Most evidence for hybrid swarm formation stemming from anthropogenic habitat disturbance comes from the breakdown of reproductive isolation between incipient species, or introgression between allopatric species following secondary contact. Human impacts on hybridization between divergent species that naturally occur in sympatry have received considerably less attention. Theory predicts that reinforcement should act to preserve reproductive isolation under such circumstances, potentially making reproductive barriers resistant to human habitat alteration. Using 15 microsatellites, we examined hybridization between sympatric populations of alewife (Alosa pseudoharengus) and blueback herring (A. aestivalis) to test whether the frequency of hybridization and pattern of introgression have been impacted by the construction of a dam that isolated formerly anadromous populations of both species in a landlocked freshwater reservoir. The frequency of hybridization and pattern of introgression differed markedly between anadromous and landlocked populations. The rangewide frequency of hybridization among anadromous populations was generally 0-8%, whereas all landlocked individuals were hybrids. Although neutral introgression was observed among anadromous hybrids, directional introgression leading to increased prevalence of alewife genotypes was detected among landlocked hybrids. We demonstrate that habitat alteration can lead to hybrid swarm formation between divergent species that naturally occur sympatrically, and provide empirical evidence that reinforcement does not always sustain reproductive isolation under such circumstances.
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Sci Rep
January 2025
Department of Electrical Engineering, Imam Khomeini Naval Science University of Nowshahr, Nowshahr, Iran.
The maximum power delivered by a photovoltaic system is greatly influenced by atmospheric conditions such as irradiation and temperature and by surrounding objects like trees, raindrops, tall buildings, animal droppings, and clouds. The partial shading caused by these surrounding objects and the rapidly changing atmospheric parameters make maximum power point tracking (MPPT) challenging. This paper proposes a hybrid MPPT algorithm that combines the benefits of the salp swarm algorithm (SSA) and hill climbing (HC) techniques.
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January 2025
Electrical Engineering Department, Kerman Branch, Islamic Azad University, Kerman, Iran.
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View Article and Find Full Text PDFSpectrochim Acta A Mol Biomol Spectrosc
December 2024
Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing 210037, China; College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China. Electronic address:
Moisture content (MC) is crucial for the storage, transportation, and processing of Camellia oleifera seeds. The purpose of this study was to investigate the feasibility for detecting MC in Camellia oleifera seeds using visible near-infrared hyperspectral imaging (VNIR-HSI) (374.98 ∼ 1038.
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December 2024
Center for Research on Microgrids (UPC CROM), Department of Electronic Engineering, Technical University of Catalonia, 08019, Barcelona, Spain.
With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system.
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December 2024
Department of Geosciences, Geotechnology, and Materials Engineering for Resources, Graduate School of International Resource Sciences, Akita University, Akita, Japan.
The present investigation employs relevance vector machine (RVM) and long short-term memory (LSTM) models to predict the time-dependent bearing capacity of concrete piles. Each RVM model (SRVM) is configured by each linear, polynomial, gaussian, sigmoid, laplacian, and exponential kernel function. Each SRVM model has been optimized by each genetic (GA_SRVM) and particle swarm optimization (PSO_RVM) algorithm.
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