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When solving multi-objective optimization problems with complex Pareto fronts characteristics, previous work generally ignores the information related to Pareto fronts provided by the population during the evolution, which is detrimental to efficiently tackle them. In order to taking full advantage of information associated with a population evolution, a multi-objective evolutionary optimization method based on online perceiving Pareto front characteristics is proposed in this study. To this end, the information associated with the Pareto front of an optimization problem is first extracted from the population. Following that, the characteristics of the Pareto front in concavity/convexity and continuity are perceived online. For the purpose of each sub-front containing only one characteristic, a Pareto front is divided based on the concavity/convexity and continuity. According to the characteristic of each sub-front, different reference points are selected to refine the distribution of reference vectors. Finally, a multi-objective evolutionary algorithm is designed targeting the characteristics of the Pareto front. The performance of the proposed method is evaluated by comparing it with 8 state-of-the-art optimizers on 31 test problems. Further, the experimental results demonstrate that the proposed method is competitive in handling multi-objective optimization problems with irregular Pareto fronts.

In differential evolution algorithm (DE), it is a widely accepted method that selecting individuals with higher fitness to generate a mutant vector. In this case, the population evolution is under a fitness-based driving force. Although the driving force is beneficial for the exploitation, it sacrifices performance on the exploration. In this paper, a novelty-hybrid-fitness driving force is introduced to trade off contradictions between the exploration and the exploitation of DE. In the new proposed DE, named as NFDDE, both fitness and novelty values of individuals are considered when choosing individuals to create mutant vectors. In addition, two adaptive scaling factors are proposed to adjust the weights of the fitness-based driving force and the novelty-based driving force, respectively, and then distinct properties of the two driving forces can be effectively utilized. At last, to save computational resources, some individuals with lower novelty are deleted when the population has converged to a certain extent. The comprehensive performance of NFDDE is extensively evaluated by comparisons between it and other 9 state-of-art DE variants based on CEC2017 test suite. In addition, distinct properties of the newly introduced strategies and involved parameters are further confirmed by a set of experiments.

The present work focuses on building up a mathematical model showing bacteria population evolution in human lungs taking into account dynamics of immune processes; the model would be useful for assessing functional damage to the lungs. Numeric modeling of processes that occur in a human body is a promising tool for analyzing and predicting impacts exerted by risk factors on health. The suggested approach was developed within a concept describing a human body as a multi-level model that allowed for interaction between various systems and functional state of examined organs given effects produced on them by different adverse factors. Since direct modeling of the structure and processes occurring in the lungs is rather complicated, these organs are usually described with a porous medium model and it requires a lot of computing resources. Damage to the lungs determined via an evolution equation was introduced into the model. The equation described dependence between damage and infiltrate distribution and effects produced on alveolar cells by toxicants excreted by bacteria. The work dwells on certain results that characterize how concentrations of immune system components and bacteria population are spatially distributed when an immune response is evolving. Our research provides a qualitative insight into reasons for quantitative changes in bacteria population under immune reactions occurring in a body under exposure to different factors. This approach can be used for obtaining more precise parameters for existing population models that show spread and clinical course of bacterial infections and for making a long-term prediction of an epidemiological situation. Results obtained with this approach can be useful for analyzing risks of communicable diseases including those occurring under exposure to adverse environmental factors.

Aimed at early maturing and poor accuracy of multi-objective firefly algorithms, we propose a multi-objective firefly algorithm based on compensation factor and elite learning (CFMOFA). Based on iterations by introducing a compensation factor into the firefly learning formula, constraints by population can be overcome and the Pareto optimal solution can be approached in a reduced period. The non-inferior solutions produced in iterations were stored in the external archive and a random external archive particle was employed as the elite particle for population evolution. In this way, the detection range of firefly was extended and diversity and accuracy of non-inferior solution set were enhanced. The conventional algorithms, the improved algorithms and the proposed multi-objective optimization algorithm were tested and compared with each other. The results indicated great advantages of the proposed algorithm in convergence, diversity, and robustness and the proposed algorithm is an effective multi-objective optimization method.

