Total Error Research Articles

Comparison of Safety and Efficacy of Four-Point Scleral Intraocular Lens Fixation and the Yamane Technique.

The purpose of our study was to compare the safety and efficacy of two scleral fixation intraocular lens (IOL) methods of four-point scleral fixation (Akreos AO60) and the Yamane technique (AcrySof MA60AC). This prospective, randomized study was conducted at the Military Institute of Medicine-National Research Institute in Warsaw between 2021 and 2023. We compared both groups for cause of aphakia, ocular history, refractive status, and complication. Our study included 50 eyes from 47 patients. Four-point fixation was performed in 25 eyes (group1), and the Yamane technique was used in 25 eyes (group2). Surgical time was 24.1min ± 8.9 in group1 and 25.1min ± 9.9 in group2 (p > 0.05). The postoperative BCVA (best-corrected visual acuity) for group1 and group2 at 1year's observation was 0.10 ± 0.15 and 0.09 ± 0.17 logMAR, respectively (p > 0.05). Postoperative total refractive error (RE) was - 0.06 ± 0.71 diopters (D) for four-point scleral fixation and 0.83 ± 0.70D for Yamane technique (p < 0.05). Endothelial cell density (ECD) loss was 0.9% in group1 and 3.5% in group2 (p > 0.05). Bleeding into the anterior chamber and vitreous body was more frequent in the group of patients operated on with the use of the Yamane technique (10 cases, 20%, p = 0.01). IOL displacement was found in one case (2%) in group2. Both analyzed techniques are well tolerated and ensure good refractive results (extremely predictable in four-point scleral fixation) and have a similar safety profile. Four-point scleral fixation of IOL would appear to be safe, effective and beneficial for young, active patients, especially after trauma or recurrent subluxation. ClinicalTrials.gov identifier NCT06389643.

Global System Errors to Simultaneously Improve the Identification of Subsystems with Mixed Data Gaussian Process Regression

Abstract This paper explores the use of Gaussian Process Regression (GPR) for system iden-&amp;#xD;tification in control engineering. It introduces two novel approaches that utilize the&amp;#xD;data from a measured global system error. The paper demonstrates these approaches&amp;#xD;by identifying a simulated system with three subsystems, a one degree of freedom&amp;#xD;mass with two antagonist muscles. The first approach uses this whole-system error&amp;#xD;data alone, achieving accuracy on the same order of magnitude as subsystem-specific&amp;#xD;data (9.28 ± 0.87 N vs. 6.96 ± 0.32 N of total model errors). This is significant, as&amp;#xD;it shows that the same data set can be used to identify unique subsystems, as op-&amp;#xD;posed to requiring a set of data descriptive of only a single subsystem. The second&amp;#xD;approach demonstrated in this paper mixes traditional subsystem-specific data with&amp;#xD;the whole system error data, achieving up to 98.71% model improvement.

Verification of the MLP network-based current sensor fault classifier for vector-controlled AC motor drives

In modern drive systems, the aim is to ensure their operational safety. Damage can occur not only to the components of the motor itself but also to the power electronic devices included in the frequency converter and the sensors in the measurement circuit. Critical damage to the electric drive that makes its further exploitation impossible can be prevented by using fault-tolerant control (FTC) algorithms. These algorithms are very often combined with diagnostic methods that assess the degree and type of damage. In this paper, a fault classification algorithm using an artificial neural network (ANN) is analysed for stator phase current sensors in AC motor drives. The authors confirm that the investigated classification algorithm works equally well on two different AC motors without the need for significant modifications, such as retraining the neural network when transferring the algorithm to another object. The method uses a stator current estimator to replace faulty sensor measurements in a vector control structure. The measured and estimated currents are then subjected to a classification process using a multilayer perceptron (MLP), which has the advantage of a small structure size compared to deep learning structures. The uniqueness of the method lies in the use of data in the training set that are not dependent on the parameters of a specific motor. Four types of current sensor faults were studied, namely total signal loss, gain error, offset, and signal saturation. Simulations were performed in a MATLAB/SIMULINK environment for drive systems with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). The results show that the algorithm correctly evaluates the type of damage in more than 99.6% of cases regardless of the type of motor. Therefore, the results presented here may help to develop universal diagnostic methods that will work on a wide variety of motors.

