Evolutionary Models Research Articles

Modelling chemical clocks. Theoretical evidences of the space and time evolution of [s/α] in the Galactic disc with Gaia-ESO survey

Chemical clocks based on s-process element/α element ratios are widely used to estimate the ages of Galactic stellar populations. However, the s/α versus age relations are not universal, varying with metallicity, location in the Galactic disc, and specific s-process elements. Moreover, current Galactic chemical evolution models struggle to reproduce the observed s/α increase at young ages, particularly for Ba. Our aim is to provide chemical evolution models for different regions of the Milky Way (MW) disc in order to identify the conditions required to reproduce the observed s/H s/Fe and s/α versus age relations. We adopted a detailed multi-zone chemical evolution model for the MW including state-of-the-art nucleosynthesis prescriptions for neutron-capture elements. The s-process elements were synthesised in asymptotic giant branch (AGB) stars and rotating massive stars, while r-process elements originate from neutron star mergers and magneto-rotational supernovae. Starting from a baseline model that successfully reproduces a wide range of neutron-capture element abundance patterns, we explored variations in gas infall/star formation history scenarios, AGB yield dependencies on progenitor stars, and rotational velocity distributions for massive stars. We compared the results of our model with the open clusters dataset from the sixth data release of the Gaia -ESO survey. A three-infall scenario for disc formation aligns better with the observed trends. The models capture the rise of s/α with age in the outer regions but fail towards the inner regions, with larger discrepancies for second s-process peak elements. Specifically, Ba production in the last 3 Gyr of chemical evolution would need to increase by slightly more than half to match the observations. The s-process contribution from low-mass ( 1.1 M_⊙) AGB stars helps reconcile predictions with data but it requires a too-strong increase that is not predicted by current nucleosynthesis calculations, even with a potential i-process contribution. Variations in the metallicity dependence of AGB yields either worsen the agreement or show inconsistent effects across elements, while distributions of massive star rotational velocities with lower velocity at high metallicities fail to improve results due to balanced effects on different elements. The predictions of our model confirm, as expected, that there is no single relationship s/α versus age and that it varies along the MW disc. However, the current prescriptions for neutron-capture element yields are not able to fully capture the complexity of evolution, particularly in the inner disc.

An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.

Variants of uncertain significance (VUS) represent variants that lack sufficient evidence to be confidently associated with a disease, thus posing a challenge in the interpretation of genetic testing results. Here we report an improved method for predicting the VUS of Arylsulfatase A (ARSA) gene as part of the Critical Assessment of Genome Interpretation challenge (CAGI6). Our method uses a transfer learning approach that leverages a pre-trained protein language model to predict the impact of mutations on the activity of the ARSA enzyme, whose deficiency is known to cause a rare genetic disorder, metachromatic leukodystrophy. Our innovative framework combines zero-shot log odds scores and embeddings from the ESM, an evolutionary scale model as features for training a supervised model on gene variants functionally related to the ARSA gene. The zero-shot log odds score feature captures the generic properties of the proteins learned due to its pre-training on millions of sequences in the UniProt data, while the ESM embeddings for the proteins in the ARSA family capture features specific to the family. We also tested our approach on another enzyme, N-acetyl-glucosaminidase (NAGLU), that belongs to the same superfamily as ARSA. Our results demonstrate that the performance of our family models (augmented ESM models) is either comparable or better than the ESM models. The ARSA model compares favorably with the majority of state-of-the-art predictors on area under precision and recall curve (AUPRC) performance metric. However, the NAGLU model outperforms all pathogenicity predictors evaluated in this study on AUPRC metric. The improved AUPRC has relevance in a diagnostic setting where variant prioritization generally entails identifying a small number of pathogenic variants from a larger number of benign variants. Our results also indicate that genes that have sparse or no experimental variant impact data, the family variant data can serve as a proxy training data for making accurate predictions. Attention analysis of active sites and binding sites in ARSA and NAGLU proteins shed light on probable mechanisms of pathogenicity for positions that are highly attended.

