True Output Research Articles

Fortifying the characterization of binary mergers in LIGO data

The study of compact binary inspirals and mergers with gravitational wave observatories amounts to optimizing a theoretical description of the data to best reproduce the true detector output. While most of the research effort in gravitational wave data modeling focuses on the gravitational waveforms themselves, here we will begin to improve our model of the instrument noise by introducing parameters that allow us to determine the background instrumental power spectrum while simultaneously characterizing the astrophysical signal. We use data from the fifth LIGO science run and simulated gravitational wave signals to demonstrate how the introduction of noise parameters results in the resilience of the signal characterization to variations in an initial estimation of the noise power spectral density. We find substantial improvement in the consistency of Bayes factor calculations when we are able to marginalize over uncertainty in the instrument noise level.

Proteomic tools for environmental microbiology—A roadmap from sample preparation to protein identification and quantification

The steadily increasing amount of (meta-)genomic sequence information of diverse organisms and habitats has a strong impact on research in microbial physiology and ecology. In-depth functional understanding of metabolic processes and overall physiological adaptation to environmental changes, however, requires application of proteomics, as the context specific proteome constitutes the true functional output of a cell. Considering the enormous structural and functional diversity of proteins, only rational combinations of various analytical approaches allow a holistic view on the overall state of the cell. Within the past decade, proteomic methods became increasingly accessible to microbiologists mainly due to the robustness of analytical methods (e.g. 2DE), and affordability of mass spectrometers and their relative ease of use. This review provides an overview on the complex portfolio of state-of-the-art proteomics and highlights the basic principles of key methods, ranging from sample preparation of laboratory or environmental samples, via protein/peptide separation (gel-based or gel-free) and different types of mass spectrometric protein/peptide analyses, to protein identification and abundance determination.

High Voltage Control for Ionized Chamber

In this paper, presenting the approach to remotely control and monitor “High Voltage Module” through Personal Computer. A programmable voltage input is provided to the analog module ranging from 0 to +4.64V equals to 0 to 100% of rated voltage output (4Kv). Current programmability allows the user to set current limit, anywhere from 0 to 100% of maximum rated current (5mA). The buffered low impedance voltage and current monitor signals can drive external circuitry directly. The IMON and VMON signal is a true output current and voltage monitoring signal. High Voltage (H.V) power supplies are used in various applications in industry. The need was to control the parameter such as voltage and current of the unit remotely and monitor the same. ADuC841 an embedded microcontroller with 8052 core of analog device is used for this application which is providing precision analog input and readout of the output voltages of high voltage module.

Pulsed I-V Auto-Measurement of High Power MOSFETs without Self-Heating

A novel Sagittarius automatic Pulsed I-V measurement system was developed. This new system eliminated the self-heating effect which affected static measurements of High Power MOSFETs using conventional methods. It provided the true output conductance and trans-conductance, solved the device modeling problems caused by negative output conductance of high power devices. The automation measurement function can fulfill the mass measurement requirements of semiconductor industry with high efficiency and accuracy.

Three-Input Majority Logic Gate and Multiple Input Logic Circuit Based on DNA Strand Displacement

In biomolecular programming, the properties of biomolecules such as proteins and nucleic acids are harnessed for computational purposes. The field has gained considerable attention due to the possibility of exploiting the massive parallelism that is inherent in natural systems to solve computational problems. DNA has already been used to build complex molecular circuits, where the basic building blocks are logic gates that produce single outputs from one or more logical inputs. We designed and experimentally realized a three-input majority gate based on DNA strand displacement. One of the key features of a three-input majority gate is that the three inputs have equal priority, and the output will be true if any of the two inputs are true. Our design consists of a central, circular DNA strand with three unique domains between which are identical joint sequences. Before inputs are introduced to the system, each domain and half of each joint is protected by one complementary ssDNA that displays a toehold for subsequent displacement by the corresponding input. With this design the relationship between any two domains is analogous to the relationship between inputs in a majority gate. Displacing two or more of the protection strands will expose at least one complete joint and return a true output; displacing none or only one of the protection strands will not expose a complete joint and will return a false output. Further, we designed and realized a complex five-input logic gate based on the majority gate described here. By controlling two of the five inputs the complex gate can realize every combination of OR and AND gates of the other three inputs.