Glycyrrhiza uralensis is a valuable medicinal legume, which occurs widely in arid and semi-arid regions. G. uralensis demand has risen steeply due to its high medical and commercial value. Interpret genome-wide information can stimulate the G. uralensis development as far as its increased bioactive compound levels, and plant yield are concerned. In this study, leaf nutrient concentration and photosynthetic chlorophyll index of G. uralensis were evaluated to determine the G. uralensis growth physiology in three habitats. We observed that G. uralensis nutrient levels and photosynthesis differed significantly in three regions (p < 0.05). Whole-genome re-sequencing of the sixty G. uralensis populations samples from different habitats was performed using an Illumina HiSeq sequencing platform to elucidate the distribution patterns, population evolution, and genetic diversity of G. uralensis. 150.06 Gb high-quality clean data was obtained after strict filtering. The 895237686 reads were mapped against the reference genome, with an average 89.7% mapping rate and 87.02% average sample coverage rate. A total of 6985987 SNPs were identified, and 117970 high-quality SNPs were obtained after filtering, which were subjected to subsequent analysis. Principal component analysis (PCA) based on interindividual SNPs and phylogenetic analysis based on interindividual SNPs showed that the G. uralensis samples could be categorized into central, southern, and eastern populations, which reflected strong genetic differentiation due to long periods of geographic isolation. In this study, a total of 131 candidate regions were screened, and 145 candidate genes (such as Glyur001802s00036258, Glyur003702s00044485, Glyur001802s00036257, Glyur007364s00047495, Glyur000028s00003476, and Glyur000398s00034457) were identified by selective clearance analysis based on Fst and θπ values. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed significant enrichment of 110 GO terms including carbohydrate metabolic process, carbohydrate biosynthetic process, carbohydrate derivative biosynthetic process, and glucose catabolic process (p < 0.05). Alpha-linolenic acid metabolism, biosynthesis of unsaturated fatty acids, and fatty acid degradation pathways were significantly enriched (p < 0.05). This study provides information on the genetic diversity, genetic structure, and population adaptability of the medicinal legumes, G. uralensis. The data obtained in this study provide valuable information for plant development and future optimization of breeding programs for functional genes.

Determining pattern in the dynamics of population evolution is a long-standing focus of evolutionary biology. Complementing the study of natural populations, microbial laboratory evolution experiments have become an important tool for addressing these dynamics because they allow detailed and replicated analysis of evolution in response to controlled environmental and genetic conditions. Key findings include a tendency for smoothly declining rates of adaptation during selection in constant environments, at least in part a reflection of antagonism between accumulating beneficial mutations, and a large number of beneficial mutations available to replicate populations leading to significant, but relatively low genetic parallelism, even as phenotypic characteristics show high similarity. Together, there is a picture of adaptation as a process with a varied and largely unpredictable genetic basis leading to much more similar phenotypic outcomes. Increasing sophistication of sequencing and genetic tools will allow insight into mechanisms behind these and other patterns.

Extreme learning machine (ELM) is an effective classification and prediction learning algorithm based on feedforward neural network (FNN). This paper presents the Phasmatodea (stick insect) population evolution algorithm (PPE), which is different from other algorithms, in which each solution represents a population and has two attributes: quantity and growth rate. Combining the concept of similar evolution and the model of population competition, it is a new local search method. The algorithm is compared with the other algorithms on benchmark functions and engineering problems. Then use it to enhance a variant of the ELM model. The results show that the proposed algorithm has a certain competitiveness.

