Fitting Method Research Articles

B-180 A Component Equation Approach to Resolving Calibration Curve Nonlinearity When Using Stable Isotope-Labeled Internal Standards for Quantifying Amino Acids With Mass Spectrometry

Abstract Background Stable isotope-labeled internal standards (SIL-IS) have been widely used with mass spectrometry to accurately quantify analytes. However, over an extended dynamic range, calibration curves calculated with the relative ratio of analytes to their corresponding SIL-IS are typically nonlinear. This makes accurate estimation of sample concentration difficult, especially at lower concentrations. Highly multiplexed clinical mass spectrometry assays routinely face this challenge. The component equation assumes interaction between an analyte and its SIL-IS causes the nonlinearity. Modeling this resulted in a linear calibration curve and high accuracy across the dynamic range. We compared the component equation with other calibration methods to quantify amino acids. Methods A total of 47 compounds were serially diluted and added with extraction solution containing a SIL-IS mixture to prepare 7 levels of calibrators. Calibrators were chromatographically separated using a Sciex ExionLC system with an Imtakt Intrada Amino Acid column and injected into a Sciex Citrine triple-quadrupole mass spectrometer. Seven different fitting methods were compared for calibration of each amino acid: non-weighted linear, quadratic and cubic models, linear models with 1/X and 1/X2 weighting, Padé[1,1] approximation, and component equation. Results Results for an example amino acid, tyrosine, are shown in the table below. Most models yield high accuracy at high concentrations, however, all models recorded significantly higher bias for the lowest calibrator than the component equation (≥55.7% vs 11.8%). The component equation outperformed all other models for mean bias (≥16.6% vs 5.9%). Similar results were obtained for the complete set of analytes. Conclusions When compared to other popular calibration methods to quantify a panel of amino acids, the component equation generated a linear curve and the greatest accuracy across all concentrations. The application of this simple equation could overcome the limitations of existing weighting and non-linear fitting approaches when using SIL-IS for quantifying analytes with mass spectrometry.

Correction: Approach to Receptance Coupling Substructure Analysis based on Full Receptance Estimation of Sub-assembly Using the Modal Fitting Method

Correction: Approach to Receptance Coupling Substructure Analysis based on Full Receptance Estimation of Sub-assembly Using the Modal Fitting Method

A new perspective on the stellar mass-metallicity relation of quiescent galaxies from the LEGA-C survey

We investigated the stellar mass-metallicity relation (MZR) using a sample of 637 quiescent galaxies with 10.4 ≤ log(M*/M⊙) < 11.7 selected from the LEGA-C survey at 0.6 ≤ z ≤ 1. We derived mass-weighted stellar metallicities using full-spectral fitting. We find that while lower-mass galaxies are both metal-rich and metal-poor, there are no metal-poor galaxies at high masses, and that metallicity is bounded at low values by a mass-dependent lower limit. This lower limit increases with mass, empirically defining a MEtallicity-Mass Exclusion (MEME) zone. We find that the spectral index MgFe ≡ √Mgb × Fe4383, a proxy for the stellar metallicity, also shows a mass-dependent lower limit resembling the MEME relation. Crucially, MgFe is independent of stellar population models and fitting methods. By constructing the metallicity enrichment histories, we find that, after the first gigayear, the star formation history of galaxies has a mild impact on the observed metallicity distribution. Finally, from the average formation times, we find that galaxies populate differently the metallicity-mass plane at different cosmic times, and that the MEME limit is recovered by galaxies that formed at z ≥ 3. Our work suggests that the stellar metallicity of quiescent galaxies is bounded by a lower limit which increases with the stellar mass. On the other hand, low-mass galaxies can have metallicities as high as galaxies ∼1 dex more massive. This suggests that, at log(M*/M⊙)≥10.4, rather than lower-mass galaxies being systematically less metallic, the observed MZR might be a consequence of the lack of massive metal-poor galaxies.

