Unmeasured Sites Research Articles

Refrigeration via purification through repeated measurements

We design a measurement-based quantum refrigerator with an arbitrary number of qubits situated in a one-dimensional array that interact through variable-range XY interactions. The method proposed is based on repeated evolution followed by a measurement on the single accessible qubit, which has the potential to reduce the temperature in the rest of the subsystems, thereby demonstrating cooling in the device. The performance of the refrigerator is quantified by the fidelity of each local subsystem with the ground state of the local Hamiltonian and the corresponding probability of success. We identify system parameters, which include the interaction strength, range of interactions, initial temperature of each qubit, and the position of the measured qubit, so that the fidelities of all the unmeasured qubits approach unity with a nonvanishing probability. We observe that although strong interactions during evolution are required to achieve cooling, the long-range interactions typically deteriorate the performance of the refrigerator, which indicates that interactions are not ubiquitous. We report the scalability and the saturation property of the success probability with respect to the system size, which turns out to be independent of the involved system parameters and the number of repeated measurements. Furthermore, we show that the number of subsystems which can be cooled changes depending on the odd or even number of sites in the refrigerator. We argue that the distribution of entanglement between unmeasured qubits can give a possible explanation of the dependence of cooling process on the measured and unmeasured sites.

Compositional and Bayesian inference analysis of the concentrations of air pollutants in Catalonia, Spain

While most countries have networks of stations for monitoring pollutant concentrations, they do not cover the whole territory continuously. Therefore, to be able to carry out a spatial and temporal study, the predictions for air pollution from unmeasured sites and time periods need to be used.The objective of this study is to predict the air pollutant concentrations of PM10, O3, NO2, SO2 and CO in sites throughout Catalonia (Spain) and time periods without a monitoring station. Compositional data (CoDa) studies the relative importance of pollutants. A novel feature in this article is combining CoDa with an indicator of total pollution. Predictions are then made using a combination of spatio-temporal models and the Bayesian Laplace Integrated Approach (INLA) inference method.The most relevant results obtained indicate that the log-ratio between NO2 and O3 has the highest variance and the best predictive accuracy in time and space. Total pollution levels are second in variance but have low spatial predictive accuracy. Third in variance is the low temporal accuracy found in the log-ratio between SO2 and the remaining pollutants. Globally, the combination of CoDa and the INLA method is suitable for making effective spatio-temporal predictions of air pollutants.

A novel computational strategy for DNA methylation imputation using mixture regression model (MRM)

BackgroundDNA methylation is an important heritable epigenetic mark that plays a crucial role in transcriptional regulation and the pathogenesis of various human disorders. The commonly used DNA methylation measurement approaches, e.g., Illumina Infinium HumanMethylation-27 and -450 BeadChip arrays (27 K and 450 K arrays) and reduced representation bisulfite sequencing (RRBS), only cover a small proportion of the total CpG sites in the human genome, which considerably limited the scope of the DNA methylation analysis in those studies.ResultsWe proposed a new computational strategy to impute the methylation value at the unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integrating information of neighboring CpGs and the similarities in local methylation patterns across subjects and across multiple genomic regions. Our method achieved a better imputation accuracy over a set of competing methods on both simulated and empirical data, particularly when the missing rate is high. By applying MRM to an RRBS dataset from subjects with low versus high bone mineral density (BMD), we recovered methylation values of ~ 300 K CpGs in the promoter regions of chromosome 17 and identified some novel differentially methylated CpGs that are significantly associated with BMD.ConclusionsOur method is well applicable to the numerous methylation studies. By expanding the coverage of the methylation dataset to unmeasured sites, it can significantly enhance the discovery of novel differential methylation signals and thus reveal the mechanisms underlying various human disorders/traits.

A Methodology for Efficient Dynamic Spatial Sampling and Reconstruction of Wafer Profiles

In semiconductor manufacturing, metrology is generally a high cost nonvalue-added operation that significantly impacts on cycle time. As such, reducing wafer metrology continues to be a major target in semiconductor manufacturing efficiency initiatives. A novel data-driven spatial dynamic sampling methodology is presented that minimizes the number of sites that need to be measured across a wafer surface while maintaining an acceptable level of wafer profile reconstruction accuracy. The methodology is based on analyzing historical metrology data using forward selection component analysis (FSCA) to determine, from a set of candidate wafer sites, the minimum set of sites that need to be monitored in order to reconstruct the full wafer profile using statistical regression techniques. Dynamic sampling is then implemented by clustering unmeasured sites in accordance with their similarity to the FSCA selected sites and temporally selecting a different sample from each cluster. In this way, the risk of not detecting previously unseen process behavior is mitigated. We demonstrate the efficacy of the proposed methodology using both simulation studies and metrology data from a semiconductor manufacturing process.

