A Method for Measuring Parameters of Defective Ellipse Based on Vision.

Ellipse detection has a very wide range of applications in the field of object detection, especially in the geometric size detection of inclined microporous parts. However, due to the processing methods applied to the parts, there are certain defects in the features. The existing ellipse detection methods do not meet the needs of rapid detection due to the problems of false detection and time consumption. This article proposes a method of quickly obtaining defective ellipse parameters based on vision. It mainly uses the approximation principle of circles to repair defective circles, then combines this with morphological processing to obtain effective edge points, and finally uses the least squares method to obtain elliptical parameters. By simulating the computer-generated images, the results demonstrate that the center fitting error of the simulated defect ellipses with major and minor axes of 600 and 400 pixels is less than 1 pixel, the major and minor axis fitting error is less than 3 pixels, and the tilt angle fitting error is less than 0.1°. Further, experimental verification was conducted on the engine injection hole. The measurement results show that the surface size deviation was less than 0.01 mm and the angle error was less than 0.15°, which means the parameters of defective ellipses can obtained quickly and effectively. It is thus suitable for engineering applications, and can provide visual guidance for the precise measurement of fiber probes.

A procedure for determining the number and pattern of digestible starch fractions in multiphasic food digestograms.

BACKGROUND: Plant-based foods are frequently heterogenous systems, containing multiple starch fractions with distinct digestion rate constants. An unbiased determination of the number and digestion pattern of these fractions is a prerequisite for understanding the digestive characteristics of food. RESULTS: A non-linear least-squares procedure based on a conditional selection of simple first-order kinetics or a combination of parallel and sequential kinetics models was developed. The procedure gave robust results fitting manually generated data, and was applied to in vitro experimental digestion data of retrograded rice starches. By correlating fitting parameters with starch structural parameters, it showed that rice starches with a lower amylose content, longer amylose chains, and amylopectin intermediate chains had more digestible starch fractions after long-term retrogradation. CONCLUSION: This procedure enables the structural basis of starch digestibility and the development of food products with slow starch digestibility to be better understood. © 2022 Society of Chemical Industry.

The susceptible-unidentified infected-confirmed (SUC) epidemic model for estimating unidentified infected population for COVID-19.

In this article, we propose the Susceptible-Unidentified infected-Confirmed (SUC) epidemic model for estimating the unidentified infected population for coronavirus disease 2019 (COVID-19) in China. The unidentified infected population means the infected but not identified people. They are not yet hospitalized and still can spread the disease to the susceptible. To estimate the unidentified infected population, we find the optimal model parameters which best fit the confirmed case data in the least-squares sense. Here, we use the time series data of the confirmed cases in China reported by World Health Organization. In addition, we perform the practical identifiability analysis of the proposed model using the Monte Carlo simulation. The proposed model is simple but potentially useful in estimating the unidentified infected population to monitor the effectiveness of interventions and to prepare the quantity of protective masks or COVID-19 diagnostic kit to supply, hospital beds, medical staffs, and so on. Therefore, to control the spread of the infectious disease, it is essential to estimate the number of the unidentified infected population. The proposed SUC model can be used as a basic building block mathematical equation for estimating unidentified infected population.

Optimization of Ring Laser Gyroscope Bias Compensation Algorithm in Variable Temperature Environment.

In a high accuracy strapdown inertial navigation system (SINS), the ring laser gyroscope's (RLG) bias changes and the performance decreases due to factors in the RLG's self-heating and changes in ambient temperature. Therefore, it is important to study the bias temperature drift characteristics of RLGs in high, low, and variable temperature environments. In this paper, a composite temperature calibration scheme is proposed. The composite temperature model introduces the derivative term and the temperature derivative cross-multiplier on the basis of the static model and sets the overlap regions for the piecewise least squares fitting. The results show that the composite temperature model can compensate the bias trend term well at ambient temperature, improve the fitting accuracy, and smooth the output curve. The compensation method has a small amount of calculations and flexible parameter design. The precision of the laser gyros in one SINS is improved by about 64.9%, 15.7%, and 3.6%, respectively, which has certain engineering application value.

Determination of multipool contributions to endogenous amide proton transfer effects in global ischemia with high spectral resolution in vivo chemical exchange saturation transfer MRI.