En este artículo, primero analizamos el stock de viviendas en España (nuevas y de segunda mano). Resumimos la evolución del modelo político de la vivienda en España, destacando las diferencias en sus etapas principales e indagando sobre las causas que han contribuido al excedente de vivienda actual. A continuación, analizamos los diferentes aspectos que explican por qué el mercado de la vivienda parece letárgico y probablemente problemático (evolución de la población y número de hogares, percepción sobre la compra de propiedades residenciales, etc.). Profundizamos en el análisis de la información sobre la evolución histórica del parque de viviendas y los cambios en los patrones de uso de la vivienda a través de los datos proporcionados por el Ministerio de Fomento y el Instituto Nacional de Estadística (INE). La compleja realidad actual del mercado de la vivienda en España no es una casualidad, sino más bien el resultado de un largo período (más de medio siglo) de políticas que apoyan el aumento de la construcción de viviendas. La población menguante y cada vez más envejecida de España pondrá en peligro la creación neta de hogares y la demanda de vivienda. Dada la situación global del mercado de la vivienda en España, creemos que estamos asistiendo al final de un ciclo prolongado de construcción de viviendas favorecido por políticas de vivienda particulares.

Whether for the Big Data or for the Internet of things, the heterogeneity of knowledge has always been an unavoidable problem. Ontologies have been acknowledged to be the core methodology for the heterogeneity of knowledge, and ontology matching is an important method to solve the heterogeneity of ontologies. This paper describes a novel ontology matching method to optimize the matching result. First, the method puts forward a novel ontology matching weight calculation method and a mathematical model to optimize the similarity integration. And then this method establishes an improved heuristic population evolution algorithm (shuffled frog leaping algorithm) to implement the optimization model. Finally, Ontology Alignment Evaluation Initiative (OAEI) test sets are used to evaluate the proposed method, and the feasibility and effectiveness of this method are proved by comparing with other matching systems in OAEI competition.

To solve dynamic multi-objective optimization problems, a dynamic multi-objective evolutionary algorithm (DMOEA) must be able to deal with the dynamics of the environment, and such modifications can lead to new optimal solutions over time. Various algorithms have been proposed that modify the way a change is handled. Among them, prediction-based methods are promising for solving this kind of problem. They provide guided direction for population evolution through a prediction mechanism that assists the DMOEA to respond quickly to new changes. Based on these strategies, we propose a dynamic non-dominated sorting differential evolution improvement with prediction in the objective space (DOSP-NSDE). The proposal uses the objective space prediction (OSP) strategy for both the static evolutionary process (between changes) and the change reaction mechanism to predict the new optimal front location. Experiments were performed on a real-world problem and four sets of test problems: FDA, dMOP, UDF, and DF. Comparison of DOSP-NSDE with several algorithms in the literature, considering three metrics, is presented, showing that the proposal is competitive with most problems.

Brain storm optimization (BSO) is a novel evolutionary algorithm which originates from the human brainstorming process. The successful applications of BSO on various problems demonstrate its validity and efficiency. To theoretically analyze the performance of algorithm from the viewpoint of population evolution, the population interaction network (PIN) is used to construct the relationship among individuals in BSO. Four experiments in different dimensions, parameters, combinatorial parameter settings and related algorithms are implemented, respectively. The experimental results indicate the frequency of average degree of BSO meets a power law distribution in the functions with low dimension, which shows the best performance of algorithm among three kinds of dimensions. The parameters of BSO are investigated to find the influence of the population interaction with the power law distribution on the performance of algorithm, and respective parameter can change the relationship among individuals. In addition, the mutual effect among parameters is analyzed to find the best combinatorial result to significantly enhance the performance of BSO. The contrast among BSO, DE and PSO demonstrates a power law distribution is more effective for boosting the population interaction to enhance the performance of algorithm.

Ultrasound-based drug delivery inspires rigorous experimental studies on the cell experiments involving microbubble-mediated ultrasound. Yet few tools can be found to accelerate the cellular exposure experiments on the heterogeneous-population-based bioeffects under various conditions. In this study, we propose a 2D model which can be used to simulate the population evolution of the co-cultured cells after microbubble-mediated ultrasound is induced. Two update rules based on the experimental data were proposed and incorporated in the simulation procedures. The bioeffects found in the previous experiment, such as cell cycle arrest, are taken into account in the model to simulate the population growth in a co-cultured setting. This model may provide a predictive tool for the multi-cell type responses to ultrasound-induced perforation and facilitate the future experiment design.

