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Three flawed practices associated with model averaging coefficients for predictor variables in regression models commonly occur when making multimodel inferences in analyses of ecological data. Model-averaged regression coefficients based on Akaike information criterion (AIC) weights have been recommended for addressing model uncertainty but they are not valid, interpretable estimates of partial effects for individual predictors when there is multicollinearity among the predictor variables. Multicollinearity implies that the scaling of units in the denominators of the regression coefficients may change across models such that neither the parameters nor their estimates have common scales, therefore averaging them makes no sense. The associated sums of AIC model weights recommended to assess relative importance of individual predictors are really a measure of relative importance of models, with little information about contributions by individual predictors compared to other measures of relative importance based on effects size or variance reduction. Sometimes the model-averaged regression coefficients for predictor variables are incorrectly used to make model-averaged predictions of the response variable when the models are not linear in the parameters. I demonstrate the issues with the first two practices using the college grade point average example extensively analyzed by Burnham and Anderson. I show how partial standard deviations of the predictor variables can be used to detect changing scales of their estimates with multicollinearity. Standardizing estimates based on partial standard deviations for their variables can be used to make the scaling of the estimates commensurate across models, a necessary but not sufficient condition for model averaging of the estimates to be sensible. A unimodal distribution of estimates and valid interpretation of individual parameters are additional requisite conditions. The standardized estimates or equivalently the t statistics on unstandardized estimates also can be used to provide more informative measures of relative importance than sums of AIC weights. Finally, I illustrate how seriously compromised statistical interpretations and predictions can be for all three of these flawed practices by critiquing their use in a recent species distribution modeling technique developed for predicting Greater Sage-Grouse ( Centrocercus urophasianus ) distribution in Colorado, USA. These model averaging issues are common in other ecological literature and ought to be discontinued if we are to make effective scientific contributions to ecological knowledge and conservation of natural resources.

When in-control process parameters are estimated, Phase II control chart performance will vary among practitioners due to the use of different Phase I data sets. The typical measure of Phase II control chart performance, the average run length (ARL), becomes a random variable due to the selection of a Phase I data set for estimation. Aspects of the ARL distribution, such as the standard deviation of the average run length (SDARL), can be used to quantify the between-practitioner variability in control chart performance. In this article, we assess the in-control performance of the exponentially weighted moving average (EWMA) control chart in terms of the SDARL and percentiles of the ARL distribution when the process parameters are estimated. Our results show that the EWMA chart requires a much larger amount of Phase I data than previously recommended in the literature in order to sufficiently reduce the variation in the chart performance. We show that larger values of the EWMA smoothing constant result in higher levels of variability in the in-control ARL distribution; thus, more Phase I data are required for charts with larger smoothing constants. Because it could be extremely difficult to lower the variation in the in-control ARL values sufficiently due to practical limitations on the amount of the Phase I data, we recommend an alternative design criterion and a procedure based on the bootstrap approach.

Based on Panel data collected from 2011 to 2020 targeted to 50 prefecture-level cities in the Yellow River Basin, this paper adopted standard deviation ellipse and spatial Dubin model to explore the nonlinear effects and spatial spillover effects of urbanization on air pollution and ecological resilience in the Yellow River Basin. The results show that the degree of air pollution in the southeast of the Yellow River Basin is higher than that in the northwest of the Yellow River Basin, the distribution range of air pollution is shrinking, the concentration of ecological resilience is enhanced, and the ecological environment is developing for the better. There is a significant U-shaped relationship between urbanization and air pollution in the Yellow River Basin, and an inverted U-shaped relationship between urbanization and ecological resilience. For every 1% increase in urbanization, air pollution decreases by 0.0873%, ecological resilience increases by 0.4046%. For every 1% increase in the square term of urbanization, air pollution increases by 0.2271%, ecological resilience decreases by 0.1789%. The urbanization of the Yellow River Basin has a spatial spillover effect on air pollution and ecological resilience, and urbanization has a significant negative impact on the ecological environment of neighboring cities. The robustness of the above conclusions is verified by introduce an inverse distance weight matrix replacing the spatial weight matrix.

The performance of the Shewhart X̄ control chart with estimated in-control parameters has been discussed a number of times in the literature. Previous studies showed that at least 400/(n - 1) phase I samples, where n > 1 is the sample size, are required so that the chart performs on average as if the in-control process parameter values were known. This recommendation was based on the in-control expected average run length (ARL) performance. The reliance on the expected ARL metric, however, averages across the practitioner-to-practitioner variability. This variability occurs due to the different historical data sets practitioners use, which results in varying parameter estimates, control limits, and in-control ARL values. In our article, we show that taking this type of variability into consideration leads to far larger amounts of phase I data than what was previously recommended. This is to ensure low levels of variation in the in-control ARL values among practitioners. The standard deviation of the ARL (SDARL) metric is used to evaluate performance for various amounts of phase I data. We show that no realistic phase I sample size is sufficient to have confidence that the attained in-control ARL is close to the desired value. We additionally investigate the effect of different process standard deviation estimators on the X̄-chart performance, showing that it is best to use a biased estimator. We also study the design of the X-chart for the case n = 1, drawing similar conclusions regarding the amount of phase I data. An alternative approach to designing control charts is recommended.

