Error Adjustment Research Articles

EVALUATION OF DOSE DELIVERY FOR TOTAL MARROW IRRADIATION USING IMAGING DATA OBTAINED WITH TOMOTHERAPY DEVICE

Fractionated total marrow irradiation and lymphoid irradiation (TMLI) is a highly conformal method of radiotherapy, requiring a high degree of dose delivery accuracy. This study presents a quantitative assessment of the delivered dose while taking into account the influence of daily positioning using one patient receiving TMLI as an example. Material and methods. Before each treatment session on TomoTherapy preliminary visualization is performed by megavoltage computer tomography (MVCT). The resulting images are used to correct the position of the patient and thereby to minimize the error of dose adjustment. In this study dose was recalculated for each treatment fraction, taking into account the current radiation geometry based on the MVCT images of the patient. The planned and delivered total dose distributions were compared. Results. The difference between the delivered and planned average dose in target comprising bone marrow and lymphoid tissue was less than 0.5%. The volume of the lungs, receiving a dose of 8 Gy did not exceed 39.3% of the total delivered dose, at the same time the coverage of the targets met prescribed requirements. Discussion. Appropriate immobilization, visualization with subsequent correction of the patient's position prior to each fraction allowed for reliable and accurate dose delivery. The evaluation of the delivered dose provides opportunity for an objective analysis of the therapy. Conclusion. The analysis of the delivered dose distribution based on MVCT visualization of the patient's body demonstrated the safety of TMLI method in terms of dose to the organs at risk, as well as the acceptable quality of the target coverage

A monthly spatio-temporal modelling of patterns for children on maintenance grant in Namibia

BACKGROUND: Child support grant (CSG) is one of the social protection strategies which is today widely seen as an intervention contributing to poverty reduction. However, despite substantial expenses, it has been documented that Namibia’s social protection benefits do not reach intended beneficiaries in an efficient manner. AIMS: This study aims to determine factors associated with spatial and temporal variation in maintenance child grant and as well as identify regions with elevated incidence rate ratios of maintenance grant in Namibia. METHODS: We fitted a Bayesian spatio-temporal regression model on maintenance grant data available over 9 years in Namibia. RESULTS: The number of children on grant has almost tripled between March 2007 and October 2015 (it changed from 50596 in 2007 to 132840). Unemployment and orphanhood were significantly associated with the incidence rate ratio of maintenance grant (CI:(1.634, 2.627) and CI: (1.000, 1.004), respectively. The adjustment of measurement error in orphanhood through the Berkson error model has ensured the stability of its effect. CONCLUSIONS: This study has shown the strength of using measurement error models for analysing child grant data. Furthermore, the study has demonstrated that the northern regions of Namibia have the highest child incidence rate ratio of maintenance grant whereas the regions in central and south are at low incidence rate at present. The maps produced in this study can be particularly helpful in allocating efficiently limited resources in poor settings.

Full-Duplex Transceiver Design in the Presence of Phase Noise and Performance Analysis

Phase noise introduced by oscillators shows significant impacts on full-duplex (FD) transceivers, even for the architecture in which the transmitter and receiver share the same oscillator. In this paper, an FD transceiver design is proposed to mitigate the phase noise contained in the multipath self-interference (SI) components. This design is featured by deploying two receive chains whose oscillator signals are originated from the transmit oscillator signal but with different delays. To work with the proposed design, an SI cancellation algorithm is developed, where the phase noise contained in the multipath SI components are extracted by dividing the two received signals. The cancellation capabilities of the proposed transceiver, as well as two typical conventional transceivers, are derived in closed-forms. The impacts of oscillator delay adjustment errors and SI channel estimation errors on the cancellation capability are also analyzed to reveal guidelines for FD transceiver designs. Simulation results show that the proposed transceiver design can expand the transmission power and relax the requirement for the analog canceller, which shows advantages in the long-range communication with high transmission power, or in the scenario where analog cancellations are not applicable.

Issues of Z-factor and an approach to avoid them for quality control in high-throughput screening studies.