PURPOSE: Chemical exchange saturation transfer (CEST) MRI has been used for quantitative assessment of dilute metabolites and/or pH in disorders such as acute stroke and tumor. However, routine asymmetry analysis (MTRasym ) may be confounded by concomitant effects such as semisolid macromolecular magnetization transfer (MT) and nuclear Overhauser enhancement. Resolving multiple contributions is essential for elucidating the origins of in vivo CEST contrast. METHODS: Here we used a newly proposed image downsampling expedited adaptive least-squares fitting on densely sampled Z-spectrum to quantify multipool contribution from water, nuclear Overhauser enhancement, MT, guanidinium, amine, and amide protons in adult male Wistar rats before and after global ischemia. RESULTS: Our results revealed the major contributors to in vivo T1 -normalized MTRasym (3.5 ppm) contrast between white and gray matter (WM/GM) in normal brain (-1.96%/second) are pH-insensitive macromolecular MT (-0.89%/second) and nuclear Overhauser enhancement (-1.04%/second). Additionally, global ischemia resulted in significant changes of MTRasym , being -2.05%/second and -1.56%/second in WM and GM, which are dominated by changes in amide (-1.05%/second, -1.14%/second) and MT (-0.88%/second, -0.62%/second). Notably, the pH-sensitive amine and amide effects account for nearly 60% and 80% of the MTRasym changes seen in WM and GM, respectively, after global ischemia, indicating that MTRasym is predominantly pH-sensitive. CONCLUSION: Combined amide and amine effects dominated the MTRasym changes after global ischemia, indicating that MTRasym is predominantly pH-sensitive and suitable for detecting tissue acidosis following acute stroke.

Perfused murine heart optical transmission spectroscopy using optical catheter and integrating sphere: Effects of ischemia/reperfusion.

Tissue transmission optical absorption spectroscopy provides dynamic information on metabolism and function. Murine genetic malleability makes it a major model for heart research. The diminutive size of the mouse heart makes optical transmission studies challenging. Using a perfused murine heart center mounted in an integrating sphere for light collection with a ventricular cavity optical catheter as an internal light source provided an effective method of optical data collection in this model. This approach provided high signal to noise optical spectra which when fit with model spectra provided information on tissue oxygenation and redox state. This technique was applied to the study of cardiac ischemia and ischemia reperfusion which generates extreme heart motion, especially during the ischemic contracture. The integrating sphere reduced motion artifacts associated with a fixed optical pickup and methods were developed to compensate for changes in tissue thickness. During ischemia, rapid decreases in myoglobin oxygenation occurred along with increases in cytochrome reduction levels. Surprisingly, when ischemic contracture occurred, myoglobin remained fully deoxygenated, while the cytochromes became more reduced consistent with a further, and critical, reduction of mitochondrial oxygen tension during ischemic contraction. This optical arrangement is an effective method of monitoring murine heart metabolism.

Vastly accelerated linear least-squares fitting with numerical optimization for dual-input delay-compensated quantitative liver perfusion mapping.

PURPOSE: To propose an efficient algorithm to perform dual input compartment modeling for generating perfusion maps in the liver. METHODS: We implemented whole field-of-view linear least squares (LLS) to fit a delay-compensated dual-input single-compartment model to very high temporal resolution (four frames per second) contrast-enhanced 3D liver data, to calculate kinetic parameter maps. Using simulated data and experimental data in healthy subjects and patients, whole-field LLS was compared with the conventional voxel-wise nonlinear least-squares (NLLS) approach in terms of accuracy, performance, and computation time. RESULTS: Simulations showed good agreement between LLS and NLLS for a range of kinetic parameters. The whole-field LLS method allowed generating liver perfusion maps approximately 160-fold faster than voxel-wise NLLS, while obtaining similar perfusion parameters. CONCLUSIONS: Delay-compensated dual-input liver perfusion analysis using whole-field LLS allows generating perfusion maps with a considerable speedup compared with conventional voxel-wise NLLS fitting. Magn Reson Med 79:2415-2421, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Coarse Alignment of Marine Strapdown INS Based on the Trajectory Fitting of Gravity Movement in the Inertial Space.

A ship experiences the random motion of sea waves during its travels. Hence, the coarse alignment of the marine strapdown Inertial Navigation System (INS) suffers from rocking disturbances such as pitch and roll. In this paper, a novel approach of marine coarse alignment was proposed for avoiding the resulting loss of accuracy from rocking disturbances. Unlike several current techniques, our alignment scheme is intuitional and concise. Moreover, the coarse alignment can be implemented without any external information. The gravity vector and its derivative expressed within the inertial frame can describe the attitude matrix between an inertial frame and the local geographic frame. We address the challenge of calculating the gravity derivative by the least-squares fitting of the trajectory of the gravity movement in the inertial frame. Meanwhile, the integration of angular rates measured by gyroscopes allows one to compute the attitude matrix between the inertial frame and the body frame. The coarse alignment can be thus accomplished by the combination of the above two attitude matrices. The experimental results show that the coarse alignment is effective with high accuracy and stability for demanding marine applications.

