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The advancement of science, as well as scientific careers, depends upon good and clear scientific writing. Science is the most democratic of human endeavours because, in principle, anyone can replicate a scientific discovery. In order for this to continue, writing must be clear enough to be understood well enough to allow replication, either in principle or in fact. In this paper I will present data on the publication process in Evolutionary Ecology, use it to illustrate some of the problems in scientific papers, make some general remarks about writing scientific papers, summarise two new paper categories in the journal which will fill gaps that appear to be expanding in the literature, and summarise new journal policies to help mitigate existing problems. Most of the suggestions about writing would apply to any scientific journal. [ABSTRACT FROM AUTHOR]

In the field of expensive optimization, numerous papers have proposed surrogate-assisted evolutionary algorithms (SAEAs) for a few thousand or even hundreds of function evaluations. However, in reality, low-cost simulations suffice for a lot of real-world problems, in which the number of function evaluations is moderately restricted, e.g., to several thousands. In such moderately restricted scenario, SAEAs become unnecessarily time-consuming and tend to struggle with premature convergence. In addition, tuning the SAEA parameters becomes impractical under the restricted budgets of function evaluations—in some cases, inadequate configuration may degrade performance instead. In this context, this paper presents a fast and auto-tunable evolutionary algorithm for solving moderately restricted expensive optimization problems. The presented algorithm is a variant of adaptive differential evolution (DE) algorithms, and is called emulation-based adaptive DE or EBADE. The primary aim of EBADE is to emulate the principle of sample-efficient optimization, such as that in SAEAs, by adaptively tuning the DE parameter configurations. Specifically, similar to Expected Improvement-based sampling, EBADE identifies parameter configurations that may produce expected-to-improve solutions, without using function evaluations. Further, EBADE incepts a multi-population mechanism and assigns a parameter configuration to each subpopulation to estimate the effectiveness of parameter configurations with multiple samples carefully. This subpopulation-based adaptation can help improve the selection accuracy of promising parameter configurations, even when using an expected-to-improve indicator with high uncertainty, by validating with respect to multiple samples. The experimental results demonstrate that EBADE outperforms modern adaptive DEs and is highly competitive compared to SAEAs with a much shorter runtime. [ABSTRACT FROM AUTHOR]

The article reports on the celebration made by the Linnean Society of London for the 150 years anniversary since the reading of the seminal evolution papers by British naturalists Charles Darwin and Alfred Russel Wallace at one of the meetings of the organization in England in 1858. According to the article, the reading of the papers marks the removal of humanity from the center of creation to promote the concept of evolution that theorizes that life came into being by a process of natural selection.

Game theory describes social behaviors in humans and other biological organisms. By far, the most powerful tool available to game theorists is the concept of a Nash Equilibrium (NE), which is motivated by perfect rationality. NE specifies a strategy for everyone, such that no one would benefit by deviating unilaterally from his/her strategy. Another powerful tool available to game theorists are evolutionary dynamics (ED). Motivated by evolutionary and learning processes, ED specify changes in strategies over time in a population, such that more successful strategies typically become more frequent. A simple game that illustrates interesting ED is the generalized Rock-Paper-Scissors (RPS) game. The RPS game extends the children's game to situations where winning or losing can matter more or less relative to tying. Here we investigate experimentally three RPS games, where the NE is always to randomize with equal probability, but the evolutionary stability of this strategy changes. Consistent with the prediction of ED we find that aggregate behavior is far away from NE when it is evolutionarily unstable. Our findings add to the growing literature that demonstrates the predictive validity of ED in large-scale incentivized laboratory experiments with human subjects. [ABSTRACT FROM AUTHOR]

This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait's average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher's fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species' weights to the lengths of DNA strands. [ABSTRACT FROM AUTHOR]

A method is described for determining the number of preferred codons in taxa in which G+C levels differ. If the hypothesis of random codon usage is not rejected, there are no preferred codons. If that hypothesis is rejected, then a model with one or two preferred codons is fitted to the data and a likelihood ratio test is used to determine whether there are one or two preferred codons. A C++ program is freely available to perform the calculations. [ABSTRACT FROM AUTHOR]

