Clustering Optimized Portrait Matting Algorithm Based on Improved Sparrow Algorithm

As a result of the influence of individual appearance and lighting conditions, aberrant noise spots cause significant mis-segmentation for frontal portraits. This paper presents an accurate portrait segmentation approach based on a combination of wavelet proportional shrinkage and an upgraded sparrow search (SSA) clustering algorithm to solve the accuracy challenge of segmentation for frontal portraits. The brightness component of the human portrait in HSV space is first subjected to wavelet scaling denoising. The elite inverse learning approach and adaptive weighting factor are then implemented to optimize the initial center location of the K-Means algorithm to improve the initial distribution and accelerate the convergence speed of SSA population members. The pixel segmentation accuracy of the proposed method is approximately 70% and 15% higher than two comparable traditional methods, while the similarity of color image features is approximately 10% higher. Experiments show that the proposed method has achieved a high level of accuracy in capricious lighting conditions

Genome-Wide Association Study for Plant Height and Grain Yield in Rice under Contrasting Moisture Regimes

Drought is one of the vitally critical environmental stresses affecting both growth and yield potential in rice. Drought resistance is a complicated quantitative trait that is regulated by numerous small effect loci and hundreds of genes controlling various morphological and physiological responses to drought. For this study, 270 rice landraces and cultivars were analyzed for their drought resistance. This was done via determination of changes in plant height and grain yield under contrasting water regimes, followed by detailed identification of the underlying genetic architecture via genome-wide association study (GWAS). We controlled population structure by setting top two eigenvectors and combining kinship matrix for GWAS in this study. Eighteen, five, and six associated loci were identified for plant height, grain yield per plant, and drought resistant coefficient, respectively. Nine known functional genes were identified, including five for plant height (OsGA2ox3, OsGH3-2, sd-1, OsGNA1 and OsSAP11/OsDOG), two for grain yield per plant (OsCYP51G3 and OsRRMh) and two for drought resistant coefficient (OsPYL2 and OsGA2ox9), implying very reliable results. A previous study reported OsGNA1 to regulate root development, but this study reports additional controlling of both plant height and root length. Moreover, OsRLK5 is a new drought resistant candidate gene discovered in this study. OsRLK5 mutants showed faster water loss rates in detached leaves. This gene plays an important role in the positive regulation of yield-related traits under drought conditions. We furthermore discovered several new loci contributing to the three investigated traits (plant height, grain yield, and drought resistance). These associated loci and genes significantly improve our knowledge of the genetic control of these traits in rice. In addition, many drought resistant cultivars screened in this study can be used as parental genotypes to improve drought resistance of rice by molecular breeding

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

Correction to: Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

The original version of this article unfortunately contained a mistake

Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes

Node localization is an essential requirement in the increasing prevalence of wireless sensor networks applications. As the most commonly used localization algorithm, the DV_Distance algorithm is more sensitive to ranging error, and also has lower localization accuracy. Therefore, this paper proposes a novel difference DV_Distance localization algorithm using correction coefficients of unknown nodes. Taking account of the fact that correction coefficients of unknown nodes should be different, the proposed method has employed the correction model based on unknown nodes. Some correction coefficients for different direction anchor nodes can be indirectly calculated using the known difference of actual Euclidean distance and corresponding accumulated hop distance between anchor nodes, and then the weighting factors for the correction coefficients of different direction anchor nodes are also computed according to their actual contribution degree, so as to make sure that the corrected distances from unknown nodes to anchor nodes, modified by the final correction coefficient, are closer to the actual distances. At last, the positions of unknown nodes can be calculated using multilateral distance measurement. The simulation results demonstrate that the proposed approach is a localization algorithm with easier implementation, and it not only has better performance on localization accuracy than existing DV_Distance localization algorithm, but also improves the localization stability under the same experimental conditions

