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RISS 인기검색어
- 검색키워드
- 저자 : Di Jia (검색결과 33 건)
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Highly Sensitive Detection of Malathion Based on FRET between Au/Fe3O4 and Rhodamine B
Di Jia,Dongqing Ma,Xiaodong Du,Lijuan An 대한화학회 2019 Bulletin of the Korean Chemical Society Vol.40 No.8
In this paper, Au/Fe3O4 nanoparticles (Au/Fe3O4 NPs) were prepared and a method for detecting malathion was constructed based on fluorescence resonance energy transfer (FRET) between Au/Fe3O4 NPs and Rhodamine B (RhB). RhB could be electrostatically adsorbed to the surface of Au/Fe3O4 NPs resulting in a low fluorescent background. The malathion hydrolysate with -SH can compete with RhB to form a stronger Au─S bond, thereby promoting the desorption of RhB from the surface of Au/Fe3O4 NPs. This process causes the fluorescence emission to recover. The result demonstrated that fluorescence quenching efficiency of Au/Fe3O4 could reach 50% at 579?nm and the fluorescence recovery efficiency of malathion hydrolysate could reach 61% at 579?nm. The limit of detection (LOD) of malathion was as low as 0.59 ?M. The linear range of detection was 27.24?99.89 ?M. Au/Fe3O4 NPs could be reused by a magnetic concentration-washing process.
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Improve coati optimization algorithm for solving constrained engineering optimization problems
Jia Heming,Shi Shengzhao,Wu Di,Rao Honghua,Zhang Jinrui,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
The coati optimization algorithm (COA) is a meta-heuristic optimization algorithm proposed in 2022. It creates mathematical models according to the habits and social behaviors of coatis: (i) In the group organization of the coatis, half of the coatis climb trees to chase their prey away, while the other half wait beneath to catch it and (ii) Coatis avoidance predators behavior, which gives the algorithm strong global exploration ability. However, over the course of our experiment, we uncovered opportunities for enhancing the algorithm’s performance. When confronted with intricate optimization problems, certain limitations surfaced. Much like a long-nosed raccoon gradually narrowing its search range as it approaches the optimal solution, COA algorithm exhibited tendencies that could result in reduced convergence speed and the risk of becoming trapped in local optima. In this paper, we propose an improved coati optimization algorithm (ICOA) to enhance the algorithm’s efficiency. Through a sound-based search envelopment strategy, coatis can capture prey more quickly and accurately, allowing the algorithm to converge more rapidly. By employing a physical exertion strategy, coatis can have a greater variety of escape options when being chased, thereby enhancing the algorithm’s exploratory capabilities and the ability to escape local optima. Finally, the lens opposition-based learning strategy is added to improve the algorithm’s global performance. To validate the performance of the ICOA, we conducted tests using the IEEE CEC2014 and IEEE CEC2017 benchmark functions, as well as six engineering problems.
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Jia Lyu,Yihua Wang,Linglong Liu,Chunming Wang,Yulong Ren,Cheng Peng,Feng Liu,Yunlong Wang,Mei Niu,Di Wang,Ming Zheng,Kunneng Zhou,Shaolu Zhao,Fuqing Wu,Haiyang Wang,Jianmin Wan 한국식물학회 2017 Journal of Plant Biology Vol.60 No.5
Differentiation from proplastids into chloroplasts isa light- and energy-dependent process. How this process isregulated is still poorly understood at the molecular level. We herein report a new putative plastidial adenine nucleotidetransporter, BRITTLE1-3 (referred to as OsBT1-3), encoded bythe rice (Oryza sativa) White Stripe Leaf 2 (WSL2) gene. Loss of OsBT1-3 function results in defective chloroplastbiogenesis, severely reduced photosynthetic efficiency, andfinally a white stripe leaf phenotype in the first four leaves. The expression levels of genes related to chlorophyllbiosynthesis and photosynthesis are drastically reduced,accompanied with over accumulation of reactive oxygenspecies (ROS) in the wsl2 mutant. OsBT1-3 is targeted tothe chloroplasts and it expresses in almost all tissues inplants, especially in young leaves. OsBT1-3 consists of 419amino acids and exhibits features of all mitochondrialcarrier proteins, including a typical transmembrane-spanningdomain and a highly conserved sequence motif designatedas the ‘mitochondrial energy transfer signatures’. Phylogeneticanalysis shows that OsBT1-3 is a putative plastidialadenine nucleotide transporter and is most closely relatedto ZmBT1-2. Together, these observations suggest that thenew putative adenine nucleotide transporter, OsBT1-3,plays an essential role in regulating chloroplast biogenesisand maintenance of ROS homeostasis during rice seedlingde-etiolation.
