Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Financial Data Modeling by Using Asynchronous Parallel Evolutionary Algorithms

In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.

The Evolutionary Modeling and Short-range Climatic Prediction for Meteorological Element Time Series

The time series of precipitation in flood season (May-September) at WuhanStation, which is set as an example of the kind of time series with chaos characters, is split intotwo parts: One includes macro climatic timescale period waves that are affected by some relativelysteady climatic factors such as astronomical factors (sunspot, etc.), some other known and/orunknown factors, and the other includes micro climatic timescale period waves superimposed on themacro one. The evolutionary modeling (EM), which develops from genetic programming (GP), is supposedto be adept at simulating the former part because it creates the nonlinear ordinary differentialequation (NODE) based upon the data series. The natural fractals (NF) are used to simulate thelatter part. The final prediction is the sum of results from both methods, thus the model canreflect multi-time scale effects of forcing factors in the climate system. The results of thisexample for 2002 and 2003 are satisfactory for climatic prediction operation. The NODE can suggestthat the data vary with time, which is beneficial to think over short-range climatic analysis andprediction. Comparison in principle between evolutionary modeling and linear modeling indicates thatthe evolutionary one is a better way to simulate the complex time series with nonlinearcharacteristics.

A Hybrid Model for the Mid-Long Term Runoff Forecasting by Evolutionary Computation Techniques

The mid-long term hydrology forecasting is one of most challenging problems in hydrological studies. This paper proposes an efficient dynamical system prediction model using evolutionary computation techniques. The new model overcomes some disadvantages of conventional hydrology forecasting ones. The observed data is divided into two parts; the slow 'smooth and steady' data, and the fast 'coarse and fluctuation' data. Under the divide and conquer strategy, the behavior of smooth data is modeled by ordinary differential equations based on evolutionary modeling, and that of the coarse data is modeled using gray correlative forecasting method. Our model is verified on the test data of the mid-long term hydrology forecast in the northeast region of China. The experimental results show that the model is superior to gray system prediction model (GSPM).

Hysteresis behavior and nonequilibrium phase transition in a one-dimensional evolutionary game model

We investigate a simple evolutionary game model in one dimension. It is found that the system exhibits a discontinuous phase transition from a defection state to a cooperation state when the b payoff of a defector exploiting a cooperator is small. Furthermore, if b is large enough, then the system exhibits two continuous phase transitions between two absorbing states and a coexistence state of cooperation and defection, respectively. The tri-critical point is roughly estimated. Moreover, it is found that the critical behavior of the continuous phase transition with an absorbing state is in the directed percolation universality class.

Dynamic Behavior Modeling in Multi-Agent System By Evolutionary Programming

In a multi-agent system, each agent must adapt itself to the environment and coordinate with other agents dynamically. TO predict or cooperate with the behavior of oiller agents. An agent should dynamically establish and evolve the cooperative behavior model of itself. In this paper, we represent the behavior model of an agent as a f-mite state machine and propose a new method of dynamically evolving the behavior model of an agent by evolutionary programming.

Parallel Implementations of Modeling Dynamical Systems by Using System of Ordinary Differential Equations

First, an asynchronous distributed parallel evolutionary modeling algorithm (PEMA) for building the model of system of ordinary differential equations for dynamical systems is proposed in this paper. Then a series of parallel experiments have been conducted to systematically test the influence of some important parallel control parameters on the performance of the algorithm. A lot of experimental results are obtained and we make some analysis and explanations to them.

Parameter Optimization Method for Gaussian Mixture Model with Data Evolution

To learn from evolutionary experimental data points effectively,an evolutionary Gaussian mixture model based on constraint consistency(EGMM)is proposed and the corresponding method of parameter optimization is presented.Here,the Gaussian mixture model(GMM)is adopted to describe the data points,and the differences between the posterior probabilities of pairwise points under the current parameters are introduced to measure the temporal smoothness.Then,parameter optimization of EGMM can be realized by evolutionary clustering.Compared with most of the existing data analysis methods by evolutionary clustering,both the whole features and individual differences of data points are considered in the clustering framework of EGMM.It decreases the algorithm sensitivity to noises and increases the robustness of evaluated parameters.Experimental result shows that the clustering sequence really reflects the shift of data distribution,and the proposed algorithm can provide better clustering quality and temporal smoothness.

