Weighted Research Articles

Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data

The Generalized Linear Autoregressive Moving Average (GLARMA) model has been used in epidemiological studies to evaluate the impact of air pollutants on health. Due to the nature of the data, a robust approach for the GLARMA model is proposed here based on the robustification of the quasi-likelihood function. Outlying observations are bounded separately by weight functions on covariates and the Huber loss function on the response variable. Some technical issues related to the robust approach are discussed and a Monte Carlo study revealed that the robust approach is more reliable than the classic one for contaminated data with additive outliers. The real data analysis investigates the impact of PM10 in the number of deaths by respiratory diseases in Vitória, Brazil.

The Optimization of Weight Functions of R3,1 Space Time by the Echo State Network

The Optimization of Weight Functions of R3,1 Space Time by the Echo State Network

Physical activity, body weight, and liver function as factors increasing Insomnia index among Korean adults: a cross-sectional analysis.

This study aimed to elucidate the relationship between physical activity, body weight, liver function, and insomnia in Korean adults, thereby providing a foundation for health promotion strategies. We recruited 11,645 adults (8,051 males and 3,594 females). Participants underwent assessments using the Korean version of the Insomnia Severity Index, measures of physical activity (PA), anthropometric data (body weight, height, body mass index [BMI], and waist circumference [WC]), and liver function (alanine aminotransferase, aspartate aminotransferase [AST], and gamma-glutamyl transferase). One-way ANOVA revealed significant differences among male groups in height (p < .001), weight (p = .036), BMI (p = .002), diastolic blood pressure (p = .008), AST (p = .036), recreational PA (p = .026), moderate PA (p < .01), vigorous PA (p < .01), and moderate-to-vigorous PA (p < .001). Similarly, significant differences were found among female groups in height (p < .001), weight (p = .001), BMI (p = .006), WC (p = .013), moderate PA (p < .001), vigorous PA (p < .001), and moderate-to-vigorous PA (p < .001). To prevent insomnia, it is essential to enhance physical activity and manage factors related to body weight and liver function, such as BMI, WC, and AST. Increasing moderate-to-vigorous physical activity is particularly crucial, as it has a substantial positive impact on reducing body weight and improving liver function.

Feasibility study of fast-ion velocity-space tomography in KSTAR via phantom tests

The fast-ion velocity distribution function is crucial for understanding fast-ion behavior and transport in future burning plasmas. However, direct measurements of this distribution are difficult due to its high-dimensional nature, necessitating inference from diagnostic data. To infer fast-ion velocity distributions in KSTAR experimental conditions, we explored the feasibility of using measurements from fast-ion Dα (FIDA) diagnostics. We assessed the reconstruction quality for two phantoms, representing a possible fast-ion distribution scenario and local velocity-space structures. We calculated the phase-space weight function of FIDA measurements, required for tomographic inversion, by modeling the measurements, and also developed a tomography code with Phillips–Tikhonov regularization. The phantom test results revealed limitations in the reconstruction capability of current FIDA systems in KSTAR, particularly near low-pitch regions. We also identified the influence of spatial bias of the weight function of the current FIDA systems. Introducing a new FIDA system to tomographic inversion process provided wider coverage in velocity space and the weight function with reduced spatial bias, thereby improving reconstruction capability, especially in low-pitch regions. We also scanned noise levels in the phantom tests and observed the benefits of using prior information to mitigate degradation of the reconstruction quality caused by noise.

Heterogeneous Weight Development of Lumpfish (Cyclopterus lumpus) Used as Cleaner Fish in Atlantic Salmon (Salmo salar) Farming

Lumpfish (Cyclopterus lumpus) are used as cleaner fish in Atlantic salmon (Salmo salar) aquaculture as one of multiple control measures against salmon lice (Lepeophtheirus salmonis). Numerous studies have documented weight as an important factor for characterising the cleaning efficacy of individual lumpfish. Hence, the efficacy of lumpfish in fish farms depends on the size at stocking and the weight development over time. Here, we aimed to quantify how weight developed over time for lumpfish deployed in salmon farming sea cages in Norway through modelling the daily growth rate. We found that the coefficient of variation for lumpfish weight increased over time, implying that the standard deviation increased more than the expected weight. The data thus showed heterogeneous growth for lumpfish in salmon farming cages, where some individuals exhibited no apparent growth, and others significant proliferation. We found that the daily growth rate was best described as bell-shaped functions of weight and temperature, consistent with a sigmoid-shaped growth curve and an optimal temperature around 10 °C. These results allow for more realistic modelling of the efficacy of lumpfish in controlling salmon lice in fish farms, as one can combine estimates of weight-dependent cleaning efficacy with weight development over time.

