Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

A prospective evaluation of plasma polyphenol levels and colon cancer risk

Polyphenols have been shown to exert biological activity in experimental models of colon cancer; however, human data linking specific polyphenols to colon cancer is limited. We assessed the relationship between pre-diagnostic plasma polyphenols and colon cancer risk in a case–control study nested within the European Prospective Investigation into Cancer and Nutrition study. Using high pressure liquid chromatography coupled to tandem mass spectrometry, we measured concentrations of 35 polyphenols in plasma from 809 incident colon cancer cases and 809 matched controls. We used multivariable adjusted conditional logistic regression models that included established colon cancer risk factors. The false discovery rate (q values ) was computed to control for multiple comparisons. All statistical tests were two-sided. After false discovery rate correction and in continuous log 2 -transformed multivariable models, equol (odds ratio [OR] per log 2 -value, 0.86, 95% confidence interval [95% CI] = 0.79–0.93; q value = 0.01) and homovanillic acid (OR per log 2 -value, 1.46, 95% CI = 1.16–1.84; q value = 0.02) were associated with colon cancer risk. Comparing extreme fifths, equol concentrations were inversely associated with colon cancer risk (OR = 0.61, 95% CI = 0.41–0.91, p trend = 0.003), while homovanillic acid concentrations were positively associated with colon cancer development (OR = 1.72, 95% CI = 1.17–2.53, p trend < 0.0001). No heterogeneity for these associations was observed by sex and across other colon cancer risk factors. The remaining polyphenols were not associated with colon cancer risk. Higher equol concentrations were associated with lower risk, and higher homovanillic acid concentrations were associated with greater risk of colon cancer. These findings support a potential role for specific polyphenols in colon tumorigenesis. © 2018 The Authors International Journal of Cancer published by John Wiley & Sons Ltd on behalf of UIC

Nut intake and 5-year changes in body weight and obesity risk in adults: results from the EPIC-PANACEA study

Purpose: There is inconsistent evidence regarding the relationship between higher intake of nuts, being an energy-dense food, and weight gain. We investigated the relationship between nut intake and changes in weight over 5 years. Methods: This study includes 373,293 men and women, 25–70 years old, recruited between 1992 and 2000 from 10 European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Habitual intake of nuts including peanuts, together defined as nut intake, was estimated from country-specific validated dietary questionnaires. Body weight was measured at recruitment and self-reported 5 years later. The association between nut intake and body weight change was estimated using multilevel mixed linear regression models with center/country as random effect and nut intake and relevant confounders as fixed effects. The relative risk (RR) of becoming overweight or obese after 5 years was investigated using multivariate Poisson regressions stratified according to baseline body mass index (BMI). Results: On average, study participants gained 2.1 kg (SD 5.0 kg) over 5 years. Compared to non-consumers, subjects in the highest quartile of nut intake had less weight gain over 5 years (−0.07 kg; 95% CI −0.12 to −0.02) (P trend = 0.025) and had 5% lower risk of becoming overweight (RR 0.95; 95% CI 0.92–0.98) or obese (RR 0.95; 95% CI 0.90–0.99) (both P trend <0.008). Conclusions: Higher intake of nuts is associated with reduced weight gain and a lower risk of becoming overweight or obese. © 2017, Springer-Verlag GmbH Germany