Weighted Research Articles

Evaluation of Three Weight Functions for Nonlocal Regularization of Sand Models

Evaluation of Three Weight Functions for Nonlocal Regularization of Sand Models

A novel controlled metabolic accelerator for the treatment of obesity-related heart failure with preserved ejection fraction: Rationale and design of the Phase 2a HuMAIN trial.

Compared with those without obesity, patients with obesity-related heart failure with preserved ejection fraction (HFpEF) have worse symptoms, haemodynamics, and outcomes. Current weight loss strategies (diet, drug, and surgical) work through decreased energy intake rather than increased expenditure and cause significant loss of skeletal muscle mass in addition to adipose tissue. This may have adverse implications for patients with HFpEF, who already have reduced skeletal muscle mass and function and high rates of physical frailty. Mitochondrial uncoupling agents may have unique beneficial effects by producing weight loss via increased catabolism rather than reduced caloric intake, thereby causing loss of adipose tissue while sparing skeletal muscle. HU6 is a controlled metabolic accelerator that is metabolized to the mitochondrial uncoupling agent 2,4-dinotrophenol. HU6 selectively increases carbon oxidation from fat and glucose while also decreasing toxic reactive oxygen species (ROS) production. In addition to sparing skeletal muscle loss, HU6 may have other benefits relevant to obesity-related HFpEF, including reduced specific tissue depots contributing to HFpEF; improved glucose utilization; and reduction in systemic inflammation via both decreased ROS production from mitochondria and decreased cytokine elaboration from excess, dysfunctional adipose. We describe the rationale and design of HuMAIN-HFpEF, a Phase 2a randomized, double-blind, placebo-controlled, dose-titration, parallel-group trial in patients with obesity-related HFpEF to evaluate the effects of HU6 on weight loss, body composition, exercise capacity, cardiac structure and function, metabolism, and inflammation, and identify optimal dosage for future Phase 3 trials. HuMAIN will test a promising novel agent for obesity-related HFpEF.

LMI feasibility analysis of 2DOF NATA model

AbstractPresent paper shows the different types of tensor product model based linear matrix inequality controller design and feasibility analysis of two degrees of freedom aeroelastic wing section model. The tensor product models are based on reducing or removing the nonlinear behavior of the system and weighting functions. The linear matrix inequality based method results globally asymptotically stable system. The goal of the paper is to examine that selecting and varying the transformation space influences the feasibility of the linear matrix inequality based controller. The paper gives a comparison between the different tensor product models in terms of controller performance. The linear matrix inequality gives feasible solution for the controller design if the transformation space is selected adequately.

Wave interaction with multiple thin flexible porous barriers in water of uniform finite depth

The interaction of water waves with multiple flexible porous barriers partially immersed in a uniform finite-depth fluid is investigated in the present study. Using linear theory, the physical problem is mathematically modeled as a boundary value problem. The eigenfunction expansion approach is used to generate the velocity potential function in terms of orthogonal eigenfunctions. The Havelock’s inversion formula is then used to convert the boundary value problem into a set of Fredholm-type integral equations. By choosing a suitable weight function, the multi-term Galerkin’s approximation technique is used to obtain the mathematical solutions of these integral equations in terms of the Chebyshev polynomial. Numerical solutions regarding the reflection and transmission coefficients, hydrodynamic forces, and dissipated wave energy at the barriers are obtained and graphically represented in several figures for various non-dimensional parameters. It is observed from the graphical representations that the permeability and flexibility of the barriers play a crucial role in the modeling of efficient breakwaters.

A robust reconstruction method based on local Bayesian estimation combined with CURE clustering

A robust reconstruction method based on local Bayesian estimation combined with CURE clustering

Generalized functionals for qualification of geological carbon storage injection sites

