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A novel nature-inspired meta-heuristic optimization algorithm, named artificial ecosystem-based optimization (AEO), is presented in this paper. AEO is a population-based optimizer motivated from the flow of energy in an ecosystem on the earth, and this algorithm mimics three unique behaviors of living organisms, including production, consumption, and decomposition. AEO is tested on thirty-one mathematical benchmark functions and eight real-world engineering design problems. The overall comparisons suggest that the optimization performance of AEO outperforms that of other state-of-the-art counterparts. Especially for real-world engineering problems, AEO is more competitive than other reported methods in terms of both convergence rate and computational efforts. The applications of AEO to the field of identification of hydrogeological parameters are also considered in this study to further evaluate its effectiveness in practice, demonstrating its potential in tackling challenging problems with difficulty and unknown search space. The codes are available at https://www.mathworks.com/matlabcentral/fileexchange/72685-artificial-ecosystem-based-optimization-aeo .

The objective of this work was to determine pressure fluctuation and transient flow characteristics, which aims to provide references to improve noise and vibration performance for the pump design and optimization when delivering sediment-laden flow. The three-dimensional (3D) transient simulations were simulated by SST k - ω turbulence model combined with Homogeneous equilibrium model (HEM). The experimental and numerical data was compared to validate the numerical accuracy. The simulation results predicted that the concentration shows strong effects on the external performance, velocity, pressure, turbulent kinetic energy distribution and peak amplitude of pulsation frequency, which all perform increasing trend with the rise of concentration. Meanwhile, the effect of the diameter size of particles on the flow field was relatively minor, which can also evidently influence the internal flow, but the effect is not simply proportional to the diameter size. The effect of diameter size on silt flow needs to be taken into account associated with the concentration distribution. The dominant frequency of solid-liquid approximately equals 0.8 times that of pure water, and the transient characteristics of sediment-laden flow perform low frequency with high amplitude features.

Parameter identification is an important method to establish the governing system of a pumped storage unit. Appropriate parameters will make the governing system obtain better control performance. Therefore, in this study, an improved artificial hummingbird algorithm (IAHA) is proposed for the parameter identification of the governing system in a pumped storage unit. The algorithm integrates two key strategies to improve the optimization ability of the algorithm. First, the Chebyshev chaotic map is employed to initialize the artificial hummingbirds, which in turn increases and enhances the global search capability of the initial population. Second, the Levy flight is introduced in the guided foraging phase to expand the search space and avoid premature convergence. The performance of the proposed IAHA algorithm is compared with that of four other algorithms on 23 standard test functions, and the results show that IAHA has higher accuracy and faster convergence than the other four algorithms. Finally, IAHA was applied to the parameter identification of the governing system of a pumped storage unit to verify the effectiveness of the algorithm in tracking real-world problems.

Background Brassica napus is one of the most important oilseed crops, and can supply considerable amounts of edible oil as well as provide raw materials for the production of biodiesel in the biotechnology industry. Lysophosphatidic acid acyltransferase (LPAT), a key enzyme in the Kennedy pathway, catalyses fatty acid chains into 3-phosphoglycerate and promotes further production of oil in the form of triacylglycerol. However, because B. napus is an allotetraploid with two subgenomes, the precise genes which involved in oil production remain unclear due to the intractability of efficiently knocking out all copies with high genetic redundancy. Therefore, a robust gene editing technology is necessary for gene function analysis. Results An efficient gene editing technology was developed for the allotetraploid plant B. napus using the CRISPR-Cas9 system. Previous studies showed poor results in either on-target or off-target activity in B. napus. In the present study, four single-gRNAs and two multi-gRNAs were deliberately designed from the conserved coding regions of BnLPAT2 which has seven homologous genes, and BnLPAT5, which has four homologous genes. The mutation frequency was found to range from 17 to 68%, while no mutation was observed in the putative off-target sites. The seeds of the Bnlpat2/Bnlpat5 mutant were wizened and showed enlarged oil bodies, disrupted distribution of protein bodies and increased accumulation of starch in mature seeds. The oil content decreased, with an average decrease of 32% for Bnlpat2 lines and 29% for Bnlpat5 lines in single-gRNA knockout lines, and a decline of 24% for Bnlpat2 mutant lines (i.e., g123) and 39% for Bnlpat2/Bnlpat5 double mutant lines (i.e., g134) in multi-gRNA knockout lines. Conclusions Seven BnLPAT2 homologous genes and four BnLPAT5 homologous genes were cleaved completely using the CRISPR-Cas9 system, which indicated that it is effective for editing all homologous genes in allotetraploid rapeseed, despite the relatively low sequence identities of both gene families. The size of the oil bodies increased significantly while the oil content decreased, confirming that BnLPAT2 and BnLPAT5 play a role in oil biosynthesis. The present study lays a foundation for further oil production improvement in oilseed crop species.

