Errors In Field Research Articles

POSSIBILITIES OF AUTOREFKERATOMETRY

In modern ophthalmology, autorefkeratometry has long become a routine diagnostic study, without which it is impossible to imagine any examination. Computerized autorefkeratometers allow you to obtain a huge amount of information about the condition of the patient’s eye in a short time. At the same time, the information obtained is not always subjected to in-depth analysis.Refractometry data should be compared with keratometry data, compared with the indicators of the paired eye, and evaluate, if possible, the dynamics of changes and their tendency. Comparison of the obtained parameters with the average statistical indicators and the age norm make it possible to establish the presence of pathology that does not belong to the field of refractive errors. Thus, the occurrence of anisometropia with similar keratometry parameters suggests opacity of the lens or vitreous pathology. Direct astigmatism is typical for young people, and oblique or reverse astigmatism is typical for patients 65+. A shift in the irido-lens diaphragm is always accompanied by a change in refraction and its instability. Keratectasias are characterized by high variability in refraction and keratometry data, while sometimes maintaining high visual acuity that does not correspond to changes in optical power. Diopter syndrome is manifested by refractive transformations in accordance with changes in the anterior-posterior axis of the eye due to retinal edema. Dry eye syndrome can be manifested by noticeable refractive instability and errors in determining keratometric parameters.High-quality performance of autorefkeratometry, taking into account the nuances, and an in-depth analysis of the data obtained are necessary for making a diagnosis and assessing the dynamics of treatment of ocular pathology.

Fundamental effects of array density and modulation frequency on image quality of diffuse optical tomography.

Diffuse optical tomography (DOT) provides three-dimensional image reconstruction of chromophore perturbations within a turbid volume. Two leading strategies to optimize DOT image quality include, (i) arrays of regular, interlacing, high-density (HD) grids of sources and detectors with closest spacing less than 15mm, or (ii) source modulated light of order ∼100MHz. However, the general principles for how these crucial design parameters of array density and modulation frequency may interact to provide an optimal system design have yet to be elucidated. Herein, we systematically evaluated how these design parameters effect image quality via multiple key metrics. Specifically, we simulated 32 system designs with realistic measurement noise and quantified localization error, spatial resolution, signal-to-noise, and localization depth of field for each of ∼85000 point spread functions in each model. We found that array density had a far stronger effect on image quality metrics than modulation frequency. Additionally, model fits for image quality metrics revealed that potential improvements diminish with regular arrays denser than 9mm closest spacing. Further, for a given array density, 300MHz source modulation provided the deepest reliable imaging compared to other frequencies. Our results indicate that both array density and modulation frequency affect the spatial sampling of tissue, which asymptotically saturates due to photon diffusivity within a turbid volume. In summary, our results provide comprehensive perspectives for optimizing future DOT system designs in applications from wearable functional brain imaging to breast tumor detection.

Inclination information-aided circumferential deformation reconstruction error correction method for CFRP belt integrated with optical fiber sensor

In view of the problem that the distributed optical fiber-based deformation reconstruction method can produce serious accumulated errors, this paper developed a smart fiber optic sensor belt that employs a carbon-fiber composite material as the matrix and strain-sensing fiber optics as the sensing element and is based on the key-point dip angle information acquired by a high-precision tilt sensor. The method of in situ correction for the cumulative error of circumferential deformation was investigated, and the particle swarm optimization algorithm was adopted to perform in situ correction for the reconstructed circumferential deformation field of the sensor belt to reduce the cumulative measurement error of the circumferential deformation field. Simulations and indoor tests were systematically performed. After the correction, the errors of the simulation and indoor tests were reduced from 210.83 and 235.82 mm to 2.17 and 4.04 mm, respectively, which verified the effectiveness and accuracy of the aforementioned method.