Health state assessment is an important measure to maintain the safety of aerospace relays. Due to the uncertainty within the relay system, the accuracy of the model assessment is challenged. In addition, the opaqueness of the process and incomprehensibility of the results tend to lose trust in the model, especially in high security fields, so it is crucial to maintain the interpretability of the model. Thus, this paper proposes a new interpretable belief rule base model with step-length convergence strategy (IBRB-Sc) for aerospace relay health state assessment. First, general interpretability criteria for BRB are considered, and strategies for maintaining model interpretability are designed. Second, the evidential reasoning (ER) method is used as the inference machine. Then, optimization is performed based on the Interpretable Projection Covariance Matrix Adaptive Evolution Strategy (IP-CMA-ES). Finally, the validity of the model is verified using the JRC-7M aerospace relay as a case study. Comparative experiments show that the proposed model maintains high accuracy and achieves advantages in interpretability. [ABSTRACT FROM AUTHOR]

Reports on the publication of a research paper that argues in favor of intelligent design in the "Proceedings of the Biological Society of Washington" in the U.S. Impact of the publication of the paper on the advocacy of getting intelligent design into the curriculum; Use of information theory and other techniques to argue that the complexity of living organisms cannot be explained by Darwinian evolution; Overview of the arguments of Stephen Meyer, the author of the paper.

Discusses the discovery of the chimp teeth from the Kapthurin Formation in the East African Rift Valley. Significance of the discovery because there is no fossils of modern apes had ever been found in Africa; Findings in the paper co-authored by Sally McBrearty, an anthropologist at the University of Connecticut, and Nina Jablonski, an anthropologist at the California Academy of Sciences; Discovery of the first chimp tooth by Jablonski from a collection of monkey fossils amassed by McBrearty from Kapthurin Formation; Implications for evolution and anthropology; Irony in the discovery of the teeth.

To improve the quality and efficiency of machine translation (MT) for low-resource languages (LRLs), this paper analyzes the application of genetic algorithm (GA) under the background of cloud computing (CC). By using GA to optimize MT for LRL, this study aims to address the limitations and challenges of traditional methods on LRL. Firstly, GA under the background of the CC platform is analyzed in detail. The CC platform offers powerful computing and storage capabilities that provide the basis for large-scale optimization using GA. GA is an optimization algorithm based on the principle of biological evolution, which seeks the optimal solution by simulating genes' genetic and evolutionary processes. In MT of LRL, GA can be used to optimize model parameters, select translation rules, and improve translation quality, thereby improving translation results. Secondly, the present situation of MT for LRL is described. Finally, the traditional LRL-oriented MT and the GA-optimized LRL-oriented MT are compared and analyzed. The results reveal that the traditional LRL-oriented MT has low accuracy and low customer satisfaction (CS). Considering the translation time, accuracy, and resource utilization rate (RUR), the highest rate is only about 47%. In contrast, GA-optimized LRL-oriented MT significantly improves translation accuracy and CS, reaching approximately 94%. The final evaluation results indicate that the accuracy of the model is usually above 70%, and up to 94%. Therefore, GA plays an important role in optimization for LRL-oriented MT. This paper provides technical support for optimizing LRL-oriented MT and contributes to the further development of MT technology. By applying GA, the limitations of traditional methods in LRL can be overcome and the quality and efficiency of translation can be improved. It is of great significance to improve the development of translation technology of LRL, enhance cross-language communication, and promote global cultural exchange. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is one of the most efficient evolution algorithms (ES) for dealing with nonlinear, complicated and difficult global optimization problems. The main contribution of this paper can be summarized in three directions: Firstly, a novel crossover rate (CR) generation scheme based on the zscore value of fitness, named fcr, is introduced. For a minimization problem, the proposed CR generation strategy always assigns a smaller CR value to individual with smaller fitness value. Therefore, the parameters of individuals with better fitness are inherited by their offspring with high probability. In the second direction, the control parameters are adjusted by unused bimodal settings in which each parameter setting is selected according to the evolution status of individual. The third direction of our work is introducing the L1 norm distance as the weights for updating the mean value of crossover rate and scale factor. Theoretically, compared with L2 norm, L1-norm is more efficient to suppress outliers in the difference vector. These modifications are first integrated with the mutation strategy of JADE, then a modified version, named JADEfcr, is proposed. In addition, to improve the optimization ability further, another variant LJADEfcr by using a linear population reduction mechanism is considered. So as to confirm and examine the performance of JADEfcr and LJADEfcr, numerical experiments are conducted on 29 optimization problems defined by CEC2017 benchmark. For JADEfcr, its experimental results are made a comparison with twelve state-of-the-art algorithms. The comparative study demonstrates that in terms of robustness, stability and solution quality, JADEfcr are better and highly competitive with these well-known algorithms. For LJADEfcr, its results are compared with JADEfcr and other nine powerful algorithms including four recent algorithms and five top algorithms on CEC2017 competition. Experimental results indicate that LJADEfcr is superior and statistically competitive with these excellent algorithms in terms of robustness, stability and the quality of the obtained solutions. [ABSTRACT FROM AUTHOR]

