Curve Fitting Method Research Articles

Design and Implementation of Bluetooth Low Energy Based Access Control System

This paper discusses the design of a Bluetooth Low Energy (BLE) based access control system intended to make access control more practical to implement on areas with high personnel turnover rate by making access rules easy to set and making access keys relatively safe to distribute compared to existing key-based access control systems. The use of BLE technology allows the system to estimate key position within the system by utilizing the curve fitting method for distance estimation and the trilateral method for positioning. The proposed system consists of a server, an admin panel, electronic locks, access keys in the form of a wearable, and access keys in the form of an Android application. The system is found to be capable of implementing access control functionalities and capable of implementing indoor positioning based on sections.

Three-dimensional information measurement and reconstruction for micro-hole array based on the modified shape from focus

The geometrical characteristics of the micro-holes and the consistency of spatial distribution among the micro-holes in the micro-hole array fabricated by laser beam drilling (LBD) directly influence the cooling performance, reliability, and longevity of the workpiece. It is of great significance to establish the relationship between the LBD processing parameters and the characteristics of the micro-hole array through the three-dimensional (3D) information measurement and reconstruction of the micro-hole array, as well as to guide and improve the LBD processing technology. However, due to the small size and high aspect ratio of the micro-holes in the micro-hole array, the 3D information measurement and reconstruction of the micro-hole array face challenges. The conventional micro-hole measurement methods suffer from low efficiency, high measurement costs, and potentially destructive measurement processes. Furthermore, the conventional micro-hole measurement methods face challenges in simultaneously obtaining the geometrical characteristics of the micro-holes and the spatial distribution among the micro-holes in the micro-hole array. Therefore, a non-contact and non-destructive 3D information measurement and reconstruction method for the micro-hole array based on the modified shape from focus is proposed. A novel focus measure operator based on grayscale difference and adaptive neighborhood size is proposed to accurately evaluate the focus value and obtain the initial focus volume matrix. A novel image processing operator based on the gradient information of the image sequence is proposed to enhance the initial focus volume matrix. The experimental results indicate that the proposed focus measure operator, combined with the novel image processing operator, is more suitable and reliable for detecting the micro-holes due to its better unimodality, higher contrast, and narrower peak width than the conventional focus measure operator. The optimal focus position of each image point is obtained using the modified Gaussian curve fitting method. On this basis, the 3D information measurement and reconstruction of the micro-hole array are realized. By comparing the results with the conventional micro-hole measurement method, the feasibility and accuracy of the proposed method have been confirmed to be an effective measurement method for the micro-hole array.

Synchronizing the Western Gotland Basin (Baltic Sea) and Lake Kälksjön (central Sweden) sediment records using common cosmogenic radionuclide production variations

Multi-archive studies of climate events and archive-specific response times require synchronous time scales. Aligning common variations in the cosmogenic radionuclide production rate via curve fitting methods provides a tool for the continuous synchronization of natural environmental archives down to decadal precision. Based on this approach, we synchronize 10Be records from Western Gotland Basin (WGB, Baltic Sea) and Lake Kälksjön (KKJ, central Sweden) sediments to the 14C production time series from the IntCal20 calibration curve during the Mid-Holocene period ~6400 to 5200 a BP. Before the synchronization, we assess and reduce non-production variability in the 10Be records by using 10Be/9Be ratios and removing common variability with the TOC record from KKJ sediments based on regression analysis. The synchronizations to the IntCal20 14C production time scale suggest decadal to multi-decadal refinements of the WGB and KKJ chronologies. These refinements reduce the previously centennial chronological uncertainties of both archives to about ± 20 (WGB) and ±40 (KKJ) years. Combining proxy time series from the synchronized archives enables us to interpret a period of ventilation in the deep central Baltic Sea basins from ~6250 to 6000 a BP as possibly caused by inter-annual cooling reducing vertical water temperature gradients allowing deep water formation during exceptionally cold winters.