Many nations have pledged to reach carbon neutrality by 2050. Embarking on the decarbonization journey, they posited geological carbon storage (GCS) as a pivotal technology within the carbon capture, utilization, and storage (CCUS) framework. The CCUS chain operates to reduce “hard-to-abate” emissions at key sectors by capturing carbon dioxide (CO2), reusing it, transporting it, or disposing of it via injection into underground geological formations for permanent storage. Despite the global success of GCS ventures, mainly driven by the oil and gas industry, GCS initiatives are still in their early stages in several developing countries. In Brazil, for instance, a full setup covering precise storage capacity databases, potential CCUS clusters, national regulatory structure, and auxiliary computer-aided engineering is underway. Intended to push the frontier in the latter subject, this paper introduces mathematical models for qualifying underground CO2 storage sites. Our research explores a family of multivariate functionals endowed with underlying reservoir features and distinct weighting functions, thus envisioning two primary objectives. Firstly, it clarifies non-linear interactions between rock and fluid properties using quality indicators. Secondly, it evaluates geographical regions considering structural traps/caprocks settings. Backed by the Matlab Reservoir Simulation Toolbox (MRST) capabilities, the methodology is a subsidiary resource for identifying suitable injection and storage sites. A case study using the UNISIM-I-D model generated dozens of volumetric quality maps that point to unique potential storage sites. Numerical simulation experiments of injection comparing legacy and novel wells reveal storage surpluses improved by up to 50%. The paper seeks to establish foundational knowledge in GCS efficiency for general underground settings. One expects that these outcomes leverage well-repurposing perspectives and stimulate field appraisal actions to scale up GCS projects both in Brazil and worldwide.

Maximum likelihood weight estimation for partial domain adaptation

Partial Domain Adaptation (PDA) aims to generalize a classification model from a labeled source domain to an unlabeled target domain, where the source label space contains the target label space. There are two main challenges in PDA that weaken the model's classification performance in the target domain: (i) the joint distribution of the source domain is related but different from that of the target domain, and (ii) the source outlier data, whose labels do not belong to the target label space, have a negative impact on learning the target classification model. To tackle these challenges, we propose a Maximum Likelihood Weight Estimation (MLWE) approach to estimate a weight function for the source domain. The weight function matches the joint source distribution of the relevant part to the joint target distribution, and reduces the negative impact of the source outlier data. To be specific, our approach estimates the weight function by maximizing a likelihood function, and the estimation leads to a nice convex optimization problem that has a global optimal solution. In the experiments, our approach demonstrates superior performance on popular benchmark datasets. Intro video and PyTorch code are available at https://github.com/sentaochen/Maximum-Likelihood-Weight-Estimation.

Effect of Ethanol Leaf Extract of Lantana camara on Fasting Blood Glucose, Body Weight and Kidney Function Indices of Alloxan-Induced Diabetic Albino Wistar Rats

The study was aimed at investigating the effect of ethanol leaf extract of Lantana camara on the fasting blood glucose, body weight and kidney function indices of alloxan-induced diabetic albino Wistar rats. Thirty male albino Wistar rats of weight 100-181 g were randomly assigned six groups of five rats each. Group 1 served as normal control. Group 2-6 animals were induced with 150 mg/kg body weight (B.W) of alloxan monohydrate intraperitoneally. Fasting blood glucose was checked 72hrs after induction and animals with blood glucose 200mg/dl and above were considered diabetic. Group 3 was treated with 50 mg/kg B.W of glibenclamide. Group 4,5 and 6 were orally treated with 600, 800 and 1000 mg/kg B.W of Lantana camara extract respectively for 21days. Blood sample was obtained by tail puncture to check glucose levels weekly with the use of glucometer. Animals treated with 600, 800 and 1000 mg/kg B.W of the extract showed significant decrease (P<0.05) in blood glucose, creatinine, urea and uric acid compared to untreated diabetic animals. Body weight of treated groups increased significantly (P<0.05) compared to the untreated group. The results obtained were synonymous to the glibenclamide treated group. This suggests that the extract possess antidiabetic potential and could be used in the management of diabetes.

Fish intake and mercury exposure in young children

Fish consumption is one of the main sources of mercury (Hg) exposure, but few studies have examined Hg exposure from fish consumption among children. This study aimed to assess the frequency of fish intake and associations with Hg and other element concentrations among 700 three-year-old children from the New Hampshire Birth Cohort Study. Usual fish intake was derived from a validated food frequency questionnaire (Block Questionnaire for ages 2–7) and toenail element concentrations were determined using ICP-MS. Multiple linear regression analysis was used to assess the association between fish intake and toenail element concentrations. A mixture analysis, using Weighted quantile sum (WQS) regression, was used to estimate the relative contribution of fish consumption to element exposures. Twenty-three percent of children were reported to consume at least one fish meal/week on average during the previous 6 months. In adjusted linear regression models, children with any type of fish consumption versus no consumption had 108% (95% confidence interval (CI: 68%, 153%)) higher toenail Hg concentrations. To a lesser extent, children consuming “other fish (not fried) including tuna” and “fried fish or fish sticks” had 120% (95% CI: 82%, 164%), and 23% (95% CI: 2%, 51%) higher toenail concentrations, respectively, than those consuming no fish. Using WQS regression, Hg was the element most strongly related to fish consumption. Fish intake among young children was related to Hg exposure even at low levels of consumption. Future studies will need to determine the health consequences of this exposure.

