The evolution of donations on scale-free networks during a COVID-19 breakout

Having a large number of timely donations during the early stages of a COVID-19 breakout would normally be considered rare. Donation is a special public goods game with zero yield, and it has the characteristics of prisoners’ dilemma. This paper discusses why timely donations in the early stages of COVID-19 occur. Based on the idea that donation is a strategy adopted by interconnected players on account of their understanding of the environment, donation-related populations are placed in social networks and the inter-correlation structure in the population is described by scale-free networks. Players in donation-related groups are of four types: donors, illegal beneficiaries, legal beneficiaries, and inactive people. We model the evolutionary game of donation on a scale-free network. Donors, illegal beneficiaries and inactive people learn and update strategies under the Fermi Update Rule, whereas the conversion between the legal beneficiaries and the other three strategies is determined by the environment surrounding the players. We study the evolution of cooperative action when the agglomeration coefficient, the parameters in the utility function, the selection strength parameter, the utility discount coefficient, the public goods discount coefficient and the initial state of the population in the scale-free network change. For population sizes of 50,100,150 and 200, we give the utility functions and the agglomeration coefficients for promoting cooperation. And we study the corresponding steady state and structural characteristics of the population. We identify the best ranges of selection strength K, the public goods discount coefficient α and the utility discount coefficient β for promoting cooperation at different population sizes. Furthermore, with an increase of the population size, the Donor Trap are found. At the same time, it is discovered that the initial state of the population has a great impact on the steady state; thus the Upper and Lower Triangle Phenomena are proposed. We also find that population size itself is also an important factor for promoting donation, pointing out the direction of efforts to further promote donation and achieve better social homeostasis under the donation model.

The joint role of coevolutionary selection and network structure in shaping trait matching in mutualisms

Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance trait matching. To address this, we simulate coevolution in topologically distinct networks under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. While network modularity enhances trait matching when coevolution is weak, network connectance does so when coevolution is strong. Third, the structural properties of networks outrank those of modules or species in determining the degree of trait matching. Our findings suggest networks can both enhance or constrain trait matching, depending on the strength of mutualistic selection.

Design Analysis of Chassis used in Self-Propelled Onion Harvester using FEA Tool

Onion farming is more commonly practiced for an irrigated crop, resulting in a high yield with large sized bulbs. Manual harvesting of an onion being meticulous requires a large amount of manpower as well as time. Thus, we have constructed and evaluated a self-propelled onion harvester which will have good performance in terms of productivity, fuel economy, less damage to crop and operator comfort. This paper is intended to discuss the results of the design and analysis of the chassis under the guidelines of the SAE TIFAN rulebook [1]. The chassis is designed using tool CATIA V5 followed by Finite element analysis (FEA) using ANSYS and the consequent results have been plotted and comparative results of old and modified chassis has proposed. During chassis designing and analysis, several factors are taken into account like material selection, strength, durability, boundary conditions, force distribution, induced stresses, optimum factor of safety, ergonomics and aesthetics. All the decisions for design are based on all pros and cons from testing and results of previous competitions.

Adaptation and Natural Selection

Here non-random shifts in allele frequencies over time are introduced, as well as how to incorporate varying levels of selection into a model of a single population through time. This chapter highlights the difference between weak and strong selection, the dynamics of single allele versus genotype-level selection, and how selection strength and population size affect allele frequency distributions over time. Finally the inference of the selection coefficient from allele frequency data is discussed, alongside the concepts of overdominance and underdominance.

Social aging: Late-life fitness gains explain the absence of a selection shadow in ants

A key hypothesis for the occurrence of senescence is a decrease in the selection strength because of low late-life fitness – the so-called selection shadow. However, in social insects, aging is considered a plastic trait and senescence seems to be absent. By life-long tracking of 102 ant colonies, we find that queens increase sexual productivity in late life regardless of their absolute lifespan or worker investment. This indicates a genetically accommodated adaptive shift towards increasingly queen-biased caste ratios over the course of a queens’ life. Furthermore, mortality decreased with age, supporting the hypothesis that aging is adaptive. We argue that selection for late life reproduction diminishes the selection shadow of old age and leads to the apparent absence of senescence in ants, in contrast to most iteroparous species.

Solving the Sample Size Problem for Resource Selection Analysis

Sample size sufficiency is a critical consideration for conducting Resource-Selection Analyses (RSAs) from GPS-based animal telemetry. Cited thresholds for sufficiency include a number of captured animals M ≥ 30 and as many relocations per animal N as possible. These thresholds render many RSA-based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations.We provide the first comprehensive solution for RSA sample size by deriving closed-form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 2 biologically meaningful quantities: habitat selection strength and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores, and herbivores living in boreal, temperate, and tropical forests, montane woodlands, swamps, and arctic tundra).Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results contradict conventional wisdom by demonstrating that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with far fewer data than is commonly assumed.We identify several critical steps in implementing these equations, including (i) a priori selection of expected model coefficients, and (ii) sampling intensity for background (absence/pseudo-absence) data within a given definition of availability. We show that random sampling of background data violates the underlying mathematics of RSA, leading to incorrect values for necessary M and N and potentially incorrect RSA model outputs. We argue that these equations should be a mandatory component for all future RSA studies.

The joint role of coevolutionary selection and network structure in shaping trait complementarity in mutualisms

Coevolution can sculpt remarkable trait similarity between mutualistic partners. Yet, it remains unclear which network topologies and selection regimes enhance such trait complementarity. To address this, we simulate coevolution in topologically-distinct net-works under a gradient of mutualistic selection strength. We describe three main insights. First, trait matching is jointly influenced by the strength of mutualistic selection and the structural properties of the network where coevolution is unfolding. Second, the strength of mutualistic selection determines the network descriptors better correlated with higher trait matching. When coevolution is weak, network modularity enhances trait matching, but when it is strong, network connectance amplifies trait matching. Third, the structural properties of networks outrank those of modules or species in determining the evolved degree of trait matching. Our findings suggest networks can both enhance or constrain trait complementary, depending on the strength of mutualistic selection.