Monotonicity-preserving bootstrapped Kriging metamodels for expensive simulations

Kriging metamodels (also called Gaussian process or spatial correlation models) approximate the Input/Output functions implied by the underlying simulation models. Such metamodels serve sensitivity analysis, especially for computationally expensive simulations. In practice, simulation analysts often know that this Input/Output function is monotonic. To obtain a Kriging metamodel that preserves this characteristic, this article uses distribution-free bootstrapping assuming each input combination is simulated several times to obtain more reliable averaged outputs. Nevertheless, these averages still show sampling variation, so the Kriging metamodel does not need to be an exact interpolator; bootstrapping gives a noninterpolating Kriging metamodel. Bootstrapping may use standard Kriging software. The method is illustrated through the popular M/M/1 model with either the mean or the 90% quantile as output; these outputs are monotonic functions of the traffic rate. The empirical results demonstrate that monotonicity-preserving bootstrapped Kriging gives higher probability of covering the true outputs, without lengthening the confidence interval.

Reliability Analysis of the Groundwater Conceptual Model

ABSTRACT The hydrologic model is the foundation of water resource management and planning. Conceptual model is the essential component of groundwater model. Due to limited understanding of natural hydrogeological conditions, the conceptual model is always constructed incompletely. Therefore, the uncertainty in the model's output is evitable when natural groundwater field is simulated by a single groundwater model. A synthetic groundwater model is built and regarded as the true model, and three alternative conceptual models are constructed by considering incomplete hydrogeological conditions. The outputs (groundwater budget terms from boundary conditions) of these groundwater models are analyzed statistically. The results show that when the conceptual model is closer to the true hydrogeological conditions, the distributions of outputs of the groundwater model are more concentrated on the true outputs. Therefore, the more reliable the structure of the conceptual model is, the more reliable the output of the groundwater model is. Moreover, the uncertainty caused by the conceptual model cannot be compensated by parameter uncertainty.

Quantum AC Voltage Standards

Standards based on the Josephson effect are being increasingly utilized for generation of quantum-accurate ac waveforms. These standards fall into two broad categories: those whose output is generated as a stepwise-approximated sine wave via programmable Josephson arrays and those with a true sinusoidal output generated by pulses supplied to Josephson junctions. This paper presents a survey of the present state of quantum ac sources and discusses future implementation of these systems for improved ac metrology. We focus mainly on developments at the National Institute of Standards and Technology but also summarize relevant work elsewhere.

Robust fault detection and isolation in stochastic systems

This article outlines the formulation of a robust fault detection and isolation (FDI) scheme that can precisely detect and isolate simultaneous actuator and sensor faults for uncertain linear stochastic systems. The given robust fault detection scheme based on the discontinuous robust observer approach would be able to distinguish between model uncertainties and actuator failures and therefore eliminate the problem of false alarms. Since the proposed approach involves estimating sensor faults, it can also be used for sensor fault identification and the reconstruction of true outputs from faulty sensor outputs. Simulation results presented here validate the effectiveness of the proposed robust FDI system.

