Identification of Linear Discrete Time Systems Using the Instrumental Variable Method.
Instrumental variable method to identify dynamic systems described by linear difference equations
Polak, E.↗
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Hedonic Price Estimates of Lake Water Quality: Valued Attribute, Instrumental Variables, and Ecological-Economic Benefits
We estimate the effect of lake water quality on residential housing prices using cross-sectional data on water clarity from 113 lakes across the United States. An instrumental-variables approach is developed to address potential endogeneity bias in the water-clarity variable. Three lake-based physical variables serve as instrumental variables: total nitrogen concentration in lake water, total phosphorus concentration in lake water, and water temperature near the lake surface. The econometric results include three methodological findings: the instruments are valid and strong; the ordinary-least-squares estimate of the coefficient on the water-clarity variable is unbiased; and water clarity is the attribute of water quality valued in the housing market. The estimated water-clarity effect shows that a one-tenth of a meter change in water clarity leads to a one-percent change in housing price, or an elasticity of 0.20 at mean clarity. Coupling this effect with estimates of an ecological production function, a lake-specific benefit index is developed that shows the effect on housing values of bringing lakes into compliance with the US Environmental Protection Agency's regional recommendations for phosphorous concentration in lake waters.
54 ENVIRONMENTAL SCIENCES↗
Instrumental variables algorithm for modal parameter identification in flutter testing
The paper is concerned with the task of estimating modal parameters from system response measurement in aircraft flutter testing. A frequency-domain derivation of an instrumental-variables algorithm is presented for a linear time-invariant dynamic system of order n. Basically, this algorithm fits a set of poles and zeros to the measured transfer function. An illustrative example is provided regarding the application of the algorithm to aeroelasticity testing. It is shown that the algorithm can be implemented for on-line data reduction with a microcomputer-based analysis system. By using instrumental variables the sensitivity of the modal parameter estimates to noise in the system-response measurements is reduced greatly. The algorithm is expected to be a powerful and valuable tool for on-line estimation of modal parameters in flutter testing and should be useful in control system and structural dynamics tests.
Johnson, W.↗
Dynamic modal estimation using instrumental variables
A method to determine the modes of dynamical systems is described. The inputs and outputs of a system are Fourier transformed and averaged to reduce the error level. An instrumental variable method that estimates modal parameters from multiple correlations between responses of single input, multiple output systems is applied to estimate aircraft, spacecraft, and off-shore platform modal parameters.
Salzwedel, H.↗
Product ion distributions using H 3 O + proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS): mechanisms, transmission effects, and instrument-to-instrument variability
Abstract. Proton-transfer-reaction mass spectrometry (PTR-MS) using hydronium ion (H3O+) ionization is widely used for the measurement of volatile organic compounds (VOCs) both indoors and outdoors. H3O+ ionization, as well as the associated chemistry in an ion–molecule reactor, is known to generate product ion distributions (PIDs) that include other product ions besides the proton-transfer product. We present a method, using gas-chromatography pre-separation, for quantifying PIDs from PTR-MS measurements of nearly 100 VOCs of different functional types including alcohols, ketones, aldehydes, acids, aromatics, organohalides, and alkenes. We characterize instrument configuration effects on PIDs and find that reactor reduced electric field strength (E/N), ion optic voltage gradients, and quadrupole settings have the strongest impact on measured PIDs. Through an interlaboratory comparison of PIDs measured from calibration cylinders, we characterized the variability of PID production from the same model of PTR-MS across seven participating laboratories. Product ion variability was generally smaller (e.g., < 20 %) for ions with larger contributions to the PIDs (e.g., > 0.30) but less predictable for product ions formed through O2+ and NO+ reactions. We present a publicly available library of H3O+ PTR-MS PIDs that will be updated periodically with user-provided data for the continued investigation into instrument-to-instrument variability of PIDs.
Link, Michael F. (ORCID:0000000218412455)↗
The radiation budget of stratocumulus clouds measured by tethered balloon instrumentation: Variability of flux measurements
Measurements of longwave and shortwave radiation were made using an instrument package on the NASA tethered balloon during the FIRE Marine Stratocumulus experiment. Radiation data from two pairs of pyranometers were used to obtain vertical profiles of the near-infrared and total solar fluxes through the boundary layer, while a pair of pyrgeometers supplied measurements of the longwave fluxes in the cloud layer. The radiation observations were analyzed to determine heating rates and to measure the radiative energy budget inside the stratocumulus clouds during several tethered balloon flights. The radiation fields in the cloud layer were also simulated by a two-stream radiative transfer model, which used cloud optical properties derived from microphysical measurements and Mie scattering theory.