Exploring Weight Functions: A Family of One-Point Methods for Multiple Roots

Exploring Weight Functions: A Family of One-Point Methods for Multiple Roots

A Rapid Detection Method for Coal Ash Content in Tailings Suspension Based on Absorption Spectra and Deep Feature Extraction

Traditional visual detection methods that employ image data are often unstable due to environmental influences like lighting conditions. However, microfiber spectrometers are capable of capturing the specific wavelength characteristics of tail coal suspensions, effectively circumventing the instability caused by lighting variations. Utilizing spectral analysis techniques for detecting ash content in tail coal appears promising as a more stable method of indirect ash detection. In this context, this paper proposes a rapid detection method for the coal ash content in tailings suspensions based on absorption spectra and deep feature extraction. Initially, a preprocessing method, the inverse time weight function (ITWF), is presented, focusing on the intrinsic connection between the sedimentation phenomena of samples. This enables the model to learn and retain spectral time memory features, thereby enhancing its analytical capabilities. To better capture the spectral characteristics of tail coal suspensions, we designed the DSFN (DeepSpectraFusionNet) model. This model has an MSCR (multi-scale convolutional residual) module, addressing the conventional models’ oversight of the strong correlation between adjacent wavelengths in the spectrum. This facilitates the extraction of relative positional information. Additionally, to uncover potential temporal relationships in sedimentation, we propose a CLSM-CS (convolutional long-short memory with candidate states) module, designed to strengthen the capturing of local information and sequential memory. Ultimately, the method employs a fused convolutional deep classifier to integrate and reconstruct both temporal memory and positional features. This results in a model that effectively correlates the ash content of suspensions with their absorption spectral characteristics. Experimental results confirmed that the proposed model achieved an accuracy of 80.65%, an F1-score of 80.45%, a precision of 83.43%, and a recall of 80.65%. These results outperformed recent coal recognition models and classical temporal models, meeting the high standards required for industrial on-site ash detection tasks.

PDHL-MARCOS method based on cumulative prospect theory for MAGDM and its application in network security evaluation

The increasingly large and complex network brings convenience to our life and production, but it also brings potential dangers. Any minor vulnerability may lead to failure of application software configuration, software maintenance, user management and system management. Eventually, our network is interrupted, and even life and production data are leaked. Therefore, establishing a scientific and effective network security evaluation model is an effective way to supervise, manage and improve network security; It is also an effective measure to reduce the loss of life and production. Therefore, we first selected six factors closely related to network security to form a complete network security evaluation index system. Then, the utility function in the traditional measurement of alternatives and ranking according to compromise solution (MARCOS) method is improved by using the value function in cumulative prospect theory (CPT), and a PDHL-MARCOS evaluation model that can reflect the decision-makers’ (DMs’) attitudes towards gains and losses is obtained in the probabilistic double hierarchy linguistic term set (PDHLTS). In addition, the relative entropy (RE) method is also used to obtain expert weights. The combination weights of attributes are obtained using the correlation coefficient and the standard deviation (CCSD) method and the weight function in CPT.

On Orlicz classes defined in terms of associated weight functions

AbstractN-functions and their growth and regularity properties are crucial in order to introduce and study Orlicz classes and Orlicz spaces. We consider N-functions which are given in terms of so-called associated weight functions. These functions are frequently appearing in the theory of ultradifferentiable function classes and in this setting additional information is available since associated weight functions are defined in terms of a given weight sequence. We express and characterize several known properties for N-functions purely in terms of weight sequences which allows to construct (counter-) examples. Moreover, we study how for abstractly given N-functions this framework becomes meaningful and finally we establish a connection between the complementary N-function and the recently introduced notion of the so-called dual sequence.