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Improved mammalian family phylogeny using gap-rare multiple sequence alignment:A timetree of extant placentals and marsupials

The timing of mammalian diversification in relation to the Cretaceous-Paleogene(KPg)mass extinction continues to be a subject of substantial debate.Previous studies have either focused on limited taxonomic samples with available whole-genome data or relied on short sequence alignments coupled with extensive species samples.In the present study,we improved an existing dataset from the landmark study of Meredith et al.(2011)by filling in missing fragments and further generated another dataset containing 120 taxa and 98 exonic markers.Using these two datasets,we then constructed phylogenies for extant mammalian families,providing improved resolution of many conflicting relationships.Moreover,the timetrees generated,which were calibrated using appropriate molecular clock models and multiple fossil records,indicated that the interordinal diversification of placental mammals initiated before the Late Cretaceous period.Additionally,intraordinal diversification of both extant placental and marsupial lineages accelerated after the KPg boundary,supporting the hypothesis that the availability of numerous vacant ecological niches subsequent to the mass extinction event facilitated rapid diversification.Thus,our results support a scenario of placental radiation characterized by both basal cladogenesis and active interordinal divergences spanning from the Late Cretaceous into the Paleogene.

Investigation on Relationship between Cost and Software Reliability

Based on the fact that the software development cost is an important factorto control the whole project, we discuss the relationship between the software development cost andsoftware reliability according to the empirieal data collected from the development process. Byevolutionary modeling we get an empirical model of the relationship between cost and softwarereliability, and validate the estimate results with the empirical data.

Hydrocarbon Generation Characteristics of Source Rocks from Different Saline Environments in the Dongpu Depression,Bohai Bay Basin,China

The Dongpu Depression is a secondary salt-bearing tectonic unit in the Bohai Bay Basin,eastern China.The depositional environment of this depression regarding its Paleogene strata is clearly different in plane,including the saltwater environment(SE)in the north,the freshwater environment(FE)in the south and the brackish water environment(BE)in the middle.The result of oil and gas exploration in the Dongpu Depression shows that more than 90%of the proven oil reserves are distributed in the northern saltwater environment.Previous studies indicate that the organic geochemistry characteristics and the hydrocarbon generation capacity of the source rocks are very clearly diverse under different environments,which results in the significant differences in the proved reserves between the north and the south.In order to further explore the differences in the hydrocarbon generation capacity of the source rocks under distinct depositional environments and the mechanism of their occurrence,three samples from different depositional environments(W18-5 for SE,H7-18 for BE,CH9 for FE)were used for confined gold tube pyrolysis experiments.The results show that the CH4 yields of W18-5,H7-18 and CH9 increase with increasing temperature,the maximum yields being 405.62 mg/g TOC,388.56 mg/g TOC and 367.89 mg/g TOC,respectively.The liquid hydrocarbon yields of W18-5,H7-18 and CH9 firstly increase with increasing temperature and then decrease after the critical temperatures.The maximum yields of C6-14 are 149.54 mg/g TOC,140.18 mg/g TOC and 116.94 mg/g TOC,the maximum yields of C14+being 852.4 mg/g TOC,652.6 mg/g TOC and 596.41 mg/g TOC,respectively for W18-5,H7-18 and CH9.To summarize,the order of hydrocarbon potential from high to low is W18-5,H7-18 and CH9.On this basis,through analyzing the influencing factors of hydrocarbon differences,this paper reveals that the saltwater environment is characterized by 4 factors:higher salinity,halophilic algae,high paleo-productivity and a strongly reducing environment,which are beneficial to the enrichment of organic matter and lead to the formation of high levels of sapropelite and exinite.According to the variation of oil and gas components in the pyrolysis experiments,the hydrocarbon generation process is divided into three stages:kerogen cracking,oil cracking and C2-5 cracking.Combined with hydrocarbon generation characteristics and stages,the evolutionary model of hydrocarbon generation for source rocks under different environments is established.