High-throughput screening (HTS) is a vital automation technology in biomedical research in both industry and academia. The well-known Z-factor has been widely used as a gatekeeper to assure assay quality in an HTS study. However, many researchers and users may not have realized that Z-factor has major issues. In this article, the following four major issues are explored and demonstrated so that researchers may use the Z-factor appropriately. First, the Z-factor violates the Pythagorean theorem of statistics. Second, there is no adjustment of sampling error in the application of the Z-factor for quality control (QC) in HTS studies. Third, the expectation of the sample-based Z-factor does not exist. Fourth, the thresholds in the Z-factor-based criterion lack a theoretical basis. Here, an approach to avoid these issues was proposed and new QC criteria under homoscedasticity were constructed so that researchers can choose a statistically grounded criterion for QC in the HTS studies. We implemented this approach in an R package and demonstrated its utility in multiple CRISPR/CAS9 or siRNA HTS studies. The R package qcSSMDhomo is freely available from GitHub: https://github.com/Karena6688/qcSSMDhomo. The file qcSSMDhomo_1.0.0.tar.gz (for Windows) containing qcSSMDhomo is also available at Bioinformatics online. qcSSMDhomo is distributed under the GNU General Public License. Supplementary data are available at Bioinformatics online.

Development and Preliminary Trajectory Verification of the Electromotor-Driven Parallel External Fixator for Deformity Correction

External fixators are widely used in deformity correction based on distraction osteogenesis. Traditionally, the rods are manually operated by patients several times a day, which will ensure the patient’s compliance, accumulative adjustment error, and trajectory deviation. To reduce the patients’ compliance and the complexity of adjustment, an electromotor-driven parallel external fixator is developed to gradually correct the deformity, which allows the fixator to be automatically adjusted and can correct any three-dimensional deformity with continuous stability. Two adjustment strategies are proposed through different trajectory control methods based on the inverse kinematics solution, and the trajectory and bone shape are generated to investigate the characteristics of the new bone more intuitively. The range of motion is performed utilizing the numerical searching method to assess the fixator’s correction capability. Finally, the trajectory verification experiment is carried out using the artificial bone model to perform the two adjustment strategies. The results show that the developed external fixator has high correction accuracy with 0.0172 mm, and can accurately and safely realize the preset correction trajectory. The developed fixator system can also be used as a teaching tool for medical training for clinicians to learn deformity correction technology.

Development and validation of a diagnostic model of antimicrobial resistance by extended-spectrum beta-lactamases in community-acquired urinary tract infections. How to adjust the prediction in variable outcome prevalences? The usefulness of contracting predictors with LASSO regression

In the case of community-acquired urinary tract infection, the identification of Enterobacteriaceae with extended spectrum beta-lactamases (ESBL) can optimize treatment, control and follow-up strategies, however the effect of variable prevalences of this resistance pattern has affected the external validity of this type of models. To develop a diagnostic predictive model that adjusts the prediction error in variable prevalences using the LASSO regression. A diagnostic predictive model of community-acquired urinary tract infection by infection by ESBL producing Enterobacteriaceae was designed. A cross-sectional study was used for both construction and validation. To assess the effect of the variable prevalence of the outcome, the validation was performed with a population in which the proportion of isolates with this resistance mechanism was lower, the participants were adult patients who consulted the emergency services of two medium-level hospital institutions. complexity of the city of Medellin. To adjust for the effect of an environment with a lower proportion of antimicrobial resistance, we used the contraction of predictors by LASSO regression. 303 patients were included for the construction of the model, six predictors were evaluated and validation was carried out in 220 patients. The adjusted model with LASSO regression favored the external validity of the model in populations with a proportion of ESBL producing isolates in urine culture of outpatients between 11 and 16%. This study provides criteria for early isolation when predictors are present in populations with proportions of resistance in ambulatory urine cultures close to 15% and proposes a methodology for the adjustment of errors in the design of prediction models for antimicrobial resistance.

