Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression

The probabilistic forecasting of electricity loads is crucial for effective scheduling and decision-making in volatile and competitive energy markets with ever-growing uncertainties. We propose a novel approach to construct the probabilistic predictors for curves (PPC) of electricity loads, which leads to properly defined predictive bands and quantiles in the context of curve-to-curve regression. The proposed predictive model provides not only accurate hourly load point forecasts, but also generates well-defined probabilistic bands and day-long trajectories of the loads at any probability level, pre-specified by managers. We also define the predictive quantile curves that exhibit future loads in extreme scenarios and provide insights for hedging risks in the supply management of electricity. When applied to the day-ahead forecasting for French half-hourly electricity loads, the PPC outperform several state-of-the-art time series and machine learning predictive methods with more accurate point forecasts (mean absolute percentage error of 1.10%, compared to 1.36%–4.88% for the alternatives), a higher coverage rate of the day-long trajectory of loads (coverage rate of 95.5%, against 31.9%–90.7% for the alternatives) and a narrower average length of the predictive bands. In a series of numerical experiments, the PPC further demonstrate robust performance and general applicability, achieving accurate coverage probabilities under a variety of data-generating mechanisms

let‐7‐repressesed S hc translation delays replicative senescence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102709/1/acel12176.pd

QTL mapping and candidate gene analysis of ferrous iron and zinc toxicity tolerance at seedling stage in rice by genome-wide association study

Background:Ferrous iron (Fe) and zinc (Zn) at high concentration in the soil cause heavy metal toxicity andgreatly affect rice yield and quality. To improve rice production, understanding the genetic and molecularresistance mechanisms to excess Fe and Zn in rice is essential. Genome-wide association study (GWAS) is aneffective way to identify loci and favorable alleles governing Fe and Zn toxicty as well as dissect the geneticrelationship between them in a genetically diverse population.Results:A total of 29 and 31 putative QTL affecting shoot height (SH), root length (RL), shoot fresh weight (SFW),shoot dry weight (SDW), root dry weight (RDW), shoot water content (SWC) and shoot ion concentrations (SFe orSZn) were identified at seedling stage in Fe and Zn experiments, respectively. Five toxicity tolerance QTL (qSdw3a,qSdw3b,qSdw12andqSFe5/qSZn5) were detected in the same genomic regions under the two stress conditionsand 22 candidate genes for 10 important QTL regions were also determined by haplotype analyses.Conclusion:Rice plants share partial genetic overlaps of Fe and Zn toxicity tolerance at seedling stage. Candidategenes putatively affecting Fe and Zn toxicity tolerance identified in this study provide valuable information forfuture functional characterization and improvement of rice tolerance to Fe and Zn toxicity by marker-assistedselection or designed QTL pyramiding

Aberrant Brain Regional Homogeneity and Functional Connectivity of Entorhinal Cortex in Vascular Mild Cognitive Impairment: A Resting-State Functional MRI Study

The aim of this study was to investigate changes in regional homogeneity (ReHo) and the functional connectivity of the entorhinal cortex (EC) in vascular mild cognitive impairment (VaMCI) and to evaluate the relationships between such changes and neuropsychological measures in VaMCI individuals. In all, 31 patients with VaMCI and 32 normal controls (NCs) underwent rs-fMRI. Differences in whole-brain ReHo and seed-based bilateral EC functional connectivity (EC-FC) were determined. Pearson's correlation was used to evaluate the relationships between regions with significant group differences and different neuropsychological measures. Vascular mild cognitive impairment (VaMCI) patients had lower scores in Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) and higher ones in Activity of Daily Living (ADL) (p < 0.05). Vascular mild cognitive impairment (VaMCI) individuals had significantly lower ReHo in the left cerebellum and right lentiform nucleus than NCs (P < 0.05, TFCE FWE correction). Vascular mild cognitive impairment (VaMCI) subjects showed significant decreases in the FC of the right EC in the right inferior frontal gyrus, right middle frontal gyrus, bilateral pre-central gyrus, and right post-central/superior parietal lobules (P < 0.05, TFCE FWE correction). Significant positive correlations were found between ReHo and MoCA scores for the right lentiform nucleus (r = 0.37, P < 0.05). The right post-central/superior parietal lobules showed a significant positive correlation between right EC-FC and MoCA scores (r = 0.37, P < 0.05). Patterns in ReHo and EC-FC changes in VaMCI patients and their correlations with neuropsychological measures may be a pathophysiological foundation of cognitive impairment, which may aid the early diagnosis of VaMCI

A Journey into the City. Migrant Workers' Relation with the Urban Space and Struggle for Existence in Xu Zechen's Early Jingpiao Fiction

In contemporary China, rural-urban migrants constitute a new urban subject with entirely new identity-related issues. This study aims at demonstrating how literature can be a valid field in investigating such evolving subjectivities, through an analysis of Xu Zechen’s early novellas depicting migrants’ vicissitudes in Beijing. Combining a close reading of the texts and a review of the main social problems characterising rural-urban migration in China, this paper focuses on the representation of the identity crisis within the migrant self in Xu’s stories, taking into account the network of meanings employed by the writer to signify the objective and subjective tension between the city and the countryside

HIGH-DIMENSIONAL NONSTATIONARY TIME SERIES MODELING WITH FUNCTIONAL DATA ANALYSIS AND DEEP LEARNING

Ph.DDOCTOR OF PHILOSOPHY (NUSGS