Prediction of Sea Surface Current Around the Korean Peninsula Using Artificial Neural Networks

AbstractThe prediction of sea surface currents is essential for various marine activities such as disaster monitoring, fishing industries, and search and rescue operations. Continuous improvements in numerical models have made it possible to predict more realistic oceans using data assimilation and fine spatial resolution. However, such complicated ocean models require high computational power and time, making them difficult to use for near‐real time forecasting. To compensate for these high computational costs, novel approaches with efficient computational costs combined with numerical model outputs need to be developed. Artificial neural networks could be one of the solutions as they require low computational power for prediction, owing to pre‐trained networks. Here, we present a prediction framework applicable to surface current prediction in the seas around the Korean Peninsula using three‐dimensional (3‐D) convolutional neural networks. The network is based on a 3‐D U‐shaped network structure and is modified to predict sea surface currents using oceanic and atmospheric variables. The forecast procedure is optimized to minimize the error of the next day's sea surface current field with a spatial resolution of 1/24° and its recursively predicting structure allows more days to be predicted. The network performance is evaluated by changing the input days and variables to find the optimal surface‐current‐prediction artificial neural network model. The optimized model shows a root mean squared error of 2.3 cm/s for the first forecast day, demonstrating its strong potential for practical use in the near future.

Modeling of the Black Sea circulation using equations of heat and salt advection–diffusion having discrete nonlinear invariants

In this work the accuracy of reconstructing the Black Sea circulation by using new approximations of nonlinear terms in the transport equations, ensuring the conservation of temperature and salinity to a power greater than two, is analyzed based on the results of forecast calculations. Three numerical experiments with differences in the schemes for calculating temperature and salinity are carried out. In the first experiment – traditional schemes are used to conserve of temperature and salinity in the first and second degrees; in the second one – the temperature is conserved in the first and fifth degrees, salinity in the first and third; in the third one – the temperature in the first and third, salinity in the first and fifth degrees. Calculations are performed on the basis of the MHI model with a resolution of 1.6 km and taking into account realistic atmospheric forcing for 2016. Validation of the results is carried out based on comparison of model fields with in-situ and satellite measurements of temperature and salinity in 2016. Analysis of mean and root mean square errors showed that new schemes for the advection-diffusion equations of heat and salt, ensuring the conservation of predictive parameters to a power greater than two, improve the accuracy of reconstructing the salinity in the Black Sea upper 100m layer throughout the year compared with traditional approximation. The root mean square errors in the salinity field are reduced by 15–20%, the thickness of the upper mixed layer in winter and the depth of the upper boundary of the thermocline layer in summer in the central part of the sea are modeled approximately 10% more accurately. Based on the results of three experiments, the smallest deviations from observational data are obtained when using approximations that ensure the conservation of temperature to the third power and salinity to the fifth power.

Coulomb Stress Change of Active Fault in South Sumatra Region Revealed from Kerinci Earthquake October 06, 1995 Mw 6.7 and Sungai Penuh Earthquake October 01, 2009 Mw 6.6

We successfully highlight the correlation of Static Stress Change (ΔCFF) in the Kerimci earthquake October 06, 1995 Mw 6.7 and Sungai Penuh earthquake October 01, 2009 Mw 6.6 earthquakes to the seismicity conditions. The data used in this study are focal mechanism obtained from Global Centroid Moment Tensor (GCMT) and seismicity obtained from USGS with M ≥ 4 after the main earthquake with a time span of 11 years. ΔCFF obtained in the Mentawai Fault System area has increased coulomb stress. ΔCFF obtained in the Suliti Segment, Ketaun Segment, and Back arc Basin of Jambi experienced an increase in stress, which can indicate the potential for future earthquakes, but there was no increase in seismicity. Besides that, ΔCFF in areas that experienced a decrease in stress, experienced an increase in seismicity. This is caused by background seismicity in the area and several factors influence the results of the calculation of ΔCFF against seismicity. Simplicity in calculation causes difficulty in explaining seismicity, especially in the blue lobe. Moreover, the use of receiver fault mechanism produces a very large error in the complex regional stress field. The use of a constant friction coefficient also produces a very large error in the calculation.

