Evolutionary Models Research Articles

The AMBRE Project: Lead abundance in Galactic stars

Context. The chemical evolution of neutron capture elements in the Milky Way is still a matter of debate. Although more and more studies investigate their chemical behaviour, there is still a lack of a significant large sample of abundances of a key heavy element: lead. Aims. Lead is the final product of the s-process nucleosynthesis channel and is one of the most stable heavy elements. The goal of this article is to present the largest catalogue of homogeneous Pb abundances, in particular for metallicities higher than −1.0 dex, and then to study the lead content of the Milky Way. Methods. We analysed high-resolution spectra from the ESO UVES and FEROS archives. Atmospheric parameters were taken from the AMBRE parametrisation. We used the automated abundance method GAUGUIN to derive lead abundances in 653 slow-rotating FGK-type stars from the 368.34 nm Pb I line. Results. We present the largest catalogue of Local Thermodynamic Equilibrium (LTE) and non-LTE lead abundances ever published with metallicities ranging from −2.9 to 0.6 dex and [Pb/Fe] from −0.7 to 3.3 dex. Within this sample, no lead-enhanced Asymptotic Giant Branch (AGB) stars were found, but nine lead-enhanced metal-poor stars ([Pb/Fe] > 1.5) were detected. Most of them were already identified as carbon-enhanced metal-poor stars with enrichments in other s-process species. The lead abundance of 13 Gaia Benchmark Stars are also provided. We then investigated the Pb content of the Milky Way disc by computing vertical and radial gradients and found a slightly decreasing [Pb/Fe] radial trend with metallicity. This trend together with other related ratios ([Pb/Eu], [Pb/Ba], and [Pb/α]) are interpreted thanks to chemical evolution models. The two-infall model closely reproduces the observed trends with respect to the metallicity. It is also found that the AGB contribution to the Pb Galactic enrichment has to be strongly reduced. Moreover, the contribution of massive stars with rather high rotational velocities should be favoured in the low-metallicity regime.

Estimation of Denudation Parameters and River Capture Events From Neural Network Inverse Modeling of River Profiles and Thermo‐ and Geochronology Data

AbstractEarth's topography represents the cumulative effects of tectonics and surface processes modulated by climate and lithology. These factors shape landscapes through time. River profiles can be inverted to estimate the rock uplift histories or lithology‐specific erodibilities. However, river systems are dynamic and evolve in response to spatial and temporal internal dynamics, such as river capture events. Here, we present a modeling framework to infer denudation rates from the inversion of river profiles and thermo‐ and geochronology data. We achieve this by coupling a landscape evolution model and an efficient inverse modeling scheme to infer poorly resolved erosional and tectonic parameters. An application of the approach is presented for the Neckar catchment, southwest Germany, characterized by stark lateral variation in bedrock erodibility and rock uplift, and that have demonstrably undergone multiple river capture events. Different end‐member scenarios are explored in the simulations. First, we test uniform and spatial variability in rock uplift rate and bedrock erodibility, and second, temporal variations in rock uplift rate and base level. Finally, we simulate river capture events by adding upstream sections (drainage area) at specific times and locations within the fluvial network. We find that spatial variation in rock uplift rate is necessary to reproduce the Neckar's river profile while honoring analytical observations. Simulations integrating river captures allow improved river profile predictions of specific tributaries of the Neckar catchment, leading to potentially more realistic erodibility and rock uplift history estimates. The time and location of the capture events determined from the modeling agree with previous estimations from geological evidence.

Equations of State, Thermodynamics, and Miscibility Curves for Jovian Planet and Giant Exoplanet Evolutionary Models

