Weighted Research Articles

Discontinuous Galerkin methods for magnetic advection-diffusion problems

We devise and analyze a class of the primal discontinuous Galerkin methods for magnetic advection-diffusion problems based on the weighted-residual approach. In addition to the upwind stabilization, we explore some terms related to convection under the vector case that provides more flexibility in constructing the schemes. Under a degenerate Friedrichs system, we show the stability and optimal error estimate, which boil down to two ingredients – the weight function and the special projection – that contain information of advection. Numerical experiments are provided to verify the theoretical results.

Inhibition of ketohexokinase prevents fructose-induced insulin resistance and endothelial dysfunction in high fat and sucrose-fed mice via enhancing energy expenditure

Abstract Introduction Cardiovascular disease linked to fatty liver poses a significant threat to health. Consumption of high-fat diets (HFD) is known to trigger a cascade of health issues including obesity, diabetes and non-alcoholic fatty liver disease (NAFLD). These conditions are exacerbated, with the addition of dietary sugar to high-fat diets (HFSD), resulting in non-alcoholic steatohepatitis (NASH). Studies have shown that fructokinase (ketohexokinase or KHK) knockdown protects mice from HFSD-induced NASH. Purpose This study focused on examining the temporal changes of metabolic perturbations, triggered by sugar consumption, particularly in the transition from fatty liver to cardiovascular disease. Methods C57/BL6 wild type (WT) and KHK-knockout mice were fed with HFD or HFSD for different durations between 6 to 20 weeks, to induce NAFLD and NASH. Western blotting and quantitative-PCR were carried out on tissues and isolated endothelial cells for protein and mRNA expressions, respectively. Glucose and insulin tolerance tests and whole-body energy measurements were performed at different time-points. Combined Laboratory Animal Monitoring System was used to monitor various parameters of energy expenditure. Functional interactions between endothelial cells and adipose tissues were carried out using an in-house Organ-On-a-chip technology. Endothelial function was studied by measuring isometric tensions in organ bath. Results KHK-deficient mice had significantly lower weight compared to WT littermates. Wild-type mice fed HFD and HFSD exhibited significant glucose intolerance, insulin resistance, and endothelial dysfunction compared to KHK-knockout mice. However, HFSD led to more severe outcomes compared to HFD, demonstrating differing temporal effects on glucose intolerance and insulin resistance. Interestingly, KHK-knockout mice were significantly protected from the effects induced by HFSD, as well as, such as increased body weight, fasting hyperglycemia, hyperinsulinemia and endothelial function. KHK-knockout mice exhibited significant increase in oxygen consumption and energy expenditure, both in HFD and HFSD-fed conditions. These data suggest that high energy-expenditure is one of the primary mechanisms, in reducing adiposity and body weight in HFSD-fed knockout mice. Notably, KHK-deficient mice were protected against sugar-induced fat accumulation, random hyperinsulinemia and significant impairment of endothelial function. Conclusions A novel finding in this study is that KHK ablation protects not only against HFSD-induced, but also HFD-induced pathologies via significantly enhancing energy expenditure. The results suggest that targeting KHK could potentially mitigate the development of fatty liver and cardiovascular diseases, induced by high calorie diets.

Estimation for conditional moment models based on martingale difference divergence

We provide a new estimation method for conditional moment models via the martingale difference divergence (MDD). Our MDD‐based estimation method is formed in the framework of a continuum of unconditional moment restrictions. Unlike the existing estimation methods in this framework, the MDD‐based estimation method adopts a non‐integrable weighting function, which could capture more information from unconditional moment restrictions than the integrable weighting function to enhance the estimation efficiency. Due to the nature of shift‐invariance in MDD, our MDD‐based estimation method can not identify the intercept parameters. To overcome this identification issue, we further provide a two‐step estimation procedure for the model with intercept parameters. Under regularity conditions, we establish the asymptotics of the proposed estimators, which are not only easy‐to‐implement with expectation‐based asymptotic variances, but also applicable to time series data with an unspecified form of conditional heteroskedasticity. Finally, we illustrate the usefulness of the proposed estimators by simulations and two real examples.

