MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy

Effect of Asymmetric Anchoring Groups on Electronic Transport in Hybrid Metal/Molecule/Graphene Single Molecule Junctions.

A combined experimental and theoretical study on molecular junctions with asymmetry in both the electrode type and in the anchoring group type is presented. A scanning tunnelling microscope is used to create the "asymmetric" Au-S-(CH2 )n-COOH-graphene molecular junctions and determine their conductance. The measurements are combined with electron transport calculations based on density functional theory (DFT) to analyze the electrical conductance and its length attenuation factor from a series of junctions of different molecular length (n). These results show an unexpected trend with a rather high conductance and a smaller attenuation factor for the Au-S-(CH2 )n -COOH-graphene configuration compared to the equivalent junction with the "symmetrical" COOH contacting using the HOOC-(CH2 )n -COOH series. Owing to the effect of the graphene electrode, the attenuation factor is also smaller than the one obtained for Au/Au electrodes. These results are interpreted through the relative molecule/electrode couplings and molecular level alignments as determined with DFT calculations. In an asymmetric junction, the electrical current flows through the less resistive conductance channel, similarly to what is observed in the macroscopic regime

Predictive value of 8-year blood pressure measures in intracerebral haemorrhage risk over 5 years

Aims The relationships between long-term blood pressure (BP) measures and intracerebral haemorrhage (ICH), as well as their predictive ability on ICH, are unclear. In this study, we aim to investigate the independent associations of multiple BP measures with subsequent 5-year ICH risk, as well as the incremental value of these measures over a single-point BP measurement in ICH risk prediction. Methods and results We included 12 398 participants from the China Kadoorie Biobank (CKB) who completed three surveys every 4–5 years. The following long-term BP measures were calculated: mean, minimum, maximum, standard deviation, coefficient of variation, average real variability, and cumulative BP exposure (cumBP). Cox proportional hazard models were used to examine the associations between these measures and ICH. The potential incremental value of these measures in ICH risk prediction was assessed using Harrell’s C statistics, continuous net reclassification improvement (cNRI), and relative integrated discrimination improvement (rIDI). The hazard ratios (95% confidence intervals) of incident ICH associated with per standard deviation increase in cumulative systolic BP and cumulative diastolic BP were 1.62 (1.25–2.10) and 1.59 (1.23–2.07), respectively. When cumBP was added to the conventional 5-year ICH risk prediction model, the C-statistic change was 0.009 (−0.001, 0.019), the cNRI was 0.267 (0.070–0.464), and the rIDI was 18.2% (5.8–30.7%). Further subgroup analyses revealed a consistent increase in cNRI and rIDI in men, rural residents, and participants without diabetes. Other long-term BP measures showed no statistically significant associations with incident ICH and generally did not improve model performance. Conclusion The nearly 10-year cumBP was positively associated with an increased 5-year risk of ICH and could significantly improve risk reclassification for the ICH risk prediction model that included single-point BP measurement

WHO cardiovascular disease risk prediction model performance in 10 regions, China

Objective To validate the World Health Organization (WHO) non-laboratory-based cardiovascular disease risk prediction model in regions of China. Methods We performed an external validation of the WHO model for East Asia using the data set of China Kadoorie Biobank, an ongoing cohort study with 512 725 participants recruited from 10 regions of China from 2004–2008. We also recalculated the recalibration parameters for the WHO model in each region and evaluated the predictive performance of the model before and after recalibration. We assessed discrimination performance by Harrell’s C index. Findings We included 412 225 participants aged 40–79 years. During a median follow-up of 11 years, 58 035 and 41 262 incident cardiovascular disease cases were recorded in women and men, respectively. Harrell's C of the WHO model was 0.682 in women and 0.700 in men but varied among regions. The WHO model underestimated the 10-year cardiovascular disease risk in most regions. After recalibration in each region, discrimination and calibration were both improved in the overall population. Harrell’s C increased from 0.674 to 0.749 in women and from 0.698 to 0.753 in men. The ratios of predicted to observed cases before and after recalibration were 0.189 and 1.027 in women and 0.543 and 1.089 in men. Conclusion The WHO model for East Asia yielded moderate discrimination for cardiovascular disease in the Chinese population and had limited prediction for cardiovascular disease risk in different regions in China. Recalibration for diverse regions greatly improved discrimination and calibration in the overall population

Long-term exposure to ambient PM2·5, active commuting, and farming activity and cardiovascular disease risk in adults in China: a prospective cohort study

