Minimization Method Research Articles

Capnodynamic assessment of mixed venous oxygen saturation in a porcine experimental endotoxemic model.

Sepsis continues to be a major cause of death and illness globally, posing significant challenges for healthcare professionals. In the pursuit of more accurate and timely monitoring tools, the concept of capnodynamically derived mixed venous oxygen saturation (Capno-SvO2) has emerged as a promising method. Capno-SvO2 provides a non-invasive way to assess and track SvO2 and could serve as an additional tool alongside more invasive methods like the pulmonary artery catheter. This could potentially be of great value in the care of critically ill patients with sepsis, where alternative minimal invasive monitoring methods may vary in reliability. The aim of the current study was to compare capno-SvO2 against values obtained through pulmonary artery blood sample CO-oximetry and continuous fiberoptic SvO2 monitoring, using a well-established porcine experimental sepsis model. Anesthetized pigs were exposed to a standardized endotoxin infusion sepsis protocol, followed by a series of maneuvers typically applied in sepsis care. Simultaneous recordings were done throughout the experiment for all three monitoring methods. Bland-Altman analysis corrected for repeated measurements was used to assess the agreement of absolute values between the paired recording of CO-oximetry and Capno-SvO2 as well as between CO-oximetry and fiberoptic SvO2. The ability of Capno-SvO2 and fiberoptic SvO2 to track changes was assessed by concordance rate. A total of 10 animals and 275 paired datapoints were included in the study. The majority of the animals displayed pronounced hemodynamical instability in response to endotoxin exposure and subsequent treatment interventions. Analysis of all paired data points showed a bias between Capno-SvO2 and CO-oximetry SvO2 of + 1% with 95% limits of agreement of -14% to + 17%. The corresponding numbers for fiberoptic SvO2 and CO-oximetry SvO2 were -4% and -15% to + 8%. The concordance rate as compared to CO-oximetry, were 97% and 93% for Capno-SvO2 and fiberoptic SvO2, respectively. In this experimental sepsis model, continuous, non-invasive Capno-SvO2 generates average absolute values comparable to the gold standard CO-oximetry albeit with relatively wide limits of agreement. Capno-SvO2 displayed a concordance rate of 97% against CO-oximetry and exhibits better trending ability compared to invasive fiberoptic SvO2.

Effects of Late-Passage Small Umbilical Cord-Derived Fast Proliferating Cells on Tenocytes from Degenerative Rotator Cuff Tears under an Interleukin 1β-Induced Tendinopathic Environment.

Tendinopathy is a chronic tendon disease. Mesenchymal stem cells (MSCs), known for their anti-inflammatory properties, may lose effectiveness with extensive culturing. Previous research introduced "small umbilical cord-derived fast proliferating cells" (smumf cells), isolated using a novel minimal cube explant method. These cells maintained their MSC characteristics through long-term culture. Thus, the purpose of the present study was to assess the anti-inflammatory effects of late-passage smumf cells at P10 on tenocytes derived from degenerative rotator cuff tears in a tendinopathic environment.Edit made in title. Kindly check and confirm.Yes, I checked the title. No problem.Kindly check and confirm affiliation 1, 2 and 3 are correctly processed.The corresponding author's affiliation has been changed to 1, 2, and 3. The mRNA expression with respect to aging of MSCs and secretion of growth factors (GFs) by smumf cells at P10 were measured. mRNA and protein synthesis in tenocytes with respect to the tenocyte phenotype, inflammatory cytokines, and matrix- degradation enzymes were measured. The inflammatory signal pathways involving nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) in tenocytes were also investigated. The proliferative response of degenerative tenocytes to co-culture with smumf cells over 7days in varying IL-1β induced tendinopathic environments was investigated.Author names: Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 3 Given name: [Sam Joongwon] Last name [Hwang], Author 6 Given name: [Chris Hyunchul] Last name [Jo]. Also, kindly confirm the details in the metadata are correct.There is no problem with the name and order. We would appreciate it if you remove "Yejin Park" from the list of authors because she left the laboratory before completing her papers and works. RESULTS: smumf cells at P10 showed no signs of aging compared to those at P3. smumf cells at P10, secreting 2,043pg/ml of hepatocyte growth factor (HGF), showed a 1.88-fold (p = .002) increase in HGF secretion in a tendinopathic environment. Degenerative tenocytes co-cultured with smumf cells showed significantly increased protein expression levels of collagen type I (Col I) and the Col I/III ratio by 1.46-fold (p < .001) and 1.66-fold (p < .001), respectively. The smumf cells at P10 reduced both mRNA and protein expression levels of matrix metalloproteinases-1, -2, -3, -8, -9, and -13 in tenocytes and attenuated NF-κB (phosphorylated IκBα/IκBα and phosphorylated p65/p65) and MAPK (phosphorylated p38/p38 and phosphorylated JNK/JNK) pathways activated by IL-1β. Removal of IL-1β from the co-culture accelerated the growth of tenocytes by 1.42-fold (p < .001). Removal of IL-1β accelerated tenocyte growth in co-cultures. Late-passage smumf cells exert anti-inflammatory effects on tenocytes derived from degenerative rotator cuff tears under a tendinopathic environment, primarily through the secretion of growth factors (GFs).