Duda, David P.↗
Product ion distributions using H3O+ proton-transfer-reaction time-of-flight mass spectrometry (PTR-ToF-MS): mechanisms, transmission effects, and instrument-to-instrument variability
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54 ENVIRONMENTAL SCIENCES↗
The Impact of Intensity Modulated Radiation Therapy on Hospitalization Outcomes in the SEER-Medicare Population With Anal Squamous Cell Carcinoma
Purpose: We examined the impact of intensity modulated radiation therapy (IMRT) on hospitalization rates in the Surveillance, Epidemiology, and End Results (SEER)–Medicare population with anal squamous cell carcinoma (SCC). Methods and Materials: We performed a retrospective cohort study using the SEER-Medicare database. We identified patients with nonmetastatic anal SCC diagnosed between 2001 and 2011 and treated with chemoradiation therapy. We assessed the relation between IMRT and first hospitalization by use of a multivariate competing-risk model, as well as instrumental variable analysis, using provider IMRT affinity as our instrument. Results: Of the 1165 patients included in our study, 458 (39%) received IMRT. IMRT use increased over time and was associated more with regional and provider characteristics than with patient characteristics. The 3- and 6-month cumulative incidences of first hospitalization were 41.9% (95% confidence interval [CI], 37.3%-46.4%) and 47.6% (95% CI, 43.0%-52.2%), respectively, for the IMRT cohort and 46.7% (95% CI, 43.0%-50.4%) and 52.1% (95% CI, 48.4%-55.7%), respectively, for the non-IMRT cohort. IMRT was associated with a decreased hazard of first hospitalization compared with 3-dimensional radiation techniques (hazard ratio, 0.70; 95% CI, 0.58-0.84; P=.0002). Instrumental variable analysis suggested an even greater reduction in hospitalizations with IMRT after controlling for unmeasured confounders. There was a trend toward improved overall survival with IMRT, with an adjusted hazard ratio of 0.77 (95% CI, 0.59-1.00; P=.05). Conclusions: The use of IMRT is associated with reduced hospitalizations in elderly patients with anal SCC. Further work is warranted to understand the long-term health and cost impact of IMRT, particularly for patient subgroups most at risk of toxicity and hospitalization.
62 RADIOLOGY AND NUCLEAR MEDICINE↗
Century-Scale Solar Variability and the Instrumental Temperature Record
Variability of the total solar irradiance (TSI) is a ptential contributor to changes in global mean temperature on time scales longer than the relaxation time of the upper ocean (i.e. a few years).
global instrumental temperature record solar featu↗
Century-Scale Solar Variability and the Instrumental Temperature Record
Variability of the total solar irradiance is a potential contributor to changes in global mean temperature on time scales longer than the relaxation time of the upper ocean.
solar effects solar irradiance global mean tempera↗
New frequency domain methods for system identification
This paper presents two new techniques for frequency domain identification of linear system parameters. The first technique uses the instrumental variables approach. The frequency domain formulation is shown to give a considerable insight into the selection of efficient and convergent instrumental variables. The new maximum likelihood formulation affords simplier numerical solution and provides a way to select parameter starting values in the gradient based optimization method.
Gupta, N. K.↗
Circulating levels of micronutrients and risk of osteomyelitis: a Mendelian randomization study
Background Few observational studies have investigated the effect of micronutrients on osteomyelitis, and these findings are limited by confounding and conflicting results. Therefore, we conducted Mendelian randomization (MR) analyses to evaluate the association between blood levels of eight micronutrients (copper, selenium, zinc, vitamin B12, vitamin C, and vitamin D, vitamin B6, vitamin E) and the risk of osteomyelitis. Methods We performed the two-sample and multivariable Mendelian randomization (MVMR) to investigate causation, where instrument variables for the predictor (micronutrients) were derived from the summary data of micronutrients from independent cohorts of European ancestry. The outcome instrumental variables were used from the summary data of European-ancestry individuals ( n = 486,484). The threshold of statistical significance was set at p < 0.00625. Results We found a significant causal association that elevated zinc heightens the risk of developing osteomyelitis in European ancestry individuals OR = 1.23 [95% confidence interval (CI) [1.07, 1.43]; p = 4.26E-03]. Similarly, vitamin B6 showed a similar significant causal effect on osteomyelitis as a risk factor OR = 2.78 (95% CI [1.34, 5.76]; p = 6.04E-03; in the secondary analysis). Post-hoc analysis suggested this result (vitamin B6). However, the multivariable Mendelian randomization (MVMR) provides evidence against the causal association between zinc and osteomyelitis OR = 0.98(95% CI [−0.11, 0.07]; p = 7.20E-1). After searching in PhenoScanner, no SNP with confounding factors was found in the analysis of vitamin B6. There was no evidence of a reverse causal impact of osteomyelitis on zinc and vitamin B6. Conclusion This study supported a strong causal association between vitamin B6 and osteomyelitis while reporting a dubious causal association between zinc and osteomyelitis.