Community detection could draw lessons from the population evolution in nature to seek the optimal detection solution. Here we propose an effective genetic algorithm TELGA for community detection based on two-level edge-knowledge learning. The proposed algorithm includes the first node-level and second partition-level edge-knowledge learning. In node-level edge-knowledge learning, the validity of the initial coding is increased by raising the appearing proportion of important edges. The combination of two objective fitness functions contributed to the aim of maximizing the community internal connectivity and minimizing the external connectivity between communities. The final solution could be quickly found by increasing the appearing proportion of the internal community edges through the partition-level edge-knowledge learning. The results of simulation experiments on several real-world networks demonstrate that TELGA could improve modularity, normalized mutual information and efficiency.

Considering the complex and inexplicit relationship between the working conditions and scheduling performance in shop floor, the paper proposed a hyper heuristic method based on gene expression programming (GEP) to customize efficient job dispatch rules and machine assignment rules for scheduling problems in shop floor. Since the traditional GEP has the shortcomings such as slow speed, inadequate precision and many parameters, which are not suitable for the actual environment, the paper made improvements on traditional GEP in two aspects: individual structure and the population evolution adaptation strategy. Through the simulation experiments the efficiency of the improved GEP machine learning approach is validated.

Monopoly can cause enormous market problems. The common characteristics of an enterprise evolution are much like those of a biodiversity evolution. In this paper, using the complex system theory and the ecological population evolution theory, we study and discover that an enterprise scale evolution trend accords with a Logistic model. After establishing a Lotka-Volterra evolutionary model of competition between two enterprises, we establish a Lotka-Volterra model of competition among multiple enterprises in a market. Through the analysis of these models, we can understand the essence of enterprise monopoly. By the analyses above, the government can forecast enterprise monopoly for a market in order to avoid corresponding risks early.

Image classifiers have been proven to be easily fooled by perturbations, but it is still exceedingly challenging to generate imperceptible disturbances, especially without the internal knowledge of the classifiers. Imperceptibility and attack capability are two main evaluating indicators of this problem, while most existing methods, so far, can only either maximize misclassification or minimize the distortion. Although there are some algorithms to consider both of them via the weighted sum method, which is equivalent to solve the multiple optimization problems, it will doubtless enlarge the computation complexity, and the strategy of setting the weights cannot make sure both indicators the optimal solutions. In this paper, we proposed an innovative general algorithm named MOEA-APGA, which is based on multi-objective evolutionary algorithm, taking both factors as the optimization objective function. A set of perturbations with diversity is generated by population evolution, and then an appropriate perturbation is selected by the proposed filtering strategy to synthesize the adversarial example. It can achieve the goal of the targeted attack without the internal knowledge of the victim networks. We tried four perturbation strategies to generate adversarial examples. The experimental results on the MNIST datasets demonstrate the effectiveness of MOEA-APGA. In addition, we refer to a slice of indicators to evaluate the power of the algorithm and the vulnerability of different samples.