SPATIAL PREDICTION OF AIR TEMPERATURE IN EAST CENTRAL ANATOLIA OF TURKEY

Abstract. Air temperature is an essential component of the factors used in landscape planning. At similar topographic conditions, vegetation may show considerable differences depending on air temperature and precipitation. In large areas, measuring temperature is a cost and time-consuming work. Therefore, prediction of climate variables at unmeasured sites at an acceptable accuracy is very important in regional resource planning. In addition, use a more proper prediction method is crucial since many different prediction techniques yield different performance in different landscape and geographical conditions. We compared inverse distance weighted (IDW), ordinary kriging (OK), and ordinary cokriging (OCK) to predict air temperature at unmeasured sites in Malatya region (East Central Anatolia) of Turkey. Malatya region is the most important apricot production area of Turkey and air temperature is the most important factor determining the apricot growing zones in this region. We used mean monthly temperatures from 1975 to 2010 measured at 28 sites in the study area and predicted temperature with IDW, OC, and OCK techniques, mapped temperature in the region, and tested the reliability of these maps. The OCK with elevation as an auxiliary variable occurred the best procedure to predict temperature against the criteria of model efficiency and relative root mean squared error.

Development of a microscale land use regression model for predicting NO2 concentrations at a heavy trafficked suburban area in Auckland, NZ

Land use regression (LUR) analysis has become a key method to explain air pollutant concentrations at unmeasured sites at city or country scales, but little is known about the applicability of LUR at microscales. We present a microscale LUR model developed for a heavy trafficked section of road in Auckland, New Zealand. We also test the within-city transferability of LUR models developed at different spatial scales (local scale and city scale). Nitrogen dioxide (NO2) was measured during summer at 40 sites and a LUR model was developed based on standard criteria. The results showed that LUR models are able to capture the microscale variability with the model explaining 66% of the variability in NO2 concentrations. Predictor variables identified at this scale were street width, distance to major road, presence of awnings and number of bus stops, with the latter three also being important determinants at the local scale. This highlights the importance of street and building configurations for individual exposure at the street level. However, within-city transferability was limited with the number of bus stops being the only significant predictor variable at all spatial scales and locations tested, indicating the strong influence of diesel emissions related to bus traffic. These findings show that air quality monitoring is necessary at a high spatial density within cities in capturing small-scale variability in NO2 concentrations at the street level and assessing individual exposure to traffic related air pollutants.

Usage of spline interpolation in catheter-based cardiac mapping

Due to their minimal invasiveness catheters are highly preferred in cardiac mapping techniques used in the source localization of rhythm disturbances in the heart. In cardiac mapping, standard steerable catheters and multielectrode basket catheters are the two alternatives for the characterization of the underlying tissue on the inner (endocardium) and outer (epicardium) surfaces of the heart. As with any discrete sampling technique, an important question for catheter-based cardiac mapping is how to determine values at locations from which direct measurements are not available. Interpolation is the most common approach for providing values at unmeasured sites using the available measurements. In this study, the usage of spline interpolation technique in catheter-based cardiac mapping was introduced for the first time in the literature and compared with the existing approaches such as the nearest-neighbor, Laplacian, and Hardy's interpolation techniques. For different sampling resolutions on the endocardium and epicardium, we reconstructed the activation-time values on unmeasured sites using measured values. To provide quantitative validation, we have applied these methods to high resolution simulated activation-time values from a realistic heart model using Aliev-Panfilov mathematical modeling approach. The results show that spline, Laplacian, and Hardy interpolation methods performed successfully and also better than nearest-neighbor method. Among all interpolation schemes except for the nearest-neighbor method the average correlation coefficient (CC) values were greater than 0.985.