The [Y/Mg] chemical clock in the Galactic disk

Context. Stellar ages are an important parameter in studies of the chemical evolution of the Galaxy. To better estimate these ages, various methods complementary to the conventional isochrone fitting method have been implemented in the past decade. Several recent studies have established the existence of a relationship between chemical clocks and stellar ages. The [Y/Mg] clock is a promising technique, but there are still several open questions, such as its validity for metal-poor stars and differences between the thin and thick disk populations. Aims. Our aim is to study the relationship between the [Y/Mg] chemical clock and stellar ages for a sample of solar-type disk stars and to provide the empirical dating relation(s) for the stellar age determination from their precise chemical abundances. We also studied the effect of metallicity and populations on this chemical clock. Methods. We derived precise stellar atmospheric parameters as well as the elemental abundances of Mg and Y through line-by-line differential spectroscopic analysis for a sample of 48 metal-poor solar-type stars based on high-quality, high-resolution ESO/HARPS spectra. From high-precision Gaia astrometric data, stellar masses and ages were estimated through isochrone fitting using Yonsei-Yale isochrones. A joint analysis of our sample, together with a sample of 185 solar twins and analogues from our previous works, was performed to calibrate the [Y/Mg] chemical clock in the Galactic disk for −0.71 < [Fe/H] < +0.34. Open clusters and stars with asteroseismic ages were used to validate our relations. Results. Two different populations are clearly seen in the [Mg/Fe]−[Fe/H] plane: the thick and thin disks. Thick disk stars show an age-metallicity relation, whereas the thin disk shows a flatter age–metallicity distribution. We find a strong, metallicity–dependent anti-correlation between the [Y/Mg] ratio and the stellar ages of our sample. For the first time in the literature, we report similar correlations for thin and thick disk stars. Conclusions. We find that the [Y/Mg] relation(s) found here for solar-type stars in a wide metallicity range are compatible with those found for solar twins in the literature. Our relation provides high accuracy and precision (0.45 and 0.99 Gyr, respectively) comparable with the best accuracy achieved for solar twins to date.

Three-Frame Random Phase-Shifting Algorithm Based on VU Decomposition Method and Ellipse Fitting

Abstract To enhance the precision and robustness of the three-frame phase shift technique, a new three-frame random phase-shifting fringe pattern phase demodulation method that combines VU decomposition and ellipse fitting techniques without the need for pre-filtering is proposed. The proposed method first performs VU decomposition on the interferograms to establish two orthogonal components of the fringe pattern. Then, the ellipse fitting method is used to obtain the relevant ellipse coefficients, thereby achieving precise phase demodulation. This method does not require an accurate phase-shifting process, thereby relaxing the stringent requirements on the phase shifter. Compared with traditional algorithms, the proposed method performs better under conditions of non-uniform background intensity and modulation amplitude. Numerical simulation experiments demonstrate that the proposed method maintains good stability across 20 repeated tests. Within the SNR range of 30 to 50 dB, the RMSE of the suggested approach is approximately 0.006 rad. This proves the effectiveness of the algorithm and provides a new approach for the development of three-frame phase-shifting technology.

Value of cardiac enzyme spectrum for the risk assessment of mortality in critically ill children: a single-centre retrospective study

ObjectivesIdentifying high-risk paediatric patients with a poor prognosis and providing timely and adequate treatment are critical. This study aimed to evaluate the effects of different types of cardiac enzyme spectrum...

Personalized Health Management Strategies Based on Deep Reinforcement Learning in the Network Environment

In order to study the optimal personalized motion push target, the author proposes a personalized health managementstrategy based on deep reinforcement learning in the network environment. Firstly, the research problem is defined, and a real-time interactive personalized motion target decision-making model is constructed; Subsequently, in response to the uncertain characteristics of user behavior in the problem, a deep reinforcement learning algorithm was adopted, combined with the departure strategy temporal difference learning method and neural network nonlinear fitting method, in order to learn strategies from user historical data; Finally, the effectiveness of the proposed method was validated using a real dataset from Fitbit. The research results indicate that personalized motion goal push services based on deep reinforcement learning can help users cultivate a healthy lifestyle and improve their personal health management level by analyzing user behavior data in real-time, providing scientific guidance and timely incentives.