The equation of state of hydrogen–helium (H–He) mixtures plays a vital role in the evolution and structure of gas giant planets and exoplanets. Recent equations of state that account for H–He interactions, coupled with H–He immiscibility curves, can now produce more physical evolutionary models, such as accounting for helium rain with greater fidelity than in the past. In this work, we present a set of tools for planetary evolution that provides a Python interface for existing tables of useful thermodynamic quantities, state-of-the-art H–He equations of state, and pressure-dependent H–He immiscibility curves. In particular, for a collection of independent variable choices, we provide scripts to calculate the variety of thermodynamic derivatives used to model convection and energy transport. These include the chemical potential derived from the internal energy, which is a modeling necessity in the presence of composition gradients when entropy is the other primary variable. Finally, an entropy-based convection formalism is presented and fully described that highlights the physical differences between adiabatic and isentropic interior models. This centralized resource is meant to facilitate both giant planet structural and evolutionary modeling and the entry of new research groups into the field of giant planet modeling. All tables of thermodynamic quantities and derivatives are available at https://github.com/Rob685/hhe_eos_misc, along with a unified Python interface. Tutorials demonstrating the interface are also available in the repository.

Modeling of Carbon Redistribution and Tetragonality Evolution in Supersaturated Ferrite

Modeling of Carbon Redistribution and Tetragonality Evolution in Supersaturated Ferrite

A dynamic evolutionary game to discourage enterprise “greenwashing”

To meet the increasing consumer demand for green products and services, governments have increased their regulation of enterprise production behaviors so that enterprises may choose “greenwashing” to obtain the premium of green products at the lowest cost under government regulation. The increase of greenwashing hinders green innovation by enterprises. To quantify this problem and seek solutions to promote enterprises cleaner production and sustainable development, we established a tripartite evolutionary game model for enterprises, consumers, and governments. Using the model, we analyzed the stability of their strategy combinations through simulations and explored the impact of some parameters on the strategy selection. We found that (1) the combination of enterprises adopting true green, consumers buying green products, and weak government regulation was the most environmentally friendly and low-cost strategy solution in the tripartite game system. (2) An increase in government penalties or subsidies to enterprises, as well as a reduction of the extra cost of producing green products, can improve the probability that enterprises will adopt cleaner production. Based on the results, the government should define reasonable subsidies and penalties, guide enterprises to assume their social responsibility, and stimulate consumer green demand. Enterprises should improve their moral qualities, actively engage in green innovation, and increase investment in green product research to guide the game system towards the ideal state.

Physics-Inspired Evolutionary Machine Learning Method: From the Schrödinger Equation to an Orbital-Free-DFT Kinetic Energy Functional.

We introduce a machine learning (ML)-supervised model function (which is in fact a functional rather than a regular function) that is inspired by the variational principle of physics. This ML hypothesis evolutionary method, termed ML-Ω, allows us to go from data to differential equation(s) underlying the physical (chemical, engineering, etc.) phenomena from which the data are derived from. The fundamental equations of physics can be derived from this ML-Ω evolutionary method when the proper training data is used. By training the ML-Ω model function with only three hydrogen-like atom energies, the method can find Schrödinger's exact functional and, from it, Schrödinger's fundamental equation. Then, in the field of density functional theory (DFT), when the model function is trained with the energies from the known Thomas-Fermi (TF) formula , it correctly finds the exact TF functional. Finally, the method is applied to find a local orbital-free (OF) functional expression of the independent electron kinetic energy functional Ts based on the γTFλvW model. By considering the theoretical energies of only five atoms (He, Be, Ne, Mg, and Ar) as the training set, the evolutionary ML-Ω method finds an ML-Ω-OF-DFT local Ts functional (γTFλvW(0.964,1/4)) that outperforms all the OF-DFT functionals of a representative group. Moreover, our ML-Ω-OF functional overcomes the difficulty of LDA's and some local generalized gradient approximation (GGA)-DFT's functionals to describe the stretched bond region at the correct spin configuration of diatomic molecules. Nonsmooth and nonclosed form functionals can be considered in the ML-Ω model function and still be effectively trained. Although our evolutionary ML-Ω model function can work without an explicit prior-form functional, by using the techniques of symbolic regression, in this work, we exploit prior-form functional expressions to make the training process simpler and faster. The ML-Ω method can be considered at the intersection of ML and the natural sciences.