Adjustment for the measurement error in evaluating biomarker performances at baseline for future survival outcomes: Time-dependent receiver operating characteristic curve within a joint modelling framework

The performance of a biomarker is defined by how well the biomarker is capable to distinguish between healthy and diseased individuals. This assessment is usually based on the baseline value of the biomarker; the value at the earliest time point of the patient follow-up, and quantified by ROC (receiver operating characteristic) curve analysis. However, the observed baseline value is often subjected to measurement error due to imperfect laboratory conditions and limited machine precision. Failing to adjust for measurement error may underestimate the true performance of the biomarker, and in a direct comparison, useful biomarkers could be overlooked. We develop a novel approach to account for measurement error when calculating the performance of the baseline biomarker value for future survival outcomes. We adopt a joint longitudinal and survival data modelling formulation and use the available longitudinally repeated values of the biomarker to make adjustment of the measurement error in time-dependent ROC curve analysis. Our simulation study shows that the proposed measurement error-adjusted estimator is more efficient for evaluating the performance of the biomarker than estimators ignoring the measurement error. The proposed method is illustrated using Mayo Clinic primary biliary cirrhosis (PBC) study.

Occupational epidemiologist's quest to tame measurement error in exposure

I aimed to assess current practices and opportunities for addressing the problem of errors in exposure in occupational epidemiology. Occupational epidemiologists appreciate that errors in exposure are a concern, but almost none correct for these errors, although there are currently no theoretical and practical barriers for this inertia. The most serious barrier to change is a faulty belief that a well-conducted epidemiologic study suffers only non-differential exposure misclassification and that its sole impact is to attenuate risk gradients, causing a false negative. On the contrary, differential exposure misclassification is the most defensible model in occupational epidemiology, and errors in exposure increase chance of both false positive and negative results. Resistance to mathematical adjustment for errors in exposure is equivalent to denying the value of more valid exposure estimates and undermines the discipline's relevance to protection of workers by informing workplace exposure limits.

Prolonged Static Whole-Body Roll-Tilt and Optokinetic Stimulation Significantly Bias the Subjective Postural Vertical in Healthy Human Subjects.

Background: Prolonged static whole-body roll-tilt has been shown to bias estimates of the direction of gravity when assessed by static paradigms such as the subjective visual vertical and the subjective haptic vertical.Objective: We hypothesized that these shifts are paradigm-independent and thus predicted a post-tilt bias as well for self-adjustments along perceived vertical (subjective postural vertical, SPV). Likewise, rotatory optokinetic stimuli, which have been shown to shift the SPV when presented at the time of adjustments, may have an lasting effect on the SPV, predicting a shift in the perceived direction of gravity in the direction of the optokinetic rotatory stimulation.Methods: Self-adjustments along perceived vertical by use of a motorized turntable were recorded at baseline and after 5 min of static whole-body roll-tilt (orientation = ±90°, adaptation period) in 10 healthy human subjects. During adaptation subjects were either in darkness (no OKN stimulation) or were presented a full-field rotatory optokinetic stimulus (velocity = ±60°/s). Statistical analysis of adjustment errors for the different conditions was performed using a generalized linear model.Results: After 5 min of static whole-body roll-tilt in darkness, we observed significant (p < 0.001) shifts in the SPV averaging −2.8° (adaptation position: −90°) and 3.1° (+90°), respectively. Adding an optokinetic rotatory stimulus resulted in an additional, significant shift of SPV adjustments toward the direction of the previously presented optokinetic rotation (optokinetic clockwise rotation: 1.4°, p = 0.034; optokinetic counter-clockwise rotation: −1.3°, p = 0.037). Trial-to-trial variability of turntable adjustments was not significantly affected by adaptation.Conclusions: Prolonged static roll-tilt results in a significant post-tilt bias of the perceived direction of gravity when assessed by the SPV, confirming previous findings from other vision-dependent and vision-independent paradigms. This finding emphasizes the impact of recent whole-body roll orientations relative to gravity. Such adaptational shifts in verticality estimates may be explained in the context of Bayesian optimal observer theory with a bias of prior knowledge (i.e., expectation biased by experience). Our findings also have clinical implications, as the observed post-tilt bias may contribute to postural instability when standing up in the morning with an increasing risk for falls and fall-related injuries in humans with preexisting balance disorders.