A dual-template molecularly imprinted electrochemical sensor assisted with BO-XGBoost algorithm for simultaneous determination of dopamine and acetaminophen

The simultaneous detection of dopamine (DA) and acetaminophen (AP) is crucial for diagnosing and treating related mental disorders. However, accurately determining DA and AP in biological samples remains challenging due to the interference from other biomolecules, such as ascorbic acid (AA), uric acid (UA), epinephrine (EP), etc. Here, we present a dual-template molecularly imprinted electrochemical sensor for the selective recognition of DA and AP. Reduced graphene(rGO)-gold nanoparticles(AuNPs) composite were utilized to enhance the effective area and increase the electron transfer rate of glassy carbon electrode(GCE), thus the electrochemical response signals were amplified. This sensor combined the advantages of nanocomposites and molecularly imprinted polymer (MIP) to achieve highly sensitive and selective detection of DA and AP. However, decreases in the current response of some analytes and variations in relationships of the peak current versus analyte concentration will occur due to adsorption competition on active sites in multianalyte mixtures, rendering the detection range constrained and traditional linear fitting methods inaccurate for predicting analyte concentrations. Therefore, a concentration prediction model based on XGBoost was developed for the sensor to achieve the reliable multi-component determination of DA and AP within the identical measurement range as individual detection. In this model, nine characteristic parameters were extracted from the differential pulse voltammetry (DPV) response curve and Bayesian optimization (BO) was adopted for automatic hyperparameter search, thereby the prediction errors were reduced and the generalization ability was improved. Experiments indicated that the MIP/AuNPs/rGO/GCE exhibited a wide detection range of 2–240 μM and 3–240 μM for DA and AP, with detection limits down to 0.26 μM and 0.33 μM, respectively. In addition, the intelligent MIP/AuNPs/rGO/GCE sensor based on BO-XGBoost model provides more accurate prediction results for untrained concentration combinations obtained from fetal bovine serum samples, indicating that the proposed detection scheme holds potential in future clinical diagnosis.

Tunnel full-space deformation measurement based on improved downsampling and registration approach by point clouds

To fully leverage the volume measurement advantage of three-dimensional laser scanning technology in tunnel deformation monitoring, a full-space deformation analysis is proposed. A Box Improved Moving Average (BIMA) method featured by the adaptive window size downsampling is proposed which aims to achieve varying levels of downsampling in different densities regions. the combination of Maximal Cliques (MAC) and Iterative Closest Point (ICP) is utilized to achieve accurate registration. Point normal information is utilized to determine the correspondence to calculate deformation which shows an average accuracy of 7 % higher than the ICP by a convergence measurement test. The deformation convergence accuracy can go beyond 76 % when the point cloud reserved rate is approximately 10 %. Comparison between the method proposed in this study and the traditional point cloud cross sectional fitting method shows that the discrepancy between these two methods is approximately within 5 mm, with a minimum value of only 0.15 mm.

A Dynamic Path Planning Method for UAVs Based on Improved Informed-RRT* Fused Dynamic Windows

In recent years, navigating rotor drones in complex and dynamic environments has been a significant challenge. This paper proposes an improved path planning method by integrating the enhanced Informed-RRT* algorithm with the Dynamic Window Approach (DWA) algorithm. Firstly, the probability weight function for sampling direction is dynamically adjusted based on the Euclidean distance between the start and goal points to obtain higher-quality sampling points. Secondly, the Artificial Potential Field (APF) concept is introduced during the generation of new nodes, applying the improved APF method to the elliptical sampling area to guide the direction of new node generation. This significantly reduces the number of redundant nodes, enhances the convergence speed, and improves the obstacle avoidance efficiency. Then, a polynomial fitting method is employed to optimize the path, yielding a smoother trajectory. Finally, the improved DWA algorithm is integrated. Initially, the Informed-RRT* algorithm generates an optimal path from the starting point to the goal point in the global space, which is then provided as an initial reference path to the DWA algorithm. During actual operation, the DWA algorithm dynamically adjusts control inputs based on the current position, speed, and reference path of the drone, ensuring that the drone can avoid dynamic obstacles and unknown static obstacles in real time while staying as close to the reference path as possible.