A Simulation Study on the Evolution of Groundwater Circulation Systems in Cenozoic Basins of Northern China

Three Cenozoic basins—the Qaidam basin, the Weihe graben-type basin and the North China plain—which are different in climatic conditions, geological settings and run—off types, are selected for the study. Based on an analysis of background information of the transect along the middle-latitude region, studies of groundwater dynamics, geochemistry, simulation of water circulation of the main elements as well as isotopic chronology, the information on global changes is collected, the formation of groundwater circulation systems and their evolution under stacked impacts of natural conditions and human activities are discussed, and a correlation is made between the evolutionary features of the above systems in these basins since 25 ka B.P. All these have laid a good foundation for further generalizing the evolutionary model of land water in northern China.

Design of Intelligent Network Performance Analysis & Forecast Support System

A system designed for supporting the network performance analysis and forecast effort is presented, based on the combination of offline network analysis and online real-time performance forecast. The off-line analysis will perform analysis of specific network node performance, correlation analysis of relative network nodes performance and evolutionary mathematical modeling of long-term network performance measurements. The online real-time network performance forecast will be based on one so-called hybrid prediction modeling approach for short-term network, performance prediction and trend analysis. Based on the module design, the system proposed has good intelligence, scalability and self-adaptability, which will offer highly effective network performance analysis and forecast tools for network managers, and is one ideal support platform for network performance analysis and forecast effort.

Regional Man-land System:Its Dynamics Characters,Structure and Evolutionary Models

From the point of view of dynamics, the author studies some important issues concerning the dynamics of the regional man land system. As a complex composition of man land interaction, the regional man land system is a semi open, nonlinear and partly controllable system which possesses a multi hierarchy interface structure and has its own evolutionary law. The dynamics characters, the dynamic structure and the evolutionary models of the regional man land system are all deeply discussed in this paper.

Predicting PM_(2.5) Concentration in the Yangtze River Delta Region Using Climate System Monitoring Indices and Machine Learning

Changing meteorological conditions during autumn and winter have considerable impact on air quality in the Yangtze River Delta(YRD)region.External climatic factors,such as sea surface temperature and sea ice,together with the atmospheric circulation,directly affect meteorological conditions in the YRD region,thereby modulating the variation in atmospheric PM_(2.5) concentration.This study used the evolutionary modeling machine learning technique to investigate the lag relationship between 144 climate system monitoring indices and autumn/winter PM_(2.5) concentration over 0-12 months in the YRD region.After calculating the contribution ratios and lagged correlation coefficients of all indices over the previous 12 months,the top 36 indices were selected for model training.Then,the nine indices that contributed most to the PM_(2.5) concentration in the YRD region,including the decadal oscillation index of the Atlantic Ocean and the consistent warm ocean temperature index of the entire tropical Indian Ocean,were selected for physical mechanism analysis.An evolutionary model was developed to forecast the average PM_(2.5) concentration in major cities of the YRD in autumn and winter,with a correlation coefficient of 0.91.In model testing,the correlation coefficient between the predicted and observed PM_(2.5) concentrations was in the range of 0.73-0.83 and the root-mean-square error was in the range of 9.5-11.6μg m-3,indicating high predictive accuracy.The model performed exceptionally well in capturing abnormal changes in PM_(2.5) concentration in the YRD region up to 50 days in advance.

Path-dependent speciation in dynamic fitness landscapes

Species is a fundamental concept in evolutionary biology and biodiversity.However,existing species definitions are often influenced by artificial factors or are challenging in practical application,leading to confusion in species classification.Due to uncertain environmental changes and random genetic drift,the fitness expectations of a population may shift,causing species to evolve to a new evolutionary state based on their current instantaneous fitness within a dynamic fitness landscape.This contrasts with the classic static fitness landscape,where fitness expectations are constant.In a dynamic fitness landscape,speciation may exhibit path dependence,where the evolution of traits follows a probabilistic path,creating feedback that shapes evolutionary trajectories.The path-dependent evolutionary mechanism suggests that species survival within an ecosystem is not directly determined by their fitness but by the probability of their evolutionary pathways.This model also indicates that species can coexist with varying probabilities under limited environmental pressures.Consequently,new species,cryptic species,or sympatric species may emerge via path-dependent evolutionary processes.Within this framework,we developed a mathematical species concept,which may guide future species classification methodologies.