Extrapolation of site‐specific weighted usable area curves and instream flow requirements to unmeasured streams within the Nooksack watershed in support of strategic watershed planning

Given the large number of locations within the study watershed (Washington, USA) where instream flow information is required, a GIS based stratification procedure using multivariate data analysis techniques was developed to extrapolate either instream flow recommendations or habitat availability curves (weighted usable area curves) upon which instream flow recommendations could be developed from study sites to other sites where no site‐specific information is available. One‐hundred‐seventy‐two drainages were divided into 21 hydrologically and geomorphologically similar subgroups. Mean annual flow versus drainage area and 60% exceedence flow versus drainage area relationships were also developed for the study area. Monthly instream flow recommendations were then extrapolated to the unmeasured streams within the hydrologically and geomorphologically similar subgroups by scaling them by a percentage of the mean annual flow; weighted usable area curves were extrapolated to the unmeasured streams within the same subgroups by scaling the flows by a percentage of the 60% exceedence flow. Extrapolated monthly instream flow recommendations ranged between 0 and 76 percent but were generally within about 20 percent or less when compared to recommendations developed from site‐specific analyses. Extrapolated weighted usable area relationships had an average root mean square error less than about 17% and reflected the basic functional relationships when compared to site specific analyses. This study demonstrates that either instream flow recommendations or weighted useable area relationships derived from site‐specific data can be extrapolated to unmeasured sites for use within the context of strategic watershed planning based on the development and application of two extrapolation methodologies.

Storm‐event rainfall–runoff modelling approach for ungauged sites in Taiwan

AbstractThis work develops a top‐down modelling approach for storm‐event rainfall–runoff model calibration at unmeasured sites in Taiwan. Twenty‐six storm events occurring in seven sub‐catchments in the Kao‐Ping River provided the analytical data set. Regional formulas for three important features of a streamflow hydrograph, i.e. time to peak, peak flow, and total runoff volume, were developed via the characteristics of storm event and catchment using multivariate regression analysis. Validation of the regional formulas demonstrates that they reasonably predict the three features of a streamflow hydrograph at ungauged sites. All of the sub‐catchments in the study area were then adopted as ungauged areas, and the three streamflow hydrograph features were calculated by the regional formulas and substituted into the fuzzy multi‐objective function for rainfall–runoff model calibration. Calibration results show that the proposed approach can effectively simulate the streamflow hydrographs at the ungauged sites. The simulated hydrographs more closely resemble observed hydrographs than hydrographs synthesized using the Soil Conservation Service (SCS) dimensionless unit hydrograph method, a conventional method for hydrograph estimation at ungauged sites in Taiwan. Copyright © 2008 John Wiley & Sons, Ltd.

Source localization of focal ventricular arrhythmias using linear estimation, correlation, and back propagation networks

Catheter-based approaches used in the localization and treatment of the source of heart rhythm disturbances (arrhythmias) have become popular, because they do not require highly invasive and risky open-chest operations. In most of the existing approaches, mapping of the outer surface (epicardium) is not possible even though arrhythmic substrates involving epicardial and subepicardial layers account for about 15% of the ventricular tachycardias. In this study, we report a feasibility study of a novel mapping approach targeting the epicardium which is based on the measurements of multielectrode catheters placed in the coronary veins. We investigated three methods in determining the most probable region of early activation, i.e., the region that contains the source of the abnormal activation on the heart, using only a set of sparse venous catheter recordings. The methods we proposed here were the linear estimation, correlation, and the back propagation networks. The linear estimation technique hypothesized the relationship between venous catheter measurements and unmeasured epicardial sites based on a previously recorded training data set. The correlation method included a comparative analysis between test and training epicardial activation time maps based on the measured values from the venous sites. In the back propagation method, the input layer consisted of the source data in the form of 42 nodes which were the activation time values from the intravenous catheter leads. We used two hidden layers with 600 and 500 nodes, respectively. The output layer consisted of 28 nodes in the output layer that corresponded to the manually selected early activation regions on the epicardium. The results of the linear estimation and the correlation methods showed that they could be used as a good predictor for the region of early activation, and thus, these approaches may be employed to direct a subsequent more focused electrophysiological study and curative radiofrequency (RF) ablation. The back propagation network approach performed relatively well for the right ventricularly paced beats and the results demonstrated its potential as a supporting technique to the estimation and correlation methods. The results of this study encourage further investigation and provides evidence that an epicardial mapping approach based on the venous catheter recordings is feasible and can provide adequate accuracy for clinical applications.