Dynamic mechanism and evolutionary game analysis of sports industry service transformation

Service transformation plays a pivotal role in achieving the sustainable development of the sports industry. This study originates from the interactive relationships among sports enterprises, consumers, and regulatory authorities, proposing a logical framework for the service transformation of the sports industry. Furthermore, a three-party evolutionary game model is constructed to explore the strategic evolution and stability conditions under both single-agent and multi-agent scenarios. The primary findings are as follows: (1) Interactive relationships among sports enterprises, consumers, and regulatory authorities exhibit a game dilemma resembling the "prisoner’s dilemma." (2) A positive promotion relationship conducive to the transformation of the sports industry towards a service model is triggered only when at least two stakeholders’ strategic choices surpass a certain threshold. (3) Fiscal subsidies play a facilitating role in encouraging service transformation for sports enterprises but have limited incentives for consumers. Finally, this paper suggests the introduction of competition mechanisms and the establishment of reward and penalty systems, offering decision-making guidance for the service transformation of the sports industry.

An evolutionary game theory analysis on the environmental impact of discharging Fukushima's nuclear wastewater: International stakeholders and strategic dynamics.

On August 24, 2023, Japan controversially decided to discharge nuclear wastewater from the Fukushima Daiichi Nuclear Power Plant into the ocean, initiating intense domestic and global debates. This study employs a mixed-method approach, integrating quantitative evolutionary game theory and qualitative data analysis to explore the strategic dynamics among Japan, other nations, and the Japan Fisheries Association regarding this decision. The data includes international environmental reports and economic statistics, served as the basis for simulating decision-making processes under various legal, economic, and environmental pressures. The evolutionary game theory model is used to predict and analyze three evolutionarily stable strategies (ESS), detailing the transition from the initiation to cessation of wastewater discharge. These strategies highlight the necessity for international cooperation, rigorous scientific research, public education, and effective wastewater treatment methods. This study aims to provide both a theoretical framework and practical guidance to foster a global consensus on nuclear wastewater management, which is vital for marine conservation and sustainable development.

Tripartite Evolutionary Game Analysis for Manufacturing Firms, Service Providers and Consumers in Collaborative Innovation Systems

With global economic growth and intensifying market competition, Chinese manufacturing firms are under pressure to enhance product competitiveness and adapt to changing consumer demands, requiring continuous product innovation and upgrading. This study introduces a tripartite evolutionary game model involving manufacturing firms, service providers and consumers to analyze the strategic stability and the impact of various factors on decision-making within this system. The results indicate that the decisions of these actors are interdependent, with a fair distribution of innovation benefits and costs being key to stimulating innovation enthusiasm and participation. The presence of free-riding behavior affects strategic choices, and a robust system of rewards and penalties can mitigate this problem. The cost of obtaining market information also influences service providers’ participation in innovation, with high costs potentially suppressing their involvement. This research provides insights to guide collaborative innovation efforts during industrial transformation, highlighting the direct impact of service providers and consumers on the success of manufacturing upgrades.

A branching model for intergenerational telomere length dynamics.

We build and study an individual based model of the telomere length's evolution in a population across multiple generations. This model is a continuous time typed branching process, where the type of an individual includes its gamete mean telomere length and its age. We study its Malthusian's behaviour and provide numerical simulations to understand the influence of biologically relevant parameters.