Development of a three-dimensional variational data assimilation system for 137Cs based on WRF-Chem model and applied to the Fukushima nuclear accident

Abstract Nuclear explosions and accidents release large amounts of radionuclides that harm human health and the environment. Accurate forecasting of nuclide pollutants and assessment of the ramifications of nuclear incidents are necessary for the emergency response and disaster assessment of nuclide pollution. In this study, we developed a three-dimensional variational (3Dvar) system to assimilate 137Cs based on the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model. The distribution of 137Cs after the Fukushima nuclear accident in Japan on 15 March 2011 was analysed. The 137Cs background field at 06:00 UTC was assimilated using a 3Dvar system and surface observational data to optimise the 137Cs analysis field. Compared with the background field, the root mean square error (RMSE) and mean bias in the 137Cs analysis field decreased by 98% and 94%, respectively. The average fraction of predictions within factors of 2 (FAC2), 5 (FAC5), and 10 (FAC10) increased from 0.67, 0.72, and 0.72 to 0.90, 1.00, and 1.00, respectively. This substantial enhancement indicated the effectiveness of the 3DVar system in mitigating the uncertainty associated with the background field. Two 12 h forecast experiments were conducted to gauge the advancement in 137Cs forecasting facilitated by data assimilation (DA). The control experiment was conducted without DA, whereas the assimilation experiment was conducted with DA. Compared with the control experiment, the average FAC2, FAC5, and FAC10 in the assimilation experiment increased by 28%, 30%, and 29%, respectively. The average RMSE decreased by 33%. The mean bias and correlation coefficient increased by 41% and 36%, respectively. These results indicated that the 3Dvar method improves the forecast accuracy of 137Cs concentration.

An Eulerian formulation for the computational modeling of phase-contrast MRI.

Computational simulation of phase-contrast MRI (PC-MRI) is an attractive way to physically interpret properties and errors in MRI-reconstructed flow velocity fields. Recent studies have developed PC-MRI simulators that solve the Bloch equation, with the magnetization transport being modeled using a Lagrangian approach. Because this method expresses the magnetization as spatial distribution of particles, influences of particle densities and their spatial uniformities on numerical accuracy are well known. This study developed an alternative method for PC-MRI modeling using an Eulerian approach in which the magnetization is expressed as a spatially smooth continuous function. The magnetization motion was described using the Bloch equation with an advection term and computed on a fixed grid using a finite difference method, and k-space sampling was implemented using the spoiled gradient echo sequence. PC-MRI scans of a fully developed flow in straight and stenosed cylinders were acquired to provide numerical examples. Reconstructed flow in a straight cylinder showed excellent agreement with input velocity profiles and mean errors were less than 0.5% of the maximum velocity. Numerical cases of flow in a stenosed cylinder successfully demonstrated the velocity profiles, with displacement artifacts being dependent on scan parameters and intravoxel dephasing due to flow disturbances. These results were in good agreement with those obtained using the Lagrangian approach with a sufficient particle density. The feasibility of the Eulerian approach to PC-MRI modeling was successfully demonstrated.

Characteristics of Deterministic and Stochastic Unsteadiness of Trailing Edge Cutback Film Cooling Flows

Abstract Trailing edge cutback film cooling flows are ubiquitous in small and medium gas turbines, but they are difficult to predict accurately due to the inherent deterministic and stochastic unsteadiness that controls the effectiveness of the cooling system. To help develop accurate closure models for such flows, the characteristics of both types of unsteadiness and their effects on the mean flows are analyzed in this research. Zonal detached eddy simulation (ZDES) is performed on a trailing edge cutback flow model, and the numerical results are validated against the measured data. Then, by using spectral proper orthogonal decomposition (SPOD) reconstruction, the original dataset is segregated into deterministic and stochastic unsteadiness. The characteristics of the stress tensor and the heat flux of each type of unsteadiness are analyzed in detail, and notable differences between the two unsteadiness are identified in terms of the stress tensor anisotropy and distribution of unsteady kinetic energy and heat flux. By propagating the unsteadiness through the Reynolds-averaged Navier–Stokes (RANS) equations, the effect of different unsteadiness on the mean flow prediction is quantified. An accurate prediction of the total stress tensor reduces the prediction error in the velocity field by 79% and cooling effectiveness by 55%. An accurate prediction of the total heat flux vector reduces the prediction error in cooling effectiveness further by 37%. These findings provide valuable knowledge for the physical understanding, turbulence modeling, and aerothermal design of cutback trailing edge flows.

Error Correction of the RapidEye Sub-Pixel Correlation: A Case Study of the 2019 Ridgecrest Earthquake Sequence.