Effect of in-hospital training on medical errors made by teams during neonatal resuscitation.

Neonatal resuscitation is one of the most critical and risky events that requires a high level of individual skill and team performance. To evaluate the effect of training of resuscitation teams on the frequency and type of medical errors (ME) that result from neonatal resuscitation. A prospective observational study was performed using a checklist to detect ME related to neonatal resuscitation. The rate of ME was 24.82%. There was a significant reduction in the percentage of errors from 17.28% in pre- pre-training phase to 7.54% in post post-training phase. Near miss MEs (98.77%) were significantly higher than adverse events. The active errors were significantly higher than latent errors, P <0.001, and decreased from 39.19% during pre-training to 19.64% in the post-training phase. The commission ME s were significantly higher than the omission, P <0.001. The latent errors percentage was 41.17% of the total errors and were not significantly reduced after training. Training sessions reduced ME that occurred during resuscitation, however careful distinguishing and recognizing the type of MEs is important to plan for further reduction of errors. Special attention to latent errors is imperative as it needs a specific approach rather than just training.

Research on water equivalence and angle dependence of medical POF scintillation dosimeters

Abstract In radiation dosimetry and medical dosimetry, optical fiber dosimeters have great advantages in application due to their small size and long transmission distance. However, in some low-dose radiation environments, the water inequivalence error and angle error of optical fiber dosimeters can have significant impacts that cannot be ignored. In this paper, the effects of ray energy, ray solid angle and optical fiber probe size on water inequivalence error and angle error are analyzed by using Monte Carlo simulation. Due to the complexity of the influencing factors of the angle error, which is difficult to be analyzed precisely and quantitatively, a correction method based on BP neural network is proposed. The final validation results demonstrate that the correction scheme can reduce the total angle error of the optical fiber dosimeter to 2.72%.&amp;#xD;

Robust Optical Physical Unclonable Function Based on Total Internal Reflection for Portable Authentication.

Physical unclonable functions (PUFs) utilize uncontrollable manufacturing randomness to yield cryptographic primitives. Currently, the fabrication of the most generally employed optical PUFs mainly depends on fluorescent, Raman, or plasmonic materials, which suffer inherent robustness issues. Herein, we construct an optical PUF with high environmental stability via total internal reflection (TIR-PUF) perturbed by randomly distributed polymer microspheres. The response image is transformed into encoded keys via an iterative binning procedure. The concentration of the polymer solution is optimized to debias the bit nonuniformity and maximize encoding capacity. The constructed TIR-PUF shows significantly high encoding capacity (2370) and markedly low total authentication error probability (1.614 × 10-23). The intra-Hamming distance is as low as 0.068, indicating the excellent readout reliability of TIR-PUF. The environmental stability of TIR-PUF has demonstrated promising results under a range of challenging conditions such as ultrasonic washing, high temperature, ultraviolet irradiation, and severe chemical environments. Moreover, the challenge-response pairs of our TIR-PUFs are demonstrated on an authentication system with low-power dissipation, lightweight components, and wireless imaging capture, rendering the possibility of portable authentication for practical applications.

Error estimation in current noisy quantum computers

One of the main important features of the noisy intermediate-scale quantum (NISQ) era is the correct evaluation and consideration of errors. In this paper, we analyse the main sources of errors in current (IBM) quantum computers and we present a useful tool (TED-qc) designed to facilitate the total error probability expected for any quantum circuit. We propose this total error probability as the best way to estimate a lower bound for the fidelity in the NISQ era, avoiding the necessity of comparing the quantum calculations with any classical one. In order to contrast the robustness of our tool we compute the total error probability that may occur in three different quantum models: 1) the Ising model, 2) the Quantum-Phase Estimation (QPE), and 3) the Grover’s algorithm. For each model, the main quantities of interest are computed and benchmarked against the reference simulator’s results as a function of the error probability for a representative and statistically significant sample size. The analysis is satisfactory in more than the 99%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99\\%$$\\end{document} of the cases. In addition, we study how error mitigation techniques are able to eliminate the noise induced during the measurement. These results have been calculated for the IBM quantum computers, but both the tool and the analysis can be easily extended to any other quantum computer.