Detecting diabetic retinopathy using a hybrid ensemble XL machine model with dual weighted-Kernel ELM and improved mayfly optimization

The uncontrolled increase of blood glucose level affects the retina and cause Diabetic Retinopathy (DR), which can permanently damage blood vessels if left untreated for a long time. Early detection helps the patient’s to minimize the severity level. However, the existing Synergic Deep Learning (SDL) model for detecting severity levels of fundus DR images lacks proper filtering methods, suffers from low image quality, and improperly tuned hyperparameters. To address these issues, a hybrid ensemble Extreme Learning (XL) machine model is proposed in detection and classification of DR. The proposed model includes preprocessing steps, such as image filtering, conversion, segmentation, and morphological operations, to improve image quality. Also the parameters are tuned effectively and the weight functions are analyzed based on the extracted features that lower the blurriness of the images. A Dual Weighted-Kernel Extreme Learning Machine (DW-KELM) is developed to classify DR images with improved accuracy, and an Improved Mayfly Optimization (IMO) algorithm is applied to optimize the DW-KELM model’s parameters. Implementing the Weighted Average Ensemble (WAE) technique enhances the model’s performance by allocating different weights. Two datasets, namely the SUSTech-SYSU, APTOS-2019 and IDRID datasets, are used for experimentation, and the results revealed that DW-KELM achieved 99% accuracy, with an overall sensitivity, specificity, precision, and F1-score of 98.52%, 96.32%, 98.10%, and 98.52% respectively. The proposed model achieved better performance in classifying 5 DR classes, including moderate DR, Proliferative DR, no DR, severe DR, and mild DR, with good image quality.

Analyticity theorems for parameter-dependent plurisubharmonic functions

In this paper, we first show that a union of upper-level sets associated to fibrewise Lelong numbers of plurisubharmonic functions is in general a pluripolar subset. Then we obtain analyticity theorems for a union of sub-level sets associated to fibrewise complex singularity exponents of some special (quasi-)plurisubharmonic functions. As a corollary, we confirm that, under certain conditions, the logarithmic poles of relative Bergman kernels form an analytic subset when the (quasi-)plurisubharmonic weight function has analytic singularities. In the end, we give counterexamples to show that the aforementioned sets are in general non-analytic even if the plurisubharmonic function is supposed to be continuous.

Subdivision Schemes Based on Weighted Local Polynomial Regression: A New Technique for the Convergence Analysis

The generation of curves and surfaces from given data is a well-known problem in Computer-Aided Design that can be approached using subdivision schemes. They are powerful tools that allow obtaining new data from the initial one by means of simple calculations. However, in some applications, the collected data are given with noise and most of schemes are not adequate to process them. In this paper, we present new families of binary univariate linear subdivision schemes using weighted local polynomial regression which are suitable for noisy data. We study their properties, such as convergence, monotonicity, polynomial reproduction and approximation and denoising capabilities. We establish conditions that connect the weight function with two essential properties: noise elimination and approximation capability. We propose a multi-objective optimization problem for improving these two capabilities and conclude that only some weight functions are Pareto optimal. For the convergence study, we develop some new theoretical results. Finally, some examples are presented to confirm the proven properties.

An improved stochastic car-following model considering the complete state information of multiple preceding vehicles under connected vehicles environment

Studying the car-following model contributes to improving vehicle driving efficiency and traffic flow stability within the context of connected automated vehicles (CAVs). Therefore, an improved stochastic car-following model (SMLVM) is proposed in this paper, building upon the traditional FVD model and BLVD model. This new model comprehensively considers four deterministic factors: acceleration, velocity, headway, and optimal speed memory of multiple preceding vehicles, as well as the comprehensive influence of stochastic factors on car-following behavior during the traveling process. Meanwhile, weight functions are utilized to represent the varying strengths of influence from vehicles ahead with different positions and speeds on the following vehicle. The SMLVM model is categorized and discussed based on practical application scenarios in this paper. When the focus is primarily on fundamental traffic flow behavior, relative motion between vehicles, and other basic principles, the SMLVM model degenerates into the MLVM model. Through stability analysis and simulation experiments on MLVM model, critical stability conditions are derived. Comparisons with other classical models demonstrate that the MLVM model exhibits stronger stability and resistance to disturbances. When the focus is primarily on various realistic driving scenarios, through simulation experiments on SMLVM models in different scenarios, it is found that the influence of stochastic factors on SMLVM models mainly depends on the magnitude of the intensity of stochastic fluctuations, which is rarely affected by the specific functional expressions of the stochastic terms. Finally, empirical analysis is conducted by selecting five sets of processed data from the next-generation simulation (NGSIM) dataset. Based on parameter estimation using the SMLVM model, and a comparison is made with the FVD model and BLVD model. The results confirm that the model proposed in this paper fits vehicle speeds better and absorbs disturbances more effectively, and exhibiting greater stability in traffic flow.

