Forecasting indoor temperatures during heatwaves: Do more complex models provide better predictions? by Matej Gustin, Rob McLeod, Kevin Lomas

“…GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. …”
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Prediction of internal temperatures during hot summer conditions with time series forecasting models by Matej Gustin, Rob McLeod, Kevin Lomas

“…The hourly results for the analysed dwellings showed a Mean Absolute Error (MAE) below 0.63°C and 0.49°C for the two case study dwellings across the 3-day forecasting period, during the 2015 heatwave. …”
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Trajectory clustering aided personalized driver intention prediction for intelligent vehicles by Dewei Yi, Jinya Su, Cunjia Liu, Wen-Hua Chen

“…The proposed system is validated by a realworld driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviours than manually defined manoeuvres due to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM clustering and CART classification provides promising intention prediction performance and is adaptive to different drivers.…”
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Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings? by Matej Gustin, Rob McLeod, Kevin Lomas

“…GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. …”
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Forecasting indoor temperatures during heatwaves using time series models by Matej Gustin, Rob McLeod, Kevin Lomas

“…The model proved capable of performing multi-step-ahead predictions during extreme heat events with an acceptable accuracy for periods up to 72 h, with hourly results achieving a Mean Absolute Error (MAE) below 0.7 °C for every forecast. …”
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Comparing efficacy of different climate indices for predicting labor loss, body temperature, and thermal perception in a wide variety of warm and hot climates by George Havenith, James Smallcombe, Simon Hodder, Ollie Jay, Josh Foster

“…Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130b.min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800W.m-2), wind (0.2-3.5m.s-1) and two clothing levels, were used to model the predictive power of ambient temperature, Universal Thermal Climate Index (UTCI), Wet Bulb Globe Temperature (WBGT), Modified Equivalent Temperature (mPET), Heat Index, Apparent Temperature (AT), and Wet Bulb Temperature ( Twb) for the calculation of PWC-loss, skin temperature ( Tskin) and core-to-skin temperature gradient, and Thermal perception( TSV) in the heat. …”
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Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods by Ciaran Broderick, Tom Matthews, Robert Wilby, Satish Bastola, Conor Murphy

“…When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments; and (iv) using a multimodel ensemble in conjunction with an appropriate averaging technique. …”
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Genetic scores of ENOS, ACE and VEGFA genes are predictive of endothelial dysfunction associated osteoporosis in postmenopausal women by Puneetpal Singh, Monica Singh, Rubanpal Khinda, Srishti Valecha, Nitin Kumar, Surinderpal Singh, Pawan K Juneja, Taranpal Kaur, Sarabjit Mastana

“…Bearers of CTAAAT (OR 2.43, p = 0.007), ACDG (OR 2.50, p = 0.002) and GATA (OR 2.10, p = 0.009) had substantial impact for osteoporosis after correcting the effects with traditional risk factors (TRD).With uncertainty measure (R2h) and Akaike information criterion (AIC), best fit models showed that CTAAAT manifested in multiplicative mode (β ± SE: 2.19 ± 0.86, p < 0.001), whereas ACDG (β ± SE: 1.73 ± 0.54, p = 0.001) and GATA (β ± SE: 3.07 ± 0.81, p < 0.001) expressed in dominant modes. …”
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DATASETS HEATSHIELD project Loughborough University, averaged per condition and individual sessions, and files showing the statistical analyses. by George Havenith, Josh Foster, James Smallcombe, Simon Hodder

“…Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130b.min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800W.m-2), wind (0.2-3.5m.s-1) and two clothing levels, were used to model the predictive power of ambient temperature, Universal Thermal Climate Index (UTCI), Wet Bulb Globe Temperature (WBGT), Modified Equivalent Temperature (mPET), Heat Index, Apparent Temperature (AT), and Wet Bulb Temperature (Twb) for the calculation of PWC-loss, skin temperature (Tskin) and core-to-skin temperature gradient, and Thermal perception( TSV) in the heat. …”
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