Microscopic Rules Research Articles

Evolutionary Dynamics of Population Games With an Aspiration-Based Learning Rule.

Agents usually adjust their strategic behaviors based on their own payoff and aspiration in gaming environments. Hence, aspiration-based learning rules play an important role in the evolutionary dynamics in a population of competing agents. However, there exist different options for how to use the aspiration information for specifying the microscopic learning rules. It is also interesting to investigate under what conditions the aspiration-based learning rules can favor the emergence of cooperative behavior in population games. A new learning rule, called as "Satisfied-Cooperate, Unsatisfied-Defect", is proposed here, which is based on aspiration. Under this learning rule, agents prefer to cooperate when their income is satisfied; otherwise, they prefer the strategy of defection. We introduce this learning rule to a population of agents playing a generalized two-person game. We, respectively, obtain the mathematical conditions in which cooperation is more abundant in finite well-mixed, infinite well-mixed, and structured populations under weak selection. Interestingly, we find that these conditions are identical, no matter whether the aspiration levels for cooperators and defectors are the same or not. Furthermore, we consider the prisoner's dilemma game (PDG) as an example and perform numerical calculations and computer simulations. Our numerical and simulation results agree well and both support our theoretical predictions in the three different types of populations. We further find that our aspiration-based learning rule can promote cooperation more effectively than alternative aspiration-based learning rules in the PDG.

Verification and Application Evaluation of Intelligent Audit Rules for The UN9000 Urine Analysis System

Objective: To apply and verify the application of intelligent audit rules for urine analysis by Cui et al. Method: A total of 1139 urine samples of hospitalized patients in Tai’an Central Hospital from September 2021 to November 2021 were randomly selected, and all samples were manually microscopic examined after the detection of the UN9000 urine analysis line. The intelligent audit rules (including the microscopic review rules and manual verification rules) were validated based on the manual microscopic examination and manual audit, and the rules were adjusted to apply to our laboratory. The laboratory turnaround time (TAT) before and after the application of intelligent audit rules was compared. Result: The microscopic review rate of intelligent rules was 25.63% (292/1139), the true positive rate, false positive rate, true negative rate, and false negative rate were 27.66% (315/1139), 6.49% (74/1139), 62.34% (710/1139) and 3.51% (40/1139), respectively. The approval consistency rate of manual verification rules was 84.92% (727/856), the approval inconsistency rate was 0% (0/856), the interception consistency rate was 12.61% (108/856), and the interception inconsistency rate was 0% (0/856). Conclusion: The intelligence audit rules for urine analysis by Cui et al. have good clinical applicability in our laboratory.

Evolution of cooperation in deme-structured populations on graphs.

Understanding how cooperation can evolve in populations despite its cost to individual cooperators is an important challenge. Models of spatially structured populations with one individual per node of a graph have shown that cooperation, modeled via the prisoner's dilemma, can be favored by natural selection. These results depend on microscopic update rules, which determine how birth, death, and migration on the graph are coupled. Recently, we developed coarse-grained models of spatially structured populations on graphs, where each node comprises a well-mixed deme, and where migration is independent from division and death, thus bypassing the need for update rules. Here, we study the evolution of cooperation in these models in the rare-migration regime, within the prisoner's dilemma. We find that cooperation is not favored by natural selection in these coarse-grained models on graphs where overall deme fitness does not directly impact migration from a deme. This is due to a separation of scales, whereby cooperation occurs at a local level within demes, while spatial structure matters between demes.

Frequent asymmetric migrations suppress natural selection in spatially structured populations.

