Evolution Rules Research Articles

Shear deformation characterization and normalized method of tricot-stitched unidirectional non-crimp fabric

Currently, the shear deformation of unidirectional non-crimp fabric (UD-NCF) is investigated sparsely, especially for the normalized method. Thus, this work systematically explored the shear behavior of UD-NCF by the off-axis extension test under different off-axis angles. The evolution rule of macro shear mechanical behavior was discussed and the effect mechanism of off-axis angle on the extension force degree and its standard deviation was revealed. Then, by analyzing the fabric morphology and deformation contour, the supporting yarn with the relatively large linear density will affect the shear deformation of UD-NCF and induce the meso buckling defect of main yarn. Most importantly, the developed normalized shear force methods suitable for the UD-NCF were derived, and the difference of normalized shear force among developed and reported methods was discussed and identified.

Geo-temperature response to reinjection in sandstone geothermal reservoirs

To study the evolution rules and behaviors of heat transport in a sandstone geothermal reservoir caused by cooled water reinjection, this research focuses on the quantitative relationship among reinjection parameters and the thermal breakthrough time of production wells. The permeation, tracer, and reinjection tests were conducted in a simulation model using a large sand tank in conjunction with the numerical simulation method based on COMSOL Multiphysics. Subsequently, sensitivity analysis and nonlinear fitting were performed to investigate the effects of fluid viscosity and density on the reinjection process, and to analyze the impact of reinjection parameters on the thermal breakthrough time of production wells, along with their underlying mechanisms and law. The results indicate that the migration velocity of reinjection water is greater in coarse sand layer compared to that in medium sand layer, and the thermal breakthrough time t is linearly correlated with reinjection rate (Q) raised to the power of − 0.85, temperature difference (ΔT) raised to the power of − 0.21, and spacing between the production and reinjection wells (R) raised to the power of 1.4. The correlation equation and analysis show that when the temperature difference between production and reinjection ΔT is more than 30 ℃, the influence of ΔT on the thermal breakthrough time of production well becomes weak, because ΔT exerts an effect on the thermal breakthrough time of production well t by influencing the relative position of the 18.5 ℃ isotherm in the temperature transition region. The error in reinjecting high-temperature fluid into low-temperature fluid may be corrected by introducing a viscosity correction coefficient αμ.

Dimensional reduction of dynamical systems by machine learning: automatic generation of the optimum extensive variables and their time-evolution map

A framework is proposed to generate a phenomenological model that extracts the essence of a dynamical system (DS) with large degrees of freedom using machine learning. For a given microscopic DS, the optimum transformation to a small number of macroscopic variables, which is expected to be extensive, and the rule of time evolution that the variables obey are simultaneously identified. The utility of this method is demonstrated through its application to the nonequilibrium relaxation of the three-state Potts model.

Evolutionary Phases of Big History

The present article is devoted to the issue of unity of laws, patterns and mechanisms of evolution at all its stages and levels of Big History and megaevolution. Despite the enormous differences between cosmic, planetological, chemical, biological, and social evolutions, there are many similarities. There are a lot of important and insightful works on the development of complexity in our Universe and in the course of Big History. Unfortunately, much less studies are devoted to analysis of universal similarities, patterns and rules within Big History. Mostly such research is focused on a few laws of Big History which are usually connected with development of complexity. However, laws in terms of the typological similarity of many patterns and rules in star-galaxy, planetological, chemical, biological and social phases of Big History, are of great importance. In the present article we will consider a number of such important similarities, which, in our opinion, clearly demonstrate the systemic-structural and functional-evolutionary unity of the world at its different levels and in different areas. The understanding of these similarities deepens our perception about all stages of Big History and its regularities, and leads us away from the false idea that social evolution in all aspects is different from the evolution of previous levels. In the first section our key goal is to give our own definitions of evolution which would cover as many variants of evolutionary changes as possible. In the second section we will try to give a rather voluminous and dialectical picture of the unfolding universal evolution instead of a short scheme: cosmic – biological – social. The notions of main and transitional phases of Big History are introduced; and the importance of its planetary and chemical phases is shown. In the third section we will show that one can reveal a number of similarities at all levels and phases of megaevolution, which can be generalized in universal laws, rules, mechanisms, patterns and principles of evolution. One should note that in fact none of the important laws and principles, not any of the important rules of evolution, have been ‘lost’ in the process of transition from lower to higher levels. They were only modified and became more complicated, and there also appeared some new principles and rules (and in retrospect one can see their rudiments at the lowest levels of evolution). Some of these laws and rules are described in this section. In the fourth section we will try to present some evolutionary and philosophical ideas that explain the profound similarity in the laws and patterns of megaevolution at all its levels and phases. In the conclusion we will discuss evolutionary and non-evolutionary matters.