Pupil and Glint Detection Using Wearable Camera Sensor and Near-Infrared LED Array.

This paper proposes a novel pupil and glint detection method for gaze tracking system using a wearable camera sensor and near-infrared LED array. A novel circular ring rays location (CRRL) method is proposed for pupil boundary points detection. Firstly, improved Otsu optimal threshold binarization, opening-and-closing operation and projection of 3D gray-level histogram are utilized to estimate rough pupil center and radius. Secondly, a circular ring area including pupil edge inside is determined according to rough pupil center and radius. Thirdly, a series of rays are shot from inner to outer ring to collect pupil boundary points. Interference points are eliminated by calculating gradient amplitude. At last, an improved total least squares is proposed to fit collected pupil boundary points. In addition, the improved total least squares developed is utilized for the solution of Gaussian function deformation to calculate glint center. The experimental results show that the proposed method is more robust and accurate than conventional detection methods. When interference factors such as glints and natural light reflection are located on pupil contour, pupil boundary points and center can be detected accurately. The proposed method contributes to enhance stability, accuracy and real-time quality of gaze tracking system.

Reaction monitoring using mid-infrared laser-based vibrational circular dichroism.

Changes in vibrational circular dichroism (VCD) were recorded on-line during a chemical reaction. The chiral complex nickel-(-)-sparteine chloride was hydrolyzed to free (-)-sparteine base in a biphasic system of sodium hydroxide solution and chloroform (CHCl(3)). Infrared (IR) and VCD spectra were iteratively recorded after pumping a sample from the CHCl(3) phase through a lab-built VCD spectrometer equipped with a tunable mid-IR quantum cascade laser light source, which allows for VCD measurements even in the presence of strongly absorbing backgrounds. Time-dependent VCD spectra were analyzed by singular value decomposition and global exponential fitting. Spectral features corresponding to the complex and free (-)-sparteine could be clearly identified in the fitted amplitude spectrum, which was associated with an exponential decay with an apparent time constant of 127 min (t(½) = 88 min).

Structural and electronic characterization of fluorine-doped La0.5Sr0.5CoO3-δ using electron energy-loss spectroscopy.

Perovskite oxides, ABO3, are potential catalysts for the oxygen evolution reaction, which is important in the production of hydrogen as a sustainable energy resource. Optimizing the chemical composition of such oxides by substitution or doping with additional elements is an effective approach to improving the activity of such catalysts. Here, we characterized the crystal and electronic structures of fluorine-doped La0.5Sr0.5CoO3-δ particles using scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy (EELS). High-resolution STEM imaging demonstrated the formation of a disordered surface phase caused by fluorine doping. In addition, spatially resolved EELS data showed that fluorine anions were introduced into the interiors of the particles and that Co ions near the surfaces were slightly reduced by fluorine doping in conjunction with the loss of oxygen ions. Peak fitting of energy-loss near-edge structure data demonstrated an unexpected nanostructure in the vicinity of the surface. An EELS characterization comprising elemental mapping together with an energy-loss near-edge structure analysis indicated that this nanostructure could not be assigned to Co-based materials but rather to the solid electrolyte BaF2. Complementary structural and electronic characterizations using STEM and EELS as demonstrated herein evidently have the potential to play an increasingly important role in elucidating the nanostructures of functional materials.

Optimization of suspended particulate transport parameters from measured concentration profiles with a new analytical model.