ISREA: An Efficient Peak-Preserving Baseline Correction Algorithm for Raman Spectra.

A critical step in Raman spectroscopy is baseline correction. This procedure eliminates the background signals generated by residual Rayleigh scattering or fluorescence. Baseline correction procedures relying on asymmetric loss functions have been employed recently. They operate with a reduced penalty on positive spectral deviations that essentially push down the baseline estimates from invading Raman peak areas. However, their coupling with polynomial fitting may not be suitable over the whole spectral domain and can yield inconsistent baselines. Their requirement of the specification of a threshold and the non-convexity of the corresponding objective function further complicates the computation. Learning from their pros and cons, we have developed a novel baseline correction procedure called the iterative smoothing-splines with root error adjustment (ISREA) that has three distinct advantages. First, ISREA uses smoothing splines to estimate the baseline that are more flexible than polynomials and capable of capturing complicated trends over the whole spectral domain. Second, ISREA mimics the asymmetric square root loss and removes the need of a threshold. Finally, ISREA avoids the direct optimization of a non-convex loss function by iteratively updating prediction errors and refitting baselines. Through our extensive numerical experiments on a wide variety of spectra including simulated spectra, mineral spectra, and dialysate spectra, we show that ISREA is simple, fast, and can yield consistent and accurate baselines that preserve all the meaningful Raman peaks.

Value Added Tax and Price Stability in Ethiopia: A Time Series Analysis

Value added tax (VAT), simply an indirect goods and services tax, which was implemented in Ethiopia starting from January, 1, 2003, is currently generating much debate especially with respect to its economic impact on aggregate level of prices. This study therefore, uses the quarterly time series data covering the period of 2004Q1 – 2014Q4 to empirically examine the relationship between VAT and price level in Ethiopia. The data was sourced from NBE, ERCA, MOF, and EEA. Johansen Co-integration is adopted in this study as it allows one to explain the effect of the value added tax in isolation from other control variables in the study. The data is tested for unit root using the Augmented Dickey-Fuller (ADF) test and the Philips-Perron (PP) test, and the result shows that variables of the study are stationary in their first differences. The Johansen co integration test revealed that there is at least one co-integrating vector implying the existence of long run relationship between VAT and inflation in Ethiopia. The coefficient of estimated error correction model (ECM) revealed that the speed of adjustment for errors in the short run is 47.9 percent indicating high speed of adjustment. The coefficient of VAT in long run is 0.188578, while it is 0.0051 in the short run enlightening that it influence price with the lower coefficient in short run. The explanatory variables accounted for 80.3 percent of the variations in inflation during the period of the study with the error term accounting for the remaining 19.7 percent. The finding of the study shows that VAT has positive and significant relationship with price in short run and long run. The positive significant impact of VAT on inflation is most likely due to the burden of the VAT on intermediate outputs. The policy implication is that well-being of the society needs to be taken into account in the process of public revenue generation. It follows that government should assure social desirability of VAT by conducting post implementation cost benefit analysis. Keywords : VAT, Price Stability, Johansen Co-integration, Ethiopia DOI: 10.7176/RJFA/11-19-02 Publication date: October 31 st 2020

How to Perform Three-Step Latent Class Analysis in the Presence of Measurement Non-Invariance or Differential Item Functioning

ABSTRACT The practice of latent class (LC) modeling using a bias-adjusted three-step approach has become widely popular. However, the current three-step approach has one important drawback – its key assumption of conditional independence between external variables and latent class indicators is often violated in practice, such as when a (nominal) covariate represents subgroups showing measurement non-invariance (MNI) or differential item functioning (DIF). In this article, we demonstrate how the current three-step approach should be modified to account for MNI; that is, covariates causing DIF should be included in the step-one model and the step-three classification error adjustment should differ across the values of the DIF covariates. We also propose a model-building strategy that makes the new methodology practically applicable also when it is unknown which of the external variables cause DIF. The new approach, implemented in the program Latent GOLD, is illustrated using a synthetic and a real data example.