Exploring the correlation between body mass index and lung function test parameters: a cross-sectional analytical study

PurposeThe correlation between body weight and health is a significant public health concern. While the adverse effects of obesity on pulmonary function are well-known, the impact of being underweight remains debated due to limited research. This study aimed to investigate the correlation between Body Mass Index (BMI) categories and lung function parameters.ResultsA study of 3077 participants found significant differences in gender, age, height, and weight across various Body Mass Index (BMI) categories. The study found non-significant variations in forced expiratory flow (FVC) across BMI categories, with underweight individuals showing lower FVC compared to normal and overweight individuals. BMI significantly impacted mean forced expiratory flow during the middle half of FVC. A significant negative correlation was observed between age and FVC, FEV1, FEV1/FVC ratio, and FEF25-75. A significant positive correlation was observed between weight, height, and lung function parameters. Multiple regression analysis revealed a decrease in lung function with advancing age, while height showed significant positive associations. The study concluded that age, sex, smoking, height, and weight collectively explained 41.0% of the variance in FVC, FEV, and FEF25-75.

Model-based meta-analysis of HbA1c reduction across SGLT2 inhibitors using dose adjusted by urinary glucose excretion

This study was aimed to evaluate whether the dose–response relationship of the sodium glucose co-transporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM)—canagliflozin, dapagliflozin, empagliflozin, ipragliflozin, luseogliflozin, and tofogliflozin—can be explained in a unified manner based on their ability to promote urinary glucose excretion (UGE). Information on HbA1c reduction at various doses of each SGLT2i was collected from literatures on randomized controlled trials and was normalized based on the daily UGE data from phase I studies. After normalizing doses, the dose–response relationship of HbA1c reduction of most of SGLT2is was represented by a unified nonlinear mixed-effect model, with the estimated maximum HbA1c (%) reduction (Emax) of 0.796 points, whereas covariate analysis showed that canagliflozin had a 1.33-fold higher Emax than those of the other drugs. Other covariates included baseline HbA1c levels, body weight, disease duration, prior treatment, and renal function. Findings from this study would influence drug selection and adjustment in clinical practice. As with SGLT2is, in cases where the efficacy cannot be easily evaluated but an appropriate pharmacodynamic marker was assessed in early clinical trials, similar approaches for other drug classes can guide strategic and evidence-based dose selection in phase III trials.

Logics for at most countable first-order structures

Abstract In this paper we present two extensions of $\omega $-logic with infinitary inference rules, denoted $Arch$-$\omega $-logic and $non$-$Arch$-$\omega $-logic. We provide the corresponding Hilbert-style axiomatizations and prove their strong completeness with respect to countable Archimedean and non-Archimedean fields, respectively. Through several examples we illustrate a natural representation of various weight functions within the proposed framework and applications to non-monotonic reasoning and neuro-symbolic computing.

Uncertainty quantification driven machine learning for improving model accuracy in imbalanced regression tasks

Several factors are known to determine the quality of machine learning models, one of which is the dataset quality. One problem related to the quality of a dataset is the imbalance issue. An imbalanced dataset contains significantly more data points for certain values of the output variable which increases the overfitting risk and negatively affects the prediction accuracy. In this article, we propose using epistemic uncertainty quantification (UQ) of machine learning models to identify rare samples in imbalanced regression problems for balancing the dataset. The developed algorithm, uncertainty quantification-driven imbalanced regression (UQDIR), is guided by UQ to restructure the training set with an adequate weight function using existent samples, eliminating the need for new data collection. After identifying rare samples with UQ, the algorithm selects a sample from the training set, assigns a resampling weight using the new weight function, and finally resamples the selected sample according to its assigned weight. We test UQDIR on several benchmark datasets and different machine learning algorithms, then compare its performance with similar imbalanced regression methods. A metamaterial design problem application is also provided for demonstrating the effectiveness of the algorithm in real-world scenarios. We show that improving the quality of UQ metrics results in improved model accuracy.