Resumen: El municipio de Moratalla, ubicado en la comarca del NO de Murcia (España) presenta por sus datos respecto a la evolución poblacional una de las pocas excepciones en el conjunto regional, ya que pierde población desde al menos cuatro décadas. Esta situación significa un progresivo deterioro en la actividad económica, el envejecimiento de la población y un importante abandono del casco histórico de la ciudad, así como en una serie de pedanías localizadas en un amplio término municipal de 954,82 km2, el tercero más extenso de la Región de Murcia que está compuesta por cuarenta y cinco términos municipales. Los análisis estadísticos, cuestionario tabulados y entrevistas de carácter cualitativo a expertos locales señalan un escenario para esta ciudad y su término municipal preocupante, que en caso de que no se consiga frenar podría desembocar a medio y largo plazo en una aguda regresión social y económica de este municipio. A través de la herramienta DAFO aplicada básicamente a la promoción turística de interior se harán propuestas que contribuyan a detener la sangría demográfica, diversificando la económica local basada en el sector primario y en la salida diaria de muchos vecinos a otras localidades, donde desarrollan sus puestos de trabajo. Palabras clave: Despoblación, vaciamiento, desarrollo local, Moratalla, estrategia territorial. Abstract: The municipality of Moratalla, located in the NW region of Murcia (Spain) presents, due to its data regarding population evolution, one of the few exceptions in the regional set, since it has lost population for at least four decades. This situation means a progressive deterioration in economic activity, the aging of the population and a significant abandonment of the historic center of the city, as well as in a series of districts located in a large municipal area of 954.82 km2, the third largest of the Murcia Region, which is made up of forty-five municipalities. Statistical analyzes, tabulated questionnaires and qualitative interviews with local experts indicate a worrying scenario for this city and its municipal area, which in the event of failure to stop could lead in the medium and long term into an acute social and economic regression of this municipality. Through the SWOT tool applied basically to inland tourism promotion, proposals will be made that help to stop demographic bleeding, diversifying the local economy based on the primary sector and the daily departure of many residents to other locations, where they develop their jobs of work. Key words: Depopulation, emptying, local development, Moratalla, territorial strategy.

To teach the most central concepts in evolutionary biology, we present an activity in pollination biology. Students play the role of either pollinator or flower and work through a set of scenarios to maximize plant fitness. This “Pollination Game” facilitates critical and inquiry-based thinking, and we accompany each round of the exercise with a set of discussion questions and answers. We have piloted and fine tuned this exercise with high school students, and improved the exercise with the input of high school teachers at a teaching conference. The activity could easily be adapted for freshman undergraduate students.

Evolutionary dynamics is traditionally considered under either an infinite population or a finite population assumptions. The stochastic effects due to sampling of finite population system play imp...

We propose a time-parameterized analogy between the thermodynamic behavior of a 3-level energy system and the progression of the HIV infection described by the cell population evolution generated by an appropriated cellular automata model. The development of internal energy and its fluctuations, and of the entropy of the 3-level energy system allows the identification of an effective temperature that uniquely characterizes the three main stages of the dynamic process of HIV infection. Furthermore, this thermodynamical equivalence allows obtaining the instants associated with particular time intervals of the evolution of internal energy and entropy, which are not quantitatively accessible by the usual dynamic models based on differential equations or cellular automata. The maximum entropy point, which marks the threshold between the states of positive and negative temperatures, also corresponds to the onset of the immune system\' exhaustion and the concomitant and inexorable progression to AIDS. Such point lies in the time interval where the energy inversion mechanisms in the populations of non-infected and infected cells occur. This time lag is also characterized by considerable fluctuations of the internal energy when different samples are compared., The manuscript contains (including the references) 14 pages, 6 figures and one table. This work was submitted and approved in Physica A

The forward time simulation tool rSHAPE (R-package for Simulated Haploid Asexual Population Evolution) was designed to complement the theoretical and empirical study of evolution. Included with rSHAPE are functions to programmatically build, run, and initially process results of an evolutionary experiment defined by the range of experimental conditions. As experimental evolution often studies both the emergence and fate ofde novomutants, I validated rSHAPE by confirming its ability to replicate seminal theoretical expectations concerning changes in fitness through time and the fixation probability of mutants. As an example of how rSHAPE can support both theoretical and empirical research, I applied rSHAPE to study how the laboratory protocol of serial passaging, common in microbial experimental evolution, affects the fixation probability ofde novomutants. Unlike related theoretical work which modelled growth as effectively exponential (Wahlet al., 2002), this study considered populations experiencing logistic growth which is common to microbes undergoing serial passaging. In contrast to exponential growth, when a population undergoes logistic growth the probability of a mutant arising and eventually fixing depends upon when a mutant arises during a growth phase. Users can download software and documentation for rSHAPE through CRAN athttps://cran.r-project.org/web/packages/rSHAPE/index.html, or via GitHub athttps://www.github.com/Jdench/SHAPE_library/.