Implementation of the EUCAST rapid antimicrobial susceptibility test (RAST) for carbapenemase/ESBL-producing Escherichia coli and Klebsiella pneumoniae isolates, and its effect on mortality.

With the rise in antimicrobial resistance, there is a growing demand for rapid antimicrobial susceptibility testing (RAST). In this study, we applied the EUCAST RAST method to ESBL/carbapenemase-producing Escherichia coli and Klebsiella pneumoniae isolates without using advanced identification systems and analysed the effect of this method on mortality rates Also the clinical impact of this method on patients infected with these bacteria and its effect on mortality rates were investigated. RAST was used for clinical blood cultures containing carbapenemase/ESBL-producing E. coli and K. pneumoniae without advanced identification systems (e.g. MALDI TOF), with preliminary identification by simple diagnostic tests (predicted RAST, or p-RAST), and its categorical agreement was investigated. The impact of the method on mortality was analysed by comparing the clinical data of patients whose blood cultures were subject to p-RAST (p-RAST group, n = 49) and those who were not subject to p-RAST (non-RAST group, n = 145). p-RAST results were analysed based on 539 antibiotic-bacteria combinations. Total error rates at 4, 6 and 8 h of incubation were 2.9%, 3.9% and 3.8%, respectively. In the p-RAST group, patients who did not receive appropriate antibiotics (29/45, 59.1%) were switched to appropriate treatment within 8 h at the latest. In contrast, in the non-RAST group, treatment of patients who received inappropriate antibiotics (79/145, 54.5%) could be changed after at least 24 h. Mortality rates were lower in the p-RAST group than in the non-RAST group (28.6% versus 51.7%, P = 0.005). p-RAST can be used safely in hospital laboratories with high rates of antimicrobial resistance and can reduce mortality rates by shortening the transition time to appropriate treatment.

Heat transfer error analysis of high-temperature wall temperature measurement using thermocouple

Thermocouples are widely employed as the temperature measuring element for an aero-engine. However, due to the unique characteristics of the aero-engine test environment, there exist significant temperature measurement errors of the thermocouple. The steady-state heat transfer error of the thermocouple can be attributed to convection heat transfer error, wire heat transfer error, and radiation heat transfer error. The flow and heat transfer process of wall temperature measurements using armored K-type thermocouples were simulated numerically by Fluent software at the Mach number of 0.1–0.5 and the temperature of 700∼1200 K. The results indicate that convective error accounts for more than 55 % of the total transfer error. To reduce excessive temperature measurement errors, a novel airflow stagnation cover structure around the thermocouple was presented. When the diameter of the stagnation cover is 2 mm, the error of the thermocouple is reduced to less than 1 %.

Using Backpropagation Neural Network for Polyvinylchloride Ceiling Price Modeling

Sales predictions on building material products today have applied an artificial neural network approach. One of the products of building material that need to be predicted for sales is polyvinylchloride (PVC) ceilings. Most companies haven’t implementing prediction technique for the sale of PVC ceilings, so this study aims to predict PVC ceiling sales with the backpropagation neural network (BPNN) method using the R algorithm. Unit gradients are calculated using the average absolute per cent error value (MAPE) to minimize the total square errors of network output. The results showed that the network architecture used was 4 to 6-1 and obtained an accuracy of 88% based on the lowest MAPE value.

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

Establishing hemolysis, icterus, and lipemia interference limits for body fluid chemistry analytes measured on the Roche cobas instrument.

