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Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. However, the DE algorithm mainly focuses on strengthening the adaptability of exploitation, which allows the sensitivity of the DE algorithm to be solved in cases where the effects of solving various types of problems are quite different. Moreover, the local search ability and population diversity have not been solved properly. Therefore, an adaptive DE algorithm using fitness distance correlation and a neighbourhood-based strategy (FNADE) is proposed. FNADE introduces the fitness distance correlation (FDC) as the basis for judging the difficulty of the problem, utilises a Voronoi diagram to increase the population diversity for complex multimodal problems and adopts the neighbourhood-based mutation strategy to strengthen the local search capability. FNADE is committed to solving unconstrained single-objective optimisation problems. The proposed algorithm is compared with six advanced DE algorithms in terms of CEC2017 benchmark functions. The experimental results show that the adaptive DE algorithm using FNADE is superior to other DE algorithms with regard to the accuracy and population diversity of the solution. [ABSTRACT FROM AUTHOR]

Differential evolution (DE), as an extremely powerful evolutionary algorithm, has recently been widely employed within complex reality optimisation problems. However, the DE algorithm mainly focuses on strengthening the adaptability of exploitation, which allows the sensitivity of the DE algorithm to be solved in cases where the effects of solving various types of problems are quite different. Moreover, the local search ability and population diversity have not been solved properly. Therefore, an adaptive DE algorithm using fitness distance correlation and a neighbourhood-based strategy (FNADE) is proposed. FNADE introduces the fitness distance correlation (FDC) as the basis for judging the difficulty of the problem, utilises a Voronoi diagram to increase the population diversity for complex multimodal problems and adopts the neighbourhood-based mutation strategy to strengthen the local search capability. FNADE is committed to solving unconstrained single-objective optimisation problems. The proposed algorithm is compared with six advanced DE algorithms in terms of CEC2017 benchmark functions. The experimental results show that the adaptive DE algorithm using FNADE is superior to other DE algorithms with regard to the accuracy and population diversity of the solution. [ABSTRACT FROM AUTHOR]

The melt-blowing process involves high velocity airflow and fiber motion, which have a significant effect on fiber attenuation. In this paper, the three-dimensional airflow field for a melt-blowing slot die was measured using the hot-wire anemometry in an experiment. The fiber motion was captured online using a high-speed camera. The characteristics of the airflow distribution and fiber motion were analyzed. The results show that the melt-blowing airflow field is asymmetrically distributed. The centerline air velocity is higher than that around it and decays quickly. The maximum airflow velocity exists near the die face, in the range of 130–160 m/s. In the region of −0.3 cm < y < 0.3 cm and 0 < z < 2 cm, the airflow has a high velocity (>100 m/s). As the distance of z reaches 5 cm and 7 cm, the maximum airflow velocity reduces to 70 m/s. The amplitude of fibers is calculated, and it increases with the increase in air dispersion area which has a significant influence on fiber attenuation. At z = 1.5 cm, 2.5 cm, 4 cm, and 5.5 cm, the average fiber amplitudes are 1.05 mm, 1.71 mm, 2.83 mm, and 3.97 mm, respectively. In the vicinity of the die, the fibers move vertically downward as straight segments. With the increase in distance from the spinneret, the fiber appears to bend significantly and forms a fiber loop. The fiber loop morphology affects the velocity of the fiber movement, causing crossover, folding, and bonding of the moving fiber. The study investigated the interaction between the fiber and airflow fields. It indicates that the airflow velocity, velocity difference, and dispersion area can affect the motion of fiber which plays an important role in fiber attenuation during the melt-blowing process. [ABSTRACT FROM AUTHOR]

The triple-frequency global positioning system (GPS), BeiDou navigation satellite system-3 (BDS-3), and Galileo can form the geometry-free and ionosphere-free (GFIF) carrier phase and pseudorange combinations, respectively. These combinations provide a precondition for which the accuracies of the carrier phase and pseudorange observations can be evaluated, respectively. The stochastic model of precise point positioning (PPP) processing is constructed using the evaluated results. The 176 International GNSS Service (IGS) stations are used to validate the presented method. The evaluated results show that accuracies of the GPS, BDS-3, and Galileo carrier phase observations are at the millimeter level, while those of pseudorange observations reach the decimeter level. In addition, BDS-3, GPS, and Galileo have different precisions. The stochastic model, which is reestablished from the evaluated accuracies, is tested using single Global Navigation Satellite System (GNSS) and multiGNSS PPP performances. The single-GNSS PPP shows that its convergence time has a mean improvement of 24%, and its 1- and 2-h mean three dimensions (3D) positioning accuracies are improved by about 17.7% and 4.5%, respectively, compared with those of empirical accuracies-based stochastic models. It also has the best performance in the multiGNSS PPP. Its mean convergence time of GPS+Galileo for 3 days is shorter than 35 min, while that of GPS+BDS-3 is less than 30 min. The mean improvements for the convergence time and 1- and 2-h 3D positioning reach 28%, 9.4%, and 2.8%, respectively, when the GPS+Galileo and GPS+BDS-3 PPP use the reestablished stochastic model instead of the empirical methods. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