A Two-Stage Evolutionary Game Model for Collaborative Emergency Management Between Local Governments and Enterprises

Enterprises play a vital role in emergency management, but few studies have considered the strategy choices behind such participation or the collaborative relationship with the government. This study contended that enterprises have at least three strategies regarding emergency management: non-participation, short-term participation, and long-term participation. We constructed a two-stage evolutionary game model to explore the behavioral evolution rules and evolutionary stability strategies of the government and enterprises, and employed numerical simulation to analyze how various factors influence the strategy selection of the government and enterprises. The results show that if and only if the utility value of participation is greater than 0, an enterprise will participate in emergency management. The evolutionary game then enters the second stage, during which system stability is affected by a synergistic relationship between participation cost, reputation benefit, and government subsidies, and by an incremental relationship between emergency management benefit, government subsidies, and emergency training cost. This study provides a new theoretical perspective for research on collaborative emergency management, and the results provide important references for promoting the performance of collaborative emergency management.

An evolutionary game for the behavior of third-party evaluators in pension public–private partnership incorporating public participation

This study analyzes the impact of public participation on the choice of third-party evaluators' behavior strategies during the service quality supervision process of China's pension public–private partnership project. An evolutionary game model between third-party evaluators and government regulators is developed, wherein the evolution rule of the two sides and public participation’s influence on their behavior under the two different conditions are analyzed, and a numerical simulation is carried out using MATLAB 2016a. It is found that third-party evaluators may choose the false evaluation strategy without public participation because of the inducement of rent-seeking or insufficient government punishment when the regulatory revenue of the government regulatory agencies is less than the regulatory cost. In contrast, in the case of public participation, the true evaluation strategy is chosen with an improvement in the level of public participation or an increase in reputation incentive. This suggests the construction and improvement of a third-party evaluation system, which shows that the construction of the service quality supervision system in China’s pension PPP project has a large operating space.

Picosecond laser-induced evolution of processing properties of 4H–SiC

The irradiation threshold of 4H–SiC under picosecond laser action was calculated, and the influence of laser parameters on the size of 4H–SiC was studied. According to the model, the evolution rule of the microgroove size was predicted. The effect of laser parameters on the mechanical properties of 4H–SiC was studied by nanoindentation. The results show that the ablation threshold energy density of the picosecond laser on 4H–SiC is 2.67 J/cm2. The effect regular of laser parameters on the size of 4H–SiC microgrooves: energy density > scanning speed > scanning interval. The error of the microgroove which is between the predicted value and measurements is within 10 %. By doing nanoindentation detection, proving that Pop-in is the elastic-plastic transition point. It shows that laser irradiation has a significant modification effect on the mechanical properties of 4H–SiC. According to the difference range of elastic modulus and hardness, showing that the influence of laser energy density on mechanical properties is dominant. The consequences of the study provide a theoretical foundation for subsequent CMP processing.

Ensemble Learning with Supervised Methods Based on Large-Scale Protein Language Models for Protein Mutation Effects Prediction.