Evolutionary Mechanism of Trust for Public–Private Partnership Projects with Public Participation

Trust plays a pivotal role in sustaining cooperative relationships and serves as the cornerstone of successful cooperation among participants in public–private partnership (PPP) projects. A decline in trust can result in unethical behavior, fractured relationships, and even project failure. Despite its critical importance, there is an insufficiency of research exploring the evolutionary dynamics of trust, particularly from the perspective of public participation. To address this, an evolutionary game model is constructed to analyze the evolution of behavioral strategies and identify their stable state from a dynamic perspective. The model examines the influence of trust levels and public participation on cooperation, revealing that trust asymmetry negatively impacts collaborative outcomes in PPP projects. There is an optimal trust level: trust positively influences cooperation up to a certain peak, beyond which excessive trust becomes detrimental and hinders cooperative outcomes. When trust levels are equal, the selection of behavioral strategies is influenced by the initial willingness of participants. A threshold exists for this initial willingness, above which cooperation is more likely to succeed. Additionally, public participation is found to significantly enhance cooperation. These conclusions advance the theoretical advancements of trust in PPP projects and offer valuable insights for policymakers and stakeholders in addressing trust-related challenges, thereby fostering sustainable collaboration.

Stress responsiveness in a wild primate predicts survival across an extreme El Niño drought.

We know more about the costs of chronic stress than the benefits of the acute stress response-an adaptive response that buffers organisms from life-threatening challenges. As yet, no primate study has empirically identified how the stress response adaptively affects evolutionary fitness. Here, we take advantage of a natural experiment-an El Niño drought-that produced unprecedented mortality for wild white-faced capuchins. Using a reaction norm approach, we provide evidence from primates that a more robust stress response to a challenge, measured using fecal glucocorticoids, predicts a greater likelihood of survival. We show that individuals with greater stress responsiveness to previous droughts later had higher survival across a severe El Niño drought. Evolutionary models need empirical data on how stress responsivity varies in adaptive ways. While we cannot buffer subjects from catastrophic events, we can use them to understand which aspects of the stress response help animals to "weather the storm."

Strategies for shore power facility utilisation under government subsidies: a three-party evolutionary game and simulation analysis

ABSTRACT The global shipping industry has prioritised reducing air pollutants by implementing various policies related to shore power in recent years. Despite these initiatives, both ports and shipping companies exhibit a low willingness to use shore power, resulting in substantial resource waste. Existing multi-party game studies on shore power simulations mostly targeted a static outcomes, lacking insights into dynamic subsidy allocation based on stakeholder interactions. Moreover, studies have often treated the influencing factors of the shipping sector as broad and vague concepts, making it challenging to identify the key determinants affecting stakeholders’ willingness to use shore power comprehensively. This paper aims to addresses these gaps by incorporating specific parameters into an evolutionary game model. By applying the stability theorem of differential equations, we determine the equilibrium points and assess their stability. Simulations are conducted in Matlab to explore the evolution of strategic choices for promoting shore power and evaluate the impact of various factors on ships’ shore power choices. The findings provide insights for enhancing shore power adoption through targeted policies and optimal incentives, thereby contributing to more effective environmental strategies within the shipping industry.

Multi-agent behavioral evolutionary game analysis of digital twin technology adoption in manufacturing enterprises

ABSTRACT Digital twin technology is crucial in breaking through the bottleneck of integrating the physical and information worlds in the manufacturing industry. However, manufacturing enterprises lack sufficient economic motivation and subjective willingness to adopt due to concerns about technological challenges and cost inputs. To elucidate the mechanisms that influence the adoption of digital twin technology, this study establishes an evolutionary game model from a stakeholder perspective, dynamically analyzes the strategy selection and system stability conditions of each participant and conducts numerical simulations. The findings reveal that the adoption process of digital twin technology in manufacturing enterprises occurs in phases. The coefficient of product attribute enhancement emerges as a key factor in promoting adoption. Government financial and consumption subsidies further encourage the adoption of digital twin technology and enhance customer preference for twin products and derivative services. The customer digital collaboration coefficient and demand matching coefficient significantly affect customers’ perceived utility benefits, which in turn influence manufacturing enterprises’ decisions to adopt digital twin technology by converting them into spillover benefits. Finally, this study provides targeted recommendations to various stakeholders to further promote the adoption of digital twin technology.