A space-adiabatic theorem for longitudinal and transversal wave motion analysis of graded metamaterials

The use of graded metamaterials is widespread in the fields of wave manipulation and energy harvesting. However, they have the drawback of causing energy scattering after a certain period due to wave reflection or trapping, known as the rainbow effect, in some cases. To address this issue, we extend the adiabatic theorem to the spatial modulation field to mitigate the impact of the rainbow effect, thereby enhancing transmission at the interface of graded metamaterials. Firstly, an analytical expression for the limiting gradient in the space-adiabatic theorem is derived. Then, it is applied to study the longitudinal wave motion in a spring-mass with graded resonators (SMGR) system and the transversal wave motion in a beam with graded resonators (BGR) system. The numerical study demonstrates that if the spatial gradient of system properties is small enough to satisfy the adiabatic theorem, wave scattering can almost be neglected, and transmission will experience a significant increase compared to the non-adiabatic case. Finally, the critical boundary between adiabatic and non-adiabatic cases is determined using the fitting method. This study introduces a design strategy for graded metamaterials, offering opportunities for communication, sensing, energy harvesting, and various other applications.

Coseismic surface ruptures of 20 strike-slip earthquakes measured from geodetic imaging data

Observations of coseismic surface ruptures are widely used for examining faulting mechanics and kinematics, and in fault displacement hazard analysis. Here, first, we provide new remote sensing observations of 20 historic surface rupturing continental strike-slip earthquakes (6.1 ≤ Mw≤ 7.9). To measure the near-field surface deformation pattern, we have processed a range of raw radar and optical images from satellite and aerial platforms with pixel tracking techniques, and in two cases using standard interferometric synthetic aperture radar (InSAR). We provide a range of observations, including surface displacement maps that constrain at least the horizontal component of surface motion (2D), and for three events the full 3D components. Second, we provide strain invariants for each event, which includes the finite dilatational strain, strain magnitudes, the vorticity, and maximum shear strain. Third, we provide a total of 134 surface fault rupture traces that are mapped manually from the displacement maps. Finally, we provide 2648 measurements of coseismic horizontal fault-parallel surface fault slip that are measured objectively using swath profiles with a function fitting method that we have developed.

Coupling Dynamics Study on Multi-Body Separation Process of Underwater Vehicles

Based on the Newton-Euler method, a coupling rigid-body dynamics model of a Trans-Medium Vehicle (TMV) separating from an Unmanned Underwater Vehicle (UUV) has been established. The modeling is based on the “holistic method” and “Kane” ideas respectively, so that most of the equations can be derived without considering the internal forces between the two bodies. The separation propulsion force, which is an internal force, only appears in the relative glide dynamics equation of the TMV along the axis of the separation tube that is installed on the UUV. This greatly reduces the workload of modeling and derivation. The UUV works entirely underwater, while the hydrodynamic shape of the TMV changes continuously during the process of the TMV separating from the UUV. Therefore, accurate hydrodynamic calculations for the UUV and TMV are the basis of numerical resolution for the two rigid bodies’ coupling dynamics model in water. A large number of numerical simulations was conducted using CFD methods to investigate the hydrodynamic performance of the UUV and TMV under various conditions. These simulations aim to establish a hydrodynamic database, and accurate hydrodynamic models were developed through fitting methods and online interpolation. In the process of solving the coupling dynamics of two bodies, the hydrodynamic model is used to calculate the hydrodynamic force experienced by the UUV and TMV. This balances the accuracy and efficiency of a numerical simulation. Finally, numerous simulations and comparative analyses were conducted under various operational conditions and separation parameters. The simulation results indicate that the impact of TMV separation on the motion state of the UUV becomes more prominent with smaller UUV to TMV mass ratios or deeper TMV separation depths. This effect can further influence the stability control of the UUV. The coupling rigid body dynamics analysis method established in this paper provides a fast and effective prediction method for use during the scheme design and separation safety evaluation phases of creating UUV-TMV systems.

Microwave diagnostics of flare plasma by the direct fitting method based on data from the Siberian Radioheliograph

In this paper, we analyze images and the frequency spectrum of microwave emission in the maximum of brightness distribution in the January 20, 2022 and July 16, 2023 flares recorded by the Siberian Radioheliograph in the 3–6 GHz and 6–12 GHz ranges. We use the obtained spectrum data for radio diagnostics of magnetic field strength and orientation, plasma density, and parameters of accelerated particles in a radio source. The radio diagnostics is carried out by a method based on minimizing the functional containing the intensities of theoretically calculated and observed frequency spectra of left-polarized and right-polarized emission. Since the form of such a multidimensional functional is quite complex, and it is not possible to minimize it by standard approaches, we employ a genetic minimization method. The radio diagnostics allows us to determine features of the dynamics of the magnetic field intensity and orientation, as well as the density and the energy spectral index of non-thermal electrons in the region of maximum brightness of the radio source. We have found that during the growth phase of the main radiation peaks the magnetic field decreases, whereas during the decay phase, on the contrary, it increases. The rate of these changes varies from a few G/s to 11 G/s for the January 20, 2022 flare and is about 1 G/s for the July 16, 2023 flare.