Abstract One metaheuristic algorithm recently introduced is atom search optimization (ASO), inspired by the physical movement of atoms based on the molecular dynamics in nature. ASO displays a unique search ability by employing the interaction force from the potential energy and the constraint force. Despite some successful applications, it still suffers from a local optima stagnation and a low search efficiency. To alleviate these disadvantages, a new adaptive hybridized optimizer named AASOPSO is proposed. In this study, the individual and group cognitive components in particle swarm optimization (PSO) are integrated into ASO to accelerate the exploitation phase, and the acceleration coefficients are introduced to adaptively achieve a good balance between exploration and exploitation. Meanwhile, to improve the search performance of the algorithm, each individual atom possesses its own force constant, which is effectively and adaptively adjusted based on the feedback of the fitness of the atom in some sequential steps. The performance of AASOPSO is evaluated on two sets of benchmark functions compared to the other population-based optimizers to show its effectiveness. Additionally, AASOPSO is applied to the optimal no-load PID design of the hydro-turbine governor. The simulation results reveal that AASOPSO is more successful than its competitors in searching the global optimal PID parameters. Graphical Abstract Graphical Abstract

Background Rapeseed is the third largest oil seed crop in the world. The seeds of this plant store lipids in oil bodies, and oleosin is the most important structural protein in oil bodies. However, the function of oleosin in oil crops has received little attention. Results In the present study, 48 oleosin sequences from the Brassica napus genome were identified and divided into four lineages (T, U, SH, SL). Synteny analysis revealed that most of the oleosin genes were conserved, and all of these genes experienced purifying selection during evolution. Three and four important oleosin genes from Arabidopsis and B. napus, respectively , were cloned and analyzed for function in Arabidopsis . Overexpression of these oleosin genes in Arabidopsis increased the seed oil content slightly, except for BnaOLE3 . Further analysis revealed that the average oil body size of the transgenic seeds was slightly larger than that of the wild type (WT), except for BnaOLE1 . The fatty acid profiles showed that the linoleic acid content (13.3% at most) increased and the peanut acid content (11% at most) decreased in the transgenic lines. In addition, the seed size and thousand-seed weight (TSW) also increased in the transgenic lines, which could lead to increased total lipid production. Conclusion We identified oleosin genes in the B. napus genome, and overexpression of oleosin in Arabidopsis seeds increased the seed weight and linoleic acid content (13.3% at most).

In this article, we proposed an active method of obstacle attached on the blade to control cavitation in centrifugal pump. The modified shear stress transport k-ω model with a local density correction for turbulent eddy viscosity combined with Kubota cavitation model was employed to simulate three-dimensional unsteady flow. The simulated external performance agreed fairly well with experiment observation. The results show that the obstacles of appropriate height can cause little disturbance to external performance. The over-high obstacle can cause larger perturbation to deteriorate the flow field at large flow rate point. The obstacle of appropriate height can induce relative high pressure and optimize the flow structure to suppress the cavitation, which is the main mechanism of cavitation control in a centrifugal pump. The passages would be blocked if over-high obstacle is arranged, which is bad to suppress the cavitation in centrifugal pumps. The effects of 1/2 the outlet width of impeller for cavitation suppression are optimal when the bubbles reach close to the obstacle, which degrade the amplitude of dominate frequency and simultaneously attenuate the bubble volume. When cavitation completely developed, the obstacle of any height can keep the cavity volume attenuating and 1/2 of the outlet width of impeller is best.