Dynamic Evolutionary Game of Knowledge-Sharing Willingness Among Enterprises in Cluster Innovation Network: Considering the Network Structure

Knowledge-sharing behavior based on cluster innovation network has become the primary way for enterprises to achieve sustainable innovation. The willingness of enterprises to share knowledge is a dynamic evolutionary game, supplemented by their periodic evaluations of changes in the network structure and strategic optimization based on game payoffs. An evolutionary game model is constructed to determine which network structure enables enterprises to attain the highest willingness to share knowledge. The results show the following: (1) The scale-free network exhibits the most significant influence on enhancing enterprises’ willingness to share knowledge, followed by small-world, random, and regular networks. In addition, the scale-free network demonstrates the slowest rate of evolutionary equilibrium. (2) The more apparent the agglomeration and community structure within a network, the greater the likelihood that enterprises will adopt knowledge-sharing strategy. (3) Compared to the network structure with smaller communities, larger communities can enhance enterprises’ willingness. However, larger communities cannot be substituted by an equivalent number of smaller communities, even if they contain the same number of enterprises. The network evolutionary mechanism will be clarified, contributing to the optimization of network structure and the promotion of knowledge-sharing collaboration among enterprises, which facilitates cluster innovation.

AGN feedback in isolated galaxies with a SMUGGLE multiphase ISM

Abstract Feedback from active galactic nuclei (AGN) can strongly impact the host galaxies by driving high-velocity winds that impart substantial energy and momentum to the interstellar medium (ISM). In this work, we study the impact of these winds in isolated galaxies using high-resolution hydrodynamics simulations. Our simulations use the explicit ISM and stellar evolution model called Stars and MUltiphase Gas in GaLaxiEs (SMUGGLE). Additionally, using a super-Lagrangian refinement scheme, we resolve AGN feedback coupling to the ISM at ∼10-100 pc scales. We find that AGN feedback efficiently regulates the growth of SMBHs. However, its effect on star formation and outflows depends strongly on the relative strengths of AGN vs local stellar feedback and the geometrical structure of the gas disk. When the energy injected by AGN is subdominant to that of stellar feedback, there are no significant changes in the star formation rates or mass outflow rates of the host galaxy. Conversely, when the energy budget is dominated by the AGN, we see a significant decline in the star formation rates accompanied by an increase in outflows. Galaxies with thin gas disks like the Milky Way allow feedback to escape easily into the polar directions without doing much work on the ISM. In contrast, galaxies with thick and diffuse gas disks confine the initial expansion of the feedback bubble within the disk, resulting in more work done on the ISM. Phase space analysis indicates that outflows primarily comprise hot and diffuse gas, with a lack of cold and dense gas.

Historical biogeography and systematics of yellow-bellied toads (Bombina variegata), with the description of a new subspecies from the Balkans

Abstract The Balkan Peninsula hosts a great proportion of Europe’s biodiversity, and this is well illustrated by amphibian richness and endemism. Among them, the yellow-bellied toad Bombina variegata has been a model in ecology and evolution, but several aspects of its phylogeography and taxonomy remain surprisingly poorly understood. In this study, we combine cytochrome b DNA barcoding data (1238 individuals from 355 localities), mitogenome phylogenetics (17.2 kb), gene-based nuclear phylogenetics (3.7 kb from four gene fragments) and multilocus phylogenomics (4759 loci / ~554 kb obtained by double digest Restriction Associated DNA sequencing; ddRAD-seq) to re-assess the diversification of B. variegata, and revisit its nomenclatural history to assign scientific names to phylogeographic lineages. The analyses support four major lineages, one assigned to B. v. variegata (Carpathians and northwestern ranges), one assigned to B. v. pachypus (Apennine Peninsula), and two assigned to B. v. scabra (Dinarides, Hellenides and Balkanides vs. the Rhodope mountains). Spatiotemporal patterns of diversification suggest a role for a Late Miocene marine incursion in the Pannonian Plain (Paratethys) as the initial trigger of divergence, followed by a vicariance event in the Apennines and a “sky island” process of Pleistocene differentiation in the Balkan Peninsula. As it reached the Dinarides during the Late Pleistocene, B. v. variegata potentially hybridized with B. v. scabra and captured its mitochondrial DNA, which resulted in a massive cyto-nuclear discordance across all northwestern European populations. Finally, we show that the two lineages of B. v. scabra significantly differ in morphology and ventral coloration patterns, and describe the Rhodope lineage as a new subspecies.