This study investigates the joint replenishment and delivery problem taking trade credits the delay in payment permitted by suppliers and capacity-constrained into consideration (CJRD-TC). The CJRD-TC aims to minimize the total system costs by finding a reasonable replenishment and delivery schedule policy. In this paper, an opposite-based RUNge Kutta optimization algorithm (OBRUN) is proposed to compare with other intelligent algorithms, including the differential evolution (DE), RUNge Kutta optimization algorithm (RUN), sine cosine algorithm (SCA), and adaptive differential evolution with optimal external archive (JADE). The proposed algorithm is tested on 14 optimization functions. The values of average, standard deviation, and the best are the lowest for 12 functions, which demonstrates OBRUN is superior to other algorithms. We also provide graphs of search history, 2D views of functions, trajectory curve, average fitness history, and convergence curve of OBRUN, the balance between exploration and exploitation over the course of iterations can be observed. The convergence curves of five algorithms are also depicted, which show OBRUN has a fast convergence rate and accuracy than other algorithms. We conduct numerical experiments, parameter tuning, and sensitivity analysis for each of the small-scale, medium-scale, and large-scale number of items. The OBRUN can always find better solutions with the lowest mean values and the best-found total cost results than other algorithms. Indicators of the average optimality gap and the optimality gap dominate by the value of zero. These computational results have demonstrated OBRUN is a suitable and effective candidate for managers to solve the practical CJRD-TC problems. [ABSTRACT FROM AUTHOR]

The author of this paper discusses the theme of the "simulated body", that is the sense of "being there" in a body that is not one's own, or that does not exist in the way one perceives it. He addresses this issue by comparing Immersive Virtual Reality technology, the phenomenological approach, and Gerald Edelman's theory of Neural Darwinism. Virtual Reality has been used to throw light on some phenomena that cannot be studied experimentally in real life, and the results of its simulations enrich the phenomenological discourse on the lived body. Virtual "Reality" seems to replicate—at least in part—the simulation mechanisms of our mind, thus favoring developments in the field of philosophy of mind. [ABSTRACT FROM AUTHOR]

The article focuses on the protein evolution study conducted by researchers Nobuhiko Tokuriki and Dan Tawfik at the Weismann Institute of Science in Rehovot, Israel. It mentions about the work of Brian Shoichet and his colleagues which gave idea on Tawfik about chaperone proteins and states that Tokuriki joined into the laboratory just as the idea is taking shape. It offers information on their work in testing the ability GroEL/GroES chaperone protein in directed evolution.

The article discusses the discovery of a fossil within Canada's Devon Island that provides a link in the evolution of pinniped ancestry. It was discovered by undergraduate researcher Elizabeth Ross while traveling with a palaeontology filed crew. The crew was headed by Canadian Museum of Nature palaeobiologist Natalia Rybczynski, who provides quotes.