Krylov subspace methods based quaternion tensor form for generalized Sylvester quaternion tensor equation with application to color video restoration

In this paper, we consider Krylov subspace methods based quaternion tensor form for a class of generalized Sylvester quaternion tensor equation. A novel quaternion tensor product and a global quaternion Arnoldi process based on the tensor form are proposed. Then, the quaternion tensor Krylov subspaces and their orthogonal bases are constructed via the proposed global quaternion Arnoldi process. The quaternion full orthogonalization method based on tensor format (denoted by QFOM−BTF) and the quaternion generalized minimal residual method based on tensor format (denoted by QGMRES−BTF) are established to solve the quaternion tensor equation. The theoretically analyze results of the proposals are discussed on the quaternion tensor form. Finally, we demonstrate the feasibility of QFOM−BTF and QGMRES−BTF methods on some numerical examples including the color video restoration.

3D Controlled‐source electromagnetic modelling in anisotropic media using secondary potentials and a cascadic multigrid solver

AbstractQuantitative interpretation of the data from controlled‐source electromagnetic methods, whether via forward modelling or inversion, requires solving a considerable number of forward problems, and multigrid methods are often employed to accelerate the solving process. In this study, a new extrapolation cascadic multigrid method is employed to solve the large sparse complex linear system arising from the finite element approximation of Maxwell's equations using secondary potentials. The equations using secondary potentials are discretized by the classic nodal finite element method on nonuniform rectilinear grids. The resulting linear systems are solved by the extrapolation cascadic multigrid method with a new prolongation operator and preconditioned Stabilized bi‐conjugate gradient method smoother. High‐order interpolation and global extrapolation formulas are utilized to construct the multigrid prolongation operator. The extrapolation cascadic multigrid method with the new prolongation operator is easier to implement and more flexible in application than the original one. Finally, several synthetic examples including layered models, models with anisotropic anomalous bodies or layers, are used to validate the accuracy and efficiency of the proposed method. Numerical results show that the extrapolation cascadic multigrid method improves the efficiency of 3D controlled‐source electromagnetic forward modelling a lot, compared with traditional iterative solvers and some state‐of‐the‐art methods or software (e.g., preconditioned flexible generalized minimal residual method, emg3d) in the considered models and grid settings. The efficiency benefit is more evident as the number of unknowns increases, and the proposed method is more efficient at low frequencies. The extrapolation cascadic multigrid method can also be used to solve systems of equations arising from related applications, such as induction logging, airborne electromagnetic, etc.

Computing Quadratic Eigenvalues and Solvent by a New Minimization Method and a Split-Linearization Technique

To solve quadratic eigenvalue problems (QEPs), especially the gyroscopic systems, two methods are proposed: an iterative direct detection method (DDM) of the complex eigenvalues of the original QEP, and a split-linearization method (SLM) for finding the solvent matrix, which results to a standard linear eigenvalue problem (LEP) being solved to compute all eigenvalues by the symmetry extension. Reducing the dimension to one-half, the LEP is recast in a simpler QEP involving the square of the solvent. We set up two new merit functions which are minimized to detect the complex eigenvalues from the original QEP and a simpler QEP. For each eigen-parameter the merit function consists of the Euclidean norm of each derived eigen-equation, whose vector variable is solved from a derived inhomogeneous linear system. Then, the golden section search algorithm is employed to minimize the merit functions and locate the complex eigenvalue as a local minimal point. The results are compared with that computed by the cyclic-reduction-based solvent (CRS) method.

