia, abnormal lymphocyte proliferation and reduced levels of T cell receptor excision circles. After confirming normal IL‐2RG expression (CD132) on T lymphocytes, signal transducer and activator of transcription‐1 (STAT‐5) phosphorylation was examined to evaluate the functionality of the common gamma chain (γc), which showed partially preserved function. Co‐immunoprecipitation experiments were performed to assess the interaction capacity of the R328X mutant with Janus kinase (JAK)3, concluding that R328X impairs JAK3 binding to γc. Here, we describe how the R328X mutation in IL‐2RG may allow partial phosphorylation of STAT‐5 through a JAK3‐independent pathway. We identified a region of three amino acids in the γc intracellular domain that may be critical for receptor stabilization and allow this alternative signaling. Identification of the functional consequences of pathogenic IL2RG variants at the cellular level is important to enable clearer understanding of partial defects leading to leaky phenotypes.
We report the biochemical and functional characterization of a nonsense mutation (p.R328X) in IL2RG present in two brothers with atypical clinical and immunological phenotype. The mutant allows partial STAT5 phosphorylation through a JAK3 independent pathway identifying a critical region for stabilization of receptors and facilitating this alternative signalling.
Including recycled waste material in cement mixes, as substitutes for natural aggregates, has resulted in diverse research projects, normally focused on mechanical capacities. In the case of recycled ...glass as an aggregate, this provides a noticeable improvement in thermal properties, depending on its dosage. This idea raises possible construction solutions that reduce the environmental impact and improves thermal behavior. For this research, an extended building typology that is susceptible to experiencing the risk of energy poverty has been chosen. The typology is typical for social housing, built using mortar blocks with crushed glass. First, the basic thermophysical properties of the mortars were determined by laboratory tests; after that, the dynamic thermal properties of representative constructive solutions using these mortars were simulated in seven representative climate zones in Chile. An analysis methodology based on periodic thermal transmittance, adaptive comfort levels and energy demand was run for the 21 proposed models. In addition, the results show that thermal comfort hours increases significantly in thermal zones 1, 2, 3 and 6; from 23 h up to 199 h during a year. It is in these zones where the distance with respect to the neutral temperature of the m50 solution reduces that of the m25 solution by half; i.e., in zone 1, from −429 °C with the m25 solution to −864 °C with the m50. This research intends to be a starting point to generate an analysis methodology for construction solutions in the built environment, from the point of view of thermal comfort.
The objective of this study was to analyze the perceived barriers to dual career success and athletic identity of student-athletes according to disability type and level of professionalization. The ...final sample consisted of 203 student-athletes with disabilities from five European countries. The questionnaires used were ESTPORT, EBBS and AIMS. Depending on disability type, it was found that student-athletes with hearing and physical impairment showed the highest difficulty in reconciling sports and studies (p = 0.001); that student-athletes with a hearing impairment showed the highest score in the barrier 'the cost of education is high' (p = 0.023); that student-athletes with a physical impairment had the highest scores in the barrier 'Exercise tires me' (p = 0.013); that student-athletes with cerebral palsy showed the highest scores in the barrier 'I do not have enough university/educational institution support' (p = 0.014) and 'Exercise facilities do not have convenient timetables for me' (p = 0.001). Depending on sports professionalization level, semi-professional student-athletes showed the highest values in the barrier 'the university/educational institution is far from my training center' (p = 0.040); while professional student-athletes had the highest score in the barrier 'exercise takes too much time from family responsibilities' (p = 0.034). In most of the variables related to identity as athletes, professional student-athletes showed the highest values, followed by semi-professional athletes (p = 0.043- < 0.001). In conclusion, the self-perception of barriers is quite relevant, with differences arising from disability type and level of professionalization, whereas the identity as an athlete is only different according to the level of professionalization.
•The paper assesses energy consumption and CO2 emissions in Chilean office buildings.•8 fundamental variables have been considered in the early stages of building design.•18 multivariable regression ...analysis was generated.•R2 between 91.81% and 98.05% for energy consumption.•R2 between 96.83% and 99.56% for CO2 emissions.
The reduction of energy consumption and CO2 emissions in buildings has become an essential field of study in the recent years. Simplified design tools, used in the first design stages, can be of great help in adopting concrete decisions that will, at the end, allow these to be reduced. This paper presents a new predictive model for office buildings in Chile. Starting from the 1,386,000 study cases pursuant the ISO 13790:2008 standard, 18 multivariable regression models have been generated, 9 for energy consumption and 9 for CO2 emissions., They have been adapted to each climatic zone in Chile. In these case studies, 8 fundamental variables have been considered to cover the design parameters. This research considers number of stories (NS), floor area (FA), form ratio (FR), window-to-wall ratio (WWR), coefficient of performance (COP), Energy efficiency ratio (EER), heating emission factors (HEF) and cooling emission factors (CEF). The models generate an R2 between 91.81% and 98.05% for energy consumption and between 96.83% and 99.56% for CO2 emissions, with the results of this research being incorporated into future regulations and into the first stages of design for office buildings in Chile. As an expected outcome, the model will contribute to reduce, or at least contain, energy consumption and CO2 emissions associated with office buildings in the future.
In recent times, studies about the accuracy of algorithms to predict different aspects of energy use in the building sector have flourished, being energy poverty one of the issues that has received ...considerable critical attention. Previous studies in this field have characterized it using different indicators, but they have failed to develop instruments to predict the risk of low-income households falling into energy poverty. This research explores the way in which six regression algorithms can accurately forecast the risk of energy poverty by means of the fuel poverty potential risk index. Using data from the national survey of socioeconomic conditions of Chilean households and generating data for different typologies of social dwellings (e.g., form ratio or roof surface area), this study simulated 38,880 cases and compared the accuracy of six algorithms. Multilayer perceptron, M5P and support vector regression delivered the best accuracy, with correlation coefficients over 99.5%. In terms of computing time, M5P outperforms the rest. Although these results suggest that energy poverty can be accurately predicted using simulated data, it remains necessary to test the algorithms against real data. These results can be useful in devising policies to tackle energy poverty in advance.
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