Cumulative entropies and sums of moments of order statistics

Generalized weighted cumulative residual entropies and generalized weighted cumulative entropies are represented by means of weighted sums of moments of upper and lower order statistics, respectively. A variety of examples is shown by specifying the weight function.

Graph-based adaptive weighted fusion SLAM using multimodal data in complex underground spaces

Accurate and robust simultaneous localization and mapping (SLAM) is essential for autonomous exploration, unmanned transportation, and emergency rescue operations in complex underground spaces. However, the demanding conditions of underground spaces, characterized by poor lighting, weak textures, and high dust levels, pose substantial challenges to SLAM. To address this issue, we propose a graph-based adaptive weighted fusion SLAM (AWF-SLAM) for autonomous robots to achieve accurate and robust SLAM in complex underground spaces. First, a contrast limited adaptive histogram equalization (CLAHE) that combined adaptive gamma correction with weighting distribution (AGCWD) in hue, saturation, and value (HSV) space is proposed to enhance the brightness and contrast of visual images in underground spaces. Then, the performance of each sensor is evaluated using a consistency check based on the Mahalanobis distance to select the optimal configuration for specific conditions. Subsequently, we elaborate an adaptive weighting function model, which leverages the residuals from point cloud matching and the inner point rate of image matching. This model fuses data from light detection and ranging (LiDAR), inertial measurement unit (IMU), and cameras dynamically, enhancing the flexibility of the fusion process. Finally, multiple primitive features are adaptively fused within the factor graph optimization, utilizing a sliding window approach. Extensive experiments were conducted to check the performance of AWF-SLAM using a self-designed mobile robot in underground parking lots, excavated subway tunnels, and complex underground coal mine spaces based on reference trajectories and reconstructions provided by state-of-the-art methods. Satisfactorily, the root mean square error (RMSE) of trajectory translation is only 0.17 m, and the mean relative robustness distance between the point cloud maps reconstructed by AWF-SLAM and the reference point cloud map is lower than 0.09 m. These results indicate a substantial improvement in the accuracy and robustness of SLAM in complex underground spaces.

Fretting fatigue life of galvanized bridge support cable wire: Experimental study and strain-based fracture mechanics analysis

The galvanized wires used in cable bridges can fail due to fretting fatigue. Experimental results have shown that defects resulting from the galvanization process are a possible source of initiation for fatigue cracks and make the life shorter than that of a wire without a galvanized layer. The investigation presented in this paper uses strain-based fracture mechanics to analyze the influence of these defects on the fatigue life of galvanized wire. The presented analysis considers nonlinear material behaviour. A 3D finite element model is used to compute the required stress field. Stress intensity factors are obtained by a proposed numerical point load weight function, and shape factors are found for the surface and deepest points of the semielliptical crack. Plain fatigue tests of galvanized wires are performed to calibrate key model parameters. The calibrated model is then shown to provide reasonably accurate but systematically conservative estimates of the fatigue life under fretting conditions typical of bridge stay cable saddle supports.

A sub-grid gas–solid interaction model for coarse-grained CFD–DEM simulations

Computational fluid dynamics has been coupled with coarse-grained discrete element method (CFD–CGDEM) to study industrial-scale gas–solid fluidized beds. However, solving the fluid flow at a grid size of several coarse-grained particles (CGPs) cannot accurately predict the gas–solid interaction, and thus reasonable correction model is required. This study develops a sub-grid model to correct the gas–solid interaction and thereby improve the accuracy of coarse-grid CFD–CGDEM simulation. First, the fluid grids with the size of several CGPs are divided into the auxiliary three-dimensional sub-grids. Then, the CGPs are mapped onto the sub-grids by the weight function to reconstruct the local solid velocities and solid fractions of the sub-grids. After that, the simplified mass and momentum conservation equations are solved to resolve the local gas velocities of sub-grids. Finally, the drag on each CGP is corrected based on the local gas-phase hydrodynamics of sub-grids. Results show that the sub-grid model resolving the local gas velocity in all the three directions works better than that just in the main stream direction. The coarse-grid CFD–CGDEM simulations with this sub-grid model agree well with the experiment for the bubbling, turbulent and rapid fluidized beds. Besides, by solving the simplified conservation equations with graphics processing units, the coarse-grid CFD–CGDEM coupled with the sub-grid model achieves 674 times speedup compared to the fine-grid simulation, demonstrating a much more efficient method to study industrial-scale gas–solid fluidized beds.