Comparison of Growth Patterns of COVID-19 Cases through the ARIMA and Gompertz Models. Case Studies: Austria, Switzerland, and Israel.

On May 19, 2020, data confirmed that coronavirus 2019 disease (COVID-19) had spread worldwide, with more than 4.7 million infected people and more than 316,000 deaths. In this article, we carry out a comparison of the methods to calculate and forecast the growth of the pandemic using two statistical models: the autoregressive integrated moving average (ARIMA) and the Gompertz function growth model. The countries that have been chosen to verify the usefulness of these models are Austria, Switzerland, and Israel, which have a similar number of habitants. The investigation to check the accuracy of the models was carried out using data on confirmed, non-asymptomatic cases and confirmed deaths from the period February 21–May 19, 2020. We use the root mean squared error (RMSE), the mean absolute percentage error (MAPE), and the regression coefficient index R2 to check the accuracy of the models. The experimental results provide promising adjustment errors for both models (R2>0.99), with the ARIMA model being the best for infections and the Gompertz best for mortality. It has also been verified that countries are affected differently, which may be due to external factors that are difficult to measure quantitatively. These models provide a fast and effective system to check the growth of pandemics that can be useful for health systems and politicians so that appropriate measures are taken and countries’ health care systems do not collapse.

Effect of errors in adjustment of quadrature channels of a coherent system on complex signal parameters estimation

Effect of errors in adjustment of quadrature channels of a coherent system on complex signal parameters estimation

A cooperative camera surveillance method based on the principle of coarse-fine coupling boresight adjustment

Visual surveillance systems play a vital role in smart manufacturing scenes. In this paper, a cooperative camera surveillance method is proposed based on the coarse-fine coupling boresight adjustment principle. A master-slave camera cooperative monitoring model is established by combining a high-resolution camera with a large-field camera, the former of which is embedded with a Risley prism pair to perform high-precision boresight pointing for monitoring a local target, while the latter can observe a wide scene and guide the former to catch a region of interest. An effective boresight adjustment strategy is put forward with a method of combining fast convergence and iterative refinement. A calibration method of the master-slave camera is proposed with the combination of binocular vision and equivalent motion plane. The error distribution, adjustment efficiency and tracking accuracy of the dual field monitoring are analyzed in detail to reveal the mapping mechanism between imaging feedback and boresight adjustment. In the surveillance experiment, the root-mean-square tracking error of the fine tracking camera can achieve better than 7.04 pixels, while the adjustment steps only need about 10 times, which validates the feasibility and effectiveness of the proposed method in industrial operation surveillance.

Application of Takagi–Sugeno fuzzy model for slab camber control in a hot strip rougher mill

ABSTRACT Control of slab camber is an important topic in hot strip rough rolling process. In this study, a slab camber control system, composed by a camber prediction model and a gap levelling value calculation model, based on the Takagi–Sugeno (T–S) fuzzy model is proposed. The performance of the developed camber prediction model is evaluated by the estimation error between the predicted and measured values of slab camber after rough rolling with the validation set from a rougher mill. The results show that the percentage of bars with estimation error within the tolerance of ±30 mm is 94.1%. Moreover, the proposed control model is finally applied in the 1580 mm hot rough rolling process. The control model can reliably provide the suggested adjustment of gap levelling, and the percentage of slabs, of which the adjustment error meets the requirement for camber quality assurance, is 95.85%.