Extension of Fejér's inequality to the class of sub-biharmonic functions

Abstract Fejér’s integral inequality is a weighted version of the Hermite-Hadamard inequality that holds for the class of convex functions. To derive his inequality, Fejér [Über die Fourierreihen, II, Math. Naturwiss, Anz. Ungar. Akad. Wiss. 24 (1906), 369–390] assumed that the weight function is symmetric w.r.t. the midpoint of the interval. In this study, without assuming any symmetry condition on the weight function, Fejér’s inequality is extended to the class of sub-biharmonic functions, namely, the set of functions f ∈ C 4 ( I ) f\in {C}^{4}\left({\mathbb{I}}) satisfying f ″ ″ ≤ 0 {f}^{^{\prime\prime} ^{\prime\prime} }\le 0 , where I {\mathbb{I}} is an interval of R {\mathbb{R}} . In the special case when the weight function is symmetric w.r.t. the midpoint of some interval, and the function f f is convex and sub-biharmonic, an interesting refinement of Fejér’s inequality is deduced. Moreover, this inequality is extended to a new class of functions defined on the plane, that we call the set of sub-biharmonic functions on the coordinates. Assuming in addition that the function is convex on the coordinates, a refinement of an inequality due to Dragomir [On the Hadamard’s inequality for convex functions on the co-ordinates in a rectangle from the plane, Taiwan. J. Math. 5 (2001), 775–788] is obtained.

Efficient algorithm for stochastic rumor blocking problem in social networks during safety accident period

Rumor sources spread negative information throughout the network, which may cause unbelievable results in real society especially for social safety field. Propagating positive information from several “protector” users is an effective method for rumor blocking once the rumor is detected. Based on the probability of each user being a rumor, “protector” nodes need to be selected in order to prepare for rumor blocking. Given a social network G=(V,E,P,Q), where P(u,v) is the probability that v is activated by u after u is activated, and Q is the weight function on node set V, Qv is the probability that v will be a rumor source. Stochastic Rumor Blocking (SRB) problem is to select k nodes as “protector” such that the expected eventually influenced users by rumor is minimized. SRB will be proved to be NP-hard and the objective function is supermodular. We present a Compound Reverse Influence Set sampling method for estimation of the objective value which can be represented as a compound set function. A randomized greedy algorithm with theoretical analysis will be presented and other two different “protector” selection strategies will be proposed for comparison. Finally, we evaluate our algorithm on real world data sets and do comparison among different strategies.

Gut Microbiome and Metabolome Alterations in Overweight or Obese Adult Population after Weight-Loss Bifidobacterium breve BBr60 Intervention: A Randomized Controlled Trial.

Probiotics, known for regulating gut microbiota, may aid those with overweight or obesity, but their mechanisms require more research. This study involved 75 overweight or obese young adults, randomly assigned to either a Bifidobacterium breve BBr60 (BBr60) group or a placebo group. Both groups received diet guidance and took either BBr60 (1 × 1010 CFU/day) or a placebo for 12 weeks. Researchers analyzed body composition, serum glucose, lipids, liver and kidney function, comprehensive metabolome, and intestinal homeostasis before and after the intervention. After 12 weeks, BBr60 significantly reduced weight and BMI compared to pretreatment levels and outperformed the placebo. The BBr60 group also showed improved blood biochemistry, with notably lower fasting blood glucose (FBG) levels than the placebo group (p < 0.05). Additionally, BBr60 influenced vital serum and fecal metabolites related to three amino acid metabolic pathways and regulated the bacteria Dialister, Klebsiella, and Bacteroides, which correlated strongly with serum metabolites. These findings indicate that BBr60 can safely and effectively regulate BMI, body weight, serum glucose, lipids, and liver function markers, which may involve BBr60's impact on key gut bacteria, which influence metabolites related to the valine, leucine, and isoleucine biosynthesis; glycine, serine, and threonine metabolism; and alanine, aspartate, and glutamate metabolism.

Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

Mild burn amplifies the locomotive depression in demyelinated mice without muscle pathophysiological changes.

Patients with mild burns take most accounts, however, the impact of mild burns is less known. Nerve destruction leads to muscle atrophy. We posit that even mild burn injury could worsen demyelinated nerves related to muscle pathophysiological impairment. Young adult C57BL/6 (male, n = 60) mice were randomly fed with either a 0.2% cuprizone diet or a regular rodent diet for 4 weeks. At week 5, all mice were then grouped into mild scald burn with 10% TBSA and sham injury groups. Mice received animal behavior tests and in situ muscle isometric force measurement before euthanasia for tissue collection. Total horizontal ambulation and vertical activity were significantly reduced in mice with mild burn injury (p<0.05). Mice with the cuprizone diet had significantly less time to fall than those with the regular diet on day 7 after burn (p<0.05). No significant difference was found in gastrocnemius tissue weight among the groups, nor muscle isometric tensions (all p>0.05). The cuprizone diet increased the maximal phosphorylating respiration in mice muscle mitochondria (p<0.05). The muscle protein expressions of caspase 3, Fbx-32, and Murf1 significantly increased in mice with the cuprizone diet 3 days after burn (p<0.05). The signal expression of S100B significantly increased in mice with the cuprizone diet, and its expression was even greater on day 7 after burn injury. (p<0.05). The cuprizone diet-induced locomotion and cognitive disorders were amplified by the mild burn injury in mice, which is associated with muscle intracellular signal alterations. However, mild burn injury does not cause mouse muscle weight loss and function impairment. The potential risk of pre-existed neural impairment could be aware when patients encounter even small or mild burns.