Tunable low-frequency broadband band gap in nonlinear locally resonant metamaterial inspired by sarcomere structure

Since traditional locally resonant metamaterials (LRMs) cannot achieve a broadband band gap in the low-frequency band, the suppression and isolation of low-frequency broadband vibration has always been a difficult problem in the field of vibration control. Therefore, in this paper, inspired by the structure of the sarcomere, a novel nonlinear LRM is proposed, aiming to open a wider band gap in the low-frequency band. Firstly, a statics model of the nonlinear local resonator (LR) is established to explore the mechanical nonlinear characteristics of the structure with different geometric parameters. And the inflection point fitting method is used to approximate the nonlinear statics model of nonlinear LR. Then a dynamics model was established, the nonlinear dispersion relationship of the infinite periodic system was obtained by Bloch's theorem and l-P perturbation method. And an analytical investigation of the theoretically calculated band gap is conducted through the band gap ratio and relative band gap width. In addition, according to the influence of different structural parameters on the real and imaginary parts of the wave vector, the regulation rules and mechanisms for nonlinear LRM to open low-frequency broadband band gaps are revealed. Finally, the Runge-Kutta method was used to numerically calculate the vibration transmissibility curve of the nonlinear LRM composed of a limited number of nonlinear LRs. And the correctness of the theoretically calculated band gap was verified. The research results provide theoretical support for the application of nonlinear LRMs in the suppression and isolation of low-frequency broadband vibrations.

A Preliminary Comparative Study on the Centering Algorithms for Cassini-ISS NAC Images

Obtaining high precision is an important consideration for astrometric studies using images from the Narrow Angle Camera (NAC) of the Cassini Imaging Science Subsystem (ISS). Selecting the best centering algorithm is key to enhancing astrometric accuracy. In this study, we compared the accuracy of five centering algorithms: Gaussian fitting, the modified moments method, and three point-spread function (PSF) fitting methods (effective PSF (ePSF), PSFEx, and extended PSF (xPSF) from the Cassini Imaging Central Laboratory for Operations (CICLOPS)). We assessed these algorithms using 70 ISS NAC star field images taken with CL1 and CL2 filters across different stellar magnitudes. The ePSF method consistently demonstrated the highest accuracy, achieving precision below 0.03 pixels for stars of magnitude 8–9. Compared to the previously considered best, the modified moments method, the ePSF method improved overall accuracy by about 10% and 21% in the sample and line directions, respectively. Surprisingly, the xPSF model provided by CICLOPS had lower precision than the ePSF. Conversely, the ePSF exhibits an improvement in measurement precision of 23% and 17% in the sample and line directions, respectively, over the xPSF. This discrepancy might be attributed to the xPSF focusing on photometry rather than astrometry. These findings highlight the necessity of constructing PSF models specifically tailored for astrometric purposes in NAC images and provide guidance for enhancing astrometric measurements using these ISS NAC images.

A novel stone fruit quality non-destructive evaluation method based on PVR by LF-NMRI

For stone fruits, the pulp ratio, indicating the proportion of pulp to the total fruit, serves as a vital metric in assessing stone fruit quality. Traditionally, this ratio is computed by measuring the mass of each component through destructive sampling, and acquired results remain controversial. In order to obtain this ratio non-destructively and more accurately, a novel evaluation method for stone fruit quality based on pulp volume ratio (PVR) by low-field nuclear magnetic resonance imaging (LF-NMRI) is proposed. This approach integrates the SwinUnet segmentation network to differentiating the fruit pulp and core structure, thereby precisely computing the volume proportion of pulp. Experimental results reveal that SwinUnet exhibits better accuracy and robustness in the LF-NMRI data compared to the other networks. Based on the segmentation results, the disparities between the PVR values computed using the ellipse fitting method and the integral method, compared to the ground truth (GT), are less than 1% and 0.5% respectively. This paper provides a new reference for selecting premium stone fruits and enriches the fruit quality evaluation system.