Kinetic analysis and dosimetric characteristics of the thermoluminescent glow curve of aquamarine (Be3Al2(SiO3)6:Fe)

Kinetic analysis of the thermoluminescent (TL) glow curve of aquamarine, a cyan variety of beryl (Be3Al2(SiO3)6), is reported. Samples were irradiated at room temperature using a90Sr/90Y β source at a rate of 0.10 Gy-1. Measurements made at 1 °C/s after irradiation from 1 to 100 Gy show a high intensity peak at 75 °C followed by three secondary peaks at 115, 189 and 302 °C respectively. The dose response of the high intensity peak (called the main peak) is linear within the first 20 Gy, but tends towards sub-linearity as the dose is close to 100 Gy. The signal decays with the time delay between irradiation and reading. It decays about 14% of its initial value, 600 s after irradiation. Reproducibility analysis shows that the material reproduces its response under identical experimental conditions with an uncertainty of 0.8% after 10 Gy beta dose irradiation. The kinetic analysis of the TL glow curve was carried out using the curve fitting method. The results show that the main peak follows first order kinetics, its activation energy is of the order of 1 eV and the frequency factor is between 1012 and 1014 s−1, while some secondary peaks follow general order kinetics.

Fast and robust demodulation of temperature from sparse sapphire fiber Bragg grating spectra with machine learning

Sapphire fiber Bragg gratings (FBGs) have demonstrated their efficacy in sensing at high-temperature harsh environments owing to their elevated melting point and outstanding stability. However, due to the extremely high volume of modes supported by the clad-less sapphire fiber, the demodulation capability of the reflected spectra is hindered due to their irregular and somewhat complicated shapes. Hence, a mode-stripping or scrambling step is typically employed beforehand, albeit at the expense of sensor robustness. Additionally, conventional interrogation of sapphire FBG sensors relies on an optical spectrum analyzer due to the high sensitivity provided by the spectrum analyzer, where the long data acquisition time restricts the system from detecting instantaneous temperature variations. In this study, we present a simple sensor configuration by directly butt-coupling the sapphire FBG multi-mode lead-out fiber to a single-mode lead-in fiber, and detect its reflected spectra via a low-cost, fast, and coarsely resolved (166 pm) spectrometer. We leverage machine learning to compensate for the under-sampling of the measured FBG spectra and achieve a temperature accuracy of 0.23 °C at a high data acquisition rate of 5 kHz (limited by the spectrometer). This represents a tenfold improvement in accuracy compared to conventional peak-searching and curve-fitting methods, as well as a significant enhancement in measurement speed that enables dynamic sensing. We further assess the robustness of our sensor by attaching one side of the sensor to a vibrator and still observe good performance (0.43 °C) even under strong shaking conditions. The introduced demodulation technology opens up opportunities for the broader use of sapphire FBG sensors in noisy and high-temperature harsh environments.

Penggabungan Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> dalam Matriks Poli (Metil Metakrilat) untuk Aplikasi Memori Tidak Meruap

MXenes, with their unique surface properties and 2D structure, have demonstrated promising potential in electronic devices, particularly in memory storage. This study explored the potential of 2D Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> for the nonvolatile memory (NVM) application. The simple solution process routes were used to fabricate the two-terminal bistable switching devices. The silver nanowires/nanocomposite/ITO structure was deposited on a glass substrate using spin coating and spray coating techniques. The Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene flakes were incorporated into a poly(methyl methacrylate) (PMMA) polymer host to form the nanocomposite and act as a charge-trapping layer. Meanwhile, PMMA acts as a dielectric layer. The measured current-voltage data showed a bistable current behavior with the presence of a memory window. The fabricated NVM memory devices were reprogrammable when the endurance test was performed and stable up to 1×104 s cycles with a distinct ON/OFF ratio of 103. The conduction mechanisms were identified using the curve-fitting method with double log plots of current-voltage (I-V) data. Based on the obtained I-V characteristics, various conduction mechanisms, especially Schottky and Poole-Frenkel emission, trapped charge limiting current, and space charge limited current, were proposed to be responsible for the bistable switching behavior. Thus, the results of this study provide an experimental basis for using MXene in non-volatile memory applications.

Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis

X-ray photoelectron spectroscopy (XPS) remains a fundamental technique in materials science, offering invaluable insights into the chemical states and electronic structure of a material. However, the interpretation of XPS spectra can be complex, requiring deep expertise and often sophisticated curve-fitting methods. In this study, we present a novel approach to the analysis of XPS data, integrating the utilization of large language models (LLMs), specifically OpenAI’s GPT-3.5/4 Turbo to provide insightful guidance during the data analysis process. Working in the framework of the CIRCE-NAPP beamline at the CELLS ALBA Synchrotron facility where data are obtained using ambient pressure X-ray photoelectron spectroscopy (APXPS), we implement robust curve-fitting techniques on APXPS spectra, highlighting complex cases including overlapping peaks, diverse chemical states, and noise presence. Post curve fitting, we engage the LLM to facilitate the interpretation of the fitted parameters, leaning on its extensive training data to simulate an interaction corresponding to expert consultation. The manuscript presents also a real use case utilizing GPT-4 and Meta’s LLaMA-2 and describes the integration of the functionality into the TANGO control system. Our methodology not only offers a fresh perspective on XPS data analysis, but also introduces a new dimension of artificial intelligence (AI) integration into scientific research. It showcases the power of LLMs in enhancing the interpretative process, particularly in scenarios wherein expert knowledge may not be immediately available. Despite the inherent limitations of LLMs, their potential in the realm of materials science research is promising, opening doors to a future wherein AI assists in the transformation of raw data into meaningful scientific knowledge.