Evolutionary Game Analysis of Knowledge Sharing among Supply Chain Enterprises Considering Incentive Strategy

This paper aims to explore and enhance the influencing factors which effect the evolution of supply chain knowledge sharing under different incentive strategies. Furthermore, it also helps supply chain managers determine whether incentives are needed and if needed to what extent they are needed to deal with the dynamic changes of factors in the evolution of supply chain knowledge sharing. The evolutionary game model of knowledge sharing without considering incentives is established respectively, and the influence of relevant influencing factors on the evolutionary results of knowledge sharing is analyzed. The results show that enterprises’strategy of selecting knowledge sharing can be attributed to the change by the degree of knowledge complementarity among enterprises when the incentive is not considered. The incentive of supply chain will stimulate knowledge sharing between enterprises but then the incentive coefficient needs to meet certain conditions; can the evolutionary system converge to the state of {knowledge sharing, knowledge sharing}, and the analysis shows that the relationship capital, knowledge’s unit value, knowledge sharing cost, knowledge sharing risk and other factors have significant effects on the evolution rate of knowledge sharing. Finally, based on the research conclusions, the countermeasures and suggestions for supply chain knowledge sharing are proposed.

On the Cosmic Variance of the Merger Rate Density of Binary Neutron Stars

The cosmic variance on the star formation history may lead to bias in the merger rate density estimation of binary neutron star (BNS) mergers by compact binary population synthesis. In this paper, we take advantage of the large box size of the Millennium Simulation combined with the semianalytic galaxy formation model GABE and the parameterized population binary star evolution model to examine how much effect the cosmic variance will introduce on the estimation of the merger rate density of BNS mergers. We find that for subbox sizes of 100 and 200 Mpc, the variance of merger rate density σ R /R at different redshifts is about 23%–35% and 13%–20%, respectively. On the one hand, as for the variance of the detection rate on BNS mergers with the current LIGO–Virgo–KAGRA (LVK) detector network, this value is very small at ≲10%, which indicates ignoring the cosmic variance is reasonable for estimating the merger rate density from current LVK observations. On the other hand, with next-generation gravitational wave detectors, it is possible to localize BNS mergers within subboxes possessing a length of 40 Mpc for a source redshift z s < 0.2. In such a small box, the cosmic variance of the merger rate density is significant, i.e., the value of σ R /R is about ∼55%. This hints that estimating the merger rate density of BNS in different sky areas may provide useful information on the cosmic variance.

Topographic metrics for unveiling fault segmentation and tectono-geomorphic evolution with insights into the impact of inherited topography, Ulsan Fault Zone, South Korea

Abstract. Quantifying today's topography can provide insights into landscape evolution and its controls, since present topography represents a cumulative expression of past and present surface processes. The Ulsan Fault Zone (UFZ) is an active fault zone on the southeastern Korean Peninsula that was reactivated as a reverse fault around 5 Ma. The UFZ strikes NNW–SSE and dips eastward. This study investigates the relative tectonic activity along the UFZ and the landscape evolution of the hanging-wall side of the UFZ, focusing on neotectonic perturbations using 10Be-derived catchment-averaged denudation rates and bedrock incision rates, topographic metrics, and a landscape evolution model. Five geological segments were identified along the fault, based on their relative tectonic activity and fault geometry. We simulated four cases of landscape evolution to investigate the geomorphic processes and accompanying topographic changes in the study area in response to fault movement. Model results reveal that the geomorphic processes and the patterns of topographic metrics (e.g., χ anomalies) depend on inherited topography (i.e., the topography that existed prior to reverse fault reactivation of the UFZ). On the basis of this important model finding and additional topographic metrics, we interpret the tectono-geomorphic history of the study area as follows: (1) the northern part of the UFZ has been in a transient state and is in topographic and geometric disequilibrium, so this segment underwent asymmetric uplift (westward tilting) prior to reverse faulting on the UFZ around 5 Ma, and (2) its southern part was negligibly influenced by the asymmetric uplift before reverse faulting. Our study demonstrates the utility of topographic metrics as reliable criteria for resolving fault segments. Together with landscape evolution modeling, topographic metrics provide powerful tools for examining the influence of inherited topography on present topography and for the elucidation of tectono-geomorphic histories.

