Simulation Of Evolution Research Articles

The Impact of Model Horizontal Resolution on Simulating Regional Climate Over East Asia Using Variable‐Resolution CESM2

AbstractIn this study, a variable‐resolution version of the Community Earth System Model (VR‐CESM) with mesh refinement (∼0.125°) over East Asia is used to simulate the regional climate in this region. For the evaluation of model performance and sensitivity to model resolution, the simulated near‐surface temperature and precipitation are compared with observations and simulation results from a globally quasi‐uniform (∼1°) CESM (UN‐CESM). Results show that VR‐CESM better simulates the spatial patterns and seasonal variations of mean temperature and precipitation than UN‐CESM over China. For extreme events, VR‐CESM improves the simulation of the occurrence frequency of wintertime daily minimum temperature and heavy precipitation. In regions with complex terrains, VR‐CESM better resolves the topographic forcing and captures the observed fine‐scale spatial patterns of temperature and precipitation, although precipitation is still overestimated. For East Asian summer monsoon precipitation, both UN‐CESM and VR‐CESM tend to overestimate (underestimate) the precipitation over northern (southern) China, which is associated with too strong meridional water vapor transport in the models and biases in the large‐scale circulation in the middle and upper troposphere. Different from previous studies with different physics parameterizations and refined domains, as the model resolution increases, simulated monsoon precipitation evolution is not obviously improved, and convective precipitation intensity decreases over eastern China. Despite this, our results indicate that VR‐CESM simulates regional climate, topographical forcing, and large‐scale circulations over East Asia reasonably well, and thus it can be applied for the future climate projection in the region.

Finding maximum independent set based on multi-stage simulated quantum adiabatic evolution

The ubiquitous nature of the maximum independent set (MIS) problem across diverse fields, including biology and chip design, presents a significant challenge. Recently, quantum algorithms have been recognized for their increased potential in addressing combinatorial optimization problems, including the MIS problem. Several quantum systems, including optical quantum circuits and Rydberg atomic systems, have been designed specifically for the MIS problem. However, elucidating the precise nature of instances that pose challenges for quantum algorithms constitutes a formidable task. In an effort to comprehend this issue, we endeavor to delineate the conditions under which instances become hard to solve by analyzing the Hamiltonian of the k-independent set (k-IS). Furthermore, we present two multi-stage strategies grounded in the simulated quantum adiabatic evolution (SQAE) of Kerr-nonlinear parametric oscillator (KPO) system for identifying the MIS in instances deemed hard to solve. More specifically, we present the algorithm leveraging SQAE of the KPO system for approximating the MIS. Our investigation of the SQAE algorithm reveals that optimal solutions are unattainable under two distinct conditions: (1) when the number of solutions is exponentially smaller to the problem’s scale, and (2) when the counts of non-optimal independent sets (IS) with m overlaps between the optimal k-IS (k>m) is exponentially smaller relative to the problem’s scale. With these two distinctive features, we propose two multi-stage SQAE strategies tailored for hard-to-solve instances. In the first approach, the SQAE algorithm is executed multiple times to identify a set of candidate vertices for the MIS. For each vertex or combinations of vertices in this set, we extract the subgraphs induced by the vertices not adjacent to them. Subsequently, the SQAE algorithm is applied to the subgraph to obtain the (k−1)-IS. In the second multi-stage strategy, we consider each vertex vi in the given graph, generating the subgraph induced by the vertices not adjacent to vi. The SQAE algorithm is then employed to find the (k−1)-IS of the subgraph. Experimental results demonstrate that both multi-stage strategies significantly enhance the performance of the SQAE algorithm for hard-to-solve instances.

Systematic and quantitative testing simulations and theories on phase coarsening by experiments

Microgravity experiments on phase coarsening in solid-liquid mixtures provided an ideal tool to closely and accurately explore the kinetics of phase coarsening because the sedimentation and convective melt flow are eliminated in the International Space Station. In this study, we employed phase-field simulations to systematically investigate the microstructure evolution during phase coarsening at various volume fractions. Simulated microstructure evolution during phase coarsening are compared quantitatively with the microstructure evolution archived from microgravity experiments. Furthermore, kinetics of phase coarsening in Pb-Sn solid-liquid mixtures at various volume fractions is studied theoretically and numerically, which is compared with microgravity experiments. In particular, particle size distribution, relative coarsening rate constants, and scaled maximum particle radii, are predicted from theories, and deduced from microgravity experiments, then calculated from phase-field simulations. This systematic and quantitative study of phase coarsening confirms the consistency to the results from phase-field simulation, microgravity experiments and theories at lower volume fractions, and stimulates more careful microgravity experiments at higher volume fractions (≥0.7).