A Literature Review of Stochastic Modeling for Phylogenetic Comparative Analysis in Trait Evolution

Evolutionary inferences from phylogenetic trees can be modeled stochastically using a range of mathematical frameworks. Among these, stochastic differential equations (SDEs) provide a particularly flexible and powerful approach to capturing the continuous-time dynamics of evolutionary processes. This review summarizes advances in stochastic modeling for trait evolution along a phylogenetic tree, with a focus on stochastic differential equations (SDEs), Gaussian and non-Gaussian processes, and time series models that can be expressed as special cases of general stochastic frameworks, depending on the questions being addressed or the types of data analyzed. We explore current developments and future research directions of stochastic modeling for phylogenetic comparative analysis in trait evolution.

Constraints on the properties of macroscopic transport in the Sun from combined lithium and beryllium depletion

The Sun is a privileged laboratory of stellar evolution, thanks to the quality and complementary nature of available constraints. Using these observations, we are able to draw a detailed picture of its internal structure and dynamics, which forms the basis of the successes of solar modelling. Amongst such observations, constraints on the depletion of lithium and beryllium are key tracers of the required efficiency and extent of macroscopic mixing just below the solar convective envelope. Thanks to revised determinations of these abundances, we may use them in conjunction with other existing spectroscopic and helioseismic constraints to study in detail the properties of macroscopic transport. We aim to constrain the efficiency of macroscopic transport at the base of the solar convective zone (BCZ) and determining the compatibility of the observations with a suggested candidate linked with the transport of angular momentum in the solar radiative interior. We use recent spectroscopic observations of lithium and beryllium abundance and include them in solar evolutionary model calibrations. We test the agreement of such models in terms of position of the convective envelope, helium mass fraction in the convective zone, sound speed profile inversions, and neutrino fluxes. We constrain the required efficiency and extent of the macroscopic mixing at the base of the BCZ, finding that a power-law density with an index, n, between 3 and 6 would reproduce the data, with efficiencies at the BCZ of about $6000; cm^ /s $, depending on the value of n. We also confirm that macroscopic mixing worsens the agreement with neutrino fluxes and that the current implementations of the magnetic Tayler instability are unable to explain the observations.

Coupled modelling and analysis of lubrication-wear evolution of textured piston/cylinder pair in high-pressure common rail pumps

Abstract Surface texturing is an important approach to enhance fluid lubrication performance and reduce friction and wear. Research on the surface texturing of the piston/cylinder pair considering various characteristics such as multi-physical fields and the evolution of friction and wear is still insufficient. The novelty of this work aims to establish a coupled numerical simulation model considering the coupling effect of multiple physical fields, wear of the cylinder bore and surface texture, and determine the effective surface texture parameters to improve the friction performance of the mechanical components of high-pressure common rail pump. The influence of surface texture on friction and wear characteristics and the evolution of lubrication-wear under different geometric cases were in-depth investigated, and the action mechanism of surface texture on lubrication and wear was revealed. Through numerical analysis, optimal parameters were identified that significantly reduce the friction coefficient and wear amount, offering practical insights for mitigating friction and wear in mechanical systems.

