Direct observation and control of near-field radiative energy transfer in a natural hyperbolic material

Heat control is a key issue in nano-electronics, where new efficient energy transfer mechanisms are highly sought after. In this respect, there is indirect evidence that high-mobility hexagonal boron nitride (hBN)-encapsulated graphene exhibits hyperbolic out-of-plane radiative energy transfer when driven out-of-equilibrium. Here we directly observe radiative energy transfer due to the hyperbolic phonon polaritons modes of the hBN encapsulant in intrinsic graphene devices under large bias, using mid-infrared spectroscopy and pyrometry. By using different hBN crystals of varied crystalline quality, we engineer the energy transfer efficiency, a key asset for compact thermal management of electronic circuits.Comment: 21 pages including Supplementary Material (Main text: 10 pages, 4 figures

Fault detection and isolation with robust principal component analysis

Principal component analysis (PCA) is a powerful fault detection and isolation method. However, the classical PCA, which is based on the estimation of the sample mean and covariance matrix of the data, is very sensitive to outliers in the training data set. Usually robust principal component analysis is applied to remove the effect of outliers on the PCA model. In this paper, a fast two-step algorithm is proposed. First, the objective was to find an accurate estimate of the covariance matrix of the data so that a PCA model might be developed that could then be used for fault detection and isolation. A very simple estimate derived from a one-step weighted variance-covariance estimate is used (Ruiz-Gazen, 1996). This is a 'local' matrix of variance which tends to emphasize the contribution of close observations in comparison with distant observations (outliers). Second, structured residuals are used for multiple fault detection and isolation. These structured residuals are based on the reconstruction principle, and the existence condition of such residuals is used to determine the detectable faults and the isolable faults. The proposed scheme avoids the combinatorial explosion of faulty scenarios related to multiple faults to be considered. Then, this procedure for outliers detection and isolation is successfully applied to an example with multiple faults

Bayes-Based Fault Discrimination in Wide Area Backup Protection

Multivariate statistical analysis is an effective tool to finish the fault location for electric power system. In Bayesian discriminant analysis as a subbranch, by the research of several populations, one can calculate the conditional probability that some samples belong to these populations, and compare the corresponding probability. The sample will be classified as population with maximum probability. In this paper, based on Bayesian discriminant analysis principle, a great number of simulation examples have confirmed that the results of Bayesian fault discriminant in wide area backup protection are accurate and reliable

Use of chemometric analyses to assess biological wastewater treatment plants by protozoa and metazoa monitoring

Protozoa and metazoa biota communities in biological wastewater treatment plants (WWTP) are known to be dependent of both the plant type (oxidation ditch, trickling filter, conventional activated sludge, among others) and the working operational conditions (incoming effluent characteristics, toxics presence, organic load, aeration, hydraulic and sludge retention times, nitrification occurrence, etc.). Thus, for analogous WWTP operating in equivalent operating conditions, similar protozoa and metazoa communities can be found. Indeed, the protozoa and metazoa biota monitoring can be considered a quite useful tool for assessing the functioning of biological WWTP. Furthermore, the use of chemometric techniques in WWTP monitoring is becoming widespread to enlighten interrelationships within the plant, especially when a large collection of data can be obtained. In the current study, the protozoa and metazoa communities of three different types of WWTP, comprising one oxidation ditch, four trickling filters, and three conventional activated sludge plants, were monitored. For that purpose, metazoa, as well as the main protozoa groups (flagellates, free-swimming, crawling and sessile ciliates, and testate amoeba) were determined in terms of contents and relative abundance. The collected data was further processed by chemometric techniques, such as cross-correlation, principal components, multivariate ANOVA, and decision trees analyses, allowing to successfully identify, and characterize, the different studied WWTP, and thus, being able to help monitoring and diagnosing operational problems.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by European Regional Development Fund under the scope of Norte2020 — Programa Operacional Regional do Norte.info:eu-repo/semantics/publishedVersio