Iterative Method Research Articles

Reconquer and divide: comparative standard-setting strategies among producer organizations

AbstractFood standards, which are used to signal adherence to sustainability goals or a specific origin, have deep political implications. Standards crafted by retailers, processors, or third-party actors such as non-governmental organizations (NGOs) often disempower farmers. Moreover, due to the liberalization and globalization of many food value chains, producer organizations (POs) lost some of their legal privileges and market protections. This paper analyzes how POs in the Global North sought to regain their control over food markets by establishing their own standards. These strategies and their consequences are considered across three dimensions: the internal life of the PO, the relevant market institutions, and the relationship between the PO and the state. Our case studies (N = 5) performed in France and in Québec, a French-speaking province of Canada, span across a variety of food sectors. Drawing on qualitative material, we designed our explanatory framework through an abductive, iterative method. Although standards crafted by POs have, in some cases, reshaped market institutions to their advantage and have repositioned them in the governance of food markets, they come at a cost. They may create tensions within POs and clash with the agrarian values of solidarity, democracy, and autonomy. Overall, this article challenges the assumption that food standards are mainly governed by private actors and sheds light on the new alliances and new identities of POs.

Scatter correction for contrast-enhanced digital breast tomosynthesis with a dual-layer detector.

Contrast-enhanced digital breast tomosynthesis (CEDBT) highlights breast tumors with neo-angiogenesis. A recently proposed CEDBT system with a dual-layer (DL) flat-panel detector enables simultaneous acquisition of high-energy (HE) and low-energy (LE) projection images with a single exposure, which reduces acquisition time and eliminates motion artifacts. However, x-ray scatter degrades image quality and lesion detectability. We propose a practical method for accurate and robust scatter correction (SC) for DL-CEDBT. The proposed hybrid SC method combines the advantages of a two-kernel iterative convolution method and an empirical interpolation strategy, which accounts for the reduced scatter from the peripheral breast region due to thickness roll-off and the scatter contribution from the region outside the breast. Scatter point spread functions were generated using Monte Carlo simulations with different breast glandular fractions, compressed thicknesses, and projection angles. Projection images and ground truth scatter maps of anthropomorphic digital breast phantoms were simulated to evaluate the performance of the proposed SC method and three other kernel- and interpolation-based methods. The mean absolute relative error (MARE) between scatter estimates and ground truth was used as the metric for SC accuracy. DL-CEDBT shows scatter characteristics different from dual-shot, primarily due to the two energy peaks of the incident spectrum and the structure of the DL detector. Compared with the other methods investigated, the proposed hybrid SC method showed superior accuracy and robustness, with MARE of for all LE and HE projection images of different phantoms in both cranial-caudal and mediolateral-oblique views. After SC, cupping artifacts in the dual-energy image were removed, and the signal difference-to-noise ratio was improved by 82.0% for 8mm iodine objects. A practical SC method was developed, which provided accurate and robust scatter estimates to improve image quality and lesion detectability for DL-CEDBT.

Fractals as Julia Sets for a New Complex Function via a Viscosity Approximation Type Iterative Methods

In this article, we examine and investigate various variants of Julia set patterns for complex exponential functions W(z)=αezn+βzm+logct, and T(z)=αezn+βzm+γ (which are analytic except at z=0) where n≥2, m,n∈N, α,β,γ∈C,c∈C∖{0} and t∈R,t≥1, by employing a viscosity approximation-type iterative method. We employ the proposed iterative method to establish an escape criterion for visualizing Julia sets. We provide graphical illustrations of Julia sets that emphasize their sensitivity to different iteration parameters. We present graphical illustrations of Julia sets; the color, size, and shape of the images change with variations in the iteration parameters. With fixed input parameters, we observe the intriguing behavior of Julia sets for different values of n and m. We hope that the conclusions of this study will inspire researchers with an interest in fractal geometry.

