If Loud Aliens Explain Human Earliness, Quiet Aliens Are Also Rare

If life on Earth had to achieve n “hard steps“ to reach humanity's level, then the chance of this event rose as time to the nth power. Integrating this over habitable star formation and planet lifetime distributions predicts >99% of advanced life appears after today, unless n < 3 and max planet duration <50 Gyr. That is, we seem early. We offer this explanation: a deadline is set by loud aliens who are born according to a hard steps power law, expand at a common rate, change their volume appearances, and prevent advanced life like us from appearing in their volumes. Quiet aliens, in contrast, are much harder to see. We fit this three-parameter model of loud aliens to data: (1) birth power from the number of hard steps seen in Earth’s history, (2) birth constant by assuming a inform distribution over our rank among loud alien birth dates, and (3) expansion speed from our not seeing alien volumes in our sky. We estimate that loud alien civilizations now control 40%–50% of universe volume, each will later control ∼ 105–3 × 107 galaxies, and we could meet them in ∼200 Myr–2 Gyr. If loud aliens arise from quiet ones, a depressingly low transition chance (<∼10−4 ) is required to expect that even one other quiet alien civilization has ever been active in our galaxy. Which seems to be bad news for the Search for Extraterrestrial Intelligence. But perhaps alien volume appearances are subtle, and their expansion speed lower, in which case we predict many long circular arcs to find in our sky.

A Thin-Film Lubrication Model for Biofilm Expansion Under Strong Adhesion

Understanding microbial biofilm growth is important to public health, because biofilms are a leading cause of persistent clinical infections. In this paper, we develop a thin-film model for microbial biofilm growth on a solid substratum to which it adheres strongly. We model biofilms as two-phase viscous fluid mixtures of living cells and extracellular fluid. The model tracks the movement, depletion, and uptake of nutrients explicitly, and incorporates cell proliferation via a nutrient-dependent source term. Notably, our thin-film reduction is two-dimensional and includes the vertical dependence of cell volume fraction. Numerical solutions show that this vertical dependence is weak for biologically-feasible parameters, reinforcing results from previous models in which this dependence was neglected. We exploit this weak dependence by writing and solving a simplified one-dimensional model that is computationally more efficient than the full model. We use both the one and two-dimensional models to predict how model parameters affect expansion speed and biofilm thickness. This analysis reveals that expansion speed depends on cell proliferation, nutrient availability, cell-cell adhesion on the upper surface, and slip on the biofilm-substratum interface. Our numerical solutions provide a means to qualitatively distinguish between the extensional flow and lubrication regimes, and quantitative predictions that can be tested in future experiments.

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.

Expansion Velocity Model of Steam-Assisted Gravity Drainage considering Thermal Convection

Steam-assisted gravity drainage (SAGD) is an important method used in the development of heavy oil. A heat transfer model in the SAGD production process is established based on the heat transfer effect caused by the temperature difference at the front edge of the steam chamber and the heat convection effect caused by the pressure difference. The observation well temperature method is used in this model to calculate the horizontal expansion speed of the steam chamber. In this manner, an expansion speed model considering heat convection and heat conduction is established for a steam chamber with a steam-assisted gravity drainage system. By comparing this with the production data extracted from the Fengcheng Oilfield target block, it is verified that the model can be effectively applied for actual field development. Simultaneously, by using the derived model, the temperature distribution at the edge of the steam chamber and production forecast can be predicted. Sensitivity analysis of the expansion rate of the steam chamber demonstrates that the larger the thermal conductivity, the faster is the steam chamber horizontal expansion speed, and the two are positively correlated; the larger the reservoir heat capacity, the slower is the steam chamber horizontal expansion speed. A larger heat capacity of the convective liquid indicates that there are more water components in the convective liquid, the viscosity of the convective liquid is low, and the expansion speed of the steam chamber increases accordingly. This research closely integrates theory with actual field production and provides theoretical support for the development of heavy oil reservoirs.

Role of bacterial persistence in spatial population expansion

Bacterial persistence, tolerance to antibiotics via stochastic phenotype switching provides a survival strategy and a fitness advantage in temporally fluctuating environments. Here we study its possible benefit in spatially varying environments using a Fisher wave approach. We study the spatial expansion of a population with stochastic switching between two phenotypes in spatially homogeneous conditions and in the presence of an antibiotic barrier. Our analytical results show that the expansion speed in growth-supporting conditions depends on the fraction of persister cells at the leading edge of the population wave. The leading edge contains a small fraction of persister cells, keeping the effect on the expansion speed minimal. The fraction of persisters increases gradually in the interior of the wave. This persister pool benefits the population when it is stalled by an antibiotic environment. In that case, the presence of persister enables the population to spread deeper into the antibiotic region and to cross an antibiotic region more rapidly. The interplay of population dynamics at the interface separating the two environments and phenotype switching in the antibiotic region results in a optimal switching rate. Overall, our results show that stochastic switching can promote population expansion in the presence of antibiotic barriers or other stressful environments.

Interspecific competition slows range expansion and shapes range boundaries

Species expanding into new habitats as a result of climate change or human introductions will frequently encounter resident competitors. Theoretical models suggest that such interspecific competition can alter the speed of expansion and the shape of expanding range boundaries. However, competitive interactions are rarely considered when forecasting the success or speed of expansion, in part because there has been no direct experimental evidence that competition affects either expansion speed or boundary shape. Here we demonstrate that interspecific competition alters both expansion speed and range boundary shape. Using a two-species experimental system of the flour beetles Tribolium castaneum and Tribolium confusum, we show that interspecific competition dramatically slows expansion across a landscape over multiple generations. Using a parameterized stochastic model of expansion, we find that this slowdown can persist over the long term. We also find that the shape of the moving range boundary changes continuously over many generations of expansion, first steepening and then becoming shallower, due to the competitive effect of the resident and density-dependent dispersal of the invader. This dynamic boundary shape suggests that current forecasting approaches assuming a constant shape could be misleading. More broadly, our results demonstrate that interactions between competing species can play a large role during range expansions and thus should be included in models and studies that monitor, forecast, or manage expansions in natural systems.