Machine learning has been increasingly utilized in the field of protein engineering, and research directed at predicting the effects of protein mutations has attracted increasing attention. Among them, so far, the best results have been achieved by related methods based on protein language models, which are trained on a large number of unlabeled protein sequences to capture the generally hidden evolutionary rules in protein sequences, and are therefore able to predict their fitness from protein sequences. Although numerous similar models and methods have been successfully employed in practical protein engineering processes, the majority of the studies have been limited to how to construct more complex language models to capture richer protein sequence feature information and utilize this feature information for unsupervised protein fitness prediction. There remains considerable untapped potential in these developed models, such as whether the prediction performance can be further improved by integrating different models to further improve the accuracy of prediction. Furthermore, how to utilize large-scale models for prediction methods of mutational effects on quantifiable properties of proteins due to the nonlinear relationship between protein fitness and the quantification of specific functionalities has yet to be explored thoroughly. In this study, we propose an ensemble learning approach for predicting mutational effects of proteins integrating protein sequence features extracted from multiple large protein language models, as well as evolutionarily coupled features extracted in homologous sequences, while comparing the differences between linear regression and deep learning models in mapping these features to quantifiable functional changes. We tested our approach on a dataset of 17 protein deep mutation scans and indicated that the integrated approach together with linear regression enables the models to have higher prediction accuracy and generalization. Moreover, we further illustrated the reliability of the integrated approach by exploring the differences in the predictive performance of the models across species and protein sequence lengths, as well as by visualizing clustering of ensemble and non-ensemble features.

Temperature-dependent behavior of CP-Ti interpreted via self-consistent crystal plasticity simulation

A commercially-pure titanium sample was investigated using an elasto-visco-plastic self-consistent polycrystal model. Temperature-dependent plastic deformation behavior was described by a dislocation density-based hardening model. The model reasonably reproduced various experimental behaviors, including stress vs. strain curves, diffraction lattice strains, twin volume fraction, and crystallographic texture. The temperature-dependent behavior was interpreted based on the dislocation evolution rules comprised of dislocation generation and recombination in accordance with the Kocks-Mecking rule. The modeling results successfully provided an in-depth interpretation of mechanical behavior of α-titanium under a wide range of temperatures.

Sintering mechanism of TiB2 reinforced Ti(C,N)-based cermet during reaction spark plasma sintering

TiB2 reinforced Ti(C,N)-based cermet was produced by the reaction spark plasma sintering of Co, Ni, Ti, C, BN, Mo2C, WC, and TaC mixtures. To understand the mechanism of reaction sintering, systematic investigation on the microstructure evolution and displacement variation rule was carried out. In addition, the influence of C/BN molar ratio on the microstructures and performances of cermet was investigated. Except an initially increased pressure, three densification patterns were found. The first method was the CoTi, TiNi, and Ti-BN diffusion reactions, resulting in a shrinkage of 22.06%. The second pattern was the viscous flow and dissolution-reprecipitation, which corresponded to the synthesis of Ti(C,N) and TiB2. In this process, the shrinkage ratio was increased by 35.62%. The final densification mainly originated from the viscous flow of metallic binder. It contributed to the transformation of macrovoids into microvoids, and an increased displacement of 30.10%. With the increasing of C/BN ratio, more C atoms diffused into Ti(C,N). Furthermore, core/rim structure-Ti(C,N) and coreless (W,Mo,Ta,Ti)(C,N) were fabricated. However, the microvoids in cermets enhanced. As a result, the hardness and fracture toughness first increased and then decreased. Results from this work are helpful to understand the reaction spark plasma sintering technique, and can enrich the preparation method of cermet.

Super/sub-loading surface model for constitutive equation of friction

In this study, an elasto–plastic constitutive model of friction incorporating the superloading surface concept was formulated as an extension of the subloading friction model. The proposed model regards the state in which the static friction coefficient is larger than the kinetic friction coefficient as the “bulkiness” of structure and the smooth transition of friction was expressed by the evolution rule of structure.The positive definiteness of the plastic multiplier, the loading condition, and a condition equation for material parameters were clearly derived. The capability of the proposed model in accordance with the subloading-friction model was confirmed in the stick–slip simulation of a mass-spring system.