Optimization of laser-texturing process parameters of Ti6Al4V alloys using metaheuristic algorithms

Manufacturing microscale textures can reduce friction and wear compared with smooth surfaces. In laser surface texturing, the resulting texture geometry is directly affected by laser parameters such as laser power, scanning speed and overlap rate. Therefore, finding the optimal parameter settings to improve the process performance is crucial. One of the biggest challenges encountered during the optimization stage is accurately determining the functional relationship between surface quality and process parameters. Another significant problem is solving the nonlinear equation system. This study aims to investigate the effect of laser-textured surfaces on the tribological behavior of titanium Ti6Al4V. Optimization of laser texture process parameters using metaheuristic algorithms is a hot topic. The first contribution of this study is the modeling of the contact angle, kurtosis and skewness quality parameters of Ti6Al4V titanium alloy based on the polynomial curve fitting method as a function of laser power, laser speed and overlap rate. Another significant contribution is to optimize the proposed nonlinear regression models using metaheuristic approaches such as genetic algorithm, particle swarm optimization, whale optimization algorithm, gray wolf optimization, equilibrium optimizer, teaching-learning-based optimization, student psychology-based optimization (SPBO), slime mould algorithm and social network search. The reason for using many metaheuristics is to test the effectiveness of different metaheuristic methods in solving this problem. Analysis of variance was performed to evaluate experimental data and compare the success of optimization methods. The results revealed that the proposed metaheuristic methods can be applied to the related problem. It has been observed that the methods give identical results. Among these methods, the SPBO method provided the best performance in the optimization of all laser output parameters.

Faradaic and Non-Faradaic Self-Discharge Mechanisms in Carbon-Based Electrochemical Capacitors.

Electrochemical capacitors (ECs) play a crucial role in electrical energy storage, offering great potential for efficient energy storage and power management. However, they face challenges such as moderate energy densities and rapid self-discharge. Addressing self-discharge necessitates a fundamental understanding of the underlying processes. This review sets itself apart from other reviews by focusing on the basic principles of self-discharge processes in carbon-based ECs, particularly examining the nature of the process and the involvement of redox reactions. This study delineates the potential conditions for various self-discharge processes and proposes plausible criteria for differentiation, complemented by mathematical modeling. Additionally, the model selection, curve fitting, and effective tuning methods are explored to control self-discharge processes.

A P–S–N curve fitting method based on mixed Weibull distribution and expectation-maximization algorithm

In practical engineering, there are many factors affecting the fatigue life of actual components, and a single Weibull distribution is insufficient to accurately describe the impact of various factors on fatigue life. In order to solve the above problems, 316L stainless steel is taken as the experimental object to study the effects of phase difference and stress levels on fatigue life based on the mixed Weibull distribution model. Firstly, the mixed Weibull distribution model is converted into the mixed extreme value distribution model, and used Euler's constant to calculate relevant parameters of extreme value distribution and Expectation-Maximization (EM) algorithm to determine the probability density function of the mixed Weibull distribution. Secondly, the probability density function is integrated to obtain the distribution function and the fatigue life under different failure probabilities is calculated. Finally, the influence of non-proportional additional reinforcement and stress amplitude on fatigue life is studied by taking them as hidden variables of mixed Weibull distribution, which is used to obtain P-S-N curves for various failure probabilities. Through the proposed method, the influence of different factors on fatigue life can be considered from the perspective of statistics, and the accuracy of fatigue life prediction can be promoted.

High remnant cholesterol as a risk factor for developing chronic kidney disease in patients with prediabetes and type 2 diabetes: a cross-sectional study of a US population.