Expected Residual Minimization Method for a Class of Stochastic Tensor Variational Inequalities

Expected Residual Minimization Method for a Class of Stochastic Tensor Variational Inequalities

Recognition and Scoring Physical Exercises via Temporal and Relative Analysis of Skeleton Nodes Extracted from the Kinect Sensor

Human activity recognition is known as the backbone of the development of interactive systems, such as computer games. This process is usually performed by either vision-based or depth sensors. So far, various solutions have been developed for this purpose; however, all the challenges of this process have not been completely resolved. In this paper, a solution based on pattern recognition has been developed for labeling and scoring physical exercises performed in front of the Kinect sensor. Extracting the features from human skeletal joints and then generating relative descriptors among them is the first step of our method. This has led to quantification of the meaningful relationships between different parts of the skeletal joints during exercise performance. In this method, the discriminating descriptors of each exercise motion are used to identify the adaptive kernels of the Constrained Energy Minimization method as a target detector operator. The results indicated an accuracy of 95.9% in the labeling process of physical exercise motions. Scoring the exercise motions was the second step after the labeling process, in which a geometric method was used to interpolate numerical quantities extracted from descriptor vectors to transform into semantic scores. The results demonstrated the scoring process coincided with the scores derived by the sports coach by a 99.5 grade in the R2 index.

Evaluating the Quality of Multiple-Choice Test Questions in the Postlicensure Environment.

Multiple-choice test questions are among the main measures of knowledge used by educators in the postlicensure environment; however, these tests are often constructed in the absence of guidelines or the means to evaluate examination quality after administration. Previously, guidance was provided on constructing quality test items. Here we present instruction for professional development specialists to use postadministration test data for an item analysis, providing insight on test flaws and opportunities for iterative examination improvement. The topics of item difficulty, index of discrimination, and distractor analysis are covered for independent analysis, and topics such as reliability are addressed for those who have access to a formal program. Three levels of strategies are described: using a learning management system for item analysis, using free open-source software, and using a minimal standards method of evaluating test items. [J Contin Educ Nurs. 2024;55(10):487-492.].

Empirical risk minimization for big data driven prescriptive analytics: An exploration of two-stage stochastic programs with recourse

In the operations research literature, data driven analyses using big data are receiving more and more interest and attention. However, big data driven operational analyses are still limited in scope, with only unconstrained big data driven problems, e.g. the newsvendor problem, being comprehensively explored. If big data driven analyses are to become a core part of operations research and practice, one must be able to formulate and solve constrained big data driven models. We therefore propose a direct empirical risk minimization (DERM) method for formulating and solving a class of constrained big data driven operation research problems. In big data driven problems, relevant operational prescriptions are dependent on a set of observed features. In this paper, we assume linear feature relationships, which enable us to fundamentally base the DERM method on linear stochastic programming. The assumption of linear feature relationships is also seen in the literature on big data driven newsvendor models. We prove that if a big data driven solution exists, the DERM method will always, for the class of constrained problems we study, arrive at a feasible solution of the initial operational problem. Moreover, we exemplify the DERM method by formulating and solving two specific big data driven operational problems. In the numerical study of the two operational problems, we show, that under linear demand, the DERM method outperforms, with regard to cost, two benchmark methods and a big data driven method from literature.

Attitude motion classification of resident space objects using light curve spectral analysis

The characterization of Earth-orbiting objects in terms of attitude motion, material composition, and shape is crucial for Space Domain Awareness. In this respect, light curves, i.e., graphs showing the brightness variation of space objects over a specific time interval, can be used to infer both their dynamical and physical properties. However, such light curve inversion problem is challenging due to measurement noise, data gaps, and the intrinsic difficulty in separating the multiple effects impacting on the object’s brightness. Existing light curve inversion methods rely on estimation filters exploiting data fusion, Machine Learning approaches, or are based on the analysis of the light-curve frequency spectra. Many of these approaches make assumptions about a-priori knowledge of target object’s characteristics, such as shape, size, surface reflective properties, and/or observation parameters, e.g., target’s orbit and observation geometry. In this framework, this paper presents an innovative architecture for the classification of the attitude motion of space objects using only photometric light curve data, not requiring any a-priori assumption or knowledge. The proposed architecture exploits the Lomb-Scargle Periodogram algorithm to retrieve a frequency spectrum from light curve measurements, and then performs the classification by analyzing the spectrum characteristics testing different candidate rotation periods through the Phase Dispersion Minimization method. The proposed classification algorithm is first tested using a light curve simulator, in which any complex geometric model and different surface properties can be generated. Finally, the method is applied to real light curves retrieved from a publicly available database to assess the classification performance.