The article is about the non-coding DNA that distinguishes us from other mammals. David Haussler, a bioinformatician, and others researched on the non-coding regions for accelerated evolution of human genome. They found that the Human accelerated region 1 (Har1), found only in the humans, and the Har1 encodes a structural RNA. This research concluded that the Har1 regions are responsible for the brain development, and differentiate us from primates and mammals.

The width spread is one of the key indices affecting hot rolling processes and product quality. The traditional Shibahara spread prediction model (SSM) does not take into account the wear and interference of production equipment. As a result, the prediction accuracy cannot satisfy the high precision and high reliability requirements. To improve the prediction accuracy, first, this paper analyzes the strip deformation mechanism and the source of prediction errors and proposes an improved Shibahara spread model (ISSM) based on wear and interference. Second, a Bayesian-optimized differential evolution and adaptive gradient descent (BDE-AGD) algorithm is proposed for ISSM parameter optimization. This algorithm adopts the Bayesian algorithm to optimize the differential evolution (BDE) algorithm in stage 1, which improves the global search ability. Furthermore, the adaptive strategy is used for gradient descent (AGD) to improve the local search capability in stage 2. Finally, the experimental results show that the BDE-AGD algorithm reduces the error by 21.9% and 3.1% compared with DE-AGD and BDE-GD, respectively. This shows that BDE-AGD has a better global optimization capability. In addition, the prediction precision of ISSM is improved by 9.77% compared with the traditional SSM. Moreover, the precision of ISSM is improved by 8.56% on average compared with seven machine learning algorithms. This shows that ISSM optimized by BDE-AGD can achieve high-precision prediction of strip spread. [ABSTRACT FROM AUTHOR]

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

Rapid growth and high carbon-fixation properties make microalgal biomass a preferred source of energy-production technologies, particularly biodiesel and bioethanol, as well as nutritional supplements and pharmaceuticals. Today, microalgal biofuel technology, which is continuing to be developed as a surrogate for fossil-fuel technology, is the focus of constant research and development, especially regarding biomass yield and lipid production. Single stressors on microalgae have been commonly examined for efficient biofuel production; however, the synergetic effects of stressors on the lipid and biomass productivity of microalgae might lead to enhanced lipid properties. In addition, considering more than one parameter might be time-consuming; thus, many models, such as the surface-response methodology, can enable significant optimization of microalgal productivity economically. As well as multiple stressors, adaptive stressors considered in "adaptive laboratory evolution" could provide robust strains that alter low lipid yields. Although the field has progressed substantially in recent years, much about microalgae, their biological potential, how to efficiently utilize them, and their metabolic responses to various stressors remain unknown. Using an extensive review of recent developments in single-stress strategies, combined-stress strategies, and adaptive stress strategies, this paper gives an overview of recent developments in microalgal biofuel production under stress conditions. Further, it has also been suggested that an integrated microalgal biorefinery that also produces high-value chemicals alongside biofuel might significantly enhance its economic feasibility. It is expected that this document will provide an understanding of how to develop commercially viable microalgae strains for biofuel applications, taking several types of stress into consideration. [ABSTRACT FROM AUTHOR]

The variation of the network topology directly indicates the knowledge network evolution, the principal composition of which is the structural evolution. Based on the principles of both the minimum coupling costs between knowledge subjects and the network average value threshold, this paper constructs the evolution model of a small-world knowledge network. Using MATLAB programming, this paper analyzes the characteristics of the evolution of knowledge network. It then examines the main parameter changes and the distribution of the network node degree as well as its probability distribution and then proceeds to investigate the variation of the subjects' knowledge level as well as the variation speed. The simulation results show that with the structural evolution of the knowledge network, the connection between the network subjects has been improved after numerous rewiring. Compared with the initial state, the total knowledge stock of the knowledge network and the average knowledge stock of the subjects are significantly enhanced. [ABSTRACT FROM AUTHOR]