A traffic-fractal-element-based congestion model considering the uneven distribution of road traffic

The uneven distribution of road traffic has been proven to play a crucial role in congestion evolution within urban transportation networks. Establishing congestion models to explore congestion evolution under its impact is of great significance for predicting congestion. Current congestion models commonly describe the uneven distribution of traffic on roads through continuous differential equations or the simulation of vehicle operational rules by which to model traffic flow dynamics. However, these models fail to directly reveal the mechanism by which the uneven distribution characteristics affect congestion, which is not conducive to uncovering the fundamental factors that affect congestion evolution and general laws for predicting congestion. Therefore, this paper first constructs traffic fractal elements (TFE) based on the fractal characteristics exhibited by uneven traffic distribution. A local parameter namely the load duty ratio (LDR) is introduced to represent the uneven distribution level of TFE. By employing the fractal interpolation function (FIF), we model road traffic as the iterations of TFE. Then we define the fractal evolution rule and the load transfer rule to inscribe the influence relationship between LDR of TFE and global congestion, ultimately establishing a TFE-based congestion model. Furthermore, the proposed TFE-based model is validated using empirical data from AMAP and simulation data from a calibrated Cellular Automata (CA) model. Finally, by comparing the results with a CA-based congestion model, the LDR of TFE is confirmed to be a comprehensive and indicative parameter affecting congestion evolution. By simulating and fitting the congestion evolution data under different LDRs, our proposed model demonstrates its value in congestion prediction and analysis.

A Multi-Scenario analysis method for collapse risks due to initial local failure of building structures

Traditional progressive failure analysis is usually computationally expensive and can only be used to analyze a limited number of cases. The resulting mechanical data on structural components are not suitable for displaying collapses or providing an intuitive reference for emergency management. In addition, numerical models do not incorporate uncertain factors in failure development, which makes it difficult to deduce multiple scenarios of future. This paper proposes a multi-scenario progressive failure analysis method based on a sub-region model for emergency management. Firstly, the material model is augmented with a possibility factor to evaluate the failure of components based on internal forces, and thereby deduce different progressive collapse scenarios. Secondly, the building structure is divided into several sub-regions based on the damage correlation of components, so that the degree of regional collapse can be demonstrated at a more macroscopic scale, which is more suitable for emergency management. By analyzing typical failure scenarios, the collapse evolution rules at the sub-region level can be summarized, which can be used for deducing the results of multiple collapse scenarios rapidly. The proposed method can identify the weak positions of buildings, deduce highly probable progressive damage scenarios, and exhibit macro collapse processes. The multi-scenario analysis results can serve as a reference for collapse safety management in risk pre-assessment and help to reconstruct the collapse process in post-disaster analysis.

A dynamic model of the packed dehumidifier

Liquid desiccant dehumidification, a clean energy technique for creating comfortable environments has received a lot of interest. Several dynamic models have been proposed to predict the outlet air humidity ratio and temperature, however, a study on presenting variations of heat and mass transfer coefficients and thermal mass of desiccant during the dynamic dehumidification process was not encountered according to authors’ best knowledge. This gap is primarily due to the complex nature of the coupled heat and mass transfer processes, which make it challenging to capture the evolution of heat and mass transfer coefficients over time. In this study, variations of these parameters are analyzed at first. A theoretical study is conducted to show the thermal mass of the desiccant can be considered constant when changing the inlet air humidity ratio or desiccant temperature, simplifying the dynamic modeling process. While the variation of heat and mass transfer coefficients show significant impact in such conditions. Subsequently, a new method based on mass conservation is provided to determine the thermal mass of desiccant. By examining the cause-and-effect relationships between heat and mass transfer coefficients and outlet air humidity ratio, the evolution of heat and mass transfer coefficients following exponential laws is deduced by inversion. Then a dynamic model of packed dehumidifiers can be completed easily based on above derivations, and its viability is confirmed by experimental results. Compared to the existing method, the prediction accuracy can improve by 50% based on the proposed methods. This study characterizes evolution rules of heat and mass transfer coefficients over time, and with this, evolutions of outlet air parameters during the dynamic dehumidification process can be acquired conveniently based on the existing steady dehumidification model.