First synoptic analysis of volcanic degassing in Papua New Guinea

We report the first satellite‐based survey of volcanic sulphur dioxide (SO2) degassing in Papua New Guinea, using Ozone Monitoring Instrument (OMI) data. OMI is sensitive to low‐level passive degassing. These observations are useful for volcano monitoring, hazard assessment (particularly aviation hazard) and assessment of arc geochemical budgets and are of immense value in remote regions with little ground‐based instrumentation, such as Papua New Guinea. We identify Bagana, Manam, Rabaul, Ulawun and Langila as the active sources of volcanic SO2 in Papua New Guinea, with Bagana being the largest source. We present an OMI SO2 time series for 2005–2008 and a total detected regional output of ∼1.8 × 109 kg SO2. About 40% of emissions were released by major eruption events at Manam (January 2005), Bagana (June 2006) and Rabaul (October 2006). Over the past century however, we estimate that major explosive eruptions contribute <5% of the arc‐scale SO2emission budget. Ground‐based DOAS measurements of SO2 degassing at five of Papua New Guinea's volcanoes are compared with our OMI observations. The total OMI SO2 output is only ∼20% of the total extrapolated from DOAS, a discrepancy which we demonstrate is consistent with other volcanic arcs. Therefore, the true total regional SO2 output may be considerably higher than that detected by OMI. Uncertainties in the OMI SO2data include the effects of in‐plume chemical processing and dilution of SO2 prior to the satellite overpass, OMI's reduced sensitivity to low levels of SO2 in the planetary boundary layer and interference by meteorological clouds.

An Efficient Predistorter Design for Compensating Nonlinear Memory High Power Amplifiers

This contribution applies digital predistorter to compensate distortions caused by memory high power amplifiers (HPAs) which exhibit true output saturation characteristics. Particle swarm optimization is first implemented to identify the Wiener HPA's parameters. The estimated Wiener HPA model is then directly used to design the predistorter. The proposed digital predistorter solution is attractive owing to its low on-line computational complexity, small memory units required and simple VLSI hardware structure implementation. Moreover, the designed predistorter is capable of successfully compensating serious nonlinear distortions and memory effects caused by the memory HPA operating in the output saturation region. Simulation results obtained are presented to demonstrate the effectiveness of this novel digital predistorter design.

First-order universality for real programs

J. Raymundo Marcial---Romero and M. H. Escardo described a functional programming language with an abstract data type Real for the real numbers and a non-deterministic operator . We show that this language is universal at first order, as conjectured by these authors: all computable, first-order total functions on the real numbers are definable. To be precise, we show that each computable function $f\colon\mathbb{R}\to\mathbb{R}$ we consider is the extension of the denotation of some program , in a model based on powerdomains, described in previous work. Whereas this semantics is only an approximate one, in the sense that programs may have a denotation strictly below their true outputs, our result shows that, to compute a given function, it is in fact always possible to find a program with a faithful denotation. We briefly indicate how our proof extends to show that functions taken from a large class of computable, first-order partial functions in several arguments are definable.

Data-Based Virtual Unmodeled Dynamics Driven Multivariable Nonlinear Adaptive Switching Control

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

DA1 Artificial Neural Network Meta-Models in Cost-Effectiveness analysis of intensive Blood-Glucose Control: A Case Study Applied to the UK prospective Diabetes Study (UKPDS) Individual Patient outcome Simulation Model

Within cost effectiveness analysis, joint uncertainty in costs and effects is commonly dealt with using probabilistic sensitivity analysis (PSA). Although economic models using patient level data can simulate more complex disease processes than cohort-based models, the computational time required to eliminate 1st-order uncertainty often makes extensive PSA impossible. To overcome this, a non-parametric artificial neural network (ANN) simulation meta-modelling method is presented using a case study that evaluates the cost-effectiveness of intensive blood-glucose monitoring in patients with type 2 diabetes. A complex individual patient simulation model (UKPDS Outcome Model version 1.3) was used with quality adjusted life years (QALY) and cost of complications as model outputs. To reduce 1st-order uncertainty, 1000 patients were simulated for each input combination selected. ANN simulation meta-models using a sample of 200 individual runs were developed and cross-validated to approximate the original simulation as these do not require any specific input-output functional relationship and can handle any number of input parameters. Performance was compared with a Gaussian Process (GP) meta-model, and a valid and better predictive meta-model was then used for PSA. From ANN meta-models, the mean absolute percentage error (defined as positive difference between the predicted and true output divided by the range in true output) was 3.8 % for costs and 1.4% for QALYs compared with 5.1% and 2.1% in GP meta-models. The distribution of errors was approximately symmetrical around zero meaning that mean costs and QALYs for an intervention are unlikely to be affected by the small inaccuracies associated with ANN approximations. ANN produces better predictive capability than GP meta-models in estimating costs and QALYs from the UKPDS outcome model. A PSA carried out using the ANN meta-model demonstrated the potential for ANN in analysing complex health economic models.

