Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation

We propose in this paper an original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (ALADIN). We particularly look at the Multi-Layer Perceptron. After optimizing our architecture with ALADIN and endogenous data previously made stationary and using an innovative pre-input layer selection method, we combined it to an ARMA model from a rule based on the analysis of hourly data series. This model has been used to forecast the hourly global radiation for five places in Mediterranean area. Our technique outperforms classical models for all the places. The nRMSE for our hybrid model ANN/ARMA is 14.9% compared to 26.2% for the na\"ive persistence predictor. Note that in the stand alone ANN case the nRMSE is 18.4%. Finally, in order to discuss the reliability of the forecaster outputs, a complementary study concerning the confidence interval of each prediction is proposedComment: Energy (2012)

Development and validation of the Multi-dimensional University Research Workplace Inventory (MDURWI)

WOS:000454839600005This study describes the development and validation of an instrument aimed toward measuring organizational features of an academic research workplace. The question pool was developed based on data from a pilot study (N = 43). The survey was deployed to academic researchers in the field of higher education research worldwide (N = 850). An exploratory factor analysis conducted on 36 questions, followed by confirmatory factor analysis, which lead to a final pool of 27 questions in five subscales, one of which divided into three lower-order factors. The final model exhibited very good fit (X2/df = 2.561; CFI = 0.972; PCFI = 0.784; RMSEA = 0.043; P[rmsea ? 0.05] < 0.001; AIC = 891.018; BCC = 987.839) and psychometric properties, in the form of factorial, convergent, and discriminant validity, as well as reliability and sensitivity. Implications of this instrument for research and policymaking are discussed, as well as future research directions.info:eu-repo/semantics/acceptedVersio

A Reliability-Generalization Study of Journal Peer Reviews: A Multilevel Meta-Analysis of Inter-Rater Reliability and Its Determinants

Background: This paper presents the first meta-analysis for the inter-rater reliability (IRR) of journal peer reviews. IRR is defined as the extent to which two or more independent reviews of the same scientific document agree. Methodology/Principal Findings: Altogether, 70 reliability coefficients (Cohen’s Kappa, intra-class correlation [ICC], and Pearson product-moment correlation [r]) from 48 studies were taken into account in the meta-analysis. The studies were based on a total of 19,443 manuscripts; on average, each study had a sample size of 311 manuscripts (minimum: 28, maximum: 1983). The results of the meta-analysis confirmed the findings of the narrative literature reviews published to date: The level of IRR (mean ICC/r 2 =.34, mean Cohen’s Kappa =.17) was low. To explain the study-to-study variation of the IRR coefficients, meta-regression analyses were calculated using seven covariates. Two covariates that emerged in the metaregression analyses as statistically significant to gain an approximate homogeneity of the intra-class correlations indicated that, firstly, the more manuscripts that a study is based on, the smaller the reported IRR coefficients are. Secondly, if the information of the rating system for reviewers was reported in a study, then this was associated with a smaller IRR coefficient than if the information was not conveyed. Conclusions/Significance: Studies that report a high level of IRR are to be considered less credible than those with a low level o