The population control bill, 2021: exploring newer perspectives

As we observe the World Population Day on 11th July, the current population stands at roughly 7.9 billion in 2021, with India bagging the second place at 1.39 billion. The net growth rate stands at 1.1% or 83 million per year and the projected world population by 2050 is estimated to be 9.7 billion. These figures are alarming to us-the millennials, who grew up writing ominous essays on ‘population explosion’ at school. Governments across the world, historically Romania to more recently China, have adopted population policies to control the rate of population growth to cater to their advantage-either economically or politically. Some of them directly against reproductive rights- to decide freely and responsibly the number, spacing and timing of their children and to be able to do so without discrimination, coercion and violence.

Reliability growth rate calculation method for hydrostatic spindle of heavy-duty machine tool using analytic network process

Aiming at the problem that the difference of the equipment between before and after implementation of reliability growth technology is not considered in calculating reliability growth rate of computer numerical control machine tools, this article takes the hydrostatic spindle of the heavy-duty machine tool as an example and introduces the concept of “net growth rate” to propose a new method to calculate reliability growth rate. First, based on the value of mean time between failures of the whole machine before implementation of reliability growth technology, a model of the “net growth rate” of the whole machine is established. Second, the reliability relationship between the whole machine and the subsystems is established through the reliability block diagram, so the problem is transformed into solving the value of mean time between failures of the subsystems. Then, a superposition model of “net growth rate” of subsystems is established by making full use of experimental data and product information. When dealing with product information, multiple factors including composition structure, design requirement, manufacture and assembly, and use environment which influence equipment reliability are considered comprehensively and analytic network process is employed to obtain the weights of the influencing factors. Based on the weights, the reliability comprehensive scores which can reflect the reliability level in the corresponding design, manufacturing, and use environment are calculated and the reliability growth rate caused by the differences of the equipment is solved. In order to add ambiguity of human judgment, interval numbers are applied to network analysis process models. Finally, this article verifies the feasibility of the proposed method with an example.

A Study on Middle-Income Population Measurement Model and Methods

Through improving the Logistic population model, this paper sets a linear relationship between the net growth rate of the population and time, gets a differential model for predicting the future population, and uses Markov chain for predicting the age structure of the population in China

Phenotypic diversity and population growth in a fluctuating environment

Organisms adapt to fluctuating environments by regulating their dynamics, and by adjusting their phenotypes to environmental changes. We model population growth using multitype branching processes in random environments, where the offspring distribution of some organism having trait t ∈ in environment e ∈ ε is given by some (fixed) distribution ϒ t,e on ℕ. Then, the phenotypes are attributed using a distribution (strategy) π t,e on the trait space . We look for the optimal strategy π t,e , t ∈ , e ∈ ε, maximizing the net growth rate or Lyapounov exponent, and characterize the set of optimal strategies. This is considered for various models of interest in biology: hereditary versus nonhereditary strategies and strategies involving or not involving a sensing mechanism. Our main results are obtained in the setting of nonhereditary strategies: thanks to a reduction to simple branching processes in a random environment, we derive an exact expression for the net growth rate and a characterization of optimal strategies. We also focus on typical genealogies, that is, we consider the problem of finding the typical lineage of a randomly chosen organism.

Phenotypic diversity and population growth in a fluctuating environment

Organisms adapt to fluctuating environments by regulating their dynamics, and by adjusting their phenotypes to environmental changes. We model population growth using multitype branching processes in random environments, where the offspring distribution of some organism having trait t ∈ in environment e ∈ ε is given by some (fixed) distribution ϒt,e on ℕ. Then, the phenotypes are attributed using a distribution (strategy) πt,e on the trait space . We look for the optimal strategy πt,e, t ∈ , e ∈ ε, maximizing the net growth rate or Lyapounov exponent, and characterize the set of optimal strategies. This is considered for various models of interest in biology: hereditary versus nonhereditary strategies and strategies involving or not involving a sensing mechanism. Our main results are obtained in the setting of nonhereditary strategies: thanks to a reduction to simple branching processes in a random environment, we derive an exact expression for the net growth rate and a characterization of optimal strategies. We also focus on typical genealogies, that is, we consider the problem of finding the typical lineage of a randomly chosen organism.

Enhanced Virulence ofSalmonella entericaSerovar Typhimurium after Passage through Mice

ABSTRACTThe interaction betweenSalmonella entericaand the host immune system is complex. The outcome of an infection is the result of a balance between thein vivoenvironment where the bacteria survive and grow and the regulation of fitness genes at a level sufficient for the bacteria to retain their characteristic rate of growth in a given host. Using bacteriological counts from tissue homogenates and fluorescence microscopy to determine the spread, localization, and distribution ofS. entericain the tissues, we show that, during a systemic infection,S. entericaadapts to thein vivoenvironment. The adaptation becomes a measurable phenotype when bacteria that have resided in a donor animal are introduced into a recipient naïve animal. This adaptation does not confer increased resistance to early host killing mechanisms but can be detected as an enhancement in the bacterial net growth rate later in the infection. The enhanced growth rate is lost upon a single passagein vitro, and it is therefore transient and not due to selection of mutants. The adapted bacteria on average reach higher intracellular numbers in individual infected cells and therefore have patterns of organ spread different from those of nonadapted bacteria. These experiments help in developing an understanding of the influence of passage in a host on the fitness and virulence ofS. enterica.