To accurately measure the carbon emission intensity of tourism, a comprehensive measurement method is proposed in this study. This method combines statistical data and standard deviation ellipse analysis, which can reflect the actual situation of carbon emission in tourism more comprehensively. The spatial autocorrelation of regional tourism is obtained by global Moran's I index and local Moran's I index, and the spatial and temporal evolution characteristics of tourism carbon emission intensity are extracted by standard deviation ellipse analysis. By calculating the consumption stripping coefficient, carbon emission intensity and total carbon emission of tourism, the carbon emission intensity of tourism is calculated. China is divided into eastern, central and western regions, and the carbon emission level and intensity in the region are calculated. The results show that: (1) from 2012 to 2021, the carbon emissions of tourism in various regions generally showed an increasing trend, but the carbon emissions in the eastern region were the highest. (2) From 2018 to 2021, the carbon emission intensity of tourism in different regions is basically the same, and the research period shows a certain downward trend. (3) The accuracy of calculating the carbon emission intensity of tourism in each region obtained by this method can reach 86.5%.

Underwater images generally suffer from quality degradations, such as low contrast, color cast, blurring, and hazy effect due to light absorption and scattering in the water medium. In applying these images to various vision tasks, single image-based underwater image enhancement has been challenging. Thus, numerous efforts have been made in the field of underwater image restoration. In this paper, we propose a successive color correction method with a minimal reddish artifact and a superpixel-based restoration using a color-balanced underwater image. The proposed successive color correction method comprises an effective underwater white balance based on the standard deviation ratio, followed by a new image normalization. The corrected image based on this color balance algorithm barely produces a reddish artifact. The superpixel-based dark channel prior is exploited to enhance the color-corrected underwater image. We introduce an image-adaptive weight factor using the mean of backscatter lights to estimate the transmission map. We perform intensive experiments for various underwater images and compare the performance of the proposed method with those of 10 state-of-the-art underwater image-enhancement methods. The simulation results show that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective quality.

Systemic lupus erythematosus (SLE) commonly damages the blood system and often manifests as blood cell abnormalities. The performance of biomarkers for predicting SLE activity still requires further improvement. This study aimed to analyze blood cell parameters to identify key indicators for a SLE activity prediction model. Clinical data of 138 patients with SLE (high activity, n  = 40; moderate activity, n  = 44; mild activity, n  = 37; low activity, n  = 17) and 100 healthy controls (HCs) were retrospectively analyzed. Data from 89 paired admission–discharge patients with SLE were collected. Differences and associations between blood cell parameters and disease indicators, as well as the relationship between the these parameters and organ damage, were examined. Machine-learning methods were employed to develop a prediction model for disease activity evaluation. Most blood cell parameters (22/26, 84.62%) differed significantly between patients with SLE and HCs. Analysis of 89 paired patients with SLE revealed significant changes in most blood cell parameters at discharge. The standard deviation of lymphocyte volume (SD-V-LY), red blood cell (RBC) count, lymphocyte percentage (LY%), hemoglobin(HGB), hematocrit(HCT), and neutrophil percentage(NE%) correlated with disease activity. By employing machine learning, an optimal model was established to predict active SLE using SD-V-LY, RBC count, and LY% (area under the curve [AUC] = 0.908, sensitivity = 0.811). External validation indicated impressive performance (AUC = 0.940, sensitivity = 0.833). Correlation analysis revealed that SD-V-LY was positively correlated with ESR, IgG, IgA, and IgM but was negatively correlated with C3 and C4. The RBC count was linked to renal and hematopoietic system impairments, whereas LY% was associated with joint/muscle involvement. In conclusion, SD-V-LY is associated with SLE disease activity. SD-V-LY combined with RBC count and LY% contributes to a prediction model, which can be utilized as an effective tool for assessing SLE activity.

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of “middle-high and low-outside.” (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level.

Patterns of sleep often vary among individuals. But sleep and activity may also vary within an individual, fluctuating in pattern across time. One possibility is that these daily fluctuations in sleep are caused by the underlying genotype of the individual. However, differences attributable to genetic causes are difficult to distinguish from environmental factors in outbred populations such as humans. We therefore employed Drosophila as a model of intra-individual variability in sleep using previously collected sleep and activity data from the Drosophila Genetic Reference Panel, a collection of wild-derived inbred lines. Individual flies had significant daily fluctuations in their sleep patterns, and these fluctuations were heritable. Using the standard deviation of sleep parameters as a metric, we conducted a genome-wide association study. We found 663 polymorphisms in 104 genes associated with daily fluctuations in sleep. We confirmed the effects of 12 candidate genes on the standard deviation of sleep parameters. Our results suggest that daily fluctuations in sleep patterns are due in part to gene activity.

Single-valued neutrosophic numbers (SVN-numbers) are a special kind of neutrosophic set on the real number set. The concept of a SVN-number is important for quantifying an ill-known quantity and ranking of SVN-number is a very difficult situation in decision-making problems. The main aim of this paper is to present a new ranking methodology of SVN-numbers for solving multi-attribute decision-making problems. Therefore, we firstly define the possibility mean, variance and standard deviation of single-valued neutrosophic numbers. Using the ratio of possibility mean and standard deviation, we have developed the proposed ranking approach and applied to MADM problems. Finally, a numerical example is examined to show the applicability and embodiment of the proposed method.