Natural microbial populations often have complex spatial structures. This can impact their evolution, in particular the ability of mutants to take over. While mutant fixation probabilities are known to be unaffected by sufficiently symmetric structures, evolutionary graph theory has shown that some graphs can amplify or suppress natural selection, in a way that depends on microscopic update rules. We propose a model of spatially structured populations on graphs directly inspired by batch culture experiments, alternating within-deme growth on nodes and migration-dilution steps, and yielding successive bottlenecks. This setting bridges models from evolutionary graph theory with Wright-Fisher models. Using a branching process approach, we show that spatial structure with frequent migrations can only yield suppression of natural selection. More precisely, in this regime, circulation graphs, where the total incoming migration flow equals the total outgoing one in each deme, do not impact fixation probability, while all other graphs strictly suppress selection. Suppression becomes stronger as the asymmetry between incoming and outgoing migrations grows. Amplification of natural selection can nevertheless exist in a restricted regime of rare migrations and very small fitness advantages, where we recover the predictions of evolutionary graph theory for the star graph.

Environment assisted quantum model for studying RNA-DNA-error correlation created due to the base tautomery

The adaptive mutation phenomenon has been drawing the attention of biologists for several decades in evolutionist community. In this study, we propose a quantum mechanical model of adaptive mutation based on the implications of the theory of open quantum systems. We survey a new framework that explain how random point mutations can be stabilized and directed to be adapted with the stresses introduced by the environments according to the microscopic rules dictated by constraints of quantum mechanics. We consider a pair of entangled qubits consist of DNA and mRNA pair, each coupled to a distinct reservoir for analyzing the spreed of entanglement using time-dependent perturbation theory. The reservoirs are physical demonstrations of the cytoplasm and nucleoplasm and surrounding environments of mRNA and DNA, respectively. Our predictions confirm the role of the environmental-assisted quantum progression of adaptive mutations. Computing the concurrence as a measure that determines to what extent the bipartite DNA-mRNA can be correlated through entanglement, is given. Preventing the entanglement loss is crucial for controlling unfavorable point mutations under environmental influences. We explore which physical parameters may affect the preservation of entanglement between DNA and mRNA pair systems, despite the destructive role of interaction with the environments.

A Statistical Mechanics Approach to Describe Cell Reorientation Under Stretch

Experiments show that when a monolayer of cells cultured on an elastic substratum is subject to a cyclic stretch, cells tend to reorient either perpendicularly or at an oblique angle with respect to the main stretching direction. Due to stochastic effects, however, the distribution of angles achieved by the cells is broader and, experimentally, histograms over the interval [0^circ , 90^circ ] are usually reported. Here we will determine the evolution and the stationary state of probability density functions describing the statistical distribution of the orientations of the cells using Fokker–Planck equations derived from microscopic rules for describing the reorientation process of the cell. As a first attempt, we shall use a stochastic differential equation related to a very general elastic energy that the cell tries to minimize and, we will show that the results of the time integration and of the stationary state of the related forward Fokker–Planck equation compare very well with experimental results obtained by different researchers. Then, in order to model more accurately the microscopic process of cell reorientation and to shed light on the mechanisms performed by cells that are subject to cyclic stretch, we consider discrete in time random processes that allow to recover Fokker–Planck equations through classical tools of kinetic theory. In particular, we shall introduce a model of reorientation as a function of the rotation angle as a result of an optimal control problem. Also in this latter case the results match very well with experiments.

An SIR model with viral load-dependent transmission

The viral load is known to be a chief predictor of the risk of transmission of infectious diseases. In this work, we investigate the role of the individuals’ viral load in the disease transmission by proposing a new susceptible-infectious-recovered epidemic model for the densities and mean viral loads of each compartment. To this aim, we formally derive the compartmental model from an appropriate microscopic one. Firstly, we consider a multi-agent system in which individuals are identified by the epidemiological compartment to which they belong and by their viral load. Microscopic rules describe both the switch of compartment and the evolution of the viral load. In particular, in the binary interactions between susceptible and infectious individuals, the probability for the susceptible individual to get infected depends on the viral load of the infectious individual. Then, we implement the prescribed microscopic dynamics in appropriate kinetic equations, from which the macroscopic equations for the densities and viral load momentum of the compartments are eventually derived. In the macroscopic model, the rate of disease transmission turns out to be a function of the mean viral load of the infectious population. We analytically and numerically investigate the case that the transmission rate linearly depends on the viral load, which is compared to the classical case of constant transmission rate. A qualitative analysis is performed based on stability and bifurcation theory. Finally, numerical investigations concerning the model reproduction number and the epidemic dynamics are presented.

