An improved golden jackal optimization for multilevel thresholding image segmentation.

Aerial photography is a long-range, non-contact method of target detection technology that enables qualitative or quantitative analysis of the target. However, aerial photography images generally have certain chromatic aberration and color distortion. Therefore, effective segmentation of aerial images can further enhance the feature information and reduce the computational difficulty for subsequent image processing. In this paper, we propose an improved version of Golden Jackal Optimization, which is dubbed Helper Mechanism Based Golden Jackal Optimization (HGJO), to apply multilevel threshold segmentation to aerial images. The proposed method uses opposition-based learning to boost population diversity. And a new approach to calculate the prey escape energy is proposed to improve the convergence speed of the algorithm. In addition, the Cauchy distribution is introduced to adjust the original update scheme to enhance the exploration capability of the algorithm. Finally, a novel "helper mechanism" is designed to improve the performance for escape the local optima. To demonstrate the effectiveness of the proposed algorithm, we use the CEC2022 benchmark function test suite to perform comparison experiments. the HGJO is compared with the original GJO and five classical meta-heuristics. The experimental results show that HGJO is able to achieve competitive results in the benchmark test set. Finally, all of the algorithms are applied to the experiments of variable threshold segmentation of aerial images, and the results show that the aerial photography images segmented by HGJO beat the others. Noteworthy, the source code of HGJO is publicly available at https://github.com/Vang-z/HGJO.

Constructing a Hierarchical Hydrophilic Crosslink Network on the Surface of a Polyvinylidene Fluoride Membrane for Efficient Oil/Water Emulsion Separation.

Oil/water mixtures from industrial and domestic wastewater adversely affect the environment and human beings. In this context, the development of a facile and improved separation method is crucial. Herein, dopamine was used as a bioadhesive to bind tea polyphenol (TP) onto the surface of a polyvinylidene fluoride (PVDF) membrane to form the first hydrophilic polymer network. Sodium periodate (NaIO4) is considered an oxidising agent for triggering self-polymerisation and can be used to introduce hydrophilic groups via surface manipulation to form the second hydrophilic network. In contrast to the individual polydopamine (PDA) and TP/NaIO4 composite coatings for a hydrophobic PVDF microfiltration membrane, a combination of PDA, TP, and NaIO4 has achieved the most facile treatment process for transforming the hydrophobic membrane into the hydrophilic state. The hierarchical superhydrophilic network structure with a simultaneous underwater superoleophobic membrane exhibited excellent performance in separating various oil-in-water emulsions, with a high water flux (1530 L.m-2 h-1.bar) and improved rejection (98%). The water contact angle of the modified membrane was 0° in 1 s. Moreover, the steady polyphenol coating was applied onto the surface, which endowed the membrane with an adequate antifouling and recovery capability and a robust durability against immersion in an acid, alkali, or salt solution. This facile scale-up method depends on in situ plant-inspired chemistry and has remarkable potential for practical applications.