Explore the vast range of titles available.

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.

Performance of PM1 cyclone separator, a measuring system based on static chamber method was set up. By utilizing 6 different sizes of polystyrene microbeads samples, the penetration curve of the BGI PM1 cyclone separator was observed after data fitting. The experiment result showed that the Da50 of PM1 cyclone is within 1.0±0.1μm, and the geometric standard deviation is within 1.2±0.1. The influence of inlet flow rates on the performance of PM1 cyclone separator was also studied. According to the experiment results, the Da50 of PM1 cyclone separator decreases as the inlet flow rate increases, and the geometric standard deviation did not fluctuate significantly. This research provides information for the study of other kinds of cyclone separators in the future.

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.

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.

Stream obstacles, naturally formed like boulders or engineered like weirs, are the major source of flow resistance; however, to quantify their flow resistance, a resistance formula needs to be selected in accordance with the specific obstacle type, that is obstacle type dependency. So far, a unified resistance formula that adequately characterizes the roughness of distinctive obstacle types remains elusive. Here, we conduct flume experiments with various natural and engineered submerged obstacles, including boulders, weirs, log jams, and transverse stones. We combine them with existing data sets containing rigid vegetation, step‐pool, and riffle‐pool to identify a unified metric for a general resistance relation. We test three roughness metrics, the widely used roughness metric D84 (84th percentile of bed grain size distribution), a bathymetric‐line‐based metric σz,centerline (the standard deviation of bed centerline elevation), and a 3D‐bathymetry‐based σz,bed (the standard deviation of elevation of the entire bed) as bed roughness, respectively. σz,bed is adopted to incorporate the roughness inhomogeneity in the transverse direction which widely exists in both natural and engineered channels, complementing the insufficiency of line‐based metric σz,centerline. Using 3‐fold cross validation, we show that the resistance formula based on σz,bed demonstrated a more consistent and superior velocity prediction capacity than those based on D84 and σz,centerline in predicting velocity across almost all obstacle types. Interestingly, when applied to channels with submerged rigid vegetation, the resistance formula based on only σz,bed can compare with those based on multiple vegetation characteristic parameters. This study shows the viability of unifying the flow resistance formula in open‐channels with submerged obstacles, avoiding obstacle‐type dependency.

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.

Recent technical innovations have increased the use of physiological information as objective indices for stress measurement. Early detection of acute stress can minimize long-term stress burdens by aiding in the prevention and treatment of stress-related physical and mental health problems. The standard deviation of pulse pressure (SDPP) has recently emerged as an objective index for measuring stress. However, the causal relationship between acute stress and SDPP has not been verified, and it is unclear whether SDPP can detect stress responses that conventional indices cannot. This study investigated whether SDPP, measured using a wearable device, can be used as a stress assessment index to detect acute psychological stress and improve the accuracy of stress estimation in healthy participants who were subjected to the trier social stress test (TSST). A total of 114 healthy volunteers were randomly divided into the stress- induced (Stressed) and non-stress (Control) groups. Heart rate (HR) and heart rate variability (HRV), which are indices of the autonomic nervous system, and salivary cortisol, an endocrine marker, were used as objective stress indices. Psychological evaluations (POMS2, STAI) were employed as subjective assessments. The relationships between these measurements and SDPP were evaluated. The results indicated that SDPP was significantly higher in the Stressed group, confirming that SDPP reflects induced stress. Furthermore, logistic regression analysis showed that adding SDPP to HR, HRV, and cortisol can improve the accuracy of estimating stress presence. Moreover, a multilevel analysis revealed that SDPP can enhance the estimation of psychological evaluation (POMS [total mood disturbance]) scores. The results suggest that SDPP can estimate the degree of stress experienced by an individual and monitor stress responses undetectable by conventional indices.

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.

The collection of traditional administrative unit-based gross domestic product (GDP) data is time-consuming and laborious, and the data lacks accurate spatial information. Long-term series nighttime light (NTL) data can provide effective spatiotemporal GDP change information, which can be used to analyze economies’ spatial distributions and development trends. In this study, we generated a spatial model of the relationship between NTL indices and GDP parameters, based on NPP-VIIRS-like NTL data for the period 2001 to 2020, conducted a multitemporal and multilevel connectivity analysis of the GDP spatialization data, and constructed a tree structure for horizontal and vertical analysis. Standard deviation ellipses and economic centers of the first-level economic connected components at the provincial and municipal levels were generated, and the economic center distribution range and development direction at the provincial and municipal levels were analyzed. The results show that GDP spatialization data, based on NPP-VIIRS-like NTL data, can intuitively reflect the GDP spatial distribution. In Henan Province, the economic levels of different regions vary, and the economic regions represented by Zhengzhou have developed rapidly, driving surrounding regional economic rapid development. Henan Province’s development trend from single-city economic centers to multicity economic centers is obvious, and the economic center has shifted to the southeast.

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.