To examine any potential links between remnant cholesterol (RC) and comorbid chronic kidney disease (CKD) in individuals with prediabetes and type 2 diabetes mellitus (T2DM). We used data from 2709 American people aged > 20 years from the National Health and Nutrition Examination Survey (NHANES) during 2011-2018. Subjects were categorized according to whether they had comorbid CKD. Logistic regression models and smoothed curve fitting methods were employed to assess the association of RC with comorbid CKD in patients with prediabetes and T2DM. The 2709 participants included 1473 patients with T2DM and 1236 with prediabetes [impaired glucose tolerance (IGT) and impaired fasting glucose (IFG)], of whom 744 (27.46%) had comorbid CKD. In multivariate-adjusted analysis, both RC and triglycerides (TG) were significantly associated with an increased risk of comorbid CKD, and a 1 mmol/L elevation of RC increased the risk by 38.1% [OR (95% CI) 1.636 (1.242, 2.156)], which was higher than the risk associated with a 1mmol/L increase in TG [1.255 (1.106, 1.424)]. Additionally, those in the highest quartile of RC had a 43.6% higher risk of concomitant renal damage than those in the lowest quartile. RC was linearly and positively associated with the incidence of comorbid CKD in this population. RC is an independent risk factor for comorbid CKD in patients with prediabetes and T2DM. This finding provides a novel insight into the management and early detection of renal disease in patients with impaired glucose metabolism.

Influence of shear yield strength of rail material on the shakedown limit in shakedown map

Rail materials with different shear yield strengths can exhibit different rolling contact fatigue (RCF) damage response during cyclic contact loading. The objective of this work is to achieve the actual shakedown limits in the shakedown map for rail materials with different shear yield strengths through RCF simulation tests of wheel-rail, and to investigate the relationship between the shear yield strength (ke) and the shakedown limit. Rolling-sliding tests were performed to investigate the RCF damage states of three types of rail materials with different shear yield strengths. Then, according to the differences of RCF damage states for each rail material and the method of nonlinear curve fitting, the actual shakedown limits for three types of rail materials were obtained. The actual shakedown limits for the three types of rail materials were lower than the original one in the classical shakedown map. With an increase in the shear yield strength (ke) of the rail material, the actual shakedown limit is reduced. Moreover, a relationship emerged which allowed a simple method to be proposed to obtain the actual shakedown limit based on the shear yield strength. The changing process and differences in the impact of different shear stress levels on the microstructural damage transformation of rail materials from the perspective of contact mechanics have been discussed. The behavior of severe accumulation of residual stresses during cyclic loading and more likely initiation of microcracks are the main reasons why rail materials with a higher shear yield strength (ke) under great contact loads are more prone to developing RCF crack damages. That is, the shakedown limit for the material with a higher shear yield strength (ke) is much lower. In view of the application of the shakedown map in the evaluation and prediction for RCF damage of rail materials with different shear yield strengths, different shakedown limits should be applied to different rail materials when analyzing their likely performance.

Revealing defect centers in PbWO4 single crystals using thermally stimulated current measurements

The trap centers have a significant impact on the electronic properties of lead tungstate (PbWO4), suggesting their crucial role in optoelectronic applications. In the present study, we investigated and revealed the presence of shallow trap centers in PbWO4 crystals through the utilization of the thermally stimulated current (TSC) method. TSC experiments were performed in the 10–280 K range by applying a constant heating rate. The TSC spectrum showed the presence of a total of four peaks, two of which were overlapped. As a result of analyzing the TSC spectrum using the curve fit method, the activation energies of revealed centers were found as 0.03, 0.11, 0.16, and 0.35 eV. The trapping centers were associated with hole centers according to the comparison of TSC peak intensities recorded by illuminating the opposite polarity contacts. Our findings not only contribute to the fundamental understanding of the charge transport mechanisms in PbWO4 crystals but also hold great promise for enhancing their optoelectronic device performance. The identification and characterization of these shallow trap centers provide valuable insights for optimizing the design and fabrication of future optoelectronic devices based on PbWO4.