A GROUNDED THEORY OF EXPERIENCING AND PERCEIVING YOUTH MENTORING AS A PROCESS OF “MIXED EMOTIONS”

Many youths from disadvantaged backgrounds globally lack positive role models and meaningful relationships with supportive adults within their families and communities. Youths’ involvement in relationships with caring adults, such as mentors, promotes their well-being. However, little is known about youth mentoring relationships in some parts of the world, particularly Zambia. This constructivist grounded theory study aimed to develop a substantive theory that explains the experiences and perceptions of mentors and mentees in Zambia. The sample included mentors and mentees who had been in relationships for at least one year. Semi-structured interviews were conducted with five mentors aged 31 to 68 and four mentees aged 18 to 20. The inductive, iterative and constant comparative method guided the coding, memoing, and theoretical sampling processes and led to the co-construction of four categories. This paper focuses on one category: “Experiencing and perceiving mentorship as a process of ‘mixed feelings’”. Mentees felt mentoring supported their emotional and psychological well-being, which enhanced their resilience and was perceived as a way of surviving. Mentors expressed that mentoring enabled them to support youths beyond their professional roles and enhanced their self-awareness, knowledge and reputation, which increased their self-worth and confidence and the desire to continue changing youths´ lives. Challenges included meeting time issues, ineffective communication, and confusion in differentiating between the mentor´s professional and mentoring roles, which caused difficulty in mentor-mentee interactions. They felt overwhelmed by their mentees´ difficult circumstances and worked with limited resources. They also felt unrecognised and unsupported. Findings suggest priority issues and potential logical solutions, such as preliminary and ongoing mentor training, recognising and supporting youth mentoring, and providing essential primary resources to disadvantaged youths in Zambia to enable effective mentoring.

Meshfree methods for nonlinear equilibrium radiation diffusion equation with interface and discontinuous coefficient

The partial differential equation describing equilibrium radiation diffusion is strongly nonlinear, which has been widely utilized in various fields such as astrophysics and others. The equilibrium radiation diffusion equation usually appears over multiple complicated domains, and the material characteristics vary between each domain. The diffusion coefficient near the interface is discontinuous. In this paper, the equilibrium radiation diffusion equation with discontinuous diffusion coefficient will be solved numerically by the unsymmetric radial basis function collocation method. The energy term T4 is linearized by utilizing the Picard-Newton and Richtmyer linearization methods on the basis of the fully implicit scheme discretization. And the successive permutation iteration and direct linearization methods are applied to linearize the diffusion terms. The accuracy of the proposed methods is proved by numerical experiments for regular and irregular domains with different types of interfaces.

Two-step nonlinear modulus-based matrix splitting iteration method for implicit complementarity problems

Two-step nonlinear modulus-based matrix splitting iteration method for implicit complementarity problems

Curvature Regularization for Non-Line-of-Sight Imaging From Under-Sampled Data.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes by using time-of-flight photon information after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is highly likely to be degraded due to noises and distortions. In this paper, we propose novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (signal and object)-domain curvature regularization model. In what follows, we develop efficient optimization algorithms relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, for which all solvers can be implemented on GPUs. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. Based on GPU computing, our algorithm is the most effective among iterative methods, balancing reconstruction quality and computational time.

Forward energy grade line analysis for tsunami inundation mapping

A simplified model using 1D topobathymetric profiles for generating tsunami inundation maps is implemented and evaluated. The approach is a modification of the ASCE Energy Grade Line Analysis, that allows estimation of the maximum inundation distances using an iterative method. The modified methodology is implemented in three coastal cities in central Chile and compared with a database of 5400 full tsunami simulations obtained from a Nonlinear Shallow Water Equations solver. The key parameter of the model is based on the Froude number, for which three parameterizations and a range of values are tested. Results show that errors in the estimation of the areal extent of the inundation can be as low as 4%, after calibration. However, calibration is site specific and the optimal solution depends on the geographical characteristics of the area of interest. A sensitivity analysis based on the aleatoric sampling of the full tsunami simulation database show that as little as 100 inundation maps are required to perform the calibration of the model. This is a manageable number that offers reduced computational costs when compared with full tsunami simulations, and even those required to train other surrogate models using machine learning.

