Uncertainty analysis using Bayesian Model Averaging: a case study of input variables to energy models and inference to associated uncertainties of energy scenarios

Background Energy models are used to illustrate, calculate and evaluate energy futures under given assumptions. The results of energy models are energy scenarios representing uncertain energy futures. Methods The discussed approach for uncertainty quantification and evaluation is based on Bayesian Model Averaging for input variables to quantitative energy models. If the premise is accepted that the energy model results cannot be less uncertain than the input to energy models, the proposed approach provides a lower bound of associated uncertainty. The evaluation of model-based energy scenario uncertainty in terms of input variable uncertainty departing from a probabilistic assessment is discussed. Results The result is an explicit uncertainty quantification for input variables of energy models based on well-established measure and probability theory. The quantification of uncertainty helps assessing the predictive potential of energy scenarios used and allows an evaluation of possible consequences as promoted by energy scenarios in a highly uncertain economic, environmental, political and social target system. Conclusions If societal decisions are vested in computed model results, it is meaningful to accompany these with an uncertainty assessment. Bayesian Model Averaging (BMA) for input variables of energy models could add to the currently limited tools for uncertainty assessment of model-based energy scenarios

Differences in segmental fat accumulation patterns by sex and ethnicity: An international approach

Excess fat on the body impacts obesity-related co-morbidity risk; however, the location of fat stores affects the severity of these risks. The purpose of this study was to examine segmental fat accumulation patterns by sex and ethnicity using international datasets. An amalgamated and cross-calibrated dataset of dual x-ray absorptiometry (DXA)-measured variables compiled segmental mass for bone mineral content (BMC), lean mass (LM), and fat mass (FM) for each participant; percentage of segment fat (PSF) was calculated as PSFsegment = (FMsegment/(BMCsegment + LMsegment + FMsegment)) × 100. A total of 30 587 adults (N = 16 490 females) from 13 datasets were included. A regression model was used to examine differences in regional fat mass and PSF. All populations followed the same segmental fat mass accumulation in the ascending order with statistical significance (arms < legs < trunk), except for Hispanic/Latinx males (arms < [legs = trunk]). Relative fat accumulation patterns differed between those with greater PSF in the appendages (Arab, Mexican, Asian, Black, American Caucasian, European Caucasian, and Australasian Caucasian females; Black males) and those with greater PSF in the trunk (Mexican, Asian, American Caucasian, European Caucasian, and Australasian Caucasian males). Greater absolute and relative fat accumulation in the trunk could place males of most ethnicities in this study at a higher risk of visceral fat deposition and associated co-morbidities