Winter wheat irrigation parameters in the Brazilian Cerrado

Wheat is the second most produced cereal in the world. The gap between production and consumption in Brazil shows that wheat production must be increased to meet the domestic demand. In this context, the objective of this study was to determine the crop coefficient of wheat and the ideal moment for irrigation, based on the soil water depletion rate (f). The study was carried out in the tropics, on an Oxisoil (Typic Haplustox), in an experimental field of Embrapa Cerrados in Planaltina, Federal District of Brazil. The experimental design consisted of randomized blocks with three replications. The treatments were differentiated by the soil water depletion index (f) of 0.2, 0.4, 0.6, and 0.8. In other words, irrigation was initiated when 20, 40, 60 and 80% of the available soil water capacity (AWC) at a soil depth of 0.4 m was consumed by the crop. Wheat yield and yield components were evaluated. The phenological development was monitored based on Zadok’s scale, in days after emergence, and the management parameters crop coefficient (Kc) and irrigation timing were determined. The results indicated a soil water depletion index of 0.4 as the best indicator for beginning wheat irrigation. Under this condition, Kc can be estimated daily as a function of days after emergence (DAE) by the quadratic polynomial regression or curve fitting method, adjusted as follows: Kc = −0.000298 × DAE² + 0.036695 × DAE + 0.332486. Alternatively, the means of Kc of each phenological crop phase can be used: Kc = 0.45 (establishment); 0.81 (tillering); 1.21 (booting); 1.43 (flowering); 1.27 (grain formation); and 0.89 (maturation). Thus, there is a limit of consumption of soil available water, at which wheat still achieves the full yield potential, with a rational use of water which varies according to the development phase.

Research on load curve fitting method based on K-means algorithm

Some areas of China are affected by the fluctuation of new energy output and load, and the power supply as a whole presents an unbalanced phenomenon of abundant electricity supply but shortage of power supply. To understand the characteristics of the load electricity consumption curve more accurately, this paper proposes a method of fitting the load electricity consumption curve based on K-mea ns algorithm. First, analyze the characteristics of the load electricity consumption based on the industry subdivision, and build the load electricity consumption characteristic index system. Then, the k-mea ns cluster analysis is carried out based on the characteristics of the load power consumption curve of the subdivided industries to determine the number of clusters, and the curve fitting and deviation test are carried out for each cluster group. This paper carries out practical analysis and theoretical research based on the data of typical industry load power consumption curve in Ningxia.

Age Estimation of Transmission Line Using Statistical Health Index and Failure Probability Curve-Fitting Method

The aging process of transmission lines has a direct impact on the reliability and safety of the power grid. Therefore, an accurate age estimation method is imperative for effective maintenance planning and infrastructure investment. This paper introduces a systematic methodology for estimating the age of overhead transmission lines, utilizing the percentage statistical health index (%SHI) and the failure probability curve-fitting (FPCF) method. The %SHI, employing a scoring and weighting approach derived from test results and inspections, is used to assess the actual condition of transmission line equipment. Additionally, the FPCF approach is applied to illustrate the connection between the SHI and the likelihood of failure, facilitating the assessment of transmission line age by fitting failure probability curves to the SHI data. This age is directly associated with the probability of experiencing a failure. The evaluation was conducted on 924 towers situated along four transmission lines connecting the 115 kV substations S1–S2, S3–S4, S5–S6, and S7–S8. These transmission lines are in four regions with diverse terrain and environments such as mountains, rice fields, and more. In the SHI calculation, practical testing results and historical failure data were applied. The results clearly indicate that there were notable disparities in the age estimations for transmission lines in diverse geographical regions of Thailand when compared to their actual ages. These discrepancies can be attributed to various factors, including the local environment, such as rainfall, flooding, and salt-laden air as well as specific geographical features like mountainous and coastal terrain. To mitigate the deterioration of transmission lines in all regions, it is essential to implement a proactive maintenance strategy. This strategy should involve more frequent inspections, the use of advanced monitoring technologies, and the establishment of robust maintenance procedures, with which it would become possible to enhance the accuracy of equipment condition assessments, ultimately resulting in an overall improvement in the reliability of transmission lines.

Learning parameter values of a fractional model of cancer employing boundary densities of tumor cells

In this paper, a free boundary model of tumor growth with drug application including the heterogeneity or different types of tumor cells (caused by mutations and different values of drug and nutrient concentrations inside the tumor) is studied. Heterogeneity is included in the model by a variable . It is assumed that converting from mutation to mutation happens with probability . A Caputo time fractional‐order hyperbolic equation describes the evolution of tumor cells depending on . It also includes two Caputo time fractional‐order parabolic equations describing the diffusions of nutrients (e.g., oxygen and glucose) and drug concentrations. Instead of integer‐order time derivatives, the fractional ones are considered. In this study, it is aimed to employ the least squares curve fitting method to fit the order of fractional derivatives, coefficient, and rates of the model. For this purpose, using the mathematical model, we have considered the boundary densities of the tumor cells of different types and near‐boundary concentrations of drug and nutrient as the functions of the unknown orders, coefficients and rates (unknown variables). Due to the complexity of the problem, we have obtained the functions numerically. For this, using a change of variable, we have changed the free boundary problem to a problem with fixed domain. Thus, Riemann–Liouville fractional‐order integrals are added to the problem. In the spatial domain, the problem is discretized using the collocation method. In the temporal domain, the fractional derivatives and integrals (with order ) are approximated in mesh points (with step size ) applying a method with error . Then, the unknown variables are obtained by fitting the functions for unknown variables to the data. In order to obtain the variables, a quadratic objective function is considered. The discretized function is substituted into the objective function, then the objective function is minimized using the trust‐region reflective algorithm. Finally, some numerical examples are presented to verify the efficiency of the method. In the examples, we have added noises generated from Gaussian distributions to the data and the effects of noise on the fitted coefficients, and rates are illustrated using some figures and tables. The noisy data are also plotted to have a clear vision of the effects of noises on the data. It is shown that despite the noise, the prediction of the radius of the tumor is acceptable.