The optical image sub-pixel correlation (SPC) technique is an important method for monitoring large-scale surface deformation. RapidEye images, distinguished by their short revisit period and high spatial resolution, are crucial data sources for monitoring surface deformation. However, few studies have comprehensively analyzed the error sources and correction methods of the deformation field obtained from RapidEye images. We used RapidEye images without surface deformation to analyze potential errors in the offset fields. We found that the errors in RapidEye offset fields primarily consist of decorrelation noise, orbit error, and attitude jitter distortions. To mitigate decorrelation noise, the careful selection of offset pairs coupled with spatial filtering is essential. Orbit error can be effectively mitigated by the polynomial fitting method. To address attitude jitter distortions, we introduced a linear fitting approach that incorporated the coherence of attitude jitter. To demonstrate the performance of the proposed methods, we utilized RapidEye images to extract the coseismic displacement field of the 2019 Ridgecrest earthquake sequence. The two-dimensional (2D) offset field contained deformation signals extracted from two earthquakes, with a maximum offset of 2.8 m in the E-W direction and 2.4 m in the N-S direction. A comparison with GNSS observations indicates that, after error correction, the mean relative precision of the offset field improved by 92% in the E-W direction and by 89% in the N-S direction. This robust enhancement underscores the effectiveness of the proposed error correction methods for RapidEye data. This study sheds light on large-scale surface deformation monitoring using RapidEye images.

Consenso mexicano de tirosinemia tipo 1

Tyrosinemia type 1 is a rare disease with autosomal recessive inheritance, featuring various clinical manifestations. These may encompass acute neonatal liver failure, neonatal cholestatic syndrome, chronic hepatitis, cirrhosis, hepatocellular carcinoma, and, alternatively, kidney disorders like renal tubular acidosis, Fanconi syndrome, hypophosphatemic rickets, among other alterations. Diagnosis relies on detecting toxic metabolites in the blood and urine, ideally confirmed through molecular testing. A consensus was reached with experts in the field of inborn errors of metabolism (EIM), including eight pediatric gastroenterologists, two EIM specialists, two geneticists, three pediatric nutritionists specialized in EIM, and a pediatric surgeon specializing in transplants. Six working groups were tasked with formulating statements and justifications, and 32 statements were anonymously voted on using the Likert scale and the Delphi method. The first virtual vote achieved an 80% consensus, with the remaining 20% determined in person. The statements were categorized into epidemiology, clinical presentation, diagnosis, nutritional and medical treatment, and genetic counseling. This consensus serves as a valuable tool for primary care physicians, pediatricians, and pediatric gastroenterologists, aiding in the prompt diagnosis and treatment of this disease. Its impact on the morbidity and mortality of patients with tyrosinemia type 1 is substantial.

Field stability of an Alvarez-type drift tube linear accelerator with small drift tubes

This study presents theoretical, simulation and experimental investigations of the field stability of a 7 MeV Alvarez-type drift tube linear accelerator (DTL) with small drift tubes (DTs). Although technical advances in permanent magnet quadrupoles make smaller DTs possible, resulting in higher shunt impedance, it is widely recognized that the DT-to-wall spacing must lie between ∼0.90 and ∼1.03 times a quarter wavelength (λ/4) to achieve field stability through post couplers (PCs). To enhance the shunt impedance of a DTL cavity, we decrease the outer diameter of the DTs and increase the inner diameter of the cavity, resulting in a DT-to-wall spacing of 1.16 times λ/4. A low-power experiment confirms that when the global tilt sensitivity slope is zero, the PC-to-DT distance is larger and there exists a stronger coupling between the PCs, resulting in a disturbed distribution of PC modes, where the frequencies of lower-order modes are overwhelmed by higher-order modes. The observed result is consistent with the analysis result given by the equivalent circuit theory and three-dimensional simulation. After field tuning by the trial-and-error method, the relative error of the axial electric field is within ±1.9% and the tilt sensitivity is within ±70%/MHz.

A multi-horizon fully coupled thermo-mechanical peridynamics

This paper presents a fully coupled thermo-mechanical peridynamic model for simulating interactive thermo-mechanical material responses and thermally induced fracturing of solids. A temperature-dependent constitutive model and a deformation-dependent heat conduction model are derived for state-based peridynamic formulation. The dispersion relation and truncation error of the state-based peridynamic heat equation are analyzed for the first time. It is found that as non-locality becoming more pronounced, the dissipative rate of heat is reduced, and the truncation error becomes larger. A small horizon can effectively mitigate oscillation while reducing the error in the temperature field. For coupled thermo-mechanical modeling, a novel multi-horizon scheme is introduced where the thermal field is solved with a different horizon than that of the mechanical field. The multi-horizon scheme allows for the implementation of a distinct degree of non-locality for different physical field. Comparing with the constant-horizon scheme, we demonstrate through numerical examples that the multi-horizon scheme offers smoother and more accurate solutions and serves a promising option for peridynamics-based multi-physics simulations.