Threshold-based network structural dynamics

The interest in dynamic processes on networks is steadily rising in recent years. In this paper, we consider the (α,β)-Threshold Network Dynamics ((α,β)-Dynamics), where α≤β, in which only structural dynamics (edge dynamics of the network) are allowed, guided by local threshold rules executed by each node. In particular, in each discrete round t, each active pair of nodes u and v, computes a value E(u,v) (the potential of the pair) as a function of the local structure of the network at round t around the two nodes. If E(u,v)<α then the link (if it exists) between u and v is removed; if α≤E(u,v)<β then an existing link among u and v is maintained; if β≤E(u,v) then a link between u and v is established if not already present. New nodes cannot be inserted as a result of the protocol, and existing nodes cannot be removed.The microscopic structure of (α,β)-Dynamics appears to be simple, so that we are able to rigorously argue about it, but still flexible, so that we are able to design meaningful microscopic local rules that give rise to interesting macroscopic behaviors. Our goals are the following: a) to investigate the properties of the (α,β)-Threshold Network Dynamics and b) to show that (α,β)-Dynamics is expressive enough to solve complex problems on networks.Our contribution in these directions is twofold. We rigorously exhibit the claim about the expressiveness of (α,β)-Dynamics, both by designing a simple protocol that provably computes the k-core of the network as well as by showing that (α,β)-Dynamics are in fact Turing-Complete. Second and most important, we construct general tools for proving stabilization that work for a subclass of (α,β)-Dynamics and prove speed of convergence in a restricted setting.

Secondary bladder cancer during long-term follow-up after iodine-125 permanent seed implantation for localized prostate cancer

IntroductionBrachytherapy for prostate cancer treatment may induce secondary bladder cancer during long-term follow-ups. This study reviews the risk and tumor characteristics of secondary bladder cancer after brachytherapy. MethodsThis single-institution retrospective study included 1162 patients treated with low-dose-rate permanent seed implantation brachytherapy with iodine-125, with or without external beam radiation therapy, for localized prostate cancer. We calculated and compared the rates of secondary bladder cancer among patients treated with brachytherapy and radical prostatectomy (n = 218) before and after a propensity score-matching analysis. Possible risk factors for secondary bladder cancer, such as patient age and external beam radiation therapy administration, were analyzed. ResultsOf 1162 patients with a median follow-up period of 11.4 (range: 0.7–15.5) years, 26 presented with urothelial carcinomas and 1 with adenocarcinoma at a median of 8.9 (range: 2.9–14.0) years after brachytherapy, although the incidence rates of secondary bladder cancer after brachytherapy were not significantly different from those after radical prostatectomy. No significant risk factors for secondary bladder cancer were identified. The initial symptoms of secondary bladder cancer were gross hematuria (74%) and microscopic hematuria with positive urine cytology (15%). Among 26 cases of secondary urothelial carcinoma, 54% were high-grade and 46% were invasive. After brachytherapy, invasive urothelial carcinoma occurred later than noninvasive urothelial carcinoma (p = 0.01). ConclusionsConsidering the aggressive malignancy of secondary bladder cancer, cystoscopy and urine cytology should be performed for further investigation of the causes of gross or microscopic hematuria and rule out secondary bladder cancer in cases followed longer than 3 years after brachytherapy.

High‐Performance Perovskite Solar Cells via Simulation Interactive Technology

CsPbBr3 film possesses high stability and easy manufacturing characteristics, rendering it attractive for applications in perovskite solar cells (PSCs). However, optical loss and energy level matching of different material layers are still the major factors, limiting the performance of PSCs. Herein, the finite element method (FEM) and density functional theory (DFT) calculations composed of simulation interaction technology are used to study the effects of different electron transport layer and hole transport layer materials on the optical performance of different PSC configurations. The effect of the charge transport layer (CTL) material and CsPbBr3 on the energy level matching and charge transport of the device is explained. The FEM simulation results show that inorganic CTL materials produce less parasitic absorption than the organic materials. The DFT calculation results give the microscopic design rules of the CTL material. In addition, PSCs with a light‐trapping structure are designed, which effectively suppressed surface reflection. Finally, through the screening of CTL materials and the design of advanced light‐trapping structures, the photocurrent of PSCs is increased by 69.8% (from 5.30 to 9.00 mA cm−2). This work provides a novel model for the screening of CTL materials for inorganic PSCs.