Locally robust Msplit estimation

Abstract Processing measurement data is an essential part of surveying engineering. One can list several methods in such a context: least squares estimation, M-estimation, R-estimation, etc. Some methods were developed by surveyors, e.g., the Danish method, IGG scheme, or Msplit estimation. The last method is, in fact, a class of estimation procedures dedicated to different problems. As a new approach to processing data, Msplit estimation is still being developed and improved. That paper concerns the local robustness of Msplit estimation and introduces a new Msplit estimation variant that is less sensitive to local outliers. Such a property seems important, especially in big data processing, such as observations from Light Detection and Ranging systems. The new variant modifies the squared Msplit estimation (SMS estimation) by implementing the adapted Tukey weight function, hence its acronym SMSTL estimation. The basic theoretical and empirical analyses, which were performed for the univariate model using, among others, the appropriate measures of robustness, confirmed the expected property of the method. The further tests, based on simulated as well as real data, show that the new method might overperform other Msplit estimation variants and classical methods for the chosen types of observation sets.

Tool State Recognition Based on POGNN-GRU under Unbalanced Data.

Accurate recognition of tool state is important for maximizing tool life. However, the tool sensor data collected in real-life scenarios has unbalanced characteristics. Additionally, although graph neural networks (GNNs) show excellent performance in feature extraction in the spatial dimension of data, it is difficult to extract features in the temporal dimension efficiently. Therefore, we propose a tool state recognition method based on the Pruned Optimized Graph Neural Network-Gated Recurrent Unit (POGNN-GRU) under unbalanced data. Firstly, design the Improved-Majority Weighted Minority Oversampling Technique (IMWMOTE) by introducing an adaptive noise removal strategy and improving the MWMOTE to alleviate the unbalanced problem of data. Subsequently, propose a POG graph data construction method based on a multi-scale multi-metric basis and a Gaussian kernel weight function to solve the problem of one-sided description of graph data under a single metric basis. Then, construct the POGNN-GRU model to deeply mine the spatial and temporal features of the data to better identify the state of the tool. Finally, validation and ablation experiments on the PHM 2010 and HMoTP datasets show that the proposed method outperforms the other models in terms of identification, and the highest accuracy improves by 1.62% and 1.86% compared with the corresponding optimal baseline model.

A Jacobi-Ritz Approach for Aeroelastic Analysis of Swept Distributed Propulsion Aircraft Wing

Abstract This paper presents a Jacobi-Ritz approach for conducting flutter and divergence analysis of a complex distributed propulsion aircraft wing like NASA X-57. The general orthogonal Jacobi polynomials are used to approximate the bending displacement and torsional rotation angle in the Ritz method based structural and aeroelastic analysis. The Jacobi polynomials satisfy the orthogonality condition using weight functions which are easily modified to satisfy different essential and natural boundary conditions. Compared to simple polynomials, Jacobi polynomials can eliminate the well-known ill-conditioning numerical issues when considering higher-order polynomials. The Jacobi-Ritz method is also found to alleviate mode switching often encountered in tracking the changes of modes with the varying airspeed. The Jacobi-Ritz method is later used to investigate the flutter and divergence speeds under distributed propulsor mass and their locations, nonuniform aerodynamic model in the presence of multiple propulsors, and the sweep angle. Results show that placing the distributed propulsors on the wing's leading edge increases the flutter speed even though the bending and torsion modal frequencies are decreased compared to those of the wing without propulsors. The presence of pods for the middle high-lift motors causes an extra aerodynamic moment which reduces the flutter speed. Parametric studies also show that the divergence speed is lower than the flutter speed for a uniform and straight distributed propulsor wing. Using swept-back wing configuration and placing the tip propulsor near the wing's leading edge can help to increase both flutter and divergence speeds.