Open sharing ofgovernment data and enterprise decision making

Data is a key factor of production in the so-called"digital economy"era.Thus,it is important to promote government data opening and sharing to advance the high-quality development of a digital economy.The article first constructs an evolutionary game model of government data opening and sharing(with local governments and enterprises as game participants)by combining realistic scenarios and evolutionary game models.Then,it discusses the evolutionary stabilization strategies under different scenarios in a categorical manner.Finally,it uses MATLAB to conduct numerical simulations to verify the accuracy of the model and analyze the key influencing factors.Several results were obtained.(1)the optimal evolutionary path to promote government data opening and sharing is for enterprises to choose to"use data"and for local governments to choose the"positive sharing"strategy,and the enterprises'decision is the internal driver.(2)The value of data assets provided by local governments when applying the"positive sharing"strategy,the cost of data used by enterprises,and the data value conversion rate of enterprises are the key factors influencing the decisions of both parties.To promote open sharing and exploitation of government data,enterprises should enhance their independent innovation capabilities,while governments should enhance the value of data assets and continuously optimize their business environments.

Drainage Responses to the Activity of the Langshan Range-Front Fault and Tectonic Implications

Langshan, a monoclinic mountain, which started to uplift since Oligocene, bounds the northwest margin of the Hetao Basin. The continuous activity of the active normal Langshan range- front fault forms the typical basin-and-range landform in Langshan area and controls the landform evolution of Langshan. Langshan is an ideal place to study relationship between quantitative geomor- phological index and active deformation. According to study on knickpoints, fitting on longitudinal channel profiles and steepness index, we demonstrate that the main controlling factors on distribution of normalized steepness index of channels are not climate （precipitation）, lithology, sediment flux, but tectonic factor, or the activity of Langshan range-front fault. The short channels in southeast flank, whose lengths are shorter than 16 km, may be still in the non-steady status. If not considering these short channels, the distribution of normalized steepness index along the Langshan range-front fault appears like M-shape pattern, while the normalized steepness index in the middle section is higher than those at both ends. This pattern is well consistent with geometrical segmentation model of the Langshan range-front fault. Combining previous active tectonic research on Langshan range-front fault, which demonstrates the Langshan range-front fault has been in the stage of linkup, we reasonably infer the Langshan range-front fault now is the result of linkup of both fault which continuously bilaterally ex- tended independently. Our tectonic geomorphological study also supports the conclusion that the Langshan range-front fault has been in the stage of linkup. The formation of several knickpoints due to tectonic factor may have been caused by slip-rate variation because of linkup of both independent faults. Based on cognition above, we also proposed the geological and geomorphological evolutionary model of the Langshan range-front fault since Oligocene.

Simulating the evolution of a clonal trait in plants with sexual and vegetative reproduction

Aims Phenotypic optimality models neglect genetics.However,especially when heterozygous genotypes are fittest,evolving allele,genotype and phenotype frequencies may not correspond to predicted optima.This was not previously addressed for organisms with complex life histories.Methods Therefore,we modelled the evolution of a fitness-relevant trait of clonal plants,stolon internode length.We explored the likely case of an asymmetric unimodal fitness profile with three model types.In constant selection models(CSMs),which are gametic,but not spatially explicit,evolving allele frequencies in the one-locus and fiveloci cases did not correspond to optimum stolon internode length predicted by the spatially explicit,but not gametic,phenotypic model.This deviation was due to the asymmetry of the fitness profile.Gametic,spatially explicit individual-based(SEIB)modeling allowed us relaxing the CSM assumptions of constant selection with exclusively sexual reproduction.Important findings For entirely vegetative or sexual reproduction,predictions of the gametic SEIB model were close to the ones of spatially explicit nongametic phenotypic models,but for mixed modes of reproduction they approximated those of gametic,not spatially explicit CSMs.Thus,in contrast to gametic SEIB models,phenotypic models and,especially for few loci,also CSMs can be very misleading.We conclude that the evolution of traits governed by few quantitative trait loci appears hardly predictable by simple models,that genetic algorithms aiming at technical optimization may actually miss the optimum and that selection may lead to loci with smaller effects in derived compared with ancestral lines.