The aims of this study were to (1) establish the maximum allowable interference limits for hemolysis, lipemia, and icterus for chemistry analytes tested in body fluid samples and (2) assess the effectiveness of serial dilution to mitigate spectral interferences. Residual body fluids from clinically ordered testing were mixed (<10% by volume) with stock solutions of interferent (spiked) and compared with a control spiked with an equal volume of 0.9% saline. The analytes were measured on the Roche cobas c501 instrument. Difference and percentage difference were calculated and compared with allowable total error limits. A subset of samples were serially diluted with 0.9% saline. Mean (SD) difference and percentage difference were calculated. The interference thresholds were lower than the package insert for lactate dehydrogenase, cholesterol, triglycerides, and total protein for hemolysis; amylase, cholesterol, and total protein for icterus; and albumin for lipemia. Only cholesterol and triglyceride results returned to baseline upon dilution of icteric samples. Interference thresholds in body fluids were lower than blood for 6 analytes. Diluting interferences that surpass these limits does not produce reliable results that are comparable to the baseline results before spiking in the interferent.

Evaluating error sources to improve precision in the co-registration of underwater 3D models

Change detection is an essential and widely used approach for investigating ecosystem dynamics. Multi-temporal 3D models increasingly underpin photogrammetry-based analyses of change for many ecologically relevant attributes. To detect change, it is necessary to accurately align 3D models collected at different times using a process referred to as co-registration. However, achieving precise co-registration is difficult in underwater habitats due to practical challenges intrinsic to surveying them. These include a lack of accurate georeferencing information, variable light, turbidity and weather conditions, and diving restrictions dictated by the diver's pressure exposure over time. Here we present an efficient co-registration workflow for 3D models that directly addresses these challenges, derived from underwater structure-from-motion methods. To test our approach, we used 3D models from across a wide range of coral reef habitats covering all those that one may encounter in shallow reefs (15 m depth and above). We then identified and empirically estimated four key sources of error: co-registration, 3D processing, image acquisition, and reference and scaling features (RSF) placement, and quantified their relative contributions to the overall error. Our proposed co-registration workflow had a mean precision of 1.37 ± 16.55 mm. Image acquisition and RSF placement errors contributed the most to the total workflow error (37% and 53%, respectively), while the contribution of co-registration and 3D processing errors was minimal (3% and 7%, respectively). As a result of our analysis, we provide ‘good practice’ guidelines to reduce errors associated with photogrammetric workflows and to facilitate efficient and reliable detection of 3D change in complex underwater ecosystems.

Long-term effects of an egg-protein hydrolysate on cognitive performance and brain vascular function: a double-blind randomized controlled trial in adults with elevated subjective cognitive failures.

Short-term intake of the egg-protein hydrolysate Newtricious (NWT)-03 improved executive function, but underlying mechanisms and long-term effects, including other cognitive domains, are unknown. A 36-week randomized controlled trial involving 44 overweight/obese individuals experiencing elevated Subjective Cognitive Failures (SCF; aged 60-75years) assessed the impact of daily consumption of 5.7g of NWT-03 or placebo powders on cognitive performance (psychomotor speed, executive function, memory) and Cerebral Blood Flow (CBF), a marker of brain vascular function. Cognitive performance was evaluated using a neurophysiological test battery (CANTAB) and CBF was measured using magnetic resonance imaging perfusion method Arterial Spin Labeling (ASL). Serum samples were collected to determine brain-derived neurotrophic factor (BDNF) concentrations. Anthropometrics, and energy and nutrient intakes remained stable throughout the trial. NWT-03 was well tolerated, and compliance was excellent (median: 99%; range: 87-103%). No overall intervention effects were observed on cognitive performance or CBF, but post-hoc analyses revealed significant improvements on executive function in women, but not men. Specifically, a reduction of 74ms in reaction latency on the multitasking task (95% CI: -134 to -15; p = 0.02), a reduction of 9 between errors (95%CI: -14 to -3; p < 0.001), and a reduction of 9 total errors (95%CI: -15 to -3; p < 0.001) on the spatial working memory task were found in women. No intervention effects were observed on serum BDNF concentrations (p = 0.31). Long-term consumption of NWT-03 improved multitasking abilities and working memory in women with elevated SCF. Brain vascular function remained unaffected. Sex differences in executive function require additional clarification.