Identification of Hammerstein Models with Known Nonlinearity Structure Using Particle Swarm Optimization

This paper investigates the use of particle swarm optimization (PSO) in the identification of Hammerstein models with known nonlinearity structure. The parameters of the Hammerstein model are estimated using PSO from the input–output data by minimizing the error between the true model output and the identified model output. Using PSO, Hammerstein models with known nonlinearity structure and unknown parameters can be identified. Moreover, systems with non-minimum phase characteristics can be identified. Extensive simulations have been used to study the convergence properties of the proposed scheme. Simulation examples are included to demonstrate the effectiveness and robustness of the proposed identification scheme.

Urinary Sodium Excretion and Cardiovascular Disease Mortality

To the Editor: Dr Stolarz-Skzypek and colleagues reported that lower urinary sodium excretion was associated with higher cardiovascular disease (CVD) mortality. These findings contradict a large body of evidence that established elevated sodium consumption as a risk factor for CVD and shed doubts on the worldwide efforts to implement population-based sodium reduction strategies. We have a number of concerns that suggest their findings should be interpreted cautiously. First, the volume of urine collected during 24 hours was significantly higher among people with high sodium excretion compared with those with low sodium excretion. It is not clear whether these differences reflect true urinary volume output or problematic completeness of urine collections. The authors assessed the accuracy of urine collection based on 24-hour urinary volume and creatinine excretion. However, these parameters are not reliable markers for completeness; instead, the proposed standard criterion in nutritional epidemiology studies is the use of paraaminobenzoic acid. Furthermore, the lowest levels of 24-hour urinary volume and sodium excretion were found in people with low educational attainment, which is in contradiction with observations from earlier studies that found sodium excretion to be higher in people with low educational level. Lower education level is also associated with unfavorable CVD risk profile and higher risk of developing CVD. Thus, although the authors adjusted for educational attainment, the findings may have been caused by misclassifying people with low socioeconomic status but higher CVD risk as having low 24-hour sodium excretion. Second, although the authors used the same study protocol, the baseline examinations in the cohorts that were combined for analysis were conducted in different time periods (a gap of roughly 10 years between recruitment) and in geographically and culturally different regions of Europe. In addition, nearly all cardiovascular deaths (81/84) occurred in FLEMENGHO (Flemish Study on Environment, Genes, and Health Outcomes; eTable 6 in the article), and mean sodium excretion was substantially lower in this cohort compared with the other study. Simple pooling of the FLEMENGHO cohort with the cohort in EPOGH (European Project on Genes in Hypertension) raises concern that ecological or temporal differences between cohorts may account for the observed association of sodium excretion with CVD mortality. It would be informative to see stratified results based on study-specific tertiles of sodium excretion. Krasimira Aleksandrova, MPH, PhD Tobias Pischon, MD, MPH Cornelia Weikert, MD, MPH