Financial Development Strategies of Countries: Identifying Priority Financial Instruments

The effectiveness of financial development strategies for countries depends on the methodology used for their formation and implementation. However, a review of the literature reveals that the methodology for implementing these strategies is not fully developed. Therefore, this study aimed to create a methodology for implementing financial development strategies by identifying priority financial instruments. The study utilized rule-based judgment, iterative methods, and the "GAP" method. As a result, the "5S+" concept was developed—an expanded version of the "5S" concept. The novelty of the "5S+" concept lies in its inclusion of financial instruments, as well as the rules and methods for determining priority financial instruments. This is the first time such a modification of the "5S" concept is proposed. The "5S+" concept was tested using data from the UK for 2007-2022. The test results allowed us to determine the UK's strategic financial development goals for 2023-2025 and identify priority financial instruments necessary to achieve these goals across the board. The expanded "5S" concept is highly representative, objective, and broadly applicable. It enhances the existing methodology for strategic financial development management and introduces new opportunities for improving the quality of strategic policy documents. Doi: 10.28991/ESJ-2024-08-06-06 Full Text: PDF

Time-Domain Load Identification and Dynamic Displacement Inversion of Discharge Sluice in Large Hydropower Stations Based on the Conjugate Gradient Least Squares Iteration Algorithm

The identification of flow-induced vibration loads is the key to solving the vibration problem of hydraulic structures induced by high-speed water flow. In this study, a time-domain load identification method for discharge sluices in large hydropower stations based on the conjugate gradient least squares (CGLS) iteration algorithm is proposed. The main idea of this method is to first construct a mathematical model through the response signal and the structural modal parameters for solving the equivalent load acting on the sluice structure. Then, it uses the iterative regularization method to solve the ill-posed problem in the load identification process of the traditional time-domain method. Numerical simulations show that, in comparison to the traditional Tikhonov regularization algorithm, the CGLS algorithm is capable of reducing the relative error of the identified dynamic loads by approximately 100%, with higher accuracy and noise immunity. In this study, the CGLS algorithm is used to identify the external dynamic loads acting on the sluice structure during the flood discharge process of a large hydropower station in China. Three equivalent dynamic loads acting on the pier and the bottom plate are obtained. The positive analysis of the vibration response of the discharge structure is conducted using the equivalent load. The results show that the error between the measured vibration response signals and the vibration response signals obtained from the positive analysis is relatively minor, with the exception of a few individual measurement points. The displacement response mean square error is less than 10%, thereby substantiating the accuracy and validity of the CGLS load identification method proposed in this study in the actual engineering.

Ultrasound-based Eigenfrequency Analysis to Determine Material Parameters of Tissue Mimicking Phantoms

Abstract A modern area of research in cancer treatment is magnetic drug targeting (MDT) with superparamagnetic iron oxide nanoparticles (SPIONs). In order to understand the processes involved in MDT in more detail and to be able to perform this therapy as efficiently as possible, a monitoring system for the spatial distribution of SPIONs in biological tissue is required. One approach is to use magnetomotive ultrasound (MMUS) to monitor the spatial distribution over time. However, the spatial distribution of SPIONs cannot be quantitatively determined applying basic MMUS algorithms. Therefore, MMUS has been extended by a simulation part to quantitatively determine the spatial distribution of SPIONs. This extended MMUS algorithm requires the material parameters and the geometry of the target tumorous tissue. In this contribution, we describe an ultrasound-based eigenfrequency analysis combined with an iterative inverse simulation-based method to determine the mechanical parameter Young's modulus of tissue mimicking phantoms. The presented approach yields a good estimate of the Young's modulus compared to the result from a compression test.

An iterative constraint energy minimizing generalized multiscale finite element method for contact problem

This work presents an Iterative Constraint Energy Minimizing Generalized Multiscale Finite Element Method (ICEM-GMsFEM) for solving the contact problem with high contrast coefficients. The model problem can be characterized by a variational inequality, where we add a penalty term to convert this problem into a non-smooth and non-linear unconstrained minimizing problem. The characterization of the minimizer satisfies the variational form of a mixed Dirilect-Neumann-Robin boundary value problem. So we apply CEM-GMsFEM combined with simismooth Newton method and introduce special boundary correctors along with multiscale spaces to achieve an optimal convergence rate. Numerical results are conducted for different highly heterogeneous permeability fields, validating the fast convergence of the CEM-GMsFEM iteration in handling the contact boundary and illustrating the stability of the proposed method with different sets of parameters. We also prove the fast convergence of the proposed iterative CEM-GMsFEM method and provide an error estimate of the multiscale solution under a mild assumption.