Similar local neuronal dynamics may lead to different collective behavior.

This report is concerned with the relevance of the microscopic rules that implement individual neuronal activation, in determining the collective dynamics, under variations of the network topology. To fix ideas we study the dynamics of two cellular automaton models, commonly used, rather in-distinctively, as the building blocks of large-scale neuronal networks. One model, due to Greenberg and Hastings (GH), can be described by evolution equations mimicking an integrate-and-fire process, while the other model, due to Kinouchi and Copelli (KC), represents an abstract branching process, where a single active neuron activates a given number of postsynaptic neurons according to a prescribed "activity" branching ratio. Despite the apparent similarity between the local neuronal dynamics of the two models, it is shown that they exhibit very different collective dynamics as a function of the network topology. The GH model shows qualitatively different dynamical regimes as the network topology is varied, including transients to a ground (inactive) state, continuous and discontinuous dynamical phase transitions. In contrast, the KC model only exhibits a continuous phase transition, independently of the network topology. These results highlight the importance of paying attention to the microscopic rules chosen to model the interneuronal interactions in large-scale numerical simulations, in particular when the network topology is far from a mean-field description. One such case is the extensive work being done in the context of the Human Connectome, where a wide variety of types of models are being used to understand the brain collective dynamics.

Review on Relationship Between the Universality Class of the Kardar-Parisi-Zhang Equation and the Ballistic Deposition Model

We have analysed the research findings on the universality class and discussed the connection between the Kardar-Parisi-Zhang (KPZ) universality class and the ballistic deposition model in microscopic rules. In one dimension and 1+1 dimensions deviations are not important in the presence of noise. At the same time, they are very relevant for higher dimensions or deterministic evolution. Mostly, in the analyses a correction scale higher than 1280 has not been studied yet. Therefore, the growth of the interface for finite system size β ≥ &lt;i&gt;0.30&lt;/i&gt; value predicted by the KPZ universality class is still predominant. Also, values of α ≥ &lt;i&gt;0.40,&lt;/i&gt; β ≥ &lt;i&gt;0.30,&lt;/i&gt; and &lt;i&gt;z&lt;/i&gt; ≥ &lt;i&gt;1.16&lt;/i&gt; obtained from literature are consistent with the expected KPZ values of α = &lt;i&gt;1&lt;/i&gt;/&lt;i&gt;2&lt;/i&gt;, β = &lt;i&gt;1&lt;/i&gt;/&lt;i&gt;3&lt;/i&gt;, and &lt;i&gt;z&lt;/i&gt; = &lt;i&gt;3&lt;/i&gt;/&lt;i&gt;2&lt;/i&gt;. A connection between the ballistic deposition and the KPZ equation through the limiting procedure and by applying the perturbation method was also presented.

Simulating crowd behaviour combining both microscopic and macroscopic rules

We propose a new method for simulating pedestrian crowd movement in a virtual environment. A crowd consists of groups of different number of people with different attributes such as gender, age, position, velocity, and energy. Each group has its own intention used to generate a trajectory for each pedestrian navigating in the virtual environment. Additionally, an agent-based model is introduced to simulate pedestrian behaviours in the groups, where various steering behaviours are introduced and combined into a single steering force to allow pedestrians in each group to walk toward their destination point. Based on the proposed method, every single pedestrian in each group can continuously adjust their attributes. Moreover, pedestrians optimize their path independently toward the desired goals, while avoiding obstacles and other pedestrians in the scene. This method takes into account the safety-space around each pedestrian to avoid collisions among pedestrians. The proposed method was implemented for several simulation scenarios under various conditions for a wide range of different parameters. Statistical analysis is carried out to evaluate the performance of the proposed method for simulating the crowd movement in the virtual environment. Results indicate that our method can generate each pedestrian’s trajectories in each group independently to reach several goal points within a reasonable computational time. Moreover, the obtained results reveal that the mean value of the computational time is not increased significantly with the increasing of the number of pedestrians in the crowd.

