Hasil untuk "Evolution"

S. Mirjalili, S. Mirjalili, A. Lewis

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 29 well-known test functions, and the results are verified by a comparative study with Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Differential Evolution (DE), Evolutionary Programming (EP), and Evolution Strategy (ES). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics. The paper also considers solving three classical engineering design problems (tension/compression spring, welded beam, and pressure vessel designs) and presents a real application of the proposed method in the field of optical engineering. The results of the classical engineering design problems and real application prove that the proposed algorithm is applicable to challenging problems with unknown search spaces.

George C. Williams

G. Simpson

G. Simpson, G. Simpson

L. Orgel

S. Arnold

M. Hauser

A. Read

J. Daltry, W. Wüster, R. Thorpe

Chang Zhou, Yang Guo, Guoyin Chen et al.

Silvia Pauciullo, Verdiana Zulian, Simone La Frazia et al.

Viral spillover represents the transmission of pathogen viruses from one species to another that can give rise to an outbreak. It is a critical concept that has gained increasing attention, particularly after the SARS-CoV-2 pandemic. However, the term is often used inaccurately to describe events that do not meet the true definition of spillover. This review aims to clarify the proper use of the term and provides a detailed analysis of the mechanisms driving zoonotic spillover, with a focus on the genetic and environmental factors that enable viruses to adapt to new hosts. Key topics include viral genetic variability in reservoir species, biological barriers to cross-species transmission, and the factors that influence viral adaptation and spread in novel hosts. The review also examines the role of evolutionary processes such as mutation and epistasis, alongside ecological conditions that facilitate the emergence of new pathogens. Ultimately, it underscores the need for more accurate predictive models and improved surveillance to better anticipate and mitigate future spillover events.

Sk Injamamul Islam, Mohamed H. Hamad, Wanarit Jitsamai et al.

Clinostomum species, a parasitic pathogen of freshwater fish, is widely distributed and infects various host species. Recently, the pathological effect due to Clinostomum metacercarial infection was described in aquaculture in Thailand; however, the global genetic diversity and population structure of this species have not been studied yet. Therefore, this study aimed to provide a detailed description of genetic diversity and population dynamics of the digenean Clinostomum isolated from Trichopodus pectoralis with globally recorded Clinostomum species. The species was characterized molecularly by analyzing 18S rDNA and inter-transcribed spacer biomarker genes (ITS1 and ITS2). A BLAST search discovered that the 18S rDNA and ITS sequence had a 100% sequence similarity with Clinostomum piscidium isolated from India and Thailand. A comprehensive analysis revealed the presence of 12 distinct haplotypes among the Clinostomum populations. This study suggests that distinct patterns of genetic variation were identified by analyzing molecular variance, pairwise Fst, and employing structure analysis. It was observed that a gradient of genetic variation exists within continents, characterized by higher levels within different groups and lower levels of genetic differentiation. Additionally, a notable presence of mixed haplotypes was observed. The results of neutrality testing suggest that there has been a significant expansion in the populations of Clinostomum in India, America, and Kenya. The discoveries from this study will provide a valuable contribution to comprehending the genetics and evolution of Clinostomum species. Furthermore, key findings will be essential in developing efficient management approaches to prevent and control this parasite.

Yulong Xue, Lingxuan Zhang, Yangming Ren et al.

We report a novel 2 × 2 broadband 3 dB coupler based on fast adiabatic mode evolution with a compact footprint and large bandwidth. The working principle of the coupler is based on the rapid adiabatic evolution of local eigenmodes of fishbone-like grating waveguides. Different from a traditional adiabatic coupling method realized by the slow change of the cross-section size of a strip waveguide, a fishbone waveguide allows faster adiabatic transition with proper structure and segment designs. The presented 3 dB coupler achieves a bandwidth range of 168 nm with an imbalance of no greater than ±0.1 dB only for a 9 μm coupling region which significantly improves existing adiabatic broadband couplers.

Darshika Manral, Doroteaciro Iovino, Olivier Jaillon et al.