Decision Analysis of Game Behavior for the Fresh Food Industry: Potential Entrants, Incumbents and Consumers

This paper conducts a decision analysis of game behavior for the fresh food industry, including potential entrants, incumbents and consumers. Potential entrants are the key research object in the analysis of game behavior. The Stackelberg model, considering the uncertainty of market, consumer preference, freshness and greenness of fresh products, is used to analyze the best entry strategies for potential entrants. This paper then establishes an evolutionary game model to further analyze the most optimal entry mode of potential entrants. The combination of two game models can help potential entrants determine the optimal price, output, product freshness, greenness and sales channels.

Tectonic Anatomy Reveals the Chinese Altai‐Kuerti‐Dulate Arc Amalgamation in the Southern Altaids

AbstractThe Erqis tectonic belt, situated at the junction of the Chinese Altai‐East Junggar, originated through the subduction of the Ob‐Zaisan Ocean, playing a pivotal role in unraveling the tectonic evolution of the southern Altaids. Tectonic and provenance analyses discern three distinct arcs: the Chinese Altai in the north exhibits a protracted history from the late Cambrian to early Permian with a slender accretionary complex (AC) termed the Supute AC; the Kuerti intra‐oceanic arc in the middle emerged during the Silurian to Devonian coevally with a minor AC known as the Tesibahan AC; and the Dulate arc in the south predominantly evolved from the Middle Devonian to Permian, giving rise to the Fuyun AC that independently developed on its northern margin at least until ∼273 Ma. Our findings indicate the existence of multiple arcs within the Ob‐Zaisan Ocean, forming an archipelago paleogeography in the Paleo‐Asian Ocean. Provenance studies lead us to propose that cryptic sutures demarcating the Chinese Altai, Kuerti, and Dulate lie approximately along the Kuerti and Tesibahan faults, respectively, and that the oceanic branches between these arcs subducted northward beneath the Chinese Altai and Kuerti arcs and southward beneath the Dulate arc. Additionally, our work demonstrates the closure of the Ob‐Zaisan Ocean most probably postdates ∼273 Ma. Combining our data with previous research, we present a novel tectonic evolution model, elucidating several arc amalgamations with multiple subduction polarities between Chinese Altai and East Junggar throughout the late Cambrian to Permian.

Stochastic Evolutionary Analysis of an Aerial Attack–Defense Game in Uncertain Environments

Aiming at the problem of random environment interference in the process of strategy interaction and the behavioral evolution of an aerial attack–defense game, this paper considers the influence of the difference in the performance and value between both game players in terms of strategy evolution; explores the randomness of the complex battlefield environment, the uncertainty of the behavioral state of game players, and the limitations of the emergent situation; constructs a mathematical model of the stochastic evolution of an aerial-coordinated attack–defense game in uncertain environments; and studies the stability of the strategy interaction and behavioral decision-making process of both players of the aerial attack–defense game. Simulation results show that many factors of the performance and value between both game players have a greater impact on the strategy evolution trend in both game players, which not only causes changes in the results of the strategy selection but also affects the rate of strategy evolution for the game players. In addition, random environmental factors cause a certain degree of interference to the strategy evolution process of the game players, which usually accelerates the game players’ strategy evolution rate and greatly affects the evolution process of the game players’ strategy. This study can provide a theoretical basis and feasible reference for improving mission decision-making, response mechanisms, and system modeling of an aerial attack–defense game, which has important theoretical value and practical significance.

An Evolutionary Analysis of Enterprise Innovation Behavior Based on Organizational Routines

Routine innovation can help enterprises eliminate old routines that do not adapt to the environment, introduce new routines with high efficiency, and improve the adaptability of enterprises and the environment. Based on this, this paper constructs an evolutionary game model between enterprises and employees, explores the game relationship between enterprise innovation incentives and employee practice innovation, and uses simulation to analyze the influence of different parameters on the evolution path of both parties in the game. The results show that there are two kinds of evolutionary stability strategies, and the final result depends on the parameter values in the payoff matrix. The relationship between income and cost is the decisive factor affecting enterprises and employees' strategy choices. One effective measure to promote enterprise innovation is the third-party reward. Establishing reasonable revenue distribution mechanisms is conducive to improving employees' innovation initiatives. This paper contributes to the advancement of research in the field of routine innovation and broadens the use of organizational routines in corporate governance.