Weighting non-IID batches for out-of-distribution detection

A standard network pretrained on in-distribution (ID) samples could make high-confidence predictions on out-of-distribution (OOD) samples, leaving the possibility of failing to distinguish ID and OOD samples in the test phase. To address this over-confidence issue, the existing methods improve the OOD sensitivity from modeling perspectives, i.e., retraining it by modifying training processes or objective functions. In contrast, this paper proposes a simple but effective method, namely Weighted Non-IID Batching (WNB), by adjusting batch weights. WNB builds on a key observation: increasing the batch size can improve the OOD detection performance. This is because a smaller batch size may make its batch samples more likely to be treated as non-IID from the assumed ID, i.e., associated with an OOD. This causes a network to provide high-confidence predictions for all samples from the OOD. Accordingly, WNB applies a weight function to weight each batch according to the discrepancy between batch samples and the entire training ID dataset. Specifically, the weight function is derived by minimizing the generalization error bound. It ensures that the weight function assigns larger weights to batches with smaller discrepancies and makes a trade-off between ID classification and OOD detection performance. Experimental results show that incorporating WNB into state-of-the-art OOD detection methods can further improve their performance.

Optimal interpolation approach for groundwater depth estimation

In arid and semi-arid regions where surface water resources are scarce, groundwater is crucial. Accurate mapping of groundwater depth is vital for sustainable management practices. This study evaluated the performance of three spatial interpolation techniques – inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF) – in predicting groundwater depth distribution across Dire Dawa City, Ethiopia. The results demonstrated the superiority of the RBF method, exhibiting the lowest RMSE (3.21 m), MAE (0.16 m), and the highest R2 (0.99) compared to IDW and OK. The IDW method emerged as the next best performer (RMSE = 4.68 m, MAE = 0.16 m, R2= 0.97), followed by OK (RMSE = 5.32 m, MAE = 0.42 m, R2= 0.95). The RBF's superior accuracy aligns with findings from other semi-arid regions, underscoring its suitability for data-scarce areas like Dire Dawa. This comparative evaluation provides valuable insights for selecting the optimal interpolation method for groundwater depth mapping, supporting informed decision-making in local water resource management.The methodological approach comprised:•Implementation of three interpolation techniques, namely, inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF), utilizing 56 groundwater depth measurements from locations dispersed throughout the study area.•Cross-validation through randomly withholding 20 % of the data for validation purposes.•Comparison of the techniques based on statistical measures of accuracy, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2).

An Optimal Family of Eighth-Order Methods for Multiple-Roots and Their Complex Dynamics

We present a new family of optimal eighth-order numerical methods for finding the multiple zeros of nonlinear functions. The methodology used for constructing the iterative scheme is based on the approach called the ‘weight factor approach’. This approach ingeniously combines weight functions to enhance convergence properties and stability. An extensive convergence analysis is conducted to prove that the proposed scheme achieves optimal eighth-order convergence, providing a significant improvement in efficiency over lower-order methods. Furthermore, the applicability of these novel methods to some real-world problems is demonstrated, showcasing their superior performance in terms of speed and accuracy. This is illustrated through a series of three examples involving basins of attraction with reflection symmetry, confirming the dominance of the new methods over existing counterparts. The examples highlight not only the robustness and precision of the proposed methods but also their practical utility in solving the complex nonlinear equations encountered in various scientific and engineering domains. Consequently, these eighth-order methods hold great promise for advancing computational techniques in fields that require the resolution of multiple roots with high precision.

Synthesis of sparse rectangular planar arrays with weight function and improved grey wolf optimization algorithm

AbstractThe present article proposes a novel hybrid approach for addressing the synthesis problem of rectangular planar arrays under multiple constraints, through joint optimization of the weight function and the improved grey wolf optimization (IGWO) algorithm. Firstly, the grey wolf optimization (GWO) algorithm is improved by using tent chaotic mapping, nonlinear convergence factor, dominant wolf dynamic belief strategy, and opposition‐based learning strategy to increase the population diversity and the ability to jump out of the local optimum. Secondly, the array elements are weighted using the weight function to generate the position distribution matrix, which reduces the thinned matrix optimization time and improves the optimization efficiency. Finally, the position distribution matrix is used to generate the thinned array, and the IGWO algorithm is applied to perform the sparse optimization with multiple constraints. The effectiveness of the method is verified through numerical simulation and full‐wave simulation experiments, demonstrating its capability to enhance array antenna performance and reduce peak sidelobe level (PSLL). These experimental results hold significant engineering implications and provide valuable references for addressing the array distribution problem under multiple constraints.