Ocean currents are a key driver of plankton dispersal across the oceanic basins. However, species specific temperature constraints may limit the plankton dispersal. We propose a methodology to estimate the connectivity pathways and timescales for plankton species with given constraints on temperature tolerances, by combining Lagrangian modeling with network theory. We demonstrate application of two types of temperature constraints: thermal niche and adaptation potential and compare it to the surface water connectivity between sample stations in the Atlantic Ocean. We find that non-constrained passive particles representative of a plankton species can connect all the stations within three years at the surface with pathways mostly along the major ocean currents. However, under thermal constraints, only a subset of stations can establish connectivity. Connectivity time increases marginally under these constraints, suggesting that plankton can keep within their favorable thermal conditions by advecting via slightly longer paths. Effect of advection depth on connectivity is observed to be sensitive to the width of the thermal constraints, along with decreasing flow speeds with depth and possible changes in pathways.

H. Orr, M. Turelli

L. Kruuk, J. Slate, J. Pemberton et al.

Bo Zhang, Yuan Gao, Tiecheng Li et al.

In order to solve the problem of long calculation time of insulated gate bipolar transistor (IGBT) junction temperature, the XGBoost machine learning algorithm is used to calculate IGBT junction temperature in the annual damage assessment process. The XGBoost machine learning algorithm can greatly reduce the calculation time of IGBT junction temperature while ensuring the accuracy, which provides conditions for finding the optimal PV system capacity ratio and power limit value by heuristic algorithm such as differential evolution algorithm later. For PV system capacity ratio and power limit, it is necessary to consider the annual damage of the PV inverter, the increase of power generation due to capacity ratio and the power generation loss due to power limit. This paper proposes an optimization goal that considers the above factors, and uses the differential evolution algorithm to obtain the optimal PV system capacity ratio and power limit value.

Gergő Bendegúz Békési, Lilla Barancsuk, István Táczi et al.

Distribution system state estimation (DSSE) is a valuable step for DSOs toward tackling the challenges of transitioning to a more sustainable energy system and the evolution and proliferation of electric cars and power electronic devices. However, on the LV level, implementation has only taken place in a few pilot projects. In this paper, an LV DSSE method is presented and implemented in four real Hungarian LV supply areas, according to well-defined scenarios. Pseudo-measurement datasets are generated from AACs and SLPs, which have been used in different combinations on networks built with different accuracies in terms of load placement. The paper focuses on the critical aspects of finding accurate and coherent information on network topology with automated management of information systems, real LV network implementation for power flow calculation and managing portions of the network characterized by uncertain or inconsistent line lengths. A refining algorithm is implemented for the integrated network information system (INIS) models. The published method estimates node voltages with a relative error of less than 1% when using AACs, and a meter-placement method to reduce the maximum value of relative errors in future scenarios is also presented. It is shown that the observation of node voltages can be improved with the usage of AACs and SLPs, and with optimal meter placement.

Fong Yew Leong, Wei-Bin Ewe, Dax Enshan Koh

Abstract Variational quantum algorithms offer a promising new paradigm for solving partial differential equations on near-term quantum computers. Here, we propose a variational quantum algorithm for solving a general evolution equation through implicit time-stepping of the Laplacian operator. The use of encoded source states informed by preceding solution vectors results in faster convergence compared to random re-initialization. Through statevector simulations of the heat equation, we demonstrate how the time complexity of our algorithm scales with the Ansatz volume for gradient estimation and how the time-to-solution scales with the diffusion parameter. Our proposed algorithm extends economically to higher-order time-stepping schemes, such as the Crank–Nicolson method. We present a semi-implicit scheme for solving systems of evolution equations with non-linear terms, such as the reaction–diffusion and the incompressible Navier–Stokes equations, and demonstrate its validity by proof-of-concept results.

LIU Baobao, WANG Heying, TAO Lu et al.

In order to tackle the problem of unbalanced distribution of educational resources in some regions, taking the real data of teachers and book resource allocation in 13 districts as example, an educational resource distribution model was proposed based on differential evolution (DE) algorithm, the effect of educational resource allocation model were compared and analyzed by simulation experiment. The results show that the model has similar allocation performance and the same time complexity, they can allocate educational resources reasonably and provide decision-making basis for education management departments, compared with the educational resource allocation model based on particle swarm (PSO) algorithm. However, with the increase of the amount of educational resources data, the model can obtain the optimal solution in fewer iterations, and the distribution result can effectively improve the problem of unbalanced distribution of educational resources. Finally, in order to verify the validity of the model, an educational resource allocation model based on artificial fish swarm algorithm is also proposed. The visualization of the distribution of educational resources data by three models is realized, which provides a certain theoretical basis for the statistics and distribution of educational resources.