Impact of on-call shifts on working memory and the role of burnout, sleep, and mental well-being in trainee physicians

ABSTRACT Background Optimal cognitive functions, including working memory (WM), are crucial to enable trainee physicians to perform and excel in their clinical practice. Several risk factors, including on-call shifts, poor mental health, burnout, and sleep problems, can impair clinical practice in trainee physicians, potentially through cognitive impairment; however, these associations have not been fully explored. Objective This study investigated the effect of on-call shifts on WM among trainee physicians and its association with burnout, depression, anxiety, affect, and sleep. Materials and methods This cross-sectional study involved 83 trainee physicians (45% male). We measured demographic and training-related factors including on-call shifts and working hours. We also assessed depressive symptoms (PHQ-9), both state and trait anxiety (STAI total score), burnout (OLBI total score), positive and negative affect scores (PANAS), and sleep disturbances (PSQI total score). WM was evaluated using spatial working memory (SWM) strategy scores that reflected performance and total error counts. Results Trainee physicians with more on-calls per month had significantly worse depressive symptoms, burnout scores, and sleep, as well as more negative affect. While controlling for covariates, being on-call more times per month was significantly associated with worse WM. Worse depressive symptoms and burnout scores were also significantly associated with impaired WM. Conclusion Working more on-call shifts is associated with compromised WM. Trainee physicians who experienced more depressive symptoms and burnout had worse WM.

Detecting New Visual Binaries in Gaia DR3 with Gaia and Two Micron All Sky Survey (2MASS) Photometry. II. Speckle Observations of 16 Low-separation Systems

Here we present speckle observations of 16 low-separation (s < 30 au) high-probability candidate binaries from the catalog by Medan et al., where secondaries typically lack astrometric solutions in Gaia. From these speckle observations, we find a second component is always detected within the field of view. To determine if the detection is consistent with a physical companion or a chance alignment with a background source, we utilize a statistic from Tokovinin & Kiyaeva that compares the apparent motion of the systems to the expected orbital motion ( μ′ ). Using simulated binary orbits, we construct likelihood distributions of μ′ assuming various total errors on the measurements. With the hypothesis that the system is a true binary, we show that large measurement errors can result in μ′ values higher than expected for bound systems. Using simulated chance alignments, we also create similar likelihoods to test this alternative hypothesis. By combining likelihoods of both true binaries and chance alignments, we find that 15 of the 16 candidates are physical systems regardless of the level of measurement error. Our findings also accommodate all 16 as physical systems if the average, relative measurement error on the binary separations and position angles is ∼4.3%, which is consistent with our knowledge of the Gaia and Gemini speckle pipelines. Importantly, beyond assessing the likelihood of a true binary versus chance alignment, this quantitative assessment of the true average measurement error will allow more robust error estimates of mass determinations from short separation binaries with Gaia and/or Gemini speckle data.

Multilevel Monte Carlo Methods for Stochastic Convection–Diffusion Eigenvalue Problems