Electrocardiography Denoising via Sparse Dictionary Learning from Small Datasets

Abstract Wearable electrocardiography monitors, e.g. embedded in textile shirts, offer new approaches in diagnosis but suffers upon limited computational capacities. Hence, we propose and evaluate a lightweight algorithm for electrocardiography denoising via sparse dictionary learning, targeting two types of noise: baseline wander and muscle artifacts. For each type of noise a dictionary is built using K-singular value decomposition. This iterative method alternates between finding a sparse representation for every training signal and then updating every atom of the dictionary on its own. A sparse representation is found using the orthogonal matching pursuit algorithm. The atoms are updated exploiting the properties of the singular value decomposition. For further sparse approximation, we use the basis pursuit denoising algorithm. Electrocardiography data stems from synthetically-generated signals as well as the freely-available Brno University of Technology ECG Quality Database. Noise is added to the signals using the MIT-BIH Noise Stress Database. Our results regarding baseline wander demonstrate that the algorithm outperforms the American Heart Association-recommended bandpass filter w.r.t. signal-to-noise ratio. Moreover, a small number of training data is sufficient for satisfying results which indicates the suitability of the method for wearable hardware with low memory and power specifications.

An iterative method for integrated hump sequencing, train makeup, and classification track assignment in railway shunting yard

In a railway shunting yard, the transformation of inbound trains into properly composed outbound trains is a complex task because it involves decisions of multiple operations processes. This study addresses the integrated optimization of hump sequencing, train makeup, and classification track assignment problem in a railway shunting yard. Several key practical yard operation constraints are considered, including train formulation constraints, hump sequencing constraints, and limitations of the maximum number and capacity of classification tracks. By introducing a new representation of block flow, the integrated problem, which adopts the extended single-stage strategy and the train-to-track policy, is formulated as a unified 0-1 integer linear programming model. The objective of the proposed model is to minimize the weighted-sum of the total dwell time of all railcars and the formulation deviation penalties of all outbound trains. Then, an iterative two-phase decomposition approach is developed to reduce the complexity of solving the integrated problem. The first phase aims to explore all feasible humping sequences using a Branch-and-Bound (B&B) algorithm. Each time a new humping sequence is generated in the first phase, the second phase containing a Branch-and-Price (B&P) algorithm is applied to solve the integrated train makeup and classification track assignment problem with the known humping sequence found in the first phase. In addition, greedy heuristics and lower bounding techniques are designed in both phases to improve computational efficiency. Comprehensive experiments are investigated based on a set of real-life instances. The results show that exact approaches provide optimal solutions, whereas heuristic approaches yield satisfactory solutions within a shorter computation time. Moreover, sensitivity analyses on the number of classification tracks and the effects of different deviation penalties are also performed to gain more managerial insights.

Newton-type methods for solving vertical linear complementarity problems

In this paper, Newton-type methods are proposed to solve the vertical linear complementarity problems. This kind of methods is different from the existing iteration methods such as the modulus-based matrix splitting methods. By analyzing the sign pattern for the solution and reformulating the vertical linear complementarity problem to an equivalent system of nonlinear equations, we propose the Newton’s method and a practical-Newton’s method that combine the Newton’s method and modulus-based matrix splitting method. Numerical experiments are given to show that these Newton-type methods have much higher efficiency than the existing modulus-based matrix splitting methods.

Advanced Variational Iteration Method to Solve Two Different Models of Third‐Order Nonlinear Emden–Fowler‐Type Equations

ABSTRACTIn this research, we propose an advanced variational iteration method by employing the Adomian polynomials with the variational iteration method to solve the system of third‐order nonlinear Emden–Fowler‐type equations arise in several applications. The third‐order nonlinear Emden–Fowler‐type equations have singularity at origin and are characterized by two models, with the shaped factor appearing two times in the first model and once in the other model. The proposed method overcomes the singularity at the origin and handles both models with different‐shaped factors. The convergence study of the proposed method for both models is also discussed in Banach space. Five models of interest are provided to test the validity and efficiency of the proposed method. The investigations confirm that the proposed method is able to solve complex problems in a few iterations with less computational time.