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Multi-strategy Remora Optimization Algorithm for solving multi-extremum problems
Jia Heming,LI YONGCHAO,Wu Di,Rao Honghua,Wen Changsheng,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
A metaheuristic algorithm that simulates the foraging behavior of remora has been proposed in recent years, called ROA. ROA mainly simulates host parasitism and host switching in the foraging behavior of remora. However, in the experiment, it was found that there is still room for improvement in the performance of ROA. When dealing with complex optimization problems, ROA often falls into local optimal solutions, and there is also the problem of too-slow convergence. Inspired by the natural rule of “Survival of the fittest”, this paper proposes a random restart strategy to improve the ability of ROA to jump out of the local optimal solution. Secondly, inspired by the foraging behavior of remora, this paper adds an information entropy evaluation strategy and visual perception strategy based on ROA. With the blessing of three strategies, a multi-strategy Remora Optimization Algorithm (MSROA) is proposed. Through 23 benchmark functions and IEEE CEC2017 test functions, MSROA is comprehensively tested, and the experimental results show that MSROA has strong optimization capabilities. In order to further verify the application of MSROA in practice, this paper tests MSROA through five practical engineering problems, which proves that MSROA has strong competitiveness in solving practical optimization problems.
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Jia Heming,You Fangkai,Wu Di,Rao Honghua,Wu Hangqu,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
The snow ablation optimizer (SAO) is a new metaheuristic algorithm proposed in April 2023. It simulates the phenomenon of snow sublimation and melting in nature and has a good optimization effect. The SAO proposes a new two-population mechanism. By introducing Brownian motion to simulate the random motion of gas molecules in space. However, as the temperature factor changes, most water molecules are converted into water vapor, which breaks the balance between exploration and exploitation, and reduces the optimization ability of the algorithm in the later stage. Especially in the face of high-dimensional problems, it is easy to fall into local optimal. In order to improve the efficiency of the algorithm, this paper proposes an improved snow ablation optimizer with heat transfer and condensation strategy (SAOHTC). Firstly, this article proposes a heat transfer strategy, which utilizes gas molecules to transfer heat from high to low temperatures and move their positions from low to high temperatures, causing individuals with lower fitness in the population to move towards individuals with higher fitness, thereby improving the optimization efficiency of the original algorithm. Secondly, a condensation strategy is proposed, which can transform water vapor into water by simulating condensation in nature, improve the deficiency of the original two-population mechanism, and improve the convergence speed. Finally, to verify the performance of SAOHTC, in this paper, two benchmark experiments of IEEE CEC2014 and IEEE CEC2017 and five engineering problems are used to test the superior performance of SAOHTC.
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Modified beluga whale optimization with multi-strategies for solving engineering problems
Jia Heming,Wen Qixian,Wu Di,Wang Zhuo,WANG YUHAO,Wen Changsheng,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.6
The beluga whale optimization (BWO) algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation, and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization (MBWO) with a multi-strategy. It was inspired by beluga whales’ two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group aggregation strategy (GAs) and a migration strategy (Ms). The GAs can improve the local development ability of the algorithm and accelerate the overall rate of convergence through the group aggregation fine search; the Ms randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO’s ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.
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Jia Heming,Lu Chenghao,Wu Di,Wen Changsheng,Rao Honghua,Abualigah Laith 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.4
In 2021, a meta-heuristic algorithm, Reptile Search Algorithm (RSA), was proposed. RSA mainly simulates the cooperative predatory behavior of crocodiles. Although RSA has a fast convergence speed, due to the influence of the crocodile predation mechanism, if the algorithm falls into the local optimum in the early stage, RSA will probably be unable to jump out of the local optimum, resulting in a poor comprehensive performance. Because of the shortcomings of RSA, introducing the local escape operator can effectively improve crocodiles' ability to explore space and generate new crocodiles to replace poor crocodiles. Benefiting from adding a restart strategy, when the optimal solution of RSA is no longer updated, the algorithm’s ability to jump out of the local optimum is effectively improved by randomly initializing the crocodile. Then joining Ghost opposition-based learning to balance the IRSA’s exploitation and exploration, the Improved RSA with Ghost Opposition-based Learning for the Global Optimization Problem (IRSA) is proposed. To verify the performance of IRSA, we used nine famous optimization algorithms to compare with IRSA in 23 standard benchmark functions and CEC2020 test functions. The experiments show that IRSA has good optimization performance and robustness, and can effectively solve six classical engineering problems, thus proving its effectiveness in solving practical problems.
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