The water body's suspended concentration reflects many coastal environmental indicators, which is important for predicting ecological hazards. The modeling of any concentration in water requires solving the settling-diffusion equation (SDE), and the values of several key input parameters therein (settling velocity ws, eddy diffusivity Ds, and erosion rates p(t)) directly determine the prediction performance. The time-consuming large-scale simulations would benefit if the parameter values could be estimated through available observations in the target sea area. The present work proposes a new optimization method for synchronously estimating the three parameters from limited concentration observations. First, an analytical solution to the one-dimensional vertical (1DV) SDE for suspended concentrations in an unsteady scenario is derived. Second, the near bottom suspended sediment concentration (SSC) profiles are measured with high-resolution observation. Third, the key parameters are optimized through the best fit of the measured SSC profiles and those modeled with the unsteady solution. Nonlinear least square fitting (NLSF) is introduced to judge the best fits automatically. The high-resolution concentration measurements in a specially-designed cylindrical tank experiment using the Yellow River Delta sediments test the proposed method. The method performs well in the initial period of turbulence generation when sediment resuspension is significant. It optimizes p(t), ws, and Ds with reasonable values and uniqueness of their combination. The proposed theory is a practical tool for quickly estimating key substance transport parameters from limited observations; it also has the potential to construct local parametric models to benefit the 3D modeling of coastal substance transport. Although the present work takes SSC as an example, it can be extended to any suspended particulate concentration in the water.

Estimation and prediction of the multiply exponentially decaying daily case fatality rate of COVID-19.

The spread of the COVID-19 disease has had significant social and economic impacts all over the world. Numerous measures such as school closures, social distancing, and travel restrictions were implemented during the COVID-19 pandemic outbreak. Currently, as we move into the post-COVID-19 world, we must be prepared for another pandemic outbreak in the future. Having experienced the COVID-19 pandemic, it is imperative to ascertain the conclusion of the pandemic to return to normalcy and plan for the future. One of the beneficial features for deciding the termination of the pandemic disease is the small value of the case fatality rate (CFR) of coronavirus disease 2019 (COVID-19). There is a tendency of gradually decreasing CFR after several increases in CFR during the COVID-19 pandemic outbreak. However, it is difficult to capture the time-dependent CFR of a pandemic outbreak using a single exponential coefficient because it contains multiple exponential decays, i.e., fast and slow decays. Therefore, in this study, we develop a mathematical model for estimating and predicting the multiply exponentially decaying CFRs of the COVID-19 pandemic in different nations: the Republic of Korea, the USA, Japan, and the UK. We perform numerical experiments to validate the proposed method with COVID-19 data from the above-mentioned four nations.

Bayesian optimization to estimate hyperfine couplings from 19F ENDOR spectra.

ENDOR spectroscopy is a fundamental method to detect nuclear spins in the vicinity of paramagnetic centers and their mutual hyperfine interaction. Recently, site-selective introduction of 19F as nuclear labels has been proposed as a tool for ENDOR-based distance determination in biomolecules, complementing pulsed dipolar spectroscopy in the range of angstrom to nanometer. Nevertheless, one main challenge of ENDOR still consists of its spectral analysis, which is aggravated by a large parameter space and broad resonances from hyperfine interactions. Additionally, at high EPR frequencies and fields (⩾94 GHz/3.4 Tesla), chemical shift anisotropy might contribute to broadening and asymmetry in the spectra. Here, we use two nitroxide-fluorine model systems to examine a statistical approach to finding the best parameter fit to experimental 263 GHz 19F ENDOR spectra. We propose Bayesian optimization for a rapid, global parameter search with little prior knowledge, followed by a refinement by more standard gradient-based fitting procedures. Indeed, the latter suffer from finding local rather than global minima of a suitably defined loss function. Using a new and accelerated simulation procedure, results for the semi-rigid nitroxide-fluorine two and three spin systems lead to physically reasonable solutions, if minima of similar loss can be distinguished by DFT predictions. The approach also delivers the stochastic error of the obtained parameter estimates. Future developments and perspectives are discussed.

Identification effect of least square fitting method in archives management.

Archives management plays an important role in the current information age. Solving the problem of identifying and classifying archives is essential for promoting the development of archives management. The Least Squares Support Vector Machine (LS-SVM) is obtained by introducing the least squares fitting method into SVM, which is good at solving nonlinear classification. A new wavelet function is used to improve the classifier. At the same time, the cross-validation method is used to optimize the kernel parameters. Finally, the fuzzy theory and LS-SVM are combined to obtain Fuzzy Least Squares Support Vector Machines (FLS-SVM). This FLS-SVM classifier can use the distance between the data points and the classification hyperplane to classify the data in the non-separable region. The performance of FLS-SVM is verified by simulation experiments. The experimental results show that the classification accuracy of FLS-SVM classifier in archive data sets is 98.7%, and the loss rate is only 0.26%. When the wavelet function is used as the kernel function, the average accuracy of the classifier reaches 98.38%. Experiments show that the proposed method has good classification performance. It verifies the feasibility and effectiveness of the least squares fitting method in file management identification and classification.

