Weighted Research Articles

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).

Effects of Laurus extract against cadmium-induced neurotoxicity in rats

Cadmium (Cd) is a hazardous heavy metal with widespread environmental presence, posing significant threats to human and animal health. This study investigates the neurotoxic effects of Cd exposure and explores the therapeutic efficacy of Laurus nobilis L. in counteracting these adverse effects. Cd exposure induces oxidative stress and neurotoxicity, leading to neuronal damage and histopathological alterations. Laurus, known for its antioxidant properties, is assessed for its potential in mitigating Cd-induced neurotoxicity through in vitro antioxidant assays, GC–MS analysis, HPLC profiling, and experimental animal models. Results demonstrate that Laurus ethanolic extract exhibits significant antioxidant activity, attributed to its phenolic and flavonoid constituents. GC–MS analysis reveals various bioactive compounds in the extract, including Hexadecanoic acid methyl ester and (Z)-13-Docosenamide, which posses neuroprotective properties. HPLC analysis identifies phenolic compounds such as ellagic acid and flavonoids like rutin and kaempferol in the extract. In vivo studies in rats exposed to Cd demonstrate that Laurus extract administration mitigates Cd-induced alterations in body and brain weight, hematological parameters, liver and kidney function, oxidative stress markers, and acetylcholinesterase (AchE) activity. Histopathological examination confirms the protective effects of Laurus against Cd-induced neuronal damage. The SOD model, validated with high resolution (2.05 Å) and strong R values (work: 0.192, free: 0.236, observed: 0.194), shows strong structural stability (C-score: 0.30). Docking studies reveal high binding affinities of kaempferol (−8.1 kcal/mol) and rutin (−8.7 kcal/mol) with SOD with kaempferol demonstrating superior solubility, lipophilicity, and drug-likenessSimulation analysis confirms the protein’s flexibility and adaptability, highlighting its therapeutic potential, especially with kaempferol. These findings suggest the therapeutic potential of Laurus. as a natural remedy for Cd-induced neurotoxicity, highlighting its antioxidant and neuroprotective properties.

LightDPH: Lightweight Dual-Projection-Head Hierarchical Contrastive Learning for Skin Lesion Classification

Effective skin cancer detection is crucial for early intervention and improved treatment outcomes. Previous studies have primarily focused on enhancing the performance of skin lesion classification models. However, there is a growing need to consider the practical requirements of real-world scenarios, such as portable applications that require lightweight models embedded in devices. Therefore, this study aims to propose a novel method that can address the major-type misclassification problem with a lightweight model. This study proposes an innovative Lightweight Dual Projection-Head Hierarchical contrastive learning (LightDPH) method. We introduce a dual projection-head mechanism to a contrastive learning framework. This mechanism is utilized to train a model with our proposed multi-level contrastive loss (MultiCon Loss), which can effectively learn hierarchical information from samples. Meanwhile, we present a distance-based weight (DBW) function to adjust losses based on hierarchical levels. This unique combination of MultiCon Loss and DBW function in LightDPH tackles the problem of major-type misclassification with lightweight models and enhances the model’s sensitivity in skin lesion classification. The experimental results demonstrate that LightDPH significantly reduces the number of parameters by 52.6% and computational complexity by 29.9% in GFLOPs while maintaining high classification performance comparable to state-of-the-art methods. This study also presented a novel evaluation metric, model efficiency score (MES), to evaluate the cost-effectiveness of models with scaling and classification performance. The proposed LightDPH effectively mitigates major-type misclassification and works in a resource-efficient manner, making it highly suitable for clinical applications in resource-constrained environments. To the best of our knowledge, this is the first work that develops an effective lightweight hierarchical classification model for skin lesion detection.