A recent publication in Nature has generated much heated discussion about evolution, its tendency towards increasing diversity and complexity, and its potential status above and beyond the known laws of fundamental physics. The argument at the heart of this controversy concerns assembly theory, a method to detect and quantify the influence of higher-level emergent causal constraints in computational worlds made of basic objects and their combinations. In this short essay, I briefly review the theory, its basic principles and potential applications. I then go on to critically examine its authors' assertions, concluding that assembly theory has merit but is not nearly as novel or revolutionary as claimed. It certainly does not provide any new explanation of biological evolution or natural selection, or a new grounding of biology in physics. In this regard, the presentation of the paper is starkly distorted by hype, which may explain some of the outrage it created. [ABSTRACT FROM AUTHOR]

The year 2009 marked both the bicentenary of Charles Darwin's birth, and the passage of 150 years since the publication of his revolutionary book, commonly referred to as The Origin of Species (Darwin ). As part of the national celebrations that took place in the UK, a meeting on 'Mathematical Models in Ecology and Evolution' was held in Bristol on September 10 and 11, with a special focus on the mathematical modelling and application of Darwin's key theories. This special issue collects together selected papers presented at the meeting, including contributions by the four invited keynote speakers, Rob Boyd, A. W. F. Edwards, Hanna Kokko and Franjo Weissing, as well as papers received after the event. The topics of these papers span the modelling of a very wide range of Darwin's ideas, particularly evolution through natural selection, the origin of species, sexual selection, altruism and cooperation, and pangenesis, as well as applications of Darwinian thinking to the behaviour of animals, humans, and even human societies. It is hoped that this collection of papers will provide a useful snapshot of the state-of-the-art in Darwinian theory after the last one and a half centuries, and help to identify potential directions for research over the next. [ABSTRACT FROM AUTHOR]

Humans are considered to be highly prosocial, especially in comparison to other species. However, most tests of prosociality are conducted in highly artificial settings among anonymous participants. To gain a better understanding of how human hyper-cooperation may have evolved, we tested humans' willingness to share in one of the most competitive fields of our current society: academia. Researchers were generally prosocial with 80% sharing a PDF of one of their latest papers, and almost 60% willing to send us their data. Intriguingly, prosociality was most prominent from male to male, and less likely among all other sex-combinations. This pattern suggests the presence of male-exclusive networks in science, and may be based on an evolutionary history promoting strong male bonds. [ABSTRACT FROM AUTHOR]

This situation began to change during the 1990s, and Dolph Schluter, more than anyone, made outstanding contributions of groundbreaking research exploring ecological speciation and its role in adaptive radiation. Ecological speciation, which is the main mechanism behind adaptive radiation, refers to when divergent natural selection in contrasting environments leads to the evolution of reproductive isolation (Schluter [12]). For example, an early review on character displacement (Schluter and McPhail [15]), and a later synthesis paper on the role of ecology in speciation (Schluter [12]) are both seminal contributions, and in a Science review he convincingly argued that Darwin was completely correct in his conclusion that natural selection is the main driver of speciation (Schluter [13]). [Extracted from the article]

Fabric of a granular assembly represents the topology of the contact network. This paper investigates the evolution of contact anisotropy (fabric) and average coordination number for a granular assembly subjected to uniaxial compression through the Discrete Element Method (DEM). A monosize three-dimensional random close-packed granular assembly with periodic boundary conditions under uniaxial compression is considered in this work. The fabric evolution is studied by post-processing the output data of the DEM simulation. The influence of cyclic loading, strain rate, and Young's modulus on the evolution of contact anisotropy and average coordination number is presented. The Young's modulus of the particle shows a significant influence on the particle contact creation during compression of the granular assembly with high strain rate. Effect of inertia on the contact anisotropy is observed to be significant during the compression of granular assemblies with different Young's modulus under high strain rate. The paper concludes with a semi-empirical model to predict the evolution of contact anisotropy as a function of the macroscopic stress state of the assembly during quasi-static uniaxial compaction. The model also introduces two microscopic non-dimensional parameters that are independent of friction between the particles and can be used to relate the macroscopic stresses with the contact anisotropy. [ABSTRACT FROM AUTHOR]