Reliability of a method to assess corticomotor excitability of lower limb muscles using a normalized EMG motor thresholding procedure

Given the importance of determining intervention-induced neuroplastic changes with lower extremity functional tasks, a reliable transcranial magnetic stimulation (TMS) methodology for proximal lower extremity muscles is needed. A pre-set fixed voltage value is typically used as the criterion for identifying a motor evoked potential (MEP) during the motor thresholding procedure. However, the fixed voltage value becomes problematic when the procedure is applied to proximal lower extremity muscles where active contractions are required. We sought to establish the reliability of a method measuring corticomotor excitability of gluteus maximus and vastus lateralis using normalized electromyography (EMG) as the criterion for identifying MEPs during the motor thresholding procedure. The active motor threshold for each muscle was determined using the lowest stimulator intensity required to elicit 5 MEPs that exceeded 20% maximal voluntary isometric contraction from 10 stimulations. TMS data were obtained from 10 participants on 2 separate days and compared using random-effect intra-class correlation coefficients (ICCs). Slopes from two input–output curve fitting methods as well as the maximum MEP of gluteus maximus and vastus lateralis were found to exhibit good to excellent reliability (ICCs ranging from 0.75 to 0.99). The described TMS method using EMG-normalized criteria for motor thresholding produced reliable results utilizing a relatively low number of TMS pulses.

A novel four phase slug single-well push–pull test with regional flux: forward modeling and parameter estimation

The single-well push–pull (SWPP) test has been widely used for several applications, including determination of transport parameters such as regional groundwater flux velocity, effective porosity and dispersivity. A complete SWPP test usually includes four phases of injection, chase, rest, and extraction, while previous models mostly ignore the chase phase for the sake of simplification. Actually, the chase phase is important to minimize the impact of well skins and wellbore storage on the shape of the breakthrough curves (BTCs), thus is necessary and should be included in the model. To this end, two simplified methods are proposed in this study based on two different time tp and ta during a four-phase SWPP test to estimate the parameters related to transport, where tp and ta correspond to the time of the peak concentration during the injection phase and the time when the center of tracer mass returns to the wellbore during the extraction phase, respectively. Specifically, the tp method can be used to estimate the regional groundwater flow velocity and dispersivity (transverse and longitudinal), while the ta method is used to estimate the regional groundwater flow velocity and the effective porosity. Finite-element numerical modeling of the SWPP test based on the coupling of two-dimensional transient groundwater flow and solute transport is employed to test the applicability of the proposed methods. Results indicate that both the tp and ta methods proposed in this study perform satisfactorily in estimation of regional groundwater flow velocity, effective porosity and dispersivity, and these two methods require fewer observations than traditional curve fitting methods and do not require any optimization algorithms. We have also compared the proposed two methods with several previous methods, and conclude that these two new methods generally perform better than those of previous studies in terms of parameter estimation reliability.

Composite and Mixture Distributions for Heavy-Tailed Data—An Application to Insurance Claims

This research provides a comprehensive analysis of two-component non-Gaussian composite models and mixture models for insurance claims data. These models have gained attraction in actuarial literature because they provide flexible methods for curve-fitting. We consider 256 composite models and 256 mixture models derived from 16 popular parametric distributions. The composite models are developed by piecing together two distributions at a threshold value, while the mixture models are developed as convex combinations of two distributions on the same domain. Two real insurance datasets from different industries are considered. Model selection criteria and risk metrics of the top 20 models in each category (composite/mixture) are provided by using the ‘single-best model’ approach. Finally, for each of the datasets, composite models seem to provide better risk estimates.