Zhang, Xu↗
Development of a hybrid neural network and transfer learning model for optimized ICP-MS/MS operation
Correct function and calibration of instrumentation is a crucial assumption for any scientific experiment. One such instrument, tandem inductively coupled plasma mass spectrometer (ICP-MS/MS), has in-depth calibration settings that range across 30+ different parameters, making it difficult to determine optimal conditions without expertise and some degree of trial and error. Often, these settings are hand-tuned, a time-intensive process prone to local maxima and human error. While some automation is available, the automation also may favor local optimizations over a global optimum. In addition to these difficulties, day to day instrument variability can further complicate the calibration process. We propose a solution to this problem as a machine learning (ML) algorithm that learns how each parameter helps determine the calibration sensitivity across several elements, and re-weights parameters over time as instrument variability changes (e.g., a global neural network (NN) with a time-dependent transfer learning (TL) component). This model would be able to generate a surface of predicted calibration sensitivities and their respective parameters, and a simple multivariate algorithm would be able to pull out the optimum results with the settings associated with them. Here-in, we describe our initial findings in working towards this goal, including data extraction from historical files, exploratory data analysis, and some initial model building to better describe the data and the feasibility of our goal.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Identification of structural systems using naturally induced vibration data in the presence of measurement noise
The method of instrument variables for the identification of the parameters of structural systems excited by white noise in the presence of white measurement noise is applied. The requisite equations and the resulting consistent estimator are derived and simulation results for least squares and instrumental variables methods, for light and heavy damping, are compared. The present method gave better results than least squares when measurement noise was presented, and verified the fact that least squares yields an inconsistent estimator unless the measurement noise is zero.
Huan, S.-L.↗
Variable-Speed Instrumented Centrifuges
Report describes conceptual pair of centrifuges, speed of which varied to produce range of artificial gravities in zero-gravity environment. Image and data recording and controlled temperature and gravity provided for 12 experiments. Microprocessor-controlled centrifuges include video cameras to record stop-motion images of experiments. Potential applications include studies of effect of gravity on growth and on production of hormones in corn seedlings, experiments with magnetic flotation to separate cells, and electrophoresis to separate large fragments of deoxyribonucleic acid.
Chapman, David K.↗
Combining Machine Learning and Comparative Effectiveness Methodology to Study Primary Care Pharmacotherapy Pathways for Veterans With Depression
Our objective is to demonstrate an innovative method combining machine learning with comparative effectiveness research techniques and to investigate a hitherto unstudied question about the effectiveness of common prescribing patterns. For Operation Enduring Freedom/Operation Iraqi Freedom veterans with major depressive disorder, we generate pharmacotherapy pathways (of antidepressants) using process mining and machine learning. We select the medication episodes that were started at subtherapeutic doses by the first assigned primary care physician and observe the paths that those medication episodes follow. Using 2-stage least squares, we test the effectiveness of starting at a low dose and staying low for longer versus ramping up fast while balancing observable and unobservable characteristics of patients and providers through instrumental variables. We leverage predetermined provider practice patterns as instruments. We collected outpatient pharmacy data for selective serotonin reuptake inhibitors and selective norepinephrine reuptake inhibitors, patient and provider characteristics (as control variables), and the instruments for our cohort. All data were extracted for the period between 2006 and 2020. There is a statistically significant positive effect (0.68, 95% CI 0.11–1.25) of “ramping up fast” on engagement in care. When we examine the effect of “ramping up slow”, we see an insignificant negative impact on engagement in care (−0.82, 95% CI −1.89 to 0.25). As expected, the probability of drop-out also seems to have a negative effect on engagement in care (−0.39, 95% CI −0.94 to 0.17). We further validate these results by testing with medication possession ratios calculated periodically as an alternative engagement in care metric. Our findings contradict the “Start low, go slow” adage, indicating that ramping up the dose of an antidepressant faster has a significantly positive effect on engagement in care for our population.
60 APPLIED LIFE SCIENCES↗
Spectrophotometers for plutonium monitoring in HB-line
This report describes the equipment, control software, calibrations for total plutonium and plutonium oxidation state, and qualification studies for the instrument. It also provides a detailed description of the uncertainty analysis, which includes source terms associated with plutonium calibration standards, instrument drift, and inter-instrument variability. Also included are work instructions for instrument, flow cell, and optical fiber setup, work instructions for routine maintenance, and drawings and schematic diagrams.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Estimating the price (in)elasticity of off-grid electricity demand
Community-scale power infrastructure may be the only electrification option for tens of millions households that remain out of reach from centralized power grids. The responsiveness of household electricity demand to price is a crucial design input for off-grid systems. While the price elasticity of electricity demand of grid-connected consumers has been abundantly studied, few studies focus on off-grid communities where substantial econometric challenges arise, including the absence of metered consumption data and electricity prices that are simultaneously determined by cost and demand considerations. This study attempts to address these challenges for the case of off-grid micro hydropower consumers. It makes two core contributions: First, we propose the surface area of the contributing hydrologic catchment as a new instrumental variable to estimate elasticity using a cross sectional dataset of existing micro hydropower infrastructure. Second, we provide a first price-elasticity estimate (-0.15) for off-grid electricity demand in Nepal. We surmise that the small (in absolute value) elasticity value found in this study arises from the low levels of consumption observed off-the-grid. We use a Monte Carlo analysis to show that failing to account for this disparity can lead to substantial financial losses caused by suboptimal power infrastructure design.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