An analytical G3 continuous corner smoothing method with adaptive constraints adjustments for five-axis machine tool

In this paper, an analytical local corner smoothing method is proposed with two specially designed quartic curves to generate smooth motion trajectories of five-axis linear segments. One symmetrical quartic B-spline curve is used to smooth the tool tip position in workpiece coordinate system (WCS); meanwhile, another asymmetrical quartic B-spline curve is used to smooth the tool orientation in machine coordinate system (MCS). With the proposed method, not only the transition error of tool tip position and the smoothing error of the tool orientation are mathematically constrained in WCS, but the G3 continuity of the tool tip position and tool orientation are guaranteed along the entire toolpath. Besides, the maximum possible machining error of tool tip position caused by transition error and chord error is also controlled analytically by transition error adjustment. What’s more, in feedrate scheduling, the feedrate of tool tip is moderately adjusted by smooth feedrate profile, which can limit the maximum feedrate of tool orientation and achieve continuous axial feedrate and acceleration. Finally, the effectiveness and feasibility of the proposed method have been validated via simulations.

Optical health analysis of visual comfort for bright screen display based on back propagation neural network

BackgroundThe visual comfort of liquid crystal display (LCD) is the subjective evaluation of the user. It is a multi-dimensional and multi-factor problem, which is affected by the luminous characteristics of the LCD screen, the physiological factors of the user, and some other environmental factors. MethodsBased on the theory of visual comfort under the guidance of ergonomics, this paper adopts a combination of objective measurement and subjective evaluation to obtain objective data such as blink frequency and pupil size changes, and subjective evaluation data on screen parameters. Correlation analysis was used to screen subjective and objective data, and an LCD visual comfort evaluation using the back propagation (BP) neural network was constructed with the aim of a concise evaluation of the LCD's own light-emitting characteristics, user's physiological factors, and environmental factors. ResultsAfter testing, the model can successfully predict the optimal visual level of the screen. After training, the relative error between the predicted value of visual comfort and the actual evaluation value is mostly within 10%. Based on this model, the display brightness and color temperature control system combined with the ambient light sensor can automatically adjust the brightness of the screen and the temperature of color parameters in correlation to user's gender, age, and ambient light changes to achieve the effect of improving visual comfort. Setting and user parameter adjustment provide a new method. The maximum adjustment error of the system after testing is 5.378%. ConclusionOur proposed technique can serve as a useful analysis platform for understanding and evaluating the visual comfort of the bright LCD screen at home or in the workplace, and enhancing optical health of humans.

Impact of an axial non-confocal antenna on the on-orbit lasercom receiver analyzed by the simplified combination method of ray tracing and diffraction theory.

For an on-orbit lasercom receiver, the space thermal environment, microgravity environment and the adjustment error will deform the optical antenna, distorting the received wavefront and deteriorating the system performance. The axial non-confocal antenna is a deformed antenna caused by the despace of the secondary or primary mirror. Based on the simplified combination method of ray tracing and diffraction theory (simplified CMRD, or SCMRD), the calculation model of the normalized intensity distribution (NID) and the normalized received power (NRP) is established for an axial non-confocal antenna. The numerical results show that, the axial non-confocal antenna will flatten the received light spot, causing interruption of the communication link and deterioration of the bit error rate (BER) performance. The quantitative relations of the NID versus the axial non-confocal distance and the NRP versus the axial non-confocal distance would provide a reference for optical design, structural design, thermal design, assembly and adjustment accuracy design and detector selection. Besides, the numerical results are compared to those calculated by the wavefront fitting method and CMRD. The pros and cons of all methods are discussed. This work would advance the development of extending the SCMRD to analyze other deformed antennas' impact on an on-orbit lasercom receiver.

Research on online parameter identification of interior permanent magnet synchronous motor based on augmented robust forgetting factor recursive least square

AbstractThe high‐performance motor control relies on the accuracy of the interior permanent magnet synchronous motor (IPMSM) parameters. In order to automatically identify the electrical parameters of IPMSM, an online parameter identification method has been developed based on robust adjustable and double tracking error to improve the identification accuracy of IPMSM parameters, which has been verified by both simulation and experiment. Also the torque closed loop was used to check the accuracy and reliability of the parameter identification. On the one hand, the simulation results show that the identification error was less than 10%, decreasing the response time and enhancing the robustness of parameter identification. On the other hand, the experimental results derived from the TC1782‐based platform verify the effectiveness and robustness of the proposed method, above all results indicating that the present identification algorithm has excellent performances of IPMSM drives system and engineering practicability.