Hydropower is a kind of clean energy, which can effectively reduce the consumption of fossil energy and is one of the main fields of new energy development. Pumped storage power station not only provides a solution for storing electric energy and generating excess, but also is a clean, efficient, economical and safe power system regulation method with high quality. Accurate modeling of a pump-turbine, as the core equipment of a pumped storage unit, is the key to safe and stable operation of the pumped storage unit. At present, a method of simplifying the external characteristics of a pump-turbine into a first-order linear model is widely used, which cannot effectively and accurately reveal the nonlinear dynamic characteristics of the unit in transition process. In order to meet the demand of high-precision simulation of the unit, a new method of identifying Taylor series expansion considering nonlinearity based on the torque characteristic formula and the flow characteristic formula is proposed, which is applied to the pump-turbine external characteristic model, and retains the second derivative term, making the model a second-order nonlinear model, and thus, the pump-turbine model becomes a nonlinear model. The nonlinear model established is used to simulate the load increase and load rejection of the unit, and the results are compared with those for the linear model. The comparison shows that the nonlinear model established for the pump-turbine can reveal the dynamic response of the unit more effectively and accurately than the linear model, and provide a further guarantee for the safe and stable operation of pumped-storage units, which is of great significance to hydropower energy development.

Magnetorheological (MR) dampers play a crucial role in various engineering systems, and how to identify the control parameters of MR damper models without any prior knowledge has become a burning problem. In this study, to identify the control parameters of MR damper models more accurately, an improved manta ray foraging optimization (IMRFO) is proposed. The new algorithm designs a searching control factor according to a weak exploration ability of MRFO, which can effectively increase the global exploration of the algorithm. To prevent the premature convergence of the local optima, an adaptive weight coefficient based on the Levy flight is designed. Moreover, by introducing the Morlet wavelet mutation strategy to the algorithm, the mutation space is adaptively adjusted to enhance the ability of the algorithm to step out of stagnation and the convergence rate. The performance of the IMRFO is evaluated on two sets of benchmark functions and the results confirm the competitiveness of the proposed algorithm. Additionally, the IMRFO is applied in identifying the control parameters of MR dampers, the simulation results reveal the effectiveness and practicality of the IMRFO in the engineering applications.

Background Manganese (Mn) deficiency and toxicity are major constraints on crop production in soil. Plants have evolved cascade strategies and specific mechanisms to tolerate these stresses. Understanding the molecular mechanisms of tolerance to Mn stress is crucial for improving the efficiency of conferring Mn tolerance and phytoremediation, which is intriguing for evolutionary research on plant adaptation to abiotic stresses. In this study, the responses of mulberry to varied concentration levels of Mn (MnSO 4 ), ranging from deficiency (0 mM and 0.03 mM), sufficiency (0.15 mM), and toxicity regimes (1.5 mM and 3 mM) were compared by elucidating the physiological, transcriptome profiling, and functional characterization of the MaCAX3 gene in mulberry leaves. Results The results show that Mn-induced deficiency and toxicity not only trigger an increase in oxidation and antioxidant parameters, including hydrogen peroxide (H 2 O 2 ), lipid peroxidase (LPO), polyphenol oxidase (PPO), and reactive oxygen species (ROS) but also concomitantly improved the activities of total antioxidant capacity (TAC) and hydroxyl radical (•OH) scavenging levels in mulberry. Results of the cell wall structural components show that cellulose, hemicellulose, and lignin contents were significantly higher, except for pectin, in the control (CK) compared to the deficiency and toxicity. Functional validation of the MaCAX3 gene via gene silencing revealed that the heterologous expression of the MaCAX3 gene increased the transport of Mn in yeast, thus inhibiting the toxic effect of Mn relative to the silenced Macax3 -VIGS. Additionally, transcriptome analysis identified a total of 811 differentially expressed genes (DEGs), with 189 and 622 being up- and downregulated, respectively. These DEGs were significantly involved in Mn transport, detoxification, oxidation, antioxidant defense, and cell wall and protein processing, which conferred tolerance to Mn in mulberry plants. Conclusion The study sheds substantial light on key molecular mechanisms and the functional characterization and validation of crucial Mn tolerance genes in mulberry leaves.