A generalization of the Exner law for sediment nonlocal transport at bedform scale

Recent researches have highlighted the significance of nonlocal processes in understanding the morphodynamics and sediment transport across landscapes. Nonlocal processes in sediment transport refer to the influence of landscape properties beyond the immediate vicinity of a given point on the sediment flux. Existing nonlocal models, employing fractional operators, aim to capture global correlated nonlocality using a global convolution operator. Nevertheless, such models tend to disregard nonlocal phenomena occurring at regional scales, potentially resulting in substantial inaccuracies due to an inadequate representation of sediment transport processes at these scales. This study presents a novel and more comprehensive mathematical formulation of the nonlocal Exner law, leveraging the peridynamic differential operator (PDDO). The proposed regional nonlocal model incorporates nonlocal sediment transport processes by utilizing a pre-defined weight function and interaction domain within the framework of the PDDO. The novel regional nonlocal model effectively bridges the gap between local and global models by integrating both short-range and long-range interactions in sediment transport. Application reveals that that the regional nonlocal model provides a significantly enhanced accuracy in depicting profiles at the bedform scale compared to the local and the global models.

Influence of Fractional Order on the Behavior of a Normalized Time-Fractional SIR Model

In this paper, we propose a novel normalized time-fractional susceptible–infected–removed (SIR) model that incorporates memory effects into epidemiological dynamics. The proposed model is based on a newly developed normalized time-fractional derivative, which is similar to the well-known Caputo fractional derivative but is characterized by the property that the sum of its weight function equals one. This unity property is crucial because it helps with evaluating how the fractional order influences the behavior of time-fractional differential equations over time. The normalized time-fractional derivative, with its unity property, provides an intuitive understanding of how fractional orders influence the SIR model’s dynamics and enables systematic exploration of how changes in the fractional order affect the model’s behavior. We numerically investigate how these variations impact the epidemiological dynamics of our normalized time-fractional SIR model and highlight the role of fractional order in improving the accuracy of infectious disease predictions. The appendix provides the program code for the model.

PULSAR Effect: Revealing potential synergies in combined radiation therapy and immunotherapy via differential equations

PULSAR (personalized ultrafractionated stereotactic adaptive radiotherapy) is a form of radiotherapy method where a patient is given a large dose or “pulse” of radiation a couple of weeks apart rather than daily small doses. The tumor response is then monitored to determine when the subsequent pulse should be given. Pre-clinical trials have shown better tumor response in mice that received immunotherapy along with pulses spaced 10 days apart. However, this was not the case when the pulses were 1 or 4 days apart. Therefore, a synergistic effect between immunotherapy and PULSAR is observed when the pulses are spaced out by a certain number of days. In our study, we aimed to develop a mathematical model that can capture the synergistic effect by considering a time-dependent weight function that takes into account the spacing between pulses. We determined feasible parameters by fitting murine tumor volume data of six treatment groups via simulated annealing algorithm. Applying these parameters to the model we simulated 4000 trials with varying sequencing of pulses. These simulations indicated that if pulses were spaced apart by at least 9 days the tumor volume was about 200 mm3 to 250 mm3 smaller when treated with PULSAR combined with immunotherapy. We successfully demonstrate that our model is simple to implement and can generate tumor volume data that is consistent with the pre-clinical trial data. Our model has the potential to aid in the development of clinical trials of PULSAR therapy.

A New Composite Dissimilarity Measure for Planar Curves Based on Higher-Order Derivatives

With the rapid development of information technology, the problem of curve matching has appeared in many application domains, including sequence analysis, signals processing, speech recognition, etc. Many similarity measures have been studied for matching curves based on Euclidean distance, which shows fragility in portraying the morphological information of curve data. In this paper, we propose a novel weighted composite curve dissimilarity metric (WCDM). First, the WCDM measures the dissimilarity based on the higher-order semantic difference between curve shapes and location difference. These two differences are calculated using the curvature difference and Euclidean distance between the curves, respectively. Second, a new dynamic weighting function is defined by employing the relationship between the trends of the curves. This function aims at adjusting the contributions of the curvature difference and the Euclidean distance to compose the dissimilarity measure WCDM. Finally, to ascertain the rationality of the WCDM, its metric properties are studied and proved theoretically. Comparison experiments on clustering and classification tasks are carried out on curve sets transformed from UCR time series datasets, and an application analysis of the WCDM is conducted on spectral data. The experimental results indicate the effectiveness of the WCDM. Specifically, clustering and classification based on the WCDM are superior to those based on ED, DTW, Hausdorff, Fréchet, and LCSS on at least 8 out of 14 datasets across all evaluation indices. In particular, the Purity and ARI on the Beetlefly dataset are improved by more than 7.5%, while accuracy on the Beef, Chinatown, and OliveOil datasets increases by 13.32%, 10.08%, and 12.83%, respectively.

