Modeling dynamic demand response using Monte Carlo simulation and interval mathematics for boundary estimation

With the rapid development of various technologies and applications in smart grid implementation, demand response has attracted growing research interests because of its potentials in enhancing power grid reliability with reduced system operation costs. This paper presents a new demand response model with elastic economic dispatch in a locational marginal pricing market. It models system economic dispatch as a feedback control process, and introduces a flexible and adjustable load cost as a controlled signal to adjust demand response. Compared with the conventional “one time use” static load dispatch model, this dynamic feedback demand response model may adjust the load to a desired level in a finite number of time steps and a proof of convergence is provided. In addition, Monte Carlo simulation and boundary calculation using interval mathematics are applied for describing uncertainty of end-user's response to an independent system operator's expected dispatch. A numerical analysis based on the modified Pennsylvania-Jersey-Maryland power pool five-bus system is introduced for simulation and the results verify the effectiveness of the proposed model. System operators may use the proposed model to obtain insights in demand response processes for their decision-making regarding system load levels and operation conditions.

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P<1.09 × 10−9) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

A comparison of DNA pools constructed following whole genome amplification for two-stage SNP genotyping designs

Genotyping in DNA pools reduces the cost and the time required to complete large genotyping projects. The aim of the present study was to evaluate pooling as part of a strategy for fine mapping in regions of significant linkage. Thirty-nine single nucleotide polymorphisms (SNPs) were analyzed in two genomic DNA pools of 384 individuals each and results compared with data after typing all individuals used in the pools. There were no significant differences using data from either 2 or 8 heterozygous individuals to correct frequency estimates for unequal allelic amplification. After correction, the mean difference between estimates from the genomic pool and individual allele frequencies was .033. A major limitation of the use of DNA pools is the time and effort required to carefully adjust the concentration of each individual DNA sample before mixing aliquots. Pools were also constructed by combining DNA after Multiple Displacement Amplification (MDA). The MDA pools gave similar results to pools constructed after careful DNA quantitation (mean difference from individual genotyping .040) and MDA provides a rapid method to generate pools suitable for some applications. Pools provide a rapid and cost-effective screen to eliminate SNPs that are not polymorphic in a test population and can detect minor allele frequencies as low as 1% in the pooled samples. With current levels of accuracy, pooling is best suited to an initial screen in the SNP validation process that can provide high-throughput comparisons between cases and controls to prioritize SNPs for subsequent individual genotyping.

The importance of modelling heterogeneity in complex disease: application to NIMH Schizophrenia Genetics Initiative data

As for other complex diseases, linkage analyses of schizophrenia (SZ) have produced evidence for numerous chromosomal regions, with inconsistent results reported across studies. The presence of locus heterogeneity appears likely and may reduce the power of linkage analyses if homogeneity is assumed. In addition, when multiple heterogeneous datasets are pooled, inter-sample variation in the proportion of linked families (alpha) may diminish the power of the pooled sample to detect susceptibility loci, in spite of the larger sample size obtained. We compare the significance of linkage findings obtained using allele-sharing LOD scores (LOD(exp))-which assume homogeneity-and heterogeneity LOD scores (HLOD) in European American and African American NIMH SZ families. We also pool these two samples and evaluate the relative power of the LOD(exp) and two different heterogeneity statistics. One of these (HLOD-P) estimates the heterogeneity parameter alpha only in aggregate data, while the second (HLOD-S) determines alpha separately for each sample. In separate and combined data, we show consistently improved performance of HLOD scores over LOD(exp). Notably, genome-wide significant evidence for linkage is obtained at chromosome 10p in the European American sample using a recessive HLOD score. When the two samples are combined, linkage at the 10p locus also achieves genome-wide significance under HLOD-S, but not HLOD-P. Using HLOD-S, improved evidence for linkage was also obtained for a previously reported region on chromosome 15q. In linkage analyses of complex disease, power may be maximised by routinely modelling locus heterogeneity within individual datasets, even when multiple datasets are combined to form larger samples.

Development and preliminary validation of the Drinking Expectancy Sexual Vulnerabilities Questionnaire (DESV-Q): A purpose-specific instrument for rape-perception research

Background Alcohol expectancies likely play a role in people’s perceptions of alcohol-involved sexual violence. However, no appropriate measure exists to examine this link comprehensively. Objective The aim of this research was to develop an alcohol expectancy measure which captures young adults’ beliefs about alcohol’s role in sexual aggression and victimization. Method Two cross-sectional samples of young Australian adults (18–25 years) were recruited for scale development (Phase 1) and scale validation (Phase 2). In Phase 1, participants (N = 201; 38.3% males) completed an online survey with an initial pool of alcohol expectancy items stated in terms of three targets (self, men, women) to identify the scale’s factor structure and most effective items. A revised alcohol expectancy scale was then administered online to 322 young adults (39.6% males) in Phase 2. To assess the predictive, convergent, and discriminant validity of the scale, participants also completed established measures of personality, social desirability, alcohol use, general and context-specific alcohol expectancies, and impulsiveness. Results Principal axis factoring (Phase 1) and confirmatory factor analysis (Phase 2) resulted in a target-equivalent five-factor structure for the final 66-item Drinking Expectancy Sexual Vulnerabilities Questionnaire (DESV-Q). The factors were labeled: - (1) Sexual Coercion - (2) Sexual Vulnerability - (3) Confidence - (4) Self-Centeredness - (5) Negative Cognitive and Behavioral Changes The measure demonstrated effective items, high internal consistency, and satisfactory predictive, convergent, and discriminant validity. Conclusions The DESV-Q is a purpose-specific instrument that could be used in future research to elucidate people’s attributions for alcohol-involved sexual aggression and victimization.

Denitrification losses from an intensively managed sub-tropical pasture: Impact of soil moisture on the partitioning of N2 and N2O emissions

Intensively managed pastures in subtropical Australia under dairy production are nitrogen (N) loaded agro-ecosystems, with an increased pool of N available for denitrification. The magnitude of denitrification losses and N2:N2O partitioning in these agro-ecosystems is largely unknown, representing a major uncertainty when estimating total N loss and replacement. This study investigated the influence of different soil moisture contents on N2 and N2O emissions from a subtropical dairy pasture in Queensland, Australia. Intact soil cores were incubated over 15 days at 80% and 100% water-filled pore space (WFPS), after the application of 15N labelled nitrate, equivalent to 50 kg N ha−1. This setup enabled the direct quantification of N2 and N2O emissions following fertilisation using the 15N gas flux method. The main product of denitrification in both treatments was N2. N2 emissions exceeded N2O emissions by a factor of 8 ± 1 at 80% WFPS and a factor of 17 ± 2 at 100% WFPS. The total amount of N-N2 lost over the incubation period was 21.27 kg ± 2.10 N2-N ha−1 at 80% WFPS and 25.26 kg ± 2.79 kg ha−1 at 100% WFPS respectively. N2 emissions remained high at 100% WFPS, while related N2O emissions decreased. At 80% WFPS, N2 emissions increased constantly over time while N2O fluxes declined. Consequently, N2/(N2 + N2O) product ratios increased over the incubation period in both treatments. N2/(N2 + N2O) product ratios responded significantly to soil moisture, confirming WFPS as a key driver of denitrification. The substantial amount of fertiliser lost as N2 reveals the agronomic significance of denitrification as a major pathway of N loss for sub-tropical pastures at high WFPS and may explain the low fertiliser N use efficiency observed for these agro-ecosystems.