Multi-Objective Hybrid Algorithm Integrating Gradient Search and Evolutionary Mechanisms

The current multi-objective evolutionary algorithm (MOEA) has attracted much attention because of its good global exploration ability, but its local search ability near the optimal value is relatively weak, and for optimization prob lems with large-scale decision variables, the number of populations and iterations required by MOEA are very large, so the optimization efficiency is low. Gradient-based optimization algorithms can overcome these problems well, but they are difficult to be applied to multi-objective problems (MOPs). Therefore, this paper introduced random weight function on the basis of weighted average gradient, developed multi-objective gradient operator, and combined it with non-dominated genetic algorithm based on reference points (NSGA- III) proposed by Deb in 2013 to develop multi-objective optimization algorithm (MOGBA) and multi-objective Hybrid Evolutionary algorithm (HMOEA). The latter greatly enhances the local search capability while retaining the good global exploration capability of NSGA-III. Numerical experiments show that HMOEA has excellent capture capability for various Pareto formations, and the efficiency is improved by times compared with typical multi-objective algorithms. And further HMOEA is applied to the multi-objective aerodynamic optimization problem of the RAE2822 airfoil, and the ideal Pareto front is obtained, indicating that HMOEA is an efficient optimization algorithm with potential applications in aerodynamic optimization design.

C-H Functionalization of Poly(ethylene oxide) - Embracing Functionality, Degradability, and Molecular Delivery.

This study presents an organocatalytic C-H functionalization approach for postpolymerization modification (PPM) of poly(ethylene oxide) (PEO). Most of PEO PPM is previously processed at the end hydroxy group, but recent advances in C-H functionalization open a way to modify the backbone position. Structurally diverse carboxylic acids are attached to PEO through a cascade process of radical generation by peroxide and oxidation to oxocarbenium by tertiary butylammonium iodide. Attaching carboxylic acids yields a series of functionalize PEO with acetal units (2-5 mol%) in a backbone, which is not accessible via conventional copolymerization of epoxides. The optimized conditions minimizes the uncontrolled degradation or crosslinking from the highly reactive radical and oxocarbenium intermediate. The newly introduced acetal units bring degradability of PEO as well as delivery of carboxylic acid molecules. Hydrolysis studies with high molecular weight functionalization PEO (Mn = 13.0kgmol-1) confirm the steady release of fragmented PEO (Mn ∼ 2.0kgmol-1) and carboxylic acid over days and the process rate is not sensitive to pH variation between pH 5 and 9. The presented method offers a versatile and efficient way to modify PEO with potential energy and medical applications.

Virtual source total focusing method for surface defects using leaky Rayleigh waves

Leaky Rayleigh waves are sensitive to surface defects and beneficial for automated detection due to their non-contact characteristics. However, the amplitude of the leaky Rayleigh waves is low due to the attenuation of waveform conversion and acoustic waves propagation, which limits the imaging quality for long-distance detection. This study introduces a novel method combining leaky Rayleigh waves detection with virtual source total focusing method. The virtual source (VS) technology enhances the emission energy of leaky Rayleigh waves. Then, the total focusing method (TFM) is applied to acquire high-accuracy images of defects. To reduce noise and artifacts, a weighting function based on the coherence factor (CF) is developed to weight the TFM superimposed signals, thereby achieving high-quality image reconstruction. Experimental results show that compared with the conventional TFM method, the proposed method can effectively improve the amplitude of imaging signals while reducing system noise and imaging artifacts. The lateral accuracy of defects is improved, with the average lateral error of defect size being 0.178 mm. The signal-to-noise ratio (SNR) of ultrasound images is increased by 27.59 dB, and the array performance index (API) of ultrasound images is decreased by 33.78 %. The proposed method provides a new and effective approach for quantitatively assessing the surface defects of metal components using leaky Rayleigh waves.