Impact of Data Corruption and Operating Temperature on Performance of Model-Based SoC Estimation

Electric vehicles (EVs) are becoming popular around the world. Making a lithium battery (LIB) pack with a robust battery management system (BMS) for an EV to operate under different complex environments is both a challenge and a requirement for engineers. A BMS can intelligently manage LIB systems by estimating the battery state of charge (SoC). Due to the nonlinear characteristics of LIB, influenced by factors such as the harsh environment and data corruption caused by electromagnetic interference (EMI) inside electric vehicles, SoC estimation should consider available capacity, model parameters, operating temperature and reductions in data sampling time. The widely used model-based algorithms, such as the extended Kalman filter (EKF) have limitations. Therefore, a detailed review of the balance between temperature, data sampling time, and different model-based algorithms is necessary. Firstly, a state of charge—open-circuit voltage (SoC-OCV) curve of LIB is obtained by the polynomial curve fitting (PCF) method. Secondly, a first-order RC (1-RC) equivalent circuit model (ECM) is applied to identify the battery parameters using a forgetting factor-based recursive least squares algorithm (FF-RLS), ensuring accurate internal battery parameters for the next step of SoC estimation. Thirdly, different model-based algorithms are utilized to estimate the SoC of LIB under various operating temperatures and data sampling times. Finally, the experimental data by dynamic stress test (DST) is collected at temperatures of 10 °C, 25 °C, and 40 °C, respectively, to verify and analyze the impact of operating temperature and data sampling time to provide a practical reference for the SoC estimation.

A multilevel lightweight fish respiratory frequency measurement method – Segmentation instead of detection

Respiratory frequency is an important physiological parameter for fish health. Traditional respiration detection methods have inherent frequency measurement error problems, and segmentation provides an innovative technique for respiration measurement. By shifting the focus from detection to segmentation, this approach provides robust and reliable support for measuring fish respiratory frequency accurately. However, the existing segmentation network models face challenges related to their large scale, high computational demands, and limited deployability at the edge. To overcome these challenges, this paper introduces an easily portable, high-precision segmentation method based on lightweight network and network slimming technique, named PM-YOLOv5s-seg (Prune MobileNetv3-YOLOv5s Segmentation). PM-YOLOv5s-seg utilizes the MobileNetv3 network as its segmentation backbone, enabling the extraction of essential fish mouth features. Simultaneously, it employs depth separable convolution to reduce network parameters and calculation amounts. Furthermore, the model undergoes additional sparsification via channel pruning, significantly improving its efficiency. Finally, respiratory frequency modeling is performed on the obtained fish mouth segmentation results, utilizing innovative techniques such as Ellipse fitting, Kalman filtering, and polynomial fitting methods. Comparison with the state-of-the-art methods demonstrates the superiority of the multilevel lightweight segmentation model PM-YOLOv5s-seg in terms of detection accuracy, computational efficiency, and model size. This model achieves an optimal balance between the speed and accuracy in fish respiratory frequency measurement, which holds significant implications for fish welfare as well as disease detection.

Continuous and Stable Printing Method of Planar Microstructure Based on Meniscus-Confined Electrodeposition.

The meniscus-confined electrodeposition (MCED) technique offers advantages such as low cost and wide applicability, making it a promising method in the field of micro/nanofabrication. However, unstable meniscal morphology and poor deposition quality during planar deposition in MCED necessitate the development of improved methods. Therefore, a planar adaptive micro-tuning deposition method (PAMTDM), which utilizes the positioning technology of scanning electrochemical cell microscopy (SECCM) and employs a singular value decomposition (SVD) planar fitting method to determine the flatness of the deposition plane, is proposed. An adaptive micro-tuning motion mode was proposed by analyzing the variation patterns of the meniscus. Moreover, a combination of multi-physics finite element simulations and orthogonal experimental methods was introduced to determine the optimal motion parameters. The experimental results demonstrate that the PAMTDM effectively addresses the issues encountered during planar growth. Compared to the point-by-point deposition method, the PAMTDM achieves a threefold increase in deposition speed for continuous deposition of 105-μm-long line segments in two-dimensional planes, with a deposition current error of less than 0.2 nA. In conclusion, the proposed method provides significant insights into the broad future applications of MCED.