The 6th International Conference in Evolutionary Linguistics (CIEL 6), jointly organized by Overseas Education College of Xiamen University and the Anthropological Society of China, was held in Xiang’an campus of Xiamen University, on November 22-23, 2014. A total of 45 scholars from China, U.S. and Estonia attended the conference. There were 7 keynote speeches, 22 oral presentations and 8 poster presentations featuring five major themes: language as a complex adaptive system; language and the brain; vertical and horizontal transmission of languages; language and population evolution in China; the ancestry of the Chinese Language.

Due to the omnipresent risk of predation, termites have evolved many antipredatory behaviors. The two related species Reticulitermes speratus and R. chinensis have been demonstrated to use homosexual tandem running to decrease individual predation risk after shedding their wings. In this study, we tested risk of predation in the termite R. flaviceps, which is distantly related to the above two species. We determined that homosexual tandem running also led to low individual predation risk in dealates of R. flaviceps. Moreover, by combining a predation model with a competition model, we observed a typical trade-off phenomenon between antipredatory behavior and pairing competition produced by male-male tandem running in the above three Reticulitermes species. Our results indicated that male-male tandem running could effectively protect disadvantaged individuals from being caught, but disadvantaged individuals would be easily eliminated in pairing competition after male-male tandem running, suggesting that male-male tandem running can promote population evolution in termites by repeatedly removing the relatively inferior male individuals.

We consider the population dynamics of two species described by the mutualistic Lotka–Volterra model with a +/+ interaction in the presence of spatial diffusions. The results demonstrate that diffusion does not affect the system’s stability but it brings two situations: one is a win–win situation where both species propagate with the same largest speed; in the other situation the aggressive species has two propagating wave fronts and the other species travels with a single slow wave front. Our model may help to understand the evolution of mutualism.

Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models.

The paper presents an analysis of major issues on human settlement sizes evolution in the XIX-th Century and particularly in the XX-th Century. The starting point is the presentation of population evolution by habitat types, it continues with an analysis of urbanization process costs and further on of de-ruralisation, while the final part includes a presentation of the programmes meant to safeguard urban and rural communities.

We provide conditions for the existence of equilibrium and non-equilibrium solutions about nonlinear population evolution systems with fixed population.

We present the time-varying critical fertility and analysis the stability of the system at the critical value. Then we get the sufficient conditions about the system stability, asymptotically stability and not. Keywords: Population development system; mortality rates; Critical fertility rate of women; stability

We perform the group classification of the generalized Lane-Emden systemxu′′+nu′+xHv=0, xv′′+nv′+xgu=0, which occurs in many applications of physical phenomena such as pattern formation, population evolution, and chemical reactions. We obtain four cases depending on the values ofn.

BACKGROUND A 7 year experiment was set up in 2002 to evaluate the long-term effects of weed management strategies based on graminicidal sulfonylureas (SUs) on the evolution of a Lolium rigidum population resistant to ACCase inhibitors in a continuous wheat cropping system. The strategies included the continued use of ALS inhibitors, the continued application of ACCase inhibitors and a simple resistance management strategy based on a biennial rotation of herbicide mode of action (MoA). RESULTS The efficacy of the tested SUs in the field decreased significantly, starting from the fourth treatment in all control strategies. Regardless of control strategy, the few survivors of the ALS treatment in the third season produced a significant number of ACCase- and ALS-resistant (multiple-resistant) progeny. Continuous ALS and biennial rotation of herbicides reduced weed densities, but L. rigidum conserved its ACCase resistance trait. Enhanced metabolism was detected in ALS-resistant plants, whereas target site was primarily involved in the ACCase-resistant individuals. CONCLUSION At the end of the experiment, multiple-resistant individuals were found in all samples coming from the control strategies investigated. The biennial rotation between ALS and other MoA appeared to delay the development of resistance to SUs over continuous treatments, but additional measures will likely need to be taken in order to make this sustainable in the long term, whereas the field efficacy of SUs remained relatively high until the end of the experiment. Integrated weed management with more diversity should be introduced in oversimplified cropping systems in order to sustainably manage resistant L. rigidum populations. Copyright © 2012 Society of Chemical Industry