A Small-Deformation Rate-Independent Continuous-Flow Model for Elasto-Plastic Frames Allowing Rapid Fatigue Predictions in Metallic Structures

Fatigue analysis in metallic frame structures can be challenging due to associated computational costs; if localized plasticity is involved, then the approach of three-dimensional (3D) continuum plasticity models for direct computation of stresses will be infeasible for the analysis of cyclic loading that would need to be modeled in medium- to high-cycle fatigue and vibratory fatigue applications. This difficulty is particularly accentuated in architected structures, for which high-resolution 3D finite element analysis (FEA) would be prohibitively expensive. In this work, we propose an alternative approach based on the use of novel elasto-plastic frame model with continuous flow (i.e. no sharp yield function) for modeling 3D frame and lattice structures. Rather than splitting the strains (as is done in classical plasticity) we split the deformation measures, extension, curvature and twist, into elastic and plastic components and postulate a rate type evolution rule for the plastic variables in terms of the stress resultants (axial force, bending moment, and torque). The combination of structural models together with the use of elasto-plastic operator split to solve the resulting boundary value problem allows for much faster determination of localized plasticity than continuum models can provide. The use of a continuous transition from elastic to rate-independent plasticity (as opposed to an abrupt change with classical plasticity models) allows us to capture localized microplasticity and determine resulting fatigue progression using a cycle-count-free, plastic work-based approach, formulated in terms of the curvatures and resultants. We demonstrate that (a) the model is able able to reproduce the response of 3D FEA with very few elements and (b) the model has the ability to rapidly predict the fatigue life under variable amplitude combined loading with relatively few frame elements.

Laser-assisted grinding of RB-SiC composites: Laser ablation behavior and mechanism

Laser ablation is an important process during Laser-Assisted Grinding (LAG) of hard and brittle materials. To realize controllable material removal during laser ablation of RB-SiC composites, ablation experiments under different Laser Energy Density (LAED) and LAG experiments are conducted. Evolution rules and mechanism of physical phase, ablation morphology and crack characteristics caused by laser irradiation are investigated. The forces of LAG and Conventional Grinding (CG) are compared. The results show that ablation surface changes from slight oxidation to obvious material removal with LAED increasing, and ablation depth increases gradually. The ablation products change from submicron SiO2 particles to nanoscale particles and floccule. High LAED promotes SiC decomposition and sublimation, which leads to the increase of C element. The SiC phase forms corrugated shape in recast layer and columnar shape in Heat Affected Zone (HAZ) at 56 J/mm2. The cold and heat cycle leads to formation of fishbone crack. For ablation specimen under 30 J/mm2, the grinding force can be reduced by a maximum of 39% and brittle damage region is reduced. The material removal and microcrack generated will significantly reduce the hardness and improve machinability, which can promote grinding efficiency.

Mechanical properties of defective kaolinite in tension and compression: A molecular dynamics study

Influenced by the external environment, clays often contain a large number of lattice defects inside, and the defects have an essential influence on the physical and mechanical properties of clays. In order to reveal the mechanical properties and deformation failure mechanism of defective kaolinite, kaolinite containing Si vacancy defects was constructed, and the mechanical properties of two types of perfect/defective kaolinite were investigated under uniaxial tensile and compressive conditions, respectively, by using molecular dynamics methods. The anisotropy of the micromechanical behavior of kaolinite and the formation mechanism was revealed. Based on the radial distribution function between atoms, the cutoff distance of individual bonds inside the crystal was determined, and the evolution rules of individual bonds inside the crystal during the deformation and destruction were elucidated. Results showed that the introduction of point defects caused different degrees of reduction in the peak strength and elastic modulus of kaolinite, and the decrease was related to the loading direction, and the decline was more significant for parallel clay layers (x- and y-) than for perpendicular clay layers (z-), with pronounced anisotropy. When the defected kaolinite was extended along the x- and y-directions, the silicon‑oxygen tetrahedral and aluminum‑oxygen octahedral sheets were fractured, and the broken sites were concentrated at the defected locations; when extended along the z-direction, the failure was dominated by the fracture of interlayer hydrogen bonds. When the defective kaolinite was compressed along the x- and y-directions, the failure was mainly caused by the gradual accumulation of the bending of the clay layer, which was distinctly different from the direct fracture under the tensile condition, and when compressed along the z-direction, the clay layer collapsed until it was failure. The tendency of the broken bonds within kaolinite corresponded well with the stress-strain curve, the number of broken bonds increased with the increase of strain, and the number of broken bonds of various bonds stabilized when the crystal structure was destroyed. The bond-break types and the number of bonds broken under different simulation conditions were somewhat different, affected by the layered structure of kaolinite and the loading patterns.

