Search Results (3)

In this article, we study the autocorrelation function (ACF), which is a crucial element in time series analysis. We compare the distribution of the ACF, both from a theoretical and empirical point of view. We focus on white noise processes (WN), i.e., uncorrelated, centered, and identically distributed variables, whose ACFs are supposed to be asymptotically independent and converge towards the same normal distribution. But, the study of the sum of the sample ACF contradicts this property. Thus, our findings reveal a deviation of the sample ACF from normality beyond a specific lag. Note that this phenomenon is observed for white noise of varying lengths, and evenforn the residuals of an ARMA($p,q$) model. This discovery challenges traditional assumptions of normality in time series modeling. Indeed, when modeling a time series, the crucial step is to validate the estimated model by checking that the associated residuals form white noise. In this study, we show that the widely used portmanteau tests are not completely accurate. Box–Pierce appears to be too conservative, whereas Ljung–Box is too liberal. We suggest an alternative method based on the ACF for establishing the reliability of the portmanteau test and the validity of the estimated model. We illustrate our methodology using money stock data in the USA. Full article

This study examined the genes related to immunity, metabolism, and antioxidants that may interact with the prevalence of postpartum endometritis in Ossimi sheep. We used fifty endometritis-positive Ossimi sheep and fifty that appeared to be normal. For the purpose of taking blood samples, each ewe had its jugular vein pierced. Nucleotide sequence differences for the immunological (alpha-2-macroglobulin, toll-like receptor 2, transforming growth factor beta, interleukin 1 receptor-associated kinase 3, high-mobility group box 1, Fc alpha and Mu receptor, and inducible nitric oxide synthase), metabolic (ADAM metallopeptidase with thrombospondin type 1 motif 20, potassium sodium-activated channel subfamily T member 2, Mitogen-activated protein kinase kinase kinase 4, FKBP prolyl isomerase 5, and relaxin family peptide receptor 1), and antioxidant (superoxide dismutase, catalase, NADH: ubiquinone oxidoreductase subunit s5, and Heme oxygenase-1) genes were found among sheep with endometritis and those in good condition utilizing PCR-DNA sequencing. Fisher’s exact test revealed a significant difference in the probability of dispersal of all significant nucleotide changes between ewe groups with and without endometritis (p ˂ 0.01). In endometritis ewes, there was a considerable up-regulation of the expression levels of A2M, TLR2, IRAK3, HMGB1, FCAMR, iNOS, ADAMTS20, KCNT2, MAP3K4, FKBP5, RXFP1, and HMOX1. Conversely, there was a down-regulation of the genes that encode TGF-β, SOD, CAT, and NDUFS5. The kind of marker and its frequency in postparturient endometrtits significantly impacted the transcript levels of the indicators under analysis. The results validate that nucleotide changes and gene manifestation outlines in these candidates are significant predictors of the prevalence of endometritis in sheep. Full article

Faults in Heating, Ventilation and Air Conditioning (HVAC) systems affect the energy efficiency of buildings. To date, there rarely exist methods to detect and diagnose faults during the operation of buildings that are both cost-effective and sufficient accurate. This study presents a method that uses artificial intelligence to automate the detection of faults in HVAC systems. The automated fault detection is based on a residual analysis of the predicted total heating power and the actual total heating power using an algorithm that aims to find an optimal decision rule for the determination of faults. The data for this study was provided by a detailed simulation of a residential case study house. A machine learning model and an ARX model predict the building operation. The model for fault detection is trained on a fault-free data set and then tested with a faulty operation. The algorithm for an optimal decision rule uses various statistical tests of residual properties such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that it is possible to predict faults for both known faults and unknown faults. The challenge is to find the optimal algorithm to determine the best decision rules. In the outlook of this study, further methods are presented that aim to solve this challenge. Full article