Urinary Sodium Excretion and Cardiovascular Disease Mortality

To the Editor: Dr Stolarz-Skzypek and colleagues reported that lower urinary sodium excretion was associated with higher cardiovascular disease (CVD) mortality. These findings contradict a large body of evidence that established elevated sodium consumption as a risk factor for CVD and shed doubts on the worldwide efforts to implement population-based sodium reduction strategies. We have a number of concerns that suggest their findings should be interpreted cautiously. First, the volume of urine collected during 24 hours was significantly higher among people with high sodium excretion compared with those with low sodium excretion. It is not clear whether these differences reflect true urinary volume output or problematic completeness of urine collections. The authors assessed the accuracy of urine collection based on 24-hour urinary volume and creatinine excretion. However, these parameters are not reliable markers for completeness; instead, the proposed standard criterion in nutritional epidemiology studies is the use of paraaminobenzoic acid. Furthermore, the lowest levels of 24-hour urinary volume and sodium excretion were found in people with low educational attainment, which is in contradiction with observations from earlier studies that found sodium excretion to be higher in people with low educational level. Lower education level is also associated with unfavorable CVD risk profile and higher risk of developing CVD. Thus, although the authors adjusted for educational attainment, the findings may have been caused by misclassifying people with low socioeconomic status but higher CVD risk as having low 24-hour sodium excretion. Second, although the authors used the same study protocol, the baseline examinations in the cohorts that were combined for analysis were conducted in different time periods (a gap of roughly 10 years between recruitment) and in geographically and culturally different regions of Europe. In addition, nearly all cardiovascular deaths (81/84) occurred in FLEMENGHO (Flemish Study on Environment, Genes, and Health Outcomes; eTable 6 in the article), and mean sodium excretion was substantially lower in this cohort compared with the other study. Simple pooling of the FLEMENGHO cohort with the cohort in EPOGH (European Project on Genes in Hypertension) raises concern that ecological or temporal differences between cohorts may account for the observed association of sodium excretion with CVD mortality. It would be informative to see stratified results based on study-specific tertiles of sodium excretion. Krasimira Aleksandrova, MPH, PhD Tobias Pischon, MD, MPH Cornelia Weikert, MD, MPH

The Income- and Expenditure-Side Estimates of U.S. Output Growth—: An Update to 2011Q2

In light of recent large revisions to the official measures of U.S. output, this update reviews the evidence in my 2010 Brookings Paper showing that the income-side estimate of output (currently called gross domestic income, or GDI) likely captures business cycle fluctuations in true output better than its better-known expenditure-side counterpart (called gross domestic product, or GDP). Most notably, over the 2007-09 downturn, the revisions moved the expenditure-side estimates closer to the income-side estimates, which showed that the downturn was considerably worse than reported initially by the expenditure-side estimates. The tendency for the expenditure-side estimates to be revised toward the income-side estimates is clearer now, as is a tendency for the smoothed income-side estimates to be revised away from the smoothed expenditure-side estimates.

High power diode-pumped 27-μm Er^3+:Y_2O_3 laser with nearly quantum defect-limited efficiency

We report diode-pumped Er3+:Y2O3 ceramic laser with ~14 W of true CW output at ~2.7 μm. This presents nearly ten-fold power increase with respect to previous best result with this laser material. We also believe this to be the highest power ever reported from Er3+ -doped bulk crystalline laser operating in a ~3-μm wavelength range. Power-scaled performance of 974-nm pumped Er3+:Y2O3 laser was achieved with the slope efficiency of ~26%.

Legal systems and auditor independence

This paper examines whether an appropriate legal system, which is a combination of a legal regime and a damage apportionment rule, effectively enhances auditor independence. Economic and psychological hypotheses derived from a one-period game model in which the auditor may commit either a technical audit failure (resulting from the auditor’s inability to detect true output given a lack of audit effort) or an independence audit failure (resulting from the auditor’s intentional misreporting on false output) are tested. Three major findings are documented. First, auditor independence affects firm investment, which in turn affects audit effort. Under this strategic dependence, no single legal system can provoke audit effort, improve auditor independence, and encourage firm investment simultaneously. To enhance auditor independence and motivate investment, a legal system consisting of both a strict regime and a proportionate rule is preferred. Second, the strict regime induces more auditor independence than the negligence regime, while the proportionate rule induces higher audit effort than the joint-and-several rule. Finally, auditors’ moral reasoning and penalty for misreporting are both positively associated with their independence. In addition, the effect of moral reasoning on auditor independence diminishes as the level of penalty increases. These two results hold only when the legal systems that auditors face are considered.