Research on the thermo-hydro-mechanical coupling simulation and deformation spatiotemporal evolution for the entire process of oil shale in-situ mining

The ground surface deformation (GSD) caused by oil shale in-situ mining poses a threat to land resources and human's lives and property. This study, for the first time, conducts a full stratum simulation of the Fuyu oil shale in-situ pyrolysis pilot base, analyzing the evolution characteristics of the temperature field, stress field, and deformation field of the entire stratum profile during the heating and cooling processes of convective heating mining. Considering the changes in the pore structure, thermophysical, and mechanical properties of the stratum, the environmental geological effects of rock deformation during in-situ mining were identified. Simulation results show that after heating, the temperature within 80 cm of the heating well reaches above the initial pyrolysis temperature of 350 °C for oil shale organic matter, and there is a significant stress concentration near the heat source. In the simulation, ground displacement rises in a wave-like manner during heating, quickly subsides after cooling, and finally stabilizes. Eventually, the entire stratum exhibited subsidence, with a subsidence amount of 0.59 cm. The spatiotemporal deformation trend obtained from SBAS-InSAR real-time monitoring results is similar to the simulation results. By comparing the monitoring results with the simulation results, the synergistic deformation mechanism of underground and ground surface co-deformation during in-situ mining of geochemical reactions in the study area was analyzed. The deformation rate is determined by the thermal hysteresis phenomenon and the temperature difference between the heated fluid and the rock layer. This provides scientific support for the geological effect evaluation of oil shale in-situ mining, which helps to improve mining safety and efficiency.

PEMPS: a phylogenetic software tool to model the evolution of metabolic pathways

BackgroundMetabolic pathways support the enzyme flux that converts input chemicals into energy and cellular building blocks. With a constant rate of input, steady-state flux is achieved when metabolite concentrations and reaction rates remain constant over time. Individual genes undergo mutation, while selection acts on higher level functions of the pathway, such as steady-state flux where applicable. Modeling the evolution of metabolic pathways through mechanistic sets of ordinary differential equations is a piece of the genotype–phenotype map model for interpreting genetic variation and inter-specific differences. Such models can generate distinct compensatory changes and adaptive changes from directional selection, indicating single nucleotide polymorphisms and fixed differences that could affect phenotype. If used for inference, this would ultimately enable detection of selection on metabolic pathways as well as inference of ancestral states for metabolic pathway function.ResultsA software tool for simulating the evolution of metabolic pathways based upon underlying biochemistry, phylogenetics, and evolutionary considerations is presented. The Python program, Phylogenetic Evolution of Metabolic Pathway Simulator (PEMPS), implements a mutation-selection framework to simulate the evolution of the pathway over a phylogeny by interfacing with COPASI to calculate the steady-state flux of the metabolic network, introducing mutations as alterations in parameter values according to a model, and calculating a fitness score and corresponding probability of fixation based on the change in steady-state flux value(s). Results from simulations are consistent with a priori expectations of fixation probabilities and systematic change in model parameters.ConclusionsThe PEMPS program simulates the evolution of a metabolic pathway with a mutation-selection modeling framework based on criteria like steady-state flux that is designed to work with SBML-formatted kinetic models, and Newick-formatted phylogenetic trees. The Python software is run on the Linux command line and is available at https://github.com/nmccloskey/PEMPS.

Cooperation and social organization depend on weighing private and public reputations

To avoid exploitation by defectors, people can use past experiences with others when deciding to cooperate or not (‘private information’). Alternatively, people can derive others’ reputation from ‘public’ information provided by individuals within the social network. However, public information may be aligned or misaligned with one’s own private experiences and different individuals, such as ‘friends’ and ‘enemies’, may have different opinions about the reputation of others. Using evolutionary agent-based simulations, we examine how cooperation and social organization is shaped when agents (1) prioritize private or public information about others’ reputation, and (2) integrate others’ opinions using a friend-focused or a friend-and-enemy focused heuristic (relying on reputation information from only friends or also enemies, respectively). When agents prioritize public information and rely on friend-and-enemy heuristics, we observe polarization cycles marked by high cooperation, invasion by defectors, and subsequent population fragmentation. Prioritizing private information diminishes polarization and defector invasions, but also results in limited cooperation. Only when using friend-focused heuristics and following past experiences or the recommendation of friends create prosperous and stable populations based on cooperation. These results show how combining one’s own experiences and the opinions of friends can lead to stable and large-scale cooperation and highlight the important role of following the advice of friends in the evolution of group cooperation.