To address constrained optimization problems (COPs), there are two crucial trade-offs in preeminent constrained evolutionary algorithms: (1) the trade-off between exploration and exploitation, and (2) the one between the constraints and the objectives. Moreover, the problem of inaccurate diversity regulation in constrained optimization evolutionary algorithms has yet to be completely solved. To achieve the first trade-off, this paper designs a diversity controller (DC) based on a small-world network. Then, the fitness distance correlation information is applied to adjust the reconnection probability of the small-world network to dynamically control the diversity. To achieve the second trade-off, we propose an infeasible–feasible regions constraint handling method (IF). There are two stages in the IF method. The first stage is to search the boundary between the infeasible and feasible regions, while the second stage adopts the self-adaptive Epsilon constraint handling method. Furthermore, combining the DC and IF constraint handing methods into the success-history-based parameter adaptive differential evolution (SHADE) algorithm, a DC-SHADE-IF algorithm is proposed. Twenty-eight constrained optimization problems provided in the CEC2017 competition on constrained real parameter optimization are employed to compare the performance between the proposed DC-SHADE-IF and seven state-of-the-art algorithms. Besides that, two real-world constrained optimization problems are employed to examine the performance of the proposed DC-SHADE-IF in real-world problems. Experimental results show the superior performance of the proposed algorithm in terms of accuracy and convergence. • A diversity controller is proposed to control population diversity. • An infeasible–feasible regions constraint handling method. • Experiments were outperform in CEC 2017 and real-world problems. [ABSTRACT FROM AUTHOR]

Monitoring the surface subsidence in mining areas is conducive to the prevention and control of geological disasters, and the prediction and early warning of accidents. Hunan Province is located in South China. The mineral resource reserves are abundant; however, large and medium-sized mines account for a low proportion of the total, and the concentration of mineral resource distribution is low, meaning that traditional mining monitoring struggles to meet the needs of large-scale monitoring of mining areas in the province. The advantages of Interferometric Synthetic Aperture Radar (InSAR) technology in large-scale deformation monitoring were applied to identify and monitor the surface subsidence of coal mining fields in Hunan Province based on a Sentinel-1A dataset of 86 images taken from 2018 to 2020, and the process of developing surface subsidence was inverted by selecting typical mining areas. The results show that there are 14 places of surface subsidence in the study area, and accidents have occurred in 2 mining areas. In addition, the railway passing through the mining area of Zhouyuan Mountain is affected by the surface subsidence, presenting a potential safety hazard. [ABSTRACT FROM AUTHOR]

Background: Early postoperative small bowel obstruction (EPSBO) is a common complication after colorectal cancer surgery. Few studies have specifically studied risk factors for early small bowel obstruction after right colectomy, especially in establishing predictive models. The purpose of the current study was to establish an effective nomogram to predict the incidence of EPSBO after right colectomy. Methods: The current study retrospectively analyzed data from a total of 424 patients who underwent right colectomy in a local hospital from January 2014 to March 2021. A logistic regression model was used to identify potential risk factors for EPSBO after right colectomy. A nomogram was established by independent risk factors, and the prediction performance of the model was evaluated using an area under the receiver operating characteristic (ROC) curve and calibration chart. Results: A total of 45 patients (10.6%) developed early small bowel obstruction after right colectomy. Male sex, emergency operation, history of abdominal surgery, open surgery, long operative time, anastomotic leakage, and preoperative albumin were closely related to EPSBO. Analysis of postoperative rehabilitation indices showed that EPSBO remarkably slowed the postoperative rehabilitation speed of patients. Multivariate logistic regression analysis showed that male sex, open surgery, operative time, and anastomotic leakage were independent risk factors (P < 0.05), and the operation time had the greatest impact on EPSBO. On the basis of multivariate logistic regression, a nomogram was constructed, which showed moderate accuracy in predicting EPSBO, with a C-statistic of 0.716. The calibration chart showed good consistency between the predicted probability and ideal probability. Conclusion: The current study constructed a nomogram based on the clinical data of patients who underwent right colectomy, which had moderate predictability and could provide reference value for clinicians to evaluate the risk of EPSBO. [ABSTRACT FROM AUTHOR]

Reference 1 Zheng H, Liu Y, Chen Z. Novel nomogram for predicting risk of early postoperative small bowel obstruction after right colectomy for cancer. B Correction to: World J Surg Oncol 20, 19 (2022) b B https://doi.org/10.1186/s12957-022-02489-2 b Following the publication of the original article [[1]], the author reported that Figure 1 was not updated as per correction and that Table 1 corrections were incorrectly carried out. [Extracted from the article]