Multi-component and multi-phase-field modelling of solidification microstructural evolution in Inconel 625 alloy during laser powder bed fusion additive manufacturing

Multi-component and multi-phase-field modelling of solidification microstructural evolution in Inconel 625 alloy during laser powder bed fusion additive manufacturing

Identifying fracture-controlled resonance modes for structural health monitoring: insights from Hunter Canyon Arch (Utah, USA)

Abstract. Progressive fracturing contributes to structural degradation of natural rock arches and other freestanding rock landforms. However, methods to detect structural changes arising from fracturing are limited, particularly at sites with difficult access and high cultural value, where non-invasive approaches are essential. This study aims to determine how fractures affect the dynamic properties of rock arches, focusing on resonance modes as indicators of structural health conditions. We hypothesize that damage resulting from fracture propagation may influence specific resonance modes that can be identified through ambient vibration modal analysis. We characterized the dynamic properties (i.e., resonance frequencies, damping ratios, and mode shapes) of Hunter Canyon Arch, Utah (USA), using spectral and cross-correlation analyses of data generated from an array of nodal geophones. Results revealed properties of nine resonance modes with frequencies between 1 and 12 Hz. Experimental data were then compared to numerical models with homogeneous and heterogeneous compositions, the latter implementing weak mechanical zones in areas of mapped fractures. All numerical solutions replicated the first two resonance modes of the arch, indicating these modes are insensitive to structural complexity derived from fractures. Meanwhile, heterogenous models with discrete fracture zones succeeded in matching the frequency and shape of one additional higher mode, indicating this mode is sensitive to the presence of fractures and thus most likely to respond to structural change from fracture propagation. An evolutionary crack damage model was then applied to simulate fracture propagation, confirming that only this higher mode is sensitive to structural damage resulting from fracture growth. While examination of fundamental modes is common practice in structural health monitoring studies, our results suggest that analysis of higher-order resonance modes can be more informative for characterizing fracture-driven structural damage.

On the Formation of Planets in the Milky Way’s Thick Disk

Abstract Exoplanet demographic surveys have revealed that close-in (≲1 au) small planets orbiting stars in the Milky Way’s thick disk are ∼50% less abundant than those orbiting stars in the Galactic thin disk. One key difference between the two stellar populations is the time at which they emerged: thick-disk stars are the likely product of cosmic noon (redshift z ∼ 2), an era characterized by high star formation rate, massive and dense molecular clouds, and strong supersonic turbulence. Solving for the background radiation field in these early star-forming regions, we demonstrate that protoplanetary disks at cosmic noon experienced radiation fields up to ∼7 orders of magnitude more intense than in solar neighborhood conditions. Coupling the radiation field to a one-dimensional protoplanetary disk evolution model, we find that external UV photoevaporation destroys protoplanetary disks in just ∼0.2–0.5 Myr, limiting the timescale over which planets can assemble. Disk temperatures exceed the sublimation temperatures of common volatile species for ≳Myr timescales, predicting more spatial homogeneity in gas chemical composition. Our calculations imply that the deficit in planet occurrence around thick-disk stars should be even more pronounced for giant planets, particularly those at wide orbital separations, predicting a higher rocky-to-giant planet ratio in the Galactic thick disk versus thin disk.

Research on accelerating the recycling efficiency of waste batteries for new energy vehicles based on a stochastic evolutionary game model

Although the rapid development of new energy vehicles (NEV) has contributed greatly to China’s carbon emission reduction, it has also brought about a problem that needs to be solved, namely the effective recycling of waste batteries. Existing recycling of waste batteries is plagued by a series of problems such as a single recycling channel, inconsistent recycling standards, lack of recycling technology, rampant irregular recycling enterprises, and low consumer participation. Meanwhile, due to the immaturity of the recycling market, the lack of clarity of existing regulations, and the lack of supervision and management, the above problems are becoming more and more serious. Therefore, to solve these problems, this paper constructs a four-party stochastic evolutionary game model including government regulators, NEV enterprises, third-party recycling enterprises, and consumers. Focus on analyzing the impact of relevant parameters on the choice of strategies by participants, and put forward proposed countermeasures to promote the effective recycling of waste batteries based on the conclusions.