Comparison of noise exposure cost and annoyance-related indicators in microscopic traffic simulations

Road traffic noise is one of the major sources of environmental noise and has been shown to increase risk of adverse health effects. For this reason, noise mapping is an efficient tool to assess and monitor city-wide noise levels in accordance with the Environmental Noise Directive (END). To relate these noise levels to the associated health effects, the noise map may be further analysed using exposure-response models, or with cost models that calculate the noise-related externalities of the traffic. With the use of microscopic traffic simulations, this assessment may be performed at a vehicle-specific level. A recently introduced methodology, the noise exposure cost, calculates the vehicle-specific contribution to the long-term social cost estimated at a macro-level based on the vehicle-specific noise contributions. However, the noise exposure cost methodology may not reflect the relative noise impact of individual vehicles, but rather solely reflect the noise contribution regardless of its relative impact to the noise levels at the time. The aim of this study is to investigate the possibility to use the linear sensitivity indicator as a basis for a non-linear weighting function in the noise exposure cost methodology, to allow the vehicle-specific noise exposure cost to better reflect the relative impact.

An enhanced proportional topology optimization method with new density filtering weight function for the minimum compliance problem

This article proposes an enhanced proportional topology optimization (EPTO) method to solve the structural topology optimization problem of minimizing compliance under material volume constraints. In the proposed method, a new filtering weight function based on the improved Heaviside threshold function is adopted to filter element density during the optimization process. The optimization process of the EPTO method consists of an inner loop and an outer loop. In the inner loop, density distribution is modified in combination with a new filtering weight function. By locally averaging the weighted element compliance, an improved density distribution function in the inner loop is put forward to make the topology configuration of the optimized structure more reasonable. In addition, the projection method is used to reduce the number of intermediate density elements, thereby obtaining optimization results with clear boundaries. In the outer loop, a new termination criterion is adopted, which terminates the optimization process by determining that the relative error of the objective function in several consecutive iterations is less than the specified value. The effectiveness and efficiency of the proposed method are demonstrated through several numerical examples involving two-dimensional (2D) and three-dimensional (3D) topology optimization problems. The results of numerical examples show that the proposed method can not only accelerate the convergence of optimization iterations, but also obtain optimized structures with smaller objective function values and better topology configurations. HIGHLIGHTS A new density filtering weight function is proposed based on an improved Heaviside threshold function. An improved inner loop density distribution formula is proposed by combining a new filtering weight function. Using projection based methods to suppress the appearance of intermediate density elements. Verify the effectiveness of the proposed algorithm by comparing the optimization results with existing algorithms such as top88 and PTO.

Data-driven inventory control for large product portfolios: A practical application of prescriptive analytics

Motivated by the real-world inventory management problem of a large network of pharmacies, this paper proposes and studies a practically relevant Prescriptive Analytics approach for data-driven dynamic inventory control of large portfolios of interrelated products. We extend existing research on weighted Sample Average Approximation by integrating a ‘global learning’ model that effectively exploits cross-learning opportunities within the product portfolio. The results of an extensive numerical evaluation on real-world data suggest that our approach outperforms relevant benchmarks—in particular, models that rely on ‘local learning’ strategies where weight functions are trained separately for each product. The numerical results also allow us to derive important practical and structural insights regarding the value of contextual information in our global learning framework.

Nonlinear Elliptic Equations with Variable Exponents Anisotropic Sobolev Weights and Natural Growth Terms

Abstract The purpose of our paper is to prove the existence of the distributional solutions for anisotropic nonlinear elliptic equations with variable exponents, which contain lower order terms dependent on the gradient of the solution and on the solution itself. The terms are weighted, and the main results rely on the possibility of comparing the weights with each other, where the right-hand side is a sum of the natural growth term and the datum f ∈ L 1(Ω). Furthermore the weight function θ(·) is in W̊ 1,p→(·) (Ω), with θ(·) &gt; 0 and connected with the coefficient b(·) ∈ L 1(Ω) of the lower order term.

On peridynamic acoustics

We consider a nonlocal (peridynamic) version of the classical forced wave equation. This scalar three-dimensional equation contains a weight function (the “micromodulus”) and a length parameter (the “horizon”) that have to be selected. We investigate various properties (the locality limit as the horizon shrinks, plane waves and group velocity), paying attention to how these properties depend on the choice of the micromodulus. We solve the forced peridynamic equation in the static case (avoiding divergent integrals) and in the time-harmonic case (with a radiation condition, when needed).