We develop new multilevel Monte Carlo (MLMC) methods to estimate the expectation of the smallest eigenvalue of a stochastic convection–diffusion operator with random coefficients. The MLMC method is based on a sequence of finite element (FE) discretizations of the eigenvalue problem on a hierarchy of increasingly finer meshes. For the discretized, algebraic eigenproblems we use both the Rayleigh quotient (RQ) iteration and implicitly restarted Arnoldi (IRA), providing an analysis of the cost in each case. By studying the variance on each level and adapting classical FE error bounds to the stochastic setting, we are able to bound the total error of our MLMC estimator and provide a complexity analysis. As expected, the complexity bound for our MLMC estimator is superior to plain Monte Carlo. To improve the efficiency of the MLMC further, we exploit the hierarchy of meshes and use coarser approximations as starting values for the eigensolvers on finer ones. To improve the stability of the MLMC method for convection-dominated problems, we employ two additional strategies. First, we consider the streamline upwind Petrov–Galerkin formulation of the discrete eigenvalue problem, which allows us to start the MLMC method on coarser meshes than is possible with standard FEs. Second, we apply a homotopy method to add stability to the eigensolver for each sample. Finally, we present a multilevel quasi-Monte Carlo method that replaces Monte Carlo with a quasi-Monte Carlo (QMC) rule on each level. Due to the faster convergence of QMC, this improves the overall complexity. We provide detailed numerical results comparing our different strategies to demonstrate the practical feasibility of the MLMC method in different use cases. The results support our complexity analysis and further demonstrate the superiority over plain Monte Carlo in all cases.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

End-user verification results of two serum separator tubes for clinical chemistry analytes according to CLSI GP34-A and CLSI GP41-A6

Tube manufacturers use different composition of gels and blood clot activator formulations in serum tube production. Our aim was to investigate the within-tube (repeatability) and between-tube variation, concordance between comparison results of BD and VacuSEL tubes. Blood samples were collected from control subjects (n = 20) and patients (n = 30) in accordance with the CLSI GP41-A6 and CLSI GP34-A guidelines. Twenty-three clinical chemistry parameters were analysed via Roche Cobas C702 Chemistry Analyzer on T0 (0 hour) and T24 (24 hour). Mean differences % were compared with Wilcoxon matched pair test. Clinical significance was evaluated based on desirable bias according to total allowable error (TEa). VacuSEL tubes demonstrated acceptable performance for the results of 20 parameters with regards to desirable bias % limits. Lactate dehydrogenase (LD) [mean difference % (%95 confidence intervals (CI) values of BD and VacuSEL tubes at T0 [6.41% (4.80–8.01%)]; sodium (Na) and total protein (TP) at T24 [-0.27% (-0.46 to −0.07%) and −1.39% (-1.87 to −0.91), respectively] were over the desirable bias limits (LD: 4.3%, Na: 0.23% and TP: 1.36%, respectively) but not exceeding total biological variation CV % [Na: 0.5 (0.0–1.0) % and TP: 2.6 (2.3–2.7) %). %95 confidence intervals (CI) of T0 LD values overlap with within-subject biological variation % (CI) limits (LD: 5.2 (4.9–5.4) %). The differences between two tubes were not medically significant and necessarily conclusive. VacuSEL serum tubes presented comparable performance with BD serum tubes.

Estimating uncertainty of mean water table depth in the contiguous United States using highly parameterized linear inverse method

Because of spatiotemporal observation gaps and prohibitive computational cost, estimating uncertainty of mean water table depth in the continental scale is a challenging problem. Based on the groundwater flow of mean water table, we first analyzed uncertainty of the mean water table depth in the contiguous US using a highly parameterized linear inverse method with unknown boundary condition and source/sink terms. To estimate uncertainty of the mean heads, the total relative errors of temporal heads and mean heads for all observed wells with more than one head instead of the relative error of temporal heads and mean head for each observed well was proposed to obtain t Location-scale and Gaussian distributions of the total relative errors in the synthetic cases. The results indicate that uncertainties of the mean heads by t Location-scale distribution are more accurate than those by Gaussian distribution. Then, we applied the inverse method and t Location-scale distribution of the total relative errors in the contiguous US to estimate uncertainty of the mean water table depth during 1900–2016. The results demonstrate that uncertainty in the K values did not result in large uncertainty of the mean water table depth. Uncertainty of the mean water table depth in the contiguous US was first estimated based on variation of hydraulic conductivity and the observed mean water table, where 100 realizations of the mean water table depth were obtained by t Location-scale distribution. The results demonstrate both the feasibility and high precision of the inverse method.