Importance of Initial Condition on Bar Secular Evolution: Role of Halo Angular Momentum Distribution Discontinuity

The dark matter halo properties, for example, mass, spin, and concentration, play a significant role in the formation and evolution of bars in disk galaxies. This study highlights the importance of a new parameter: the dark matter halo angular momentum distribution in the disk’s central region. We experiment with N-body galaxy models having a disk and dark matter similar to Milky Way–type galaxies. In these models, we vary the discontinuity of the angular momentum distribution of the halo (the total spin is the same for all models). Our N-body experiments suggest that bar forms in all models after a few Gyr of disk evolution. However, in the secular evolution of the bar, as we evolve these models until 9.78 Gyr, the bar gains its strength in the model with the most continuous halo angular momentum distribution, and the bar loses strength for the most discontinuous halo angular momentum distribution. The secular evolution of the bar suggests that box/peanut/x-shaped bulges similar to those found in the Milky Way disk should be more pronounced in halos with continuous halo angular momentum distributions. This study demonstrates the importance of the initial condition setup of galaxy systems, namely the discontinuity in the dark matter halo angular momentum distribution for a given density distribution, on the bar secular evolution in the disk galaxy simulations. Further, this study helps reconcile the conflicting results of bar secular evolution in a high-spinning halo of the recent literature.

Revisiting the evolution of bow-tie architecture in signaling networks

Bow-tie architecture is a layered network structure that has a narrow middle layer with multiple inputs and outputs. Such structures are widely seen in the molecular networks in cells, suggesting that a universal evolutionary mechanism underlies the emergence of bow-tie architecture. The previous theoretical studies have implemented evolutionary simulations of the feedforward network to satisfy a given input-output goal and proposed that the bow-tie architecture emerges when the ideal input-output relation is given as a rank-deficient matrix with mutations in network link intensities in a multiplicative manner. Here, we report that the bow-tie network inevitably appears when the link intensities representing molecular interactions are small at the initial condition of the evolutionary simulation, regardless of the rank of the goal matrix. Our dynamical system analysis clarifies the mechanisms underlying the emergence of the bow-tie structure. Further, we demonstrate that the increase in the input-output matrix reduces the width of the middle layer, resulting in the emergence of bow-tie architecture, even when evolution starts from large link intensities. Our data suggest that bow-tie architecture emerges as a side effect of evolution rather than as a result of evolutionary adaptation.

Improved prediction of site-rates from structure with averaging across homologs.

Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site-specific rates based on protein structure and a thermodynamic stability model have shown a significant but modest ability to predict empirical site-specific rates calculated from sequence. Models that use detailed atomistic models of protein energetics do not outperform simpler approaches using packing density. We demonstrate that a fundamental reason for this is that empirical site-specific rates are the result of the average effect of many different microenvironments in a phylogeny. By analyzing the results of evolutionary dynamics simulations, we show how averaging site-specific rates across many extant protein structures can lead to correct recovery of site-rate prediction. This result is also demonstrated in natural protein sequences and experimental structures. Using predicted structures, we demonstrate that atomistic models can improve upon contact density metrics in predicting site-specific rates from a structure. The results give fundamental insights into the factors governing the distribution of site-specific rates in protein families.

Warped disk evolution in grid-based simulations

Warped disk evolution in grid-based simulations

Free-energy calculations in condensed matter: from early challenges to the advent of umbrella sampling

The investigation of condensed matter transformations hinges on the precision of free-energy calculations. This article charts the evolution of molecular simulations, tracing their development from the techniques of the early 1960s, through the emergence of free-energy calculations toward the end of the decade, and leading to the advent of umbrella sampling in 1977. The discussion explores the inherent challenges and limitations of early simulational endeavors, such as the struggle with accurate phase-space sampling and the need for innovative solutions like importance sampling and multistage sampling methods. Taken together, the initial hurdles and subsequent adoption of advanced techniques exemplify the leap from analytical methods to effective computational strategies that enabled more reliable simulations. Further analysis of this narrative reveals the methodological breakthroughs as well as the setbacks that transformed the theoretical and practical understanding of condensed matter phenomena. A follow-up study will examine the shifts in free-energy calculations from the late 1970s into the 1980s.