Background Increased physical activity is associated with a reduced risk of cardiovascular disease, but outdoor physical activity can be accompanied by increased inhalation of fine particulate matter (PM2·5). The extent to which long-term exposure to PM2·5 can offset the cardiovascular benefits of physical activity is unknown. We aimed to evaluate whether the associations between active commuting or farming activity and incident risks of cerebrovascular disease and ischaemic heart disease were consistent between populations with different ambient PM2·5 exposures. Methods We did a prospective cohort study using data from people aged 30–79 years without cardiovascular disease at baseline from the China Kadoorie Biobank (CKB). Active commuting and farming activity were assessed at baseline using questionnaires. A high-resolution (1 × 1 km) satellite-based model was used to estimate annual average PM2·5 exposure during the study period. Participants were stratified according to PM2·5 exposure (54 μg/m3 or greater vs less than 54 μg/m3). Hazard ratios (HRs) and 95% CIs for incident cerebrovascular disease and ischaemic heart disease by active commuting and farming activity were estimated using Cox proportional hazard models. Effect modifications by PM2·5 exposure were tested by likelihood ratio tests. Analyses were restricted to the period from Jan 1, 2005, to Dec 31, 2017. Findings Between June 25, 2004, and July 15, 2008, 512 725 people were enrolled in the CKB cohort. 322 399 eligible participants completed the baseline survey and were included in the analysis of active commuting (118 274 non-farmers and 204 125 farmers). Among 204 125 farmers, 2985 reported no farming time and 201 140 were included in the farming activity analysis. During a median follow-up of 11 years, 39 514 cerebrovascular disease cases and 22 313 ischaemic heart disease cases were newly identified. Among non-farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting was associated with lower risks of cerebrovascular disease (highest active commuting vs lowest active commuting HR 0·70, 95% CI 0·65–0·76) and ischaemic heart disease (0·60, 0·54–0·66). However, among non-farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, there was no association between active commuting and cerebrovascular disease or ischaemic heart disease. Among farmers with exposure to annual average PM2·5 concentrations of less than 54 μg/m3, increased active commuting (highest active commuting vs lowest active commuting HR 0·77, 95% CI 0·63–0·93) and increased farming activity (highest activity vs lowest activity HR 0·85, 95% CI 0·79–0·92) were both associated with a lower cerebrovascular disease risk. However, among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater, increases in active commuting (highest active commuting vs lowest active commuting HR 1·12, 95% CI 1·05–1·19) and farming activity (highest activity vs lowest activity HR 1·18, 95% CI 1·09–1·28) were associated with an elevated cerebrovascular disease risk. The above associations differed significantly between PM2·5 strata (all interaction p values <0·0001). Interpretation For participants with long-term exposure to higher ambient PM2·5 concentrations, the cardiovascular benefits of active commuting and farming activity were significantly attenuated. Higher levels of active commuting and farming activity even increased the cerebrovascular disease risk among farmers with exposure to annual average PM2·5 concentrations of 54 μg/m3 or greater. Funding National Natural Science Foundation of China, National Key Research and Development Program of China, Kadoorie Charitable Foundation, UK Wellcome Trust

Destructive quantum interference in <i>meta</i>-oligo(phenyleneethynylene) molecular wires with gold-graphene heterojunctions.

Quantum interference (QI) is well recognised as a significant contributing factor to the magnitude of molecular conductance values in both single-molecule and large area junctions. Numerous structure-property relationship studies have shown that para-connected oligo(phenyleneethynylene) (OPE) based molecular wires exemplify the impact of constructive quantum interference (CQI), whilst destructive quantum interference (DQI) effects are responsible for the orders of magnitude lower conductance of analogous meta-contacted OPE derivatives, despite the somewhat shorter effective tunnelling distance. Since molecular conductance is related to the value of the transmission function, evaluated at the electrode Fermi energy, T(EF), which in turn is influenced by the presence and relative energy of (anti)resonances, it follows that the relative single-molecule conductance of para- and meta-contacted OPE-type molecules is tuned both by the anchor group and the nature of the electrode materials used in the construction of molecular junctions (gold|molecule|gold vs. gold|molecule|graphene). It is shown here that whilst amine-contacted junctions show little influence of the electrode material on molecular conductance due to the similar electrode-molecule coupling through this anchor group to both types of electrodes, the weaker coupling between thiomethyl and ethynyl anchors and the graphene substrate electrode results in a relative enhancement of the DQI effect. This work highlights an additional parameter space to explore QI effects and establishes a new working model based on the electrode materials and anchor groups in modulating QI effects beyond the chemical structure of the molecular backbone

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P &gt; 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field