Multitasking Evolutionary Algorithm Based on Adaptive Seed Transfer for Combinatorial Problem

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs

Adaptive Feature Weights Based Double-Layer Multi-Objective Method for SAR Image Segmentation

The recently proposed multi-objective clustering methods convert the segmentation problem to a multi-objective optimization problem by extracting multiple features from an image to be segmented as clustering data. However, most of these methods fail to consider the impacts of different features on segmentation results when calculating the similarity using the Euclidean distance. In this paper, feature domination is defined to segment the image efficiently, and then an adaptive feature weights based double-layer multi-objective method (AFWDLMO) for image segmentation is presented. The proposed method mainly contains two layers: a weight determination layer and a clustering layer. In the weight determination layer, AFWDLMO adaptively identifies the dominant feature of an image to be segmented and specifies its optimal weight through differential evolution. In the clustering layer, multi-objective clustering functions are established and optimized based on the acquired optimal weight, and a set of solutions with high segmentation accuracy is found. The segmentation results on several texture images and SAR images show that the proposed method is better than several existing state-of-the-art segmentation algorithms

Application and Principle of Bolt-Mesh-Cable Control Technology in Extremely Soft Coal Seam Roadway

The extremely soft coal seam roadway has the problems of low surrounding rock strength and difficult support. Based on the engineering background of the mining roadway in the extremely soft coal seam of the Quandian coal mine, this paper adopts the research method combining numerical simulation and field measurement. The stress field, displacement field, support force, and loading arch structure characteristics of roadway surrounding rock under bolt-mesh-cable support and existing shed-cable support are compared, and the control principle of bolt-mesh-cable in extremely soft coal seam roadway is expounded. Our study indicated that compared with nonsupport and shed-cable support, the pressure stress range of the surrounding rock in extremely soft coal seam increases, and the stress gradient decreases when bolt-mesh-cable support is used. The roadway displacement and displacement difference decrease. The components of the support achieve stress coordination. The thickness of the loading arch formed in the surrounding rock is large, and the compressive stress in the arch is evenly distributed. The bolt-mesh-cable significantly improves the stress environment and the stability of the roadway. The field practice shows that the roadway deformation is small after the bolt-mesh-cable support is adopted, and the roadway section during excavation and mining meets the requirements of safety production

Aggregation‐induced narrowband isomeric fluorophores for ultraviolet nondoped OLEDs by engineering multiple nonbonding interactions

Abstract Traditional donor‐acceptor type organic luminescent materials usually suffer from unfavorable spectral broadening and fluorescence quenching problems arising from strong inter/intra‐chromophore interactions in aggregation state. Here, two ultraviolet carbazole‐pyrimidine isomers (named o‐DCz‐Pm and m‐DCz‐Pm) with novel aggregation‐induced narrowband phenomenon are constructed and systematic investigated by experiments and theoretical simulations. Benefitting from strengthened steric hindrance and multiple noncovalent interactions, the nonradiative decay, vibrational motion, and structural relaxation of singlet state can be effectively suppressed in aggregation state. Consequently, the electroluminescence peak of 397 nm, full width at half maximum of 21 nm and external quantum efficiency of 3.4% are achieved simultaneously in nondoped o‐DCz‐Pm‐based device. This work paves an avenue toward the development of high‐performance narrowband nondoped ultraviolet materials and organic light‐emitting diodes

Creep Instability Mechanism and Control Technology of Soft Coal Roadways Based on Fracture Evolution Law

To address the challenging issues of large deformation, control difficulties, and susceptibility to failure in the support structure of soft coal roadways, this study utilizes the CVISC block creep model in UDEC software. The model incorporates Coulomb slip without cohesive contact to simulate the characteristics of soft coal, such as its loose, fragile, and small-block nature. Additionally, a soft coal nonlinear discrete element creep model is developed to investigate the creep characteristics of soft coal under triaxial compression, with the aim of revealing the underlying creep destabilization mechanism in soft coal tunnels. Based on the research findings, a primary, strong active support technology is proposed. This approach involves the use of high-preload, high-strength anchor rods and anchor cables, as well as the implementation of steel mesh and plastic woven mesh to enhance surface protection. The study highlights that: (1) The shear cracks inside the coal body of the soft coal specimen transform into tensile cracks under external force, leading to an increase in the number of tensile cracks. This is an important symbol of accelerated creep in soft coal. Improvement in peripheral pressure helps inhibit the generation of tensile cracks inside the specimen. (2) The rapid development of creep and inter-particle tensile fissures within the coal body particles themselves, along with the change in stress state after the excavation of the roadway, are the main reasons for the overall creep damage of the roadway. (3) The support force in the early stage of shed cable support is small, which cannot inhibit the accelerated development of tensile fissures. This leads to continuous deformation of the roadway, resulting in the failure of the support structure in the later stage and aggravated roadway damage. (4) The new support technology helps control surface deformation by enhancing the strength of the roadway protection surface. This suppresses the development speed and number of tensile fissures during roadway deformation, improves the starting strength of the roadway for accelerated creep, and enables effective control of the overall deformation of the soft coal roadway. Thus, the effectiveness of roadway support is remarkable

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Abstract Background Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. Methods An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts. Results All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively. Conclusions The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images