Automatic control method of spherical wave exposure interference field based on the Moiré alignment principle.

Aberration-corrected gratings are widely applied in spectral analysis owing to their dispersion and convergence properties. However, the phase distribution error of the exposure interference field reduces the accuracy of the groove density distribution, making it difficult to satisfy the needs of high-precision spectral instruments. Therefore, this paper establishes an error model for the phase distribution of the spherical wave exposure interference field, describing the relationship between the phase distribution error and the recording parameter error. This model is used to propose a method of automatically controlling a spherical wave exposure interference field based on Moiré alignment principle. This method automatically measures the phase of the interference field by extracting the phase from the Moiré fringes generated by the superposition of the interference field and the reference grating, and then inversely calculates the recording parameters. The measurement results are then fed back to the automatic calibration mechanism for compensation, thereby achieving automatic control of the exposure interference field. Applying this method to calibrate the exposure interference field reduces the average relative error of the groove density of the produced plane aberration-corrected grating by two orders of magnitude compared with that of the traditional control method. This method significantly enhances the control accuracy for the spherical wave exposure interference field, improving the distribution accuracy of the groove density of the aberration-corrected grating, thereby supporting spectral analysis.

Off-axis spectrum and small signal gain of dual harmonic planar undulator

The spectral properties of the spontaneous undulator radiation generated from the dual harmonic planar undulator configuration are analyzed. The effect of changing the observation point away from the axis on radiation is demonstrated. The expressions for off-axis undulator radiation and off-axis small signal gain are derived using the generalized Bessel function. The emission of radiation at odd and even harmonics and the effect of the additional harmonic field on the intensity and small signal gain are analyzed. The present work can also be used for the diagnostic study of errors in the undulator field and the detector setting for FEL measurements.

Primary Atopic Disorders: Expanding Field of Inborn Errors of Immunity

Primary Atopic Disorders: Expanding Field of Inborn Errors of Immunity

Verification of neuronavigated TMS accuracy using structured-light 3D scans

Objective. To investigate the reliability and accuracy of the manual three-point co-registration in neuronavigated transcranial magnetic stimulation (TMS). The effect of the error in landmark pointing on the coil placement and on the induced electric and magnetic fields was examined. Approach. The position of the TMS coil on the head was recorded by the neuronavigation system and by 3D scanning for ten healthy participants. The differences in the coil locations and orientations and the theoretical error values for electric and magnetic fields between the neuronavigated and 3D scanned coil positions were calculated. In addition, the sensitivity of the coil location on landmark accuracy was calculated. Main results. The measured distances between the neuronavigated and 3D scanned coil locations were on average 10.2 mm, ranging from 3.1 to 18.7 mm. The error in angles were on average from two to three degrees. The coil misplacement caused on average a 29% relative error in the electric field with a range from 9% to 51%. In the magnetic field, the same error was on average 33%, ranging from 10% to 58%. The misplacement of landmark points could cause a 1.8-fold error for the coil location. Significance. TMS neuronavigation with three landmark points can cause a significant error in the coil position, hampering research using highly accurate electric field calculations. Including 3D scanning to the process provides an efficient method to achieve a more accurate coil position.

