To study evolution of conditional dispersal, a Lotka-Volterra reaction-diffusion-advection model for two competing species in a heterogeneous environment is proposed and investigated. The two species are assumed to be identical except their dispersal strategies: both species disperse by random diffusion and advection along environmental gradients, but one species has stronger biased movement (i.e., advection along the environmental gradients) than the other one. It is shown that at least two scenarios can occur: if only one species has a strong tendency to move upward the environmental gradients, the two species can coexist since one species mainly pursues resources at places of locally most favorable environments while the other relies on resources from other parts of the habitat; if both species have such strong biased movements, it can lead to overcrowding of the whole population at places of locally most favorable environments, which causes the extinction of the species with stronger biased movement. These results provide a new mechanism for the coexistence of competing species, and they also imply that selection is against excessive advection along environmental gradients, and an intermediate biased movement rate may evolve.
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http://dx.doi.org/10.1007/s00285-008-0166-2 | DOI Listing |
J Mol Model
January 2025
Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
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January 2025
Shanxi Jiangyang Chemical Limited Company, Taiyuan, 030041, Shanxi, China.
Context: DNAN/DNB cocrystals, as a newly developed type of energetic material, possess superior safety and thermal stability, making them a suitable alternative to traditional melt-cast explosives. Nonetheless, an exploration of the thermal degradation dynamics of the said cocrystal composite has heretofore remained uncharted. Consequently, we engaged the ReaxFF/lg force field modality to delve into the thermal dissociation processes of the DNAN/DNB cocrystal assembly across a spectrum of temperatures, encompassing 2500, 2750, 3000, 3250, and 3500 K.
View Article and Find Full Text PDFGlob Chang Biol
January 2025
Department of Renewable Resources, University of Alberta, Edmonton, Canada.
Soil microorganisms transform plant-derived C (carbon) into particulate organic C (POC) and mineral-associated C (MAOC) pools. While microbial carbon use efficiency (CUE) is widely recognized in current biogeochemical models as a key predictor of soil organic carbon (SOC) storage, large-scale empirical evidence is limited. In this study, we proposed and experimentally tested two predictors of POC and MAOC pool formation: microbial necromass (using amino sugars as a proxy) and CUE (by O-HO approach).
View Article and Find Full Text PDFSci Rep
January 2025
Division of Engineering, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
This study advances microfluidic probe (MFP) technology through the development of a 3D-printed Microfluidic Mixing Probe (MMP), which integrates a built-in pre-mixer network of channels and features a lined array of paired injection and aspiration apertures. By combining the concepts of hydrodynamic flow confinements (HFCs) and "Christmas-tree" concentration gradient generation, the MMP can produce multiple concentration-varying flow dipoles, ranging from 0 to 100%, within an open microfluidic environment. This innovation overcomes previous limitations of MFPs, which only produced homogeneous bioreagents, by utilizing the pre-mixer to create distinct concentration of injected biochemicals.
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January 2025
Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, Faculty of Dentistry, Taif University, 21944, Taif, Saudi Arabia.
This study investigates the use of machine learning models to predict solubility of rivaroxaban in binary solvents based on temperature (T), mass fraction (w), and solvent type. Using a dataset with over 250 data points and including solvents encoded with one-hot encoding, four models were compared: Gradient Boosting (GB), Light Gradient Boosting (LGB), Extra Trees (ET), and Random Forest (RF). The Jellyfish Optimizer (JO) algorithm was applied to tune hyperparameters, enhancing model performance.
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