Toward a Universal Model for Spatially Structured Populations.

A key question in evolution is how likely a mutant is to take over. This depends on natural selection and on stochastic fluctuations. Population spatial structure can impact mutant fixation probabilities. We introduce a model for structured populations on graphs that generalizes previous ones by making migrations independent of birth and death. We demonstrate that by tuning migration asymmetry, the star graph transitions from amplifying to suppressing natural selection. The results from our model are universal in the sense that they do not hinge on a modeling choice of microscopic dynamics or update rules. Instead, they depend on migration asymmetry, which can be experimentally tuned and measured.

Insights into structure and properties of P2O5-based binary systems through molecular dynamics simulations

In order to clarify the microscopic characteristics and evolution rules of varying P2O5-based binary melt structures, molecular dynamics simulation (MD) was utilized to conduct a comprehensive study on its structure and performance. The investigation results demonstrated that the basic unit of P2O5-based binary melt network structure is [PO4]3−, and its structure is close to the standard tetrahedron. With the increase of P2O5 content, Q0, Q1 and Q2 will gradually shift to Q3 structure, resulting the network structure of each phosphate system more complicated. For a given P2O5 content in different systems, as the size of the metal cation decreases, the PO structure becomes more disordered and the degree of polymerization of systems increase. The order of polymerization degree of distinct binary systems is BaOP2O5＜K2OP2O5＜Na2OP2O5＜CaOP2O5. In addition, by linking the change law of the degree of polymerization of P2O5-based binary systems with macroscopic thermodynamic properties of dephosphorization, it is concluded that the dephosphorization capacity of varying binary systems is inversely proportional to their degree of polymerization.

A new insight into pyrolysis mechanism of three typical actual biomass: The influence of structural differences on pyrolysis process

Biomass pyrolysis involves complex structural changes and numerous chemical reactions. At present, most studies on actual biomass pyrolysis focus on the macroscopic thermal degradation process, while the studies on microscopic pyrolysis rules are still very limited. This work provides a new insight into the pyrolysis mechanism of three types of actual biomass, namely hardwood (eucalyptus saligna), softwood (pinus sylvestris) and straw (corn straw), by combining in-situ characterization of the evolution of biomass structures with density functional theory (DFT) calculations of pyrolysis pathways. Firstly, the evolution of various characteristic structures during biomass pyrolysis was monitored by in-situ diffuse reflectance infrared Fourier transform spectroscopy (in-situ DRIFT). It was found that the thermal stability of the same functional group in different biomass samples are not exactly the same, and the structural differences in biomass would lead to different pyrolysis behaviors. Then, considering the great structural difference of hemicellulose in different biomass, the key initial pyrolysis reaction paths including the cleavage of glycosidic bonds, the dissociation of side chains and the opening of sugar ring of hemicellulose were studied by DFT calculations. It was found that the cleavage of glycosidic bonds and the dissociation of O-acetyl side chains of glucomannan in softwood hemicellulose are more advantageous than those of xylan in hardwood and straw hemicellulose. In addition, the cleavage mechanism of hemicellulose-lignin bond (LC), a characteristic connection structure between different components of actual biomass, was investigated. The result showed that the cleavage of Cα-O mainly occurred by concerted mechanism for α-ether bond of LC.