A virtual rodent predicts the structure of neural activity across behaviours.

Animals have exquisite control of their bodies, allowing them to perform a diverse range of behaviours. How such control is implemented by the brain, however, remains unclear. Advancing our understanding requires models that can relate principles of control to the structure of neural activity in behaving animals. Here, to facilitate this, we built a 'virtual rodent', in which an artificial neural network actuates a biomechanically realistic model of the rat1 in a physics simulator2. We used deep reinforcement learning3-5 to train the virtual agent to imitate the behaviour of freely moving rats, thus allowing us to compare neural activity recorded in real rats to the network activity of a virtual rodent mimicking their behaviour. We found that neural activity in the sensorimotor striatum and motor cortex was better predicted by the virtual rodent's network activity than by any features of the real rat's movements, consistent with both regions implementing inverse dynamics6. Furthermore, the network's latent variability predicted the structure of neural variability across behaviours and afforded robustness in a way consistent with the minimal intervention principle of optimal feedback control7. These results demonstrate how physical simulation of biomechanically realistic virtual animals can help interpret the structure of neural activity across behaviour and relate it to theoretical principles of motor control.

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Constraint-preserved numerical schemes with decoupling structure for the Ericksen–Leslie model with variable density

In this paper, first- and second-order numerical schemes are developed for the Ericksen–Leslie model with variable density. The influence of variable density leads to some conflicts between decoupling and unconditional energy stability. We overcome it by special discretization and extra-fractional-step method. The fully decoupled structures are obtained by using the scalar auxiliary variable (SAV) method with only one SAV. This allows only one additional ODE to be solved so that the schemes are computationally cheaper. The non-convex constraint |b|=1 is preserved by rewriting the orientation field with polar coordinates. It also improves computational efficiency because the vector function of the orientation field is replaced by scalar function. We first reformulate the Ericksen–Leslie model as an equivalent new form by introducing a SAV and polar coordinate form of the orientation field. Secondly, two linear schemes and the corresponding unconditional energy stabilities are established. Then, we show the detailed implementations for proving that the schemes are fully decoupled and uniquely solvable. Finally, the convergence rates and energy dissipations are tested by performing some numerical experiments. The evolutionary simulations are also given.

LinG3D: visualizing the spatio-temporal dynamics of clonal evolution.

Cancers are spatially heterogenous, thus their clonal evolution, especially following anti-cancer treatments, depends on where the mutated cells are located within the tumor tissue. For example, cells exposed to different concentrations of drugs, such as cells located near the vessels in contrast to those residing far from the vasculature, can undergo a different evolutionary path. However, classical representations of cell lineage trees do not account for this spatial component of emerging cancer clones. Here, we propose routines to trace spatial and temporal clonal evolution in computer simulations of the tumor evolution models. The LinG3D (Lineage Graphs in 3D) is an open-source collection of routines (in MATLAB, Python, and R) that enables spatio-temporal visualization of clonal evolution in a two-dimensional tumor slice from computer simulations of the tumor evolution models. These routines draw traces of tumor clones in both time and space, and may include a projection of a selected microenvironmental factor, such as the drug or oxygen distribution within the tumor, if such a microenvironmental factor is used in the tumor evolution model. The utility of LinG3D has been demonstrated through examples of simulated tumors with different number of clones and, additionally, in experimental colony growth assay. This routine package extends the classical lineage trees, that show cellular clone relationships in time, by adding the space component to show the locations of cellular clones within the 2D tumor tissue patch from computer simulations of tumor evolution models.

Playful rehearsals of animal subjugation: The naturalization of animal labour in videogames

In videogames, players commonly encounter virtual animals who perform labour for human benefit. Animal labour is not only physical, but increasingly involves labours of bonding and love which invest the player in the animal's liveliness alongside their utility. This article analyses Stardew Valley, interrogating the ways in which the player encounters and builds relationships with labouring virtual animals. It argues that, through these player-animal relationships, the player rehearses orientations towards animal life which take for granted their subjugation. In Stardew Valley, animals express love in ways which not only obscure their subjugated position, relative to humans, in relationships of domination, but also encourage the player to reproduce those relationships on an expanding scale. This is a naturalization of animal subjugation which, in part, justifies real practices of industrial animal agriculture which lead not only to constant cycles of mass animal death, but also contribute to climate disaster.