Theory for measuring electric charge density of a ring from scanning force microscopy

We consider a ring that carries an arbitrary electric charge density in the presence of a scanning force microscopy tip. We propose an algorithm that predicts this charge density from knowledge of the electrostatic ring-tip contribution to the total scanning force microscopy force–distance curves. We first solve the direct electrostatic problem of finding the electrostatic forces by the ring on a scanning force microscopy conducting probe. These forces are in the pN and nN range and, therefore, measurable with current technologies. Finally, we describe a method based on the least squares minimization method to measure the charge on the ring.

Minimizing phase delay of feedback control signals for electromagnetic vibrators to reduce measurement uncertainty in ultra-low frequency vibration calibration

Suppressing the distortion of ultra-low frequency (ULF) vibration waveforms produced by an electromagnetic vibrator (EMV) presents significant challenges, leading to considerable measurement uncertainty during vibration calibration. A phase delay minimization method for feedback control signals is proposed to reduce the harmonic distortion of ULF vibration waveforms. The method quantitatively describes the influence law of the active viscoelastic approach on system frequency response characteristics. By selecting appropriate active stiffness and damping coefficients, the method minimizes the phase delay of the feedback control signal, thereby enhancing the disturbance suppression capability of the displacement and velocity composite negative feedback control. Experimental results indicate that harmonic distortion of the vibration waveform is reduced to less than 1.70 % within the ULF frequency range of 0.001 Hz ≤ f ≤ 1 Hz. Furthermore, the amplitude sensitivity calibration uncertainty, which arises from vibration waveform distortion, has been reduced to 0.038 % (k = 1.8), thereby improving the accuracy of vibration calibration.

Low-intensity parent- and clinician-delivered support for young autistic children in Aotearoa New Zealand: a randomised controlled trial

Aotearoa New Zealand does not provide publicly-funded intensive autism support. While parent-mediated supports are promising, children and families may also benefit from direct clinician support. We tested the efficacy of a low-intensity programme involving parent- and clinician-delivered support for autistic children. This single-blind, two-arm randomised controlled trial assessed outcomes of a six-month low-intensity parent- and clinician-delivered support (2-3h per week) based on the Early Start Denver Model compared to a control group who received monthly support calls and assistance with referrals. Children aged 1-4.5 years who were autistic or showing signs of autism and their parents were randomised to the low-intensity or control group by a blinded statistician using the Urn minimisation method. Assessments were conducted at baseline and immediately following the support period (24-weeks post-baseline). The primary outcome was child engagement during an interaction with their parent. The trial was pre-registered with ANZCTR: U1111-1260-2529. From March 2021 to May 2023, 56 families were randomised to either the low-intensity or control group. Following drop-outs, 21 families in the low-intensity group and 24 in the control group were included in analysis. There was large and significantly greater improvement in children's engagement in the low-intensity group compared to the control group (F (1, 43)=21.47, p<0.0001, ηp 2=0.33). There was one recorded adverse event unrelated to the support and two adverse effects related to the support. A low-intensity parent- and clinician-delivered support can improve engagement between an autistic child and their parent during play. Low-intensity supports may be beneficial in areas where access to clinical autism supports is limited. Emerging Researcher First Grant from the Health Research Council of New Zealand.

Baseline Survey of Plant Species Along Salang Road in Afghanistan

The Salang Road, a critical transportation corridor, serves as a vital link connecting the northern and southern parts of Afghanistan. In response to the government's decision to upgrade this road, assessing and documenting the plant species in the region is crucial. This study was conducted to catalog the plant species along the Salang Road, aiming to establish a comprehensive understanding of the area's botanical diversity. Plants were recorded using the quadrate sampling method at 35 sampling points. A transect line was set up at each sampling point, 5 quadrats were established along each line, and the occurrence of plants within the quadrat frame was recorded. Quadrat sizes, determined using the minimal area method, were 10×10 m for trees, 2×5 m for shrubs, and 1 m2 for herbs. Plants were identified with the help of literature and by comparing with the deposited specimen of Kabul University Faculty of Sciences Herbarium (KUFS). Data were analyzed using Microsoft Excel. 135 diverse species, including trees, shrubs, and herbs, representing various ecological niches were documented. These species belong to 46 plant families, and the most abundant ones were Asteraceae, Apiaceae, Rosaceae, Lamiaceae, Fabaceae, and Polygonaceae. The survey additionally examined road construction's impact on plant species' distribution and diversity. The survey provides valuable insights into the Salang corridor area's botanical diversity and ecological significance, serving as a foundational dataset for future environmental conservation and land management efforts. The information obtained from this study can support qualified decision-making and sustainable development practices in the region.