Se presenta un estudio de la variación craneofacial en muestras del noroccidente de Suramérica, distribuidas ampliamente en un sentido espacial y temporal, con el objetivo de proponer hipótesis sobre el poblamiento inicial. Se llevaron a cabo diferentes análisis (matriz-R, correlación de matrices y morfometría geométrica) para estudiar la historia y la estructura poblacional a nivel regional. Los resultados sugieren una alta diversidad morfológica, así como la existencia de patrones de estructuración espacial y temporal; también indican que ciertos eventos poblacionales y la acción de fuerzas evolutivas afectaron notablemente la diversidad craneofacial a través del tiempo. A partir de la comparación de distintos tipos de evidencia se propone un modelo sobre la forma y el tiempo del poblamiento temprano de la región y la subsecuente evolución poblacional a lo largo del Holoceno.

The classic problem for a logistically evolving single species population being harvested involves three parameters: rate constant, carrying capacity and harvesting rate, which are taken to be positive constants. However, in real world situations, these parameters may vary with time. This paper considers the situation where these vary on a time scale much longer than that intrinsic to the population evolution itself. Application of a multiple time scale approach gives approximate explicit closed form expressions for the changing population, that compare favorably with those generated from numerical solutions.

In a recent paper, Wagner and Graf derived a population evolution equation for an ensemble of convective plumes, an analog with the Lotka–Volterra equation, from the energy equations for convective plumes provided by Arakawa and Schubert. Although their proposal is interesting, as the present note shows, there are some problems with their derivation.

The apiarian and agricultural sectors got together to set up an apiary survey in Eure-et-Loir, in 2010, during oilseed rape flowering. The aim of this observatory was to highlight what can occur during oilseed rape flowering regarding bee population evolution and agricultural practices. The year 2010 was marked by important larvae and brood mortalities which induced a national investigation. Among the 4 surveyed apiaries, 2 were also concerned by these mortalities but this could not be explained by the observations and the analysis that were carried out. However, this survey enabled us to identify apiarian and agricultural practices around the apiaries and to combine expert testimonies of apiarian and agricultural professionals.

In this paper, we proposed a LW-PSO (PSO using a local world evolving model) by combining a local world evolving model with the “survival of the fittest” evolution mechanism. The concept of population structural entropy, a new diversity measure is defined and used to control the evolution process of the population topology of LW-PSO so the balance between the exploration and exploitation can be preserved during the population evolution. The experimental results show that LW-PSO can outperform the standard PSO with the lbest topology and the gbest topology especially in the multimodal problems and the extra time cost is negligible.

Is there an overriding principle of nature, hitherto overlooked, that governs all population behavior? A single principle that drives all the regimes observed in nature - exponential-like growth, saturated growth, population decline, population extinction, oscillatory behavior? In current orthodox population theory, this diverse range of population behaviors is described by many different equations - each with its own specific justification. The signature of an overriding principle would be a differential equation which, in a single statement, embraces all the panoply of regimes. A candidate such governing equation is proposed. The principle from which the equation is derived is this: The effect on the environment of a population's success is to alter that environment in a way that opposes the success., Comment: Revised equation-numbering to correspond to published version

Aims: A research was undertaken to explore the possibility to use Biolog system of microbial metabolic characterization for the monitoring of yeast population evolution during alcoholic fermentation for wine production. Methods and Results: An application of Biolog system was employed for the characterization of yeasts of oenological interest, in pure cultures and mixed consortia, in various cell concentrations. The system’s capacity to discriminate among different cell concentrations of the same yeast strain was ascertained, along with the capacity to discriminate between mixed and pure populations. Conclusions: The tested application of Biolog system resulted suitable for a quick recognition (24 h) of the presence of starter cultures within mixed populations of autochthonous yeasts. Such discrimination was confirmed with the one resulting from molecular techniques. Significance and Impact of the Study: The study suggests the possibility to employ Biolog system for an early monitoring of yeast evolution in modern wine-making fermentations, where specialized yeasts are more and more frequently used as starters and their ability to overcome autochthonous yeast populations is crucial.

