Influence of the load of the power transformer on the magnetizing current

Data on the magnetizing current of power transformers are taken from the experience of idling. It is considered that it does not change under load. The experience of idling does not take into account the uneven saturation of the magnetic core when working under load. The hypothesis of a significant error caused by this assumption is put forward. The experiments carried out confirmed the hypothesis. The differences in the measurement of the magnetizing current at idle and under load in the experiments reached 28-32%. This determines the inaccuracy in the calculations of currents and losses in power transformers, which, taking into account the continuous operation of transformers and their large number, can be significant. It is proposed to add the experience of working at rated load when testing power transformers. This experience will not only allow us to clarify the val-ue of the magnetizing current under load and magnetic losses, but also to re-fine the design of the transformer in the direction of reducing the magnetizing current by eliminating uneven saturation of the magnetic circuit when working under load, due to the influence of magnetic scattering fields. This is possible by locally increasing the cross-section of the magnetic circuit in the busiest places of the magnetic circuit.

PaIRKAT: A pathway integrated regression-based kernel association test with applications to metabolomics and COPD phenotypes

High-throughput data such as metabolomics, genomics, transcriptomics, and proteomics have become familiar data types within the "-omics" family. For this work, we focus on subsets that interact with one another and represent these "pathways" as graphs. Observed pathways often have disjoint components, i.e. nodes or sets of nodes (metabolites, etc.) not connected to any other within the pathway which notably lessens testing power. In this paper we propose the Pathway Integrated Regression-based Kernel Association Test (PaIRKAT), a new kernel machine regression method for incorporating known pathway information into the semi-parametric kernel regression framework. This paper also contributes an application of a graph kernel regularization method for overcoming disconnected pathways. By incorporating a regularized or "smoothed" graph into a score test, PaIRKAT is capable of providing more powerful tests for associations between biological pathways and phenotypes of interest and will be helpful in identifying novel pathways for targeted clinical research. We evaluate this method through several simulation studies and an application to real metabolomics data from the COPDGene study. Our simulation studies illustrate the robustness of this method to incorrect and incomplete pathway knowledge, and the real data analysis shows meaningful improvements of testing power in pathways. PaIRKAT was developed for application to metabolomic pathway data, but the techniques are easily generalizable to other data sources with a graph-like structure.

Spatial Autocorrelation in Soil Compaction and Its Impact on Earthwork Acceptance Testing

The compaction quality of soil embankments is critical to the long-term performance of the pavements placed on them. In current quality assurance (QA) practice, state highway agencies (SHAs) rely on in-situ testing at a small number of point locations to decide whether to accept or reject the product, assuming that the samples taken at random locations are independent of each other. This assumption, however, is invalid because soil properties are spatially autocorrelated – the properties at nearby locations are correlated to each other. Consequently, if the sampling locations are close to each other, the effective number of samples is reduced, which in turn increases the risk of incorrect accept/reject decisions. This study addressed this spatial autocorrelation issue in soil acceptance testing. Soil data from the U.S Highway 31 project, collected by intelligent compaction (IC) in the format of compaction meter value (CMV), were used to prove the existence of spatial autocorrelation using the semivariogram and Moran’s I analysis. The impact of spatial autocorrelation on soil acceptance testing was assessed by comparing the testing power under two scenarios (with and without spatial autocorrelation). The results suggest that the existence of spatial autocorrelation decreases the testing power, resulting in a greater risk to the SHA. Based on these findings, this study proposed two spatial indices to mitigate the negative impact of spatial autocorrelation by controlling the spatial pattern of random samples. A web tool was also developed as an implementation to augment the random sampling process in field QA practice by incorporating the spatial pattern of samples.