A Wafer Pre-Alignment Algorithm Based on Weighted Fourier Series Fitting of Circles and Least Squares Fitting of Circles.

The wafer pre-aligner is a crucial component in the lithography process to correct the wafer center and notch orientation. To improve the precision and the efficiency of pre-alignment, a new method to calibrate the center and the orientation of a wafer based on the weighted Fourier series fitting of circles (WFC) method and the least squares fitting of circles (LSC) method, respectively, is proposed. The WFC method effectively suppressed the influence of the outliers and had high stability compared with the LSC method when fitted to the center of the circle. While the weight matrix degenerated to the identity matrix, the WFC method degenerated into the Fourier series fitting of circles (FC) method. The fitting efficiency of the FC method is 28% higher than that of the LSC method, and the fitting accuracy of the center of the FC method is the same as that of the LSC method. In addition, the WFC method and the FC method perform better than the LSC method in radius fitting. The pre-alignment simulation results showed that the absolute position accuracy of the wafer was ±2 µm, the absolute direction accuracy was 0.01°, and the total calculation time was less than 3.3 s in our platform.

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks.

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6−33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10−35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28−43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

A stochastic Bayesian bootstrapping model for COVID-19 data.

We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021, which encompasses four waves. Each wave is appropriately described by a generalized logistic growth curve. Accordingly, the four waves are modeled through a sum of four generalized logistic growth curves. Pointwise values of the twenty input parameters are fitted by a least-squares optimization procedure. Taking into account the significant variability in the daily reported cases, the input parameters and the errors are regarded as random variables on an abstract probability space. Their probability distributions are inferred from a Bayesian bootstrap procedure. This framework is shown to offer a more accurate estimation of the COVID-19 reported cases than the deterministic formulation.

WhatEELS. A python-based interactive software solution for ELNES analysis combining clustering and NLLS.

The analysis of energy loss near edge structures in EELS is a powerful method for a precise characterization of elemental oxidation states and local atomic coordination with an outstanding lateral resolution, down to the atomic scale. Given the complexity and sizes of the EELS spectrum images datasets acquired by the state-of-the-art instrumentation, methods with low convergence times are usually preferred for spectral unmixing in quantitative analysis, such as multiple linear least squares fittings. Nevertheless, non-linear least squares fitting may be a superior choice for analysis in some cases, as it eliminates the need of calibrated reference spectra and provides information for each of the individual components included in the fitted model. To avoid some of the problems that the non-linear least squares algorithms may suffer dealing with mixed-composition samples and, thus, a model comprised by a large number of individual curves we proposed the combination of clustering analysis for segmentation and non-linear least squares fitting for spectral analysis. Clustering analysis is capable of a fast classification of pixels in smaller subsets divided by their spectral characteristics, and thus increases the control over the model parameters in separated regions of the samples, classified by their specific compositions. Furthermore, along with this manuscript we provide access to a self-contained and expandable modular software solution called WhatEELS. It was specifically designed to facilitate the combined use of clustering and NLLS, and includes a set of tools for white-lines analysis and elemental quantification. We successfully demonstrated its capabilities with a control sample of mesoporous cerium oxide doped with praseodymium and gadolinium, which posed challenging case-study given its spectral characteristics.

Controlling COVID-19 Outbreaks with Financial Incentives.

In this paper, we consider controlling coronavirus disease 2019 (COVID-19) outbreaks with financial incentives. We use the recently developed susceptible-unidentified infected-confirmed (SUC) epidemic model. The unidentified infected population is defined as the infected people who are not yet identified and isolated and can spread the disease to susceptible individuals. It is important to quickly identify and isolate infected people among the unidentified infected population to prevent the infectious disease from spreading. Considering financial incentives as a strategy to control the spread of disease, we predict the effect of the strategy through a mathematical model. Although incentive costs are required, the duration of the disease can be shortened. First, we estimate the unidentified infected cases of COVID-19 in South Korea using the SUC model, and compute two parameters such as the disease transmission rate and the inverse of the average time for confirming infected individuals. We assume that when financial incentives are provided, there are changes in the proportion of confirmed patients out of unidentified infected people in the SUC model. We evaluate the numbers of confirmed and unidentified infected cases with respect to one parameter while fixing the other estimated parameters. We investigate the effect of the incentives on the termination time of the spread of the disease. The larger the incentive budget is, the faster the epidemic will end. Therefore, financial incentives can have the advantage of reducing the total cost required to prevent the spread of the disease, treat confirmed patients, and recover overall economic losses.