Tracing the History of Obscured Star Formation with the SIMBA Cosmological Galaxy Evolution Simulation

We explore the cosmic evolution of the fraction of dust-obscured star formation predicted by the simba cosmological hydrodynamic simulations featuring an on-the-fly model for dust formation, evolution, and destruction. We find that up to z = 3, our results are broadly consistent with previous observational results of little to no evolution in obscured star formation. However, at z > 3 we find strong evolution at fixed galaxy stellar mass toward greater amounts of obscured star formation, in tension with high-redshift observations. We explain the trend of increasing obscuration at higher redshifts by evolving star-dust geometry, as the dust-to-stellar mass ratios remain relatively constant across cosmic time. We additionally see that at a fixed redshift, more massive galaxies have a higher fraction of their star formation obscured, which is explained by increased dust-to-stellar mass ratios at higher stellar masses. Finally, we estimate the contribution of dust-obscured star formation to the total star formation rate budget and find that the dust-obscured star formation history peaks around z ∼ 2−3, and becomes subdominant at z ≳ 5. The dominance of obscured star formation at redshifts z ≲ 4 is consistent with our results for the evolution of the obscured star formation fraction at fixed stellar mass to higher values at higher redshift because there exist fewer massive, heavily obscured galaxies at high redshift.

Flat silk cocoons: A candidate material for fabricating lightweight and impact-resistant composites

The utilization of silk cocoons in the production of lightweight and tough composites has been gaining increasing attention. However, the limited applications of normal silk cocoons (NSC) are attributed to their small size and irregular shape. To overcome this deficiency, flat silk cocoons (FSC) with a similar structure and controllable size were prepared. Next, we systematically characterized and compared the microstructures, morphologies, compositions, thermal properties, and mechanical properties of FSC with NSC. Subsequently, FSC was successfully utilized to fabricate a novel silk fibroin fiber reinforced sericin matrix composite (HPFSC) using a hot pressing method, followed by the analysis of its microstructure evolution, mechanical properties, failure modes, and theoretical modeling. This composite has outstanding mechanical properties including hardness, modulus, and strength. HPFSC has a relatively low density of ~1.3 g/cm3, whose absorbed impact energy can reach a maximum value of 11.1 J/mm, exceeding that of most engineering materials, such as aluminum alloy, ceramics, glass, and carbon fiber composites. The exceptional performance of HPFSC can be attributed to the reduced porosity, enhanced bonding between silk fibroin fibers facilitated by sericin, and their structural transformation. This study offers valuable guidance for the fabrication of lightweight and impact-resistant composites using flat silk cocoons.

Flow-based Generative Emulation of Grids of Stellar Evolutionary Models

We present a flow-based generative approach to emulate grids of stellar evolutionary models. By interpreting the input parameters and output properties of these models as multidimensional probability distributions, we train conditional normalizing flows to learn and predict the complex relationships between grid inputs and outputs in the form of conditional joint distributions. Leveraging the expressive power and versatility of these flows, we showcase their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters. In addition, we describe a simple Bayesian approach for estimating stellar parameters using these flows and demonstrate its application to asteroseismic data sets of red giants observed by the Kepler mission. By applying this approach to red giants in open clusters NGC 6791 and NGC 6819, we illustrate how large age uncertainties can arise when fitting only to global asteroseismic and spectroscopic parameters without prior information on initial helium abundances and mixing length parameter values. We also conduct inference using the flow at a large scale by determining revised estimates of masses and radii for 15,388 field red giants. These estimates show improved agreement with results from existing grid-based modeling, reveal distinct population-level features in the red clump, and suggest that the masses of Kepler red giants previously determined using the corrected asteroseismic scaling relations have been overestimated by 5%–10%.