An Artificial Bee Colony Algorithm with Random Location Updating

As a novel swarm intelligence algorithm, artificial bee colony (ABC) algorithm inspired by individual division of labor and information exchange during the process of honey collection has advantage of simple structure, less control parameters, and excellent performance characteristics and can be applied to neural network, parameter optimization, and so on. In order to further improve the exploration ability of ABC, an artificial bee colony algorithm with random location updating (RABC) is proposed in this paper, and the modified search equation takes a random location in swarm as a search center, which can expand the search range of new solution. In addition, the chaos is used to initialize the swarm population, and diversity of initial population is improved. Then, the tournament selection strategy is adopted to maintain the population diversity in the evolutionary process. Through the simulation experiment on a suite of unconstrained benchmark functions, the results show that the proposed algorithm not only has stronger exploration ability but also has better effect on convergence speed and optimization precision, and it can keep good robustness and validity with the increase of dimension

A High Precision Approach to Calibrate a Structured Light Vision Sensor in a Robot-Based Three-Dimensional Measurement System

A robot-based three-dimensional (3D) measurement system is presented. In the presented system, a structured light vision sensor is mounted on the arm of an industrial robot. Measurement accuracy is one of the most important aspects of any 3D measurement system. To improve the measuring accuracy of the structured light vision sensor, a novel sensor calibration approach is proposed to improve the calibration accuracy. The approach is based on a number of fixed concentric circles manufactured in a calibration target. The concentric circle is employed to determine the real projected centres of the circles. Then, a calibration point generation procedure is used with the help of the calibrated robot. When enough calibration points are ready, the radial alignment constraint (RAC) method is adopted to calibrate the camera model. A multilayer perceptron neural network (MLPNN) is then employed to identify the calibration residuals after the application of the RAC method. Therefore, the hybrid pinhole model and the MLPNN are used to represent the real camera model. Using a standard ball to validate the effectiveness of the presented technique, the experimental results demonstrate that the proposed novel calibration approach can achieve a highly accurate model of the structured light vision sensor

An Enhanced DV-Hop Localization Scheme Based on Weighted Iteration and Optimal Beacon Set

Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning performance of the standard DV-Hop, an enhanced DV-Hop based on weighted iteration and optimal beacon set is presented in this paper. Firstly, different weights are assigned to beacons based on the per-hop error, and the weighted minimum mean square error (MMSE) is performed iteratively to find the optimal average hop size (AHS) of beacon nodes. After that, the approach of estimating the distance between unknown nodes and beacons is redefined. Finally, considering the influence of beacon nodes with different distances to the unknown node, the nearest beacon nodes are given priority to compute the node position. The optimal coordinates of the unknown nodes are determined by the best beacon set derived from a grouping strategy, rather than all beacons directly participating in localization. Simulation results demonstrate that the average localization error of our proposed DV-Hop reaches about 3.96 m, which is significantly lower than the 9.05 m, 7.25 m, and 5.62 m of the standard DV-Hop, PSO DV-Hop, and Selective 3-Anchor DV-Hop

An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization

In real-world scenarios, images may be affected by additional noise during compression and transmission, which interferes with postprocessing such as image segmentation and feature extraction. Image noise can also be induced by environmental variables and imperfections in the imaging equipment. Robust principal component analysis (RPCA), one of the traditional approaches for denoising images, suffers from a failure to efficiently use the background’s low-rank prior information, which lowers its effectiveness under complex noise backgrounds. In this paper, we propose a robust PCA method based on a nonconvex low-rank approximation and total variational regularization (TV) to model the image denoising problem in order to improve the denoising performance. Firstly, we use a nonconvex γ-norm to address the issue that the traditional nuclear norm penalizes large singular values excessively. The rank approximation is more accurate than the nuclear norm thanks to the elimination of matrix elements with substantial approximation errors to reduce the sparsity error. The method’s robustness is improved by utilizing the low sensitivity of the γ-norm to outliers. Secondly, we use the l1-norm to increase the sparsity of the foreground noise. The TV norm is used to improve the smoothness of the graph structure in accordance with the sparsity of the image in the gradient domain. The denoising effectiveness of the model is increased by employing the alternating direction multiplier strategy to locate the global optimal solution. It is important to note that our method does not require any labeled images, and its unsupervised denoising principle enables the generalization of the method to different scenarios for application. Our method can perform denoising experiments on images with different types of noise. Extensive experiments show that our method can fully preserve the edge structure information of the image, preserve important features of the image, and maintain excellent visual effects in terms of brightness smoothing