Study of magnetic relaxation in MHD simulations of energetically different flares

The scenario of magnetic energy dissipation in solar flares due to reconnection merits investigation from the perspective of magnetohydrodynamic (MHD) relaxation. For this purpose, we carry out data-constrained MHD simulations with the EULAG-MHD numerical model for three energetically different flares, identified as B6.4, C4.0, and M1.1 in the GOES scheme. A magnetic field reconstruction in the solar atmosphere using a non-force-free field extrapolation model identifies magnetic null points for the B6.4 and C4.0 flares and a hyperbolic flux tube for the M1.1 flare as primary reconnection sites. The simulated evolution of the magnetofluid exhibits reconnection at these sites—exemplified by the slipping reconnection in the null point topology of the B6.4 flare. An estimation of the dissipated magnetic energy using three different volumes of integration within the computational domain amounts to ≈7%, 16.8%, and 33% of the available free magnetic energy in the simulation of B6.4, C4.0, and M1.1 flares. The angle (θ) between the current density and the magnetic field at the reconnection site decreases by 75.92°, 41.37°, and 40.13°, respectively, implying more alignment. The amount of dissipated magnetic energy in the simulated dynamics of each flare is in concurrence with the general energy relation between the classes of chosen flares. Furthermore, the increase in alignment at the reconnection sites suggests the occurrence of magnetic relaxation locally.

The effects of precursory velocity changes on earthquake nucleation and stress evolution in dynamic earthquake cycle simulations

Seismic velocity changes in earthquake cycles have been observed over a wide range of timescales and may be a good indicator of the onset of future earthquakes. Understanding the effects of precursory velocity changes right before seismic and slow-slip events could potentially elucidate the onset and timing of fault failure. We use numerical models to simulate fully dynamic earthquake cycles in 2D strike-slip fault systems with antiplane geometry, surrounded by a narrow fault-parallel damage zone. By imposing S-wave velocity changes inside fault damage zones, we investigate the effects of these precursors on multiple stages of the seismic cycle, including nucleation, coseismic, postseismic, and interseismic stages. Our modeling results show a wide spectrum of fault slip behaviors including fast earthquakes, slow-slip events, and variable creep. One primary effect of the imposed velocity precursor is on the earthquake nucleation phase, and earlier onset of precursors causes earthquakes to nucleate sooner with a smaller nucleation size that is not predicted by theoretical equations. Furthermore, such precursors affect the nucleation of dynamic earthquakes and slow-slip events. Our results highlight the importance of short- and long-term monitoring of fault zone structures for better assessment of regional seismic hazard.

Late Pleistocene island weathering and precipitation in the Western Pacific Warm Pool

Deciphering past climate variability in the Western Pacific Warm Pool (WPWP), the Earth’s largest heat and moisture centre, is vital for understanding the global climate system. Nevertheless, its long-term evolution remains controversial, largely due to ambiguities in existing proxy interpretations and discrepancies between records. Here, we present a weathering and erosion reconstruction from the WPWP spanning the last 140,000 years, based on the mineralogy and geochemistry of a sediment core from offshore of northern New Guinea. The paleo-weathering reconstruction is consistent with the simulated precipitation evolution on nearby islands, thereby suggesting a close coupling between climate variability and island weathering in a tropical setting. In addition, our combined data-model interpretation of WPWP climate history shows many similarities to the East Asian Summer Monsoon (EASM) variability over orbital timescales. Overall, our study highlights the critical role of precession-paced interhemispheric energy redistribution, via the West Pacific meridional sea-surface pressure gradient, in linking orbital-scale WPWP climate and EASM variability.

On the effective thermal conductivity of gallium-based liquid metal filled elastomers

Gallium based alloys are liquid at ambient temperature, combining both metal-like high thermal conductivity and fluidity characteristics, which contributes to their applications in thermal management, soft robotics, stretchable electronics and etc. A great number of experimental researches have been performed in preparing Ga-based liquid metal filled elastomers (LMFEs) with various constituent systems, processing techniques, droplet morphology and filler sizes in attempt to obtain the best thermal conductive efficiency. The thermal conductive mechanisms in these LMFEs are still lacking a comprehensive understanding up to date, and therefore the present work aimed to conduct a thorough analysis on this topic using both theoretical models and finite element method (FEM) simulations. The predictability of four classic micromechanics models was compared with the experiments and FEM simulations, and their relevant thermal conductive principles were identified from the simulated thermal flux evolution. Some deficiencies of these analytical models are identified and amended by reconsidering their special architectures. This work is favorable for establishing a general and quantitative relationship between the effective thermal conductivity and microstructures for most of the synthesized LMFEs available in the literatures.