Comparative analysis of continuum angiogenesis models

Although discrete approaches are increasingly employed to model biological phenomena, it remains unclear how complex, population-level behaviours in such frameworks arise from the rules used to represent interactions between individuals. Discrete-to-continuum approaches, which are used to derive systems of coarse-grained equations describing the mean-field dynamics of a microscopic model, can provide insight into such emergent behaviour. Coarse-grained models often contain nonlinear terms that depend on the microscopic rules of the discrete framework, however, and such nonlinearities can make a model difficult to mathematically analyse. By contrast, models developed using phenomenological approaches are typically easier to investigate but have a more obscure connection to the underlying microscopic system. To our knowledge, there has been little work done to compare solutions of phenomenological and coarse-grained models. Here we address this problem in the context of angiogenesis (the creation of new blood vessels from existing vasculature). We compare asymptotic solutions of a classical, phenomenological “snail-trail” model for angiogenesis to solutions of a nonlinear system of partial differential equations (PDEs) derived via a systematic coarse-graining procedure (Pillay et al. in Phys Rev E 95(1):012410, 2017. https://doi.org/10.1103/PhysRevE.95.012410). For distinguished parameter regimes corresponding to chemotaxis-dominated cell movement and low branching rates, both continuum models reduce at leading order to identical PDEs within the domain interior. Numerical and analytical results confirm that pointwise differences between solutions to the two continuum models are small if these conditions hold, and demonstrate how perturbation methods can be used to determine when a phenomenological model provides a good approximation to a more detailed coarse-grained system for the same biological process.

Effect of Savings on a Gas-Like Model Economy with Credit and Debt.

In kinetic exchange models, agents make transactions based on well-established microscopic rules that give rise to macroscopic variables in analogy to statistical physics. These models have been applied to study processes such as income and wealth distribution, economic inequality sources, economic growth, etc., recovering well-known concepts in the economic literature. In this work, we apply ensemble formalism to a geometric agents model to study the effect of saving propensity in a system with money, credit, and debt. We calculate the partition function to obtain the total money of the system, with which we give an interpretation of the economic temperature in terms of the different payment methods available to the agents. We observe an interplay between the fraction of money that agents can save and their maximum debt. The system’s entropy increases as a function of the saved proportion, and increases even more when there is debt.

Shale Gas Transport in Nanopores: Contribution of Different Transport Mechanisms and Influencing Factors

The classical Darcy’s law cannot effectively describe the microscopic flow rules of shale gas. In addition, conducting gas transport experiments in nanopores is difficult, and the correctness of the simulation results is not guaranteed. Studies on the flow and transmission of shale gas in microscopic nanopores can effectively guide the macroscopic numerical simulation of shale gas reservoirs, which is of great significance to the economical and efficient development of such reservoirs. In this work, the dimensionless relaxation time expression is modified, and the Peng–Robinson equation of state (P–R EOS) is introduced to the microscale gas flow lattice Boltzmann model. The influences of viscous flow, slippage effect, boundary Knudsen layer, adsorbed gas layer, and surface diffusion are considered, and the results are combined with the real isothermal adsorption experimental data of shale samples collected from the Longmaxi formation in Sichuan Basin. Finally, the contributions of various transport mechanisms to shale gas flow in nanopores and their influencing factors are studied. Results show that the gas velocity and mass flux (Q) obtained using the ideal gas EOS are higher than those obtained using P–R EOS under high pressure. When the effective pore diameter (He) is less than 5 nm, surface diffusion and its induced free flow are the main transport mechanisms of shale gas flow in nanopores. Viscous flow becomes the main transport mechanism when He exceeds 20 nm. He, pressure, and shale adsorption capacity significantly affect the contribution rate of each transport mechanism to the total Q of shale gas. By comparison, the influence of temperature on the Q of shale gas is relatively small and can be neglected under high pressure.

Self-organization in many-body systems with short-range interactions: clustering, correlations and topology

We investigate the self-organization of point-particles with short-range interactions modeled via simple 1D and 2D Hubbard-like models. We show how various properties emerge such as, boson-like ordering leading to topological structures in real space, via the clustering of the particles at discrete energy levels, which can be analyzed using a network/graph mathematical language. We calculate analytically the number of clusters, the correlations between them and topological numbers like the Euler characteristic, deriving different organizational schemes and entanglement entropy scaling laws. All calculations are performed for an arbitrary number of particles N and energy of the system E. Our results demonstrate how orders with diverse topological/geometric and entanglement features, emerge in strongly interacting many-body systems, that follow minimal microscopic interaction rules.