In recent years, evaluating graph distance has become more and more important in a variety of real applications and many graph distance measures have been proposed. Among all of those measures, structure-based graph distance measures have become the research focus due to their independence of the definition of cost functions. However, existing structure-based graph distance measures have low degree of precision because only node and edge information of graphs are employed in these measures. To improve the precision of graph distance measures, we define substructure abundance vector (SAV) to capture more substructure information of a graph. Furthermore, based on SAV, we propose unified graph distance measures which are generalization of the existing structure-based graph distance measures. In general, the unified graph distance measures can evaluate graph distance in much finer grain. We also show that unified graph distance measures based on occurrence mapping and some of their variants are metrics. Finally, we apply the unified graph distance metric and its variants to the population evolution analysis and construct distance graphs of marker networks in three populations, which reflect the single nucleotide polymorphism (SNP) linkage disequilibrium (LD) differences among these populations.

A population balance method in which continuum and discrete phases are integrated is developed to simulate the evolution of polydisperse population of bubbles in a turbulent pipeline flow. The investigation is focused on the effect of the coalescence efficiency on this evolution. A dilute system of bubbles under microgravity conditions is considered. It is found that if the initial coalescence efficiency is low, a slight increase produces a significant effect on the bubble coalescence rate, and thus on the evolution of the population. If, however, the initial coalescence efficiency is high, its increase results in a marginal effect on the way the population evolves. The results of simulations are validated against experimental data on the population mean.

A general population evolution model is considered. Any individual of the population is characterized by its score. Certain general conditions are assumed concerning the number of the individuals and their scores. Asymptotic theorems are obtained for the number of individuals having a fixed score. Then it is proved that the score distribution is scale free. The result is applied to obtain the weight distribution of cliques in a random graph evolution model.

trees sifter 1.0 implements an approximate method to estimate the time to the most recent common ancestor (TMRCA) of a set of DNA sequences, using population evolution modelling. In essence, the program simulates genealogies with a user-defined model of coalescence of lineages, and then compares each simulated genealogy to the genealogy inferred from the real data, through two summary statistics: (i) the number of mutations on the genealogy (Mn), and (ii) the number of different sequence types (alleles) observed (Kn). The simulated genealogies are then submitted to a rejection algorithm that keeps only those that are the most likely to have generated the observed sequence data. At the end of the process, the accepted genealogies can be used to estimate the posterior probability distribution of the TMRCA.

In the origin of life community there has been a dispute about whether metabolism or replication came first. Yet both of these approaches are in implicit agreement that the first forms of life were basically passive. That shared assumption has begun to be challenged by a new generation of metabolism-first approaches, emphasizing that movement and adaptive behavior could have played an important role right from the start. After introducing recent research on this behavior-based approach to the origin of life, I offer a preliminary assessment of what this new approach implies for the origins of the genetic system. Current state of the field Metabolismand replicator-first approaches, while differing in many respects, implicitly agree that the first forms of life were passive and encapsulated individuals. Both failed to consider the intermediate timescales between chemical self-constitution and population evolution: no mention is made of motility nor of development (Froese et al., 2011). Yet new metabolismfirst approaches emphasize that motility and adaptive behavior could have played a crucial role from the start (Froese et al., 2014; Hanczyc, 2011; Egbert et al., 2012). Such a behaviorbased approach can refresh thinking on several classic issues. For example, it was long believed that pre-biotic evolution of a hypercycle of autocatalytic processes is unstable because it can be invaded by parasitic compounds. But later on it was realized that this is only the case in a well-mixed (non-spatial) environment, while spatial embedding can make hypercycles immune to parasites. We can push this change in perspective even further. It has been shown that it is possible to make use of the instabilities introduced by the parasites as a source of spontaneous motility, a capacity which can be adaptive under some conditions (e.g., Froese et al., 2012). Indeed, there may even have been forms of minimal cognition already at the origin of life (Hanczyc & Ikegami, 2010).

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