Fast fluid–structure interaction simulation method based on deep learning flow field modeling

The rapid acquisition of high-fidelity flow field information is of great significance for engineering applications such as multi-field coupling. Current research in flow field modeling predominantly focuses on low Reynolds numbers and periodic flows, exhibiting weak generalization capabilities and notable issues with temporal inferring error accumulation. Therefore, we establish a reduced order model (ROM) based on Convolutional Auto-Encoder (CAE) and Long Short-Term Memory (LSTM) neural network and propose an unsteady flow field modeling method for the airfoil with a high Reynolds number and strong nonlinear characteristics. The attention mechanism and weak physical constraints are integrated into the model architecture to improve the modeling accuracy. A broadband excitation training strategy is proposed to overcome the error accumulation problem of long-term inferring. With only a small amount of latent codes, the relative error of the flow field reconstructed by CAE can be less than 5‰. By training LSTM with broadband excitation signals, stable dynamic evolution can be achieved in the time dimension. CAE-LSTM can accurately predict the forced response and complex limit cycle behavior of the airfoil in a wide range of amplitude and frequency under subsonic/transonic conditions. The relative errors of predicted variables and integral force are less than 1%. The fluid–structure interaction framework is built by coupling the ROM and motion equations of the structure. CAE-LSTM predicts the time series response of pitch displacement and moment coefficient at different reduced frequencies, which is in good agreement with computational fluid dynamics, and the simulation time savings exceed one order of magnitude.

Rapid and sparse reconstruction of high-speed steady-state and transient compressible flow fields using physics-informed graph neural networks

Explosion flow fields are characterized by shock waves with varying intensity and position (i.e., explosive loads), which are the primary causes of structural damage. Accurate and rapid prediction of explosive loads is crucial for structural blast-resistant design and daily security management. While existing empirical models and numerical simulation methods can capture the propagation characteristics of explosive shock waves, high-precision simulation requires a massive computational workload, which is insufficient to meet the fast computational demands of various explosive scenarios. To address this contradiction, this study constructed a sparse reconstruction model for two-dimensional explosion fields based on machine learning algorithms. The model utilizes sparse observational data to establish a mapping relationship to the distribution of the entire flow field. The model is built by a physics-informed graph neural network (PIGN). The graph neural network is employed to associate node features, while the physical network is utilized to control model convergence, aiming to enhance model performance. Using the constructed dataset, the PIGN model was tested. Performance and generalization capabilities of the model were assessed by comparing its results with numerical simulation. This evaluation analyzed the relative error distribution and error statistical results of the reconstructed flow field. The results indicate that the PIGN model can effectively reconstruct explosion fields, with an average error in the reconstructed flow field below 4%. Furthermore, when the number of probe points reaches 10, the average error of the flow field reconstructed by the model is close to 6%. This model not only provides a highly reliable distribution of explosion overpressure and pressure-time variations but also, with a well-trained model, accomplishes flow field reconstruction within 1 ms. It offers a novel approach for achieving rapid and reasonable prediction of explosion fields or two-dimensional compressible flow fields.

A Simple Statistical Postprocessing Scheme for Enhancing the Skill of Seasonal SST Predictions in the Tropics

Abstract Seasonal prediction systems are subject to systematic errors, including those introduced during the initialization procedure, that may degrade the forecast skill. Here we use a novel statistical postprocessing correction scheme that is based on canonical correlation analysis (CCA) to relate errors in ocean temperature arising during initialization with errors in the predicted sea surface temperature fields at 1–12-month lead time. In addition, the scheme uses CCA of simultaneous SST fields from the prediction and corresponding observations to correct pattern errors. Finally, simple scaling is used to mitigate systematic location and phasing errors as a function of lead time and calendar month. Applying this scheme to an ensemble of seven seasonal prediction models suggests that moderate improvement of prediction skill is achievable in the tropical Atlantic and, to a lesser extent, in the tropical Pacific and Indian Ocean. The scheme possesses several adjustable parameters, including the number of CCA modes retained, and the regions of the left and right CCA patterns. These parameters are selected using a simple tuning procedure based on the average of four skill metrics. The results of the present study indicate that errors in ocean temperature fields due to imperfect initialization and SST variability errors can have a sizable negative impact on SST prediction skill. Further development of prediction systems may be able to remedy these impacts to some extent. Significance Statement The prediction of year-to-year climate variability patterns, such as El Niño, offers potential benefits to society by aiding mitigation and adaptation efforts. Current prediction systems, however, may still have substantial room for improvement due to systematic model errors and due to imperfect initialization of the oceanic state at the start of predictions. Here we develop a statistical correction scheme to improve prediction skill after forecasts have been completed. The scheme shows some moderate success in improving the skill for predicting El Niño and similar climate patterns in seven prediction systems. Our results not only indicate a potential for improving prediction skill after the fact but also point to the importance of improving the way prediction systems are initialized.