Impact of Disease Severity and Disease-Modifying Therapies on Myostatin Levels in SMA Patients.

Clinical trials with treatments inhibiting myostatin pathways to increase muscle mass are currently ongoing in spinal muscular atrophy. Given evidence of potential myostatin pathway downregulation in Spinal Muscular Atrophy (SMA), restoring sufficient myostatin levels using disease-modifying treatments (DMTs) might arguably be necessary prior to considering myostatin inhibitors as an add-on treatment. This retrospective study assessed pre-treatment myostatin and follistatin levels' correlation with disease severity and explored their alteration by disease-modifying treatment in SMA. We retrospectively collected clinical characteristics, motor scores, and mysotatin and follistatin levels between 2018 and 2020 in 25 Belgian patients with SMA (SMA1 (n = 13), SMA2 (n = 6), SMA 3 (n = 6)) and treated by nusinersen. Data were collected prior to treatment and after 2, 6, 10, 18, and 30 months of treatment. Myostatin levels correlated with patients' age, weight, SMA type, and motor function before treatment initiation. After treatment, we observed correlations between myostatin levels and some motor function scores (i.e., MFM32, HFMSE, 6MWT), but no major effect of nusinersen on myostatin or follistatin levels over time. In conclusion, further research is needed to determine if DMTs can impact myostatin and follistatin levels in SMA, and how this could potentially influence patient selection for ongoing myostatin inhibitor trials.

Sample Size Calculation Under Nonproportional Hazards Using Average Hazard Ratios.

Many clinical trials assess time-to-event endpoints. To describe the difference between groups in terms of time to event, we often employ hazard ratios. However, the hazard ratio is only informative in the case of proportional hazards (PHs) over time. There exist many other effect measures that do not require PHs. One of them is the average hazard ratio (AHR). Its core idea is to utilize a time-dependent weighting function that accounts for time variation. Though propagated in methodological research papers, the AHR is rarely used in practice. To facilitate its application, we unfold approaches for sample size calculation of an AHR test. We assess the reliability of the sample size calculation by extensive simulation studies covering various survival and censoring distributions with proportional as well as nonproportional hazards (N-PHs). The findings suggest that a simulation-based sample size calculation approach can be useful for designing clinical trials with N-PHs. Using the AHR can result in increased statistical power to detect differences between groups with more efficient samplesizes.

Effects of Probiotic Supplementation on Body Weight, Growth Performance, Immune Function, Intestinal Microbiota and Metabolites in Fallow Deer.

Intestinal diseases are one of the diseases that affect the growth and immunity of deer. Currently, more lactic acid bacteria (LAB) are available as feed additives to improve the intestinal ecological balance of ruminants in production practices. In this study, Enterococcus faecalis was supplemented in the feed of fallow deer for 170 d, and body weights, blood indices and immune levels of fallow deer were counted at 35, 65 and 170 d. The effects of Enterococcus faecalis on the intestinal microbiota and the metabolism of fallow deer were analysed using 16S rDNA and UPLC-MS/MS methods. The results showed that the addition of Enterococcus faecalis to the diet improved body weight and immune function and increased the aggregation of gut microbiota in fallow deer. The addition of Enterococcus faecalis altered the community structure of intestinal microorganisms in fallow deer and increased the number of beneficial bacteria. In addition, combined with metabolomics analysis, it was found that supplementation with Enterococcus faecalis significantly altered the metabolites of fallow deer, mainly regulating lipid metabolism, carbohydrate metabolism and phospholipid metabolism. In conclusion, this study presents, for the first time, evidence that the LAB strain Enterococcus faecalis can be used as a potential probiotic for deer and points to a new direction for the treatment of intestinal disorders in the deer family.

Complexity of 2-Rainbow Total Domination Problem

In this paper, we extend the findings of recent studies on k-rainbow total domination by placing our focus on its computational complexity aspects. We show that the problem of determining whether a graph has a 2-rainbow total dominating function of a given weight is NP-complete. This complexity result holds even when restricted to planar graphs. Along the way tight bounds for the k-rainbow total domination number of rooted product graphs are established. In addition, we obtain the closed formula for the k-rainbow total domination number of the corona product G∗H\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$G*H$$\\end{document}, provided that H has enough vertices.