Research on green innovation efficiency measurement and influencing factors in the three major coastal urban agglomerations in China

Introduction: Behind China’s booming economy lies a series of environmental and resource consumption issues. After continuous research and exploration, scholars generally agree that green innovation is a crucial way to solve this problem. As the core regions of China’s economic development, studying the green innovation level of the three major urban agglomerations can help understand China’s progress in this area. It can provide beneficial experience and inspiration for other urban agglomerations and the formulation of relevant policies in the future.Methods: This paper selects the panel data of 40 cities in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2010 to 2020 as the research object. The super-efficiency SBM (slack-based measure) model and Malmquist-Luenberger (ML) index are used to measure the green innovation efficiency (GIE) and its dynamic evolution rules, and the Tobit regression model is constructed to analyze the influencing factors of GIE.Results: The PRD urban agglomeration has the highest GIE level, while the GIE level in BTH and YRD shifted around 2015. Technical efficiency and technological progress together lead to increased GIE, with technological progress having a higher impact than technical efficiency. The regression coefficients of urbanization level, industrial structure, and science and technology level are 0.0078, 0.0071, and 0.0616, respectively, significantly promoting GIE. The coefficients of economic development level, foreign direct investment, environmental regulations, and SO2 emissions are −0.2198, −0.1163, −0.005, and −0.011, respectively, significantly inhibiting GIE. The coefficient of vegetation cover of 0.0228 has no significant effect on GIE.Conclusions: The overall GIE of the three major urban agglomerations is relatively high. Still, there is spatial variability in GIE among different cities, accompanied by the phenomenon of two-level differentiation. The study suggests that improving GIE requires enhanced interventions at both the city level and the level of influencing factors. This study enriches the theoretical results on the meso-level of GIE and provides theoretical guidance and practical directions for promoting green innovation in urban agglomerations, achieving peaking carbon and carbon neutrality, and promoting green and high-quality development.

Mean-Field Analysis with Random Perturbations to Detect Gliders in Cellular Automata

Cellular automata are mathematical models that represent systems with complex behavior through simple interactions between their individual elements. These models can be used to study unconventional computational systems and complexity. One notable aspect of cellular automata is their ability to create structures known as gliders, which move in a regular pattern to represent the manipulation of information. This paper introduces the modification of mean-field theory applied to cellular automata, using random perturbations based on the system’s evolution rule. The original aspect of this approach is that the perturbation factor is tailored to the nature of the rule, altering the behavior of the mean-field polynomials. By combining the properties of both the original and perturbed polynomials, it is possible to detect when a cellular automaton is more likely to generate gliders without having to run evolutions of the system. This methodology is a useful approach to finding more examples of cellular automata that exhibit complex behavior. We start by examining elementary cellular automata, then move on to examples of automata that can generate gliders with more states. To illustrate the results of this methodology, we provide evolution examples of the detected automata.

Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks

The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking neural network based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the potential for integrating multiple biological principles. Existing researches on LSM focus on different certain perspectives, including high-dimensional encoding or optimization of the liquid layer, network architecture search, and application to hardware devices. There is still a lack of in-depth inspiration from the learning and structural evolution mechanism of the brain. Considering these limitations, this paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules. For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property. For brain-inspired learning of LSM, we propose a dopamine-modulated Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term dopamine regulation and local trace-based BCM synaptic plasticity. Comparative experimental results on different decision-making tasks show that introducing structural evolution of the liquid layer, and the DA-BCM regulation of the liquid layer and the readout layer could improve the decision-making ability of LSM and flexibly adapt to rule reversal. This work is committed to exploring how evolution can help to design more appropriate network architectures and how multi-scale neuroplasticity principles coordinated to enable the optimization and learning of LSMs for relatively complex decision-making tasks.