Fifty years ago, a small one and a half page paper without a single reference was published in the leading journal Nature. The paper laid out the most general mathematical formulation of natural selection that would work for all kinds of selection processes and under any form of inheritance (not just biological evolution and Mendelian genes), although the paper discussed the issue in a genetical framework. Written by a maverick American expatriate in England, with no prior background of studying evolution or genetics, the paper had initially been turned down by the editor of Nature as too difficult to understand. Largely ignored by the evolutionary biology community till the 1990s, the Price Equation is now widely recognized as an extremely useful conceptualization, permitting the incorporation of non-genetic inheritance into evolutionary models, serving to clarify the relationship between kin-selection and group-selection, unifying varied approaches used in the past to model evolutionary change, and forming the foundation of multi-level selection theory. [ABSTRACT FROM AUTHOR]

In order to improve the resistive switching performance of SiO2-based resistance random access memory (RRAM) devices, the evolution from pure oxide-based devices to metallic nanoparticle dispersed oxide-based devices is investigated in this paper. It is found that a high ratio of dispersed-oxide/pure-oxide and thin SiO2 film can increase the uniformity and endurance of RRAM devices. Pure TiN/SiO2/Au RRAM devices show large distributions and poor endurance for repeated resistive switching process, while the TiN/SiO2:FePt/SiO2/Au RRAM devices exhibit higher uniformity and better endurance. Moreover, the fluctuations and degradation of high-resistance state and low-resistance state are observed in the former devices; by increasing the ratio of dispersed-oxide/pure-oxide in TiN/SiO2/SiO2:FePt/SiO2/Au, high consistency resistive switching is obtained. It is believed that the random formation of conducting filaments (CFs) causes poor performance of RRAM devices, and with a higher ratio of dispersed-oxide/pure-oxide the morphology of CFs is controlled due to the local field enhancement. The results in this paper may provide a method of fabricating high-performance RRAM devices for industry application. [ABSTRACT FROM AUTHOR]

Socialization is the essential building process of any society in natural ecosystems. Effective socialization processes have been investigated for both "biotic" (human) and "abiotic" (virtual) entities, also within digital ecosystems in the perspective of common and self-adaptive languages. In this paper, we propose an approach for socialization, language self-adaptation, and evolution that enables an effective communicative interaction among digital entities acting in a digital ecosystem. The proposed method relies on an adaptable and extensible grammatical formalism, named Digital Ecosystem Grammar (DEG). This grammar enables digital entities to interpret the messages sent by other entities by using interaction, learning and evolution actions. Moreover, a grammar learning algorithm is applied to provide the self-adaptation mechanisms that allow the digital environment to adapt the interaction language according to new messages. The approach was suitable to support the characteristics of self-adaptation, context-awareness, evolvability, and semanticity of a digital ecosystem language. [ABSTRACT FROM AUTHOR]

Evolution is a key concept of biology, fundamental to understand the world and address important societal problems, but research studies show that it is still not widely understood and accepted. Several factors are known to influence evolution acceptance and understanding, but little information is available regarding the impacts of the curriculum on these aspects. Very few curricula have been examined to assess the coverage of biological evolution. The available studies do not allow comparative analyses, due to the different methodologies employed by the authors. However, such an analysis would be useful for research purposes and for the development of appropriate educational policies to address the problem of a lack of evolution acceptance in some countries. In this paper we describe the steps through which we developed a valid and reliable instrument for curricula analysis known as FACE: "Framework to Assess the Coverage of biological Evolution by school curricula." This framework was developed based on the "Understanding Evolution Conceptual Framework" (UECF). After an initial pilot study, our framework was reformulated based on identified issues and experts' opinions. To generate validity and reliability evidence in support of the framework, it was applied to four European countries' curricula. For each country, a team of a minimum of two national and two foreign coders worked independently to assess the curriculum using this framework for content analysis. Reliability evidence was estimated using Krippendorf's alpha and resulted in appropriate values for coding the examined curricula. Some issues that coders faced during the analysis were discussed and, to ensure better reliability for future researchers, additional guidelines and one extra category were included in the framework. The final version of the framework includes six categories and 34 subcategories. FACE is a useful tool for the analysis and the comparison of curricula and school textbooks regarding the coverage of evolution, and such results can guide curricula development. [ABSTRACT FROM AUTHOR]

This Special Issue on