Numerical optimization of separation processes in a distillation column

An optimal control problem when modeling the rectification process in the column is considered. The process is described by a system of first-order hyperbolic equations with dynamic boundary conditions. A specific peculiarity of the model under consideration is in the boundary conditions of a special type. At each of the boundaries, boundary conditions are determined from a system of ordinary differential equations, which also includes unknown values of functions on another boundary. Three variants of numerical methods for solving the considered problem are proposed. The first method is based on a linearized maximum principle. Structurally it coincides with a conditional gradient method in a corresponding functional space. The second method is a modification of a two-parameter maximum principle method. This method makes it possible to work effectively with internal admissible controls in contrast to standard iterative maximum principle methods. The third method is based on the procedure of discretization of the problem and the use of the standard SciPy Python library to solve the corresponding mathematical programming problem. The results of numerical experiments are illustrated by a series of tables and graphs. As a result, the conditional gradient method may be recommended for solving the problem under consideration.

Convergence theory of efficient parametric iterative methods for solving the Yang-Baxter-like matrix equation

PurposeIn this study, we present a novel parametric iterative method for computing the polar decomposition and determining the matrix sign function.Design/methodology/approachThis method demonstrates exceptional efficiency, requiring only two matrix-by-matrix multiplications and one matrix inversion per iteration. Additionally, we establish that the convergence order of the proposed method is three and four, and confirm that it is asymptotically stable.FindingsIn conclusion, we extend the iterative method to solve the Yang-Baxter-like matrix equation. The efficiency indices of the proposed methods are shown to be superior compared to previous approaches.Originality/valueThe efficiency and accuracy of our proposed methods are demonstrated through various high-dimensional numerical examples, highlighting their superiority over established methods.

A novel deep learning based method for myocardial strain quantification

Purpose.This paper introduces a deep learning method for myocardial strain analysis while also evaluating the efficacy of the method across a public and a private dataset for cardiac pathology discrimination.Methods.We measure the global and regional myocardial strain in cSAX CMR images by first identifying a ROI centered in the LV, obtaining the cardiac structures (LV, RV and Myo) and estimating the motion of the myocardium. Finally, we compute the strain for the heart coordinate system and report the global and regional strain.Results.We validated our method in two public datasets (ACDC, 80 subjects, and CMAC, 16 subjects) and a private dataset (SSC, 75 subjects), containing healthy and pathological cases (acute myocardial infarction, DCM and HCM). We measured the mean Dice coefficient and Hausdorff distance for segmentation accuracy, and the absolute end point error for motion accuracy, and we conducted a study of the discrimination power of the strain and strain rate between populations of healthy and pathological subjects. The results demonstrated that our method effectively quantifies myocardial strain and strain rate, showing distinct patterns across different cardiac conditions achieving notable statistical significance. Results also show that the method's accuracy is on par with iterative non-parametric registration methods and is also capable of estimating regional strain values.Conclusion.Our method proves to be a powerful tool for cardiac strain analysis, achieving results comparable to other state-of-the-art methods, and computational efficiency over traditional methods.

Study of the resonant motion of a test particle inside Kepler 69 using circular restricted three body problem

Summary: Scientific researches to discover extraterrestrial life are very intense and important. For a long time, humanity has speculated about the existence of the planetary systems different from our solar system, where extraterrestrial life can be existing elsewhere in the universe. The understanding of the origin of planets and planetary systems has indeed become a major focus of research in modern Astrophysics. Exoplanet research is one of the most field developing subjects in Astrophysics and Astronomy. Today, the number of discovered exoplanets, where life is thought to exist, is large. One of the main goals of scientists is to find Earth 2, where there is possibility of extraterrestrial life. Kepler's mission was, and is very important in the field of exoplanet research. This mission has revolutionized our understanding of exoplanets. The number of discovered exoplanets has exceeded 5780 in 4314 planetary systems, with 969 systems having more than one planet [1]. Among them, one of the most interesting is Kepler 69 c, a super-Earth around Kepler 69. This exoplanet is located at the boundary between the habitable zone and the outer one. In this study let’s focus on the classical gravitational problem called Circular Restricted Three-Body Problem (CRTBP) [2, 3] and use an iterative method to perform numerical calculations through MATLAB software. Therefore, in this paper, let’s try to study the movement of a “test particle”, under the action of the gravitational field of the system Kepler 69–Kepler 69 c. The aim of this paper is to study the motion of a “test particle” (for example an extrasolar asteroid test) inside the exoplanetary system Kepler 69. Results are presented in the (x, y) plane in the rotating and inertia system. Also, some of the results are presented in the phase planes (x, vx) and (y, vy)