The erosion of large primary atmospheres typically leaves behind substantial secondary atmospheres on temperate rocky planets

Exoplanet exploration has revealed that many—perhaps most—terrestrial exoplanets formed with substantial H2-rich envelopes, seemingly in contrast to solar system terrestrials, for which there is scant evidence of long-lived primary atmospheres. It is not known how a long-lived primary atmosphere might affect the subsequent habitability prospects of terrestrial exoplanets. Here, we present a new, self-consistent evolutionary model of the transition from primary to secondary atmospheres. The model incorporates all Fe-C-O-H-bearing species and simulates magma ocean solidification, radiative-convective climate, thermal escape, and mantle redox evolution. For our illustrative example TRAPPIST-1, our model strongly favors atmosphere retention for the habitable zone planet TRAPPIST-1e. In contrast, the same model predicts a comparatively thin atmosphere for the Venus-analog TRAPPIST-1b, which would be vulnerable to complete erosion via non-thermal escape and is consistent with JWST observations. More broadly, we conclude that the erosion of primary atmospheres typically does not preclude surface habitability, and frequently results in large surface water inventories due to the reduction of FeO by H2.

An empirical isochrone archive for nearby open clusters

Context. The ages of star clusters and co-moving stellar groups contain essential information about the Milky Way. Their special properties and placement throughout the galactic disk make them excellent tracers of galactic structure and key components to unlocking its star formation history. Yet, even though the importance of stellar population ages has been widely recognized, their determination remains a challenging task often associated with highly model-dependent and uncertain results. Aims. We propose a new approach to this long-standing problem, which relies on empirical isochrones of known clusters extracted from high-quality observational data. These purely observation-based data products open up the possibility of relative age determination, free of stellar evolution model assumptions. Methods. For the derivation of the empirical isochrones, we used a combination of the statistical analysis tool principal component analysis for preprocessing and the supervised machine learning method support vector regression for curve extraction. To improve the statistical reliability of our result, we defined the empirical isochrone of a color-magnitude diagram (CMD) of a cluster as the median calculated from a set of nboot = 1000 curves derived from bootstrapped data. The algorithm requires no physical priors, is computationally fast, and can easily be generalized over a large range of CMD combinations and evolutionary stages of clusters. Results. We provide empirical isochrones in all Gaia DR2 and DR3 color combinations for 83 nearby clusters (d &lt; 500 pc), which cover an estimated age range of 7 Myr to 3 Gyr. In doing so, we pave the way for a relative comparison between individual stellar populations based on an age-scaling ladder of empirical isochrones of known clusters. Furthermore, due to the exceptional precision of the available observational data, we report accurate lower main sequence empirical isochrones for many clusters in our sample, which are of special interest as this region is known to be especially complex to model. We validate our method and results by comparing the extracted empirical isochrones to cluster ages in the literature. We also investigate the added information that empirical isochrones covering the lower main sequence can provide on case studies of the IC 4665 cluster and the Meingast 1 stream. Conclusions. The archive of empirical isochrones offers a novel approach to validating age estimates and can be used as an age-scaling ladder or age brackets for new populations and serve as calibration data for further constraining stellar evolution models.

Emergent time scales of epistasis in protein evolution

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis.

Experimental study on tribological behavior of a binderless cemented carbide at high contact stress

Knowledge of frictional evolution and associated damage mechanism during sliding wear conditions using binderless WC-based cemented carbide is lacking. In this study, the frictional evolution and corresponding transformation of microstructure, and wear mechanisms of Al2O3/WC-based cemented carbide due to the effect of different contact pressure, especially high pressure have been explored using a reciprocating ball-on-flat sliding wear tester, which was experimentally simulated following the ASTM G133–02 standard. The dynamic curves in friction coefficient with the sliding time were described. The microscopic 3D topography of contact surface was obtained by the MFP-3D atomic force microscope. The material removal and the wear rate were discussed. Worn surface morphologies at different moments, cross-sectional images of wear tracks at different loads, and wear debris were taken by field emission scanning electron microscope. The results suggested that the pressure plays a decisive role in frictional evolution and wear characteristics. Two models of frictional evolution were declared. A novel “surge” phenomena in friction coefficient was found and explained at high contact pressure. Wear transition from mild wear to severe wear was confirmed. More than one wear mechanism was observed, including micro-cutting (polishing), generation and propagation of cracks, cracking-induced spalling, plastic deformation, and the formation of tribolayer.