Nonexistence results for a time-fractional biharmonic diffusion equation

We consider weak solutions of the nonlinear time-fractional biharmonic diffusion equation ∂tαu+∂tβu+uxxxx=h(t,x)|u|p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\partial _{t}^{\\alpha }u+\\partial _{t}^{\\beta }u+u_{xxxx}=h(t,x)|u|^{p}$\\end{document} in (0,∞)×(0,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$(0,\\infty )\ imes (0,1)$\\end{document} subject to the initial conditions u(0,x)=u0(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$u(0,x)=u_{0}(x)$\\end{document}, ut(0,x)=u1(x)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$u_{t}(0,x)=u_{1}(x)$\\end{document} and the Navier boundary conditions u(t,1)=uxx(t,1)=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$u(t,1)=u_{xx}(t,1)=0$\\end{document}, where α∈(0,1)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\alpha \\in (0,1)$\\end{document}, β∈(1,2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\beta \\in (1,2)$\\end{document}, ∂tα\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\partial _{t}^{\\alpha}$\\end{document} (resp. ∂tβ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\partial _{t}^{\\beta}$\\end{document}) is the fractional derivative of order α (resp. β) with respect to the time-variable in the Caputo sense, p>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$p>1$\\end{document} and h is a measurable positive weight function. Using nonlinear capacity estimates specifically adapted to the fourth-order differential operator ∂4∂x4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\frac{\\partial ^{4}}{\\partial x^{4}}$\\end{document}, the domain, the initial conditions and the boundary condition, a general nonexistence result is established. Next, some special cases of weight functions h are discussed.

Carleman estimate and applications for a heat equation with multiple singularities

The goal of this article is the analyses of inverse source problem and null controllability for a heat equation with multipolar inverse-square potentials: ∑ j = 1 m μ j | x − x j | 2 , where x 1 , x 2 , … , x m are different points, defined in a bounded C 2 domain Ω ⊂ R n , and μ 1 , … , μ m are real constants. More precisely, we show that for any μ j ≤ ( n − 2 ) 2 4 , j = 1 , … , m , we can obtain a Lipschitz stability result in inverse source problems and we will show that the system is null controllable. Our proofs are mainly based on Carleman estimates, and due to multiple singularities, we give a new Carleman estimate by choosing an appropriate weight function.

Monascus red pigments alleviate high‐fat and high‐sugar diet‐induced NAFLD in mice by modulating the gut microbiota and metabolites

AbstractMonascus red pigments (MRP) may have benefits against NAFLD with an unclear mechanism. This study aimed to explore the protective effect of MRP supplementation against NAFLD through regulation of gut microbiota and metabolites. The C57BL/6 mice animals were randomly allocated into the normal diet (NC), HFHS diet‐induced NAFLD model, and MRP intervention group fed with HFHS diet. Serum lipid profiles and liver function parameters were measured. Liver and colon histopathology analysis was conducted to determine the injury in the liver and colon. 16S rRNA gene sequencing was employed to analyze gut microbial composition from fecal samples. Untargeted metabonomics was performed to analyze changes in metabolites in serum and fecal samples. MRP supplementation significantly improved the HFHS‐induced alterations in body weight, lipid profiles, and liver function (p < .01). MRP supplementation decreased the abundance of Akkermansia, Candidatus saccharimonas, Dubosiella, and Oscillibacter, while increasing Lactobacillus, Lachnospiraceae NK4A136 group, and Rikenella in mice fed the HFHS diet. Furthermore, MRP supplementation improved the serum and fecal metabolic profiles induced by the HFHS diet, primarily involving the arachidonic acid metabolism, unsaturated fatty acid biosynthesis, and adipocyte lipolysis pathways. Liver function and lipid profiles were closely associated with the abundance of Lactobacillus, Streptococcus, Oscillibacter, Akkemansia, and Desulfovibrio (p < .01). These findings revealed that MRP supplementation may help restore gut microbiota composition and balance its metabolites, thereby improving NAFLD. This study presents a novel outlook on the potential benefits of MRP supplementation in ameliorating NAFLD and supports the application of MRP as a new functional food.