Optimization of fast-ion diagnostic sets in tokamaks and stellarators using diagnostic weight functions.

The fast-ion phase-space distribution function in the magnetic fusion devices is always underdiagnosed, and every new fast-ion diagnostic should be carefully assessed before installation to minimize redundancies in measurements and maximize the information from the yet undiagnosed part of the fast-ion phase space distribution function. Here, we present a novel method of assessing the added value of a considered fast-ion diagnostic, taking actual geometry and an existing set of fast-ion diagnostics into account. The new method is based on a reformulation of the diagnostic weight functions in constants of motion (COM). We compare the proposed method with the previous approach using Monte Carlo simulations.

A backward problem for stochastic Kuramoto-Sivashinsky equation: Conditional stability and numerical solution

In this paper, we consider a backward problem in time for a linear stochastic Kuramoto-Sivashinsky equation. Firstly, we present two Carleman estimates incorporating weight functions independent of the variable x for the stochastic Kuramoto-Sivashinsky equation. Subsequently, we employ these two Carleman estimates to establish conditional stability for the backward problem in two distinct scenarios: when 0<t0<T and when t0=0. Lastly, we transform the backward problem in time into the minimization of a regularized Tikhonov functional. This functional is solved by the conjugate gradient algorithm based on the gradient formula tailored for the regularized functional. Numerical examples related to the recovery of continuous and discontinuous initial values illustrate the effectiveness of the conjugate gradient algorithm.

Localized solutions of the wave equation

A family of solutions of the wave equation is presented. The simplest waveforms represent sub-cycle pulses; oscillatory pulses with a dominant wavenumber are also discussed. These solutions are sufficiently localized to have finite energy, momentum, and angular momentum when applied to acoustic and electromagnetic pulses. The energy, momentum, and angular momentum of the pulses are simply expressed in terms of the wavenumber weight function.

Never stop or never start? Optimal stopping under a mixture of CPT and EUT preferences

We consider the problem of finding the best time to stop a diffusion process for an agent with a preference model that is a mixture of expected utility theory (EUT) and cumulative prospect theory (CPT). In view of time-inconsistency, we consider two types of agents: a naive agent, who is not aware of the time-inconsistency and thus re-plans at every instant, and a sophisticated agent, who is aware of the time-inconsistency and takes a so-called intra-personal equilibrium strategy by correctly anticipating her actions in the future. We show that under a wide range of the CPT preference parameter values, the naive agent will never stop. For a sophisticated agent, we use a different notion of intra-personal equilibrium from the one employed by Ebert and Strack (2018). We show that any two-threshold strategy, which is to stop when the diffusion process reaches either an upper threshold or a lower threshold, cannot be an intra-personal equilibrium if the agent overweights worst, unlikely outcomes disproportionally. We derive a sufficient and necessary condition for the strategy of stopping everywhere to be an intra-personal equilibrium and show that this condition does not hold and thus the sophisticated agent may choose to start the diffusion process for some commonly used probability weighting functions.

Spectral weighting functions for localization of complex sound. III. The effect of sensorineural hearing lossa).

Spectral weighting functions for sound localization were measured in participants with bilateral mild sloping to moderately severe, high-frequency sensorineural hearing loss (SNHL) and compared to normal hearing (NH) participants with and without simulated SNHL. Each participant group localized three types of complex tones, comprised of seven frequency components spatially jittered and presented from the horizontal frontal field. A threshold-elevating noise masker was implemented in the free field to simulate SNHL for participants with NH. On average, participants with SNHL and NH (in quiet and simulated SNHL) placed the greatest perceptual weight on components within the interaural time difference "dominance region," found previously to peak around 800 Hz [Folkerts and Stecker, J. Acoust. Soc. Am. 151, 3409-3425 (2022)]. In addition to the peak at 800 Hz, both participant groups (including NH participants in quiet) placed near equal weight on 400 Hz, resulting in a broadened "peak" in the dominance region, most likely due to the reduction of audibility to higher frequency components. However, individual weighting strategies were more variable across participants with SNHL than participants with NH. Localization performance was reduced for participants with SNHL but not for NH participants with simulated hearing loss when compared to NH participants in quiet.