Robust Underwater Direction-of-Arrival Estimation Method Using Acoustic Sensor Array under Unknown Swing Deviation Elements

This paper presents a strategy called the alternating iterative minimization method (AIMM), aimed at enhancing the precision of direction of arrival (DOA) estimation when utilizing an acoustic vector sensor array (AVSA) with unknown swing deviation elements (SDEs). The AVSA model with unknown SDEs is formulated by incorporating the swing deviation parameter. Later, to estimate the swing deviation matrix (SDM) and the sparse signal power by using the alternating iteration method, the auxiliary cost functions with respect to SDM and the sparse signal power are formulated based on the regularized weighted least squares (RWLS) and regularized covariance matrix fitting (RCMF) criteria. Furthermore, their analytical expressions have also been quantified. In order to mitigate the effect of unknown SDEs on the accuracy of DOA estimation, any sub-time segment (STS) in the dataset is selected as the reference to convert the received data of different STS into the reference STS using the estimated SDM. The simulation and experimental outcomes conclusively represent the effectiveness of the suggested TSIM approach using AVSA in handling unknown SDEs.

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.

Микроволновая диагностика вспышечной плазмы методом фитирования по данным Cибирского радиогелиографа

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.

Experimental validation of an innovative method for minimization of deformation tolerances of reinforced concrete ceilings

AbstractThe static design of reinforced concrete ceilings is mostly based on finite element calculations. If the calculated deformation is too big, a superelevation is carried out in order to optimize the thickness of the ceiling. The superelevation is achieved by setting parts of the formwork a fixed amount higher than the edge areas of the ceilings before concreting. In this contribution, a validation of various numerical methods and a new load approach for the deformation of concrete slabs by means of measurement results is shown whereas the focus is on the multistory support of ceilings, which have not arrived the full Young's modulus.

A Combined Quasi-Newton-Type Method Using 4th-Order Directional Derivatives for Solving Degenerate Problems of Unconstrained Optimization

Introduction. Methods of unconstrained optimization play a significant role in machine learning [1–6]. When solving practical problems in machine learning, such as tuning nonlinear regression models, the extremum point of the chosen optimality criterion is often degenerate, which significantly complicates its search. Therefore, degenerate problems are the most challenging in optimization. Known numerical methods for solving the general unconstrained optimization problem, up to the second order, have very low convergence rates when solving degenerate problems [7, 8]. This is explained by the fact that for a significant improvement in convergence rate in this case, it is necessary to use higher-order derivatives in the method than the second order [10]. The purpose of the paper is to develop an efficient quasi-Newton method for solving degenerate unconstrained optimization problems, the idea of which (unlike regularization) involves dividing the entire space into the sum of two orthogonal subspaces. This idea was introduced in [23]. The space division at each iteration of the method is based on the spectral decomposition of the matrix approximating the Hessian of the objective function using the BFGS formula [3]. Each subspace exhibits its own behavior of the objective function, and therefore, an appropriate minimization method is applied on it. Results. A combined quasi-Newton method is presented for solving degenerate unconstrained optimization problems, based on orthogonal decomposition of the Hessian approximation matrix and division of the entire space into the sum of two orthogonal subspaces. On one subspace (the kernel of the Hessian approximation matrix), a method is applied where derivatives in the direction of the 4th order are computed, while on the orthogonal complement to it, a quasi-Newtonian method is applied. A separate one-dimensional search is applied on each of these subspaces to determine the step multiplier in the respective direction. The effectiveness of the presented combined method is confirmed by numerical experiments conducted on widely accepted test functions for unconstrained optimization problems. The proposed method allows obtaining fairly accurate solutions to test tasks in case of degeneracy of the minimum point with significantly lower computational costs for gradient calculations compared to optimization procedures of well-known mathematical packages. Conclusions. The idea of dividing the entire space into the sum of two (or possibly more) orthogonal subspaces when solving complex optimization problems is quite promising in terms of applying a combination of different numerical methods on separate subspaces. In the future, it is planned to conduct theoretical research on the convergence rate of the presented combined method in solving degenerate unconstrained optimization problems. Keywords: unconstrained optimization, quasi-Newton methods, degenerate minimum point, spectral matrix decomposition, Machine Learning.

A Smart Pipe Length Splitting Method for Cost Minimization of a Pressurized Irrigation Network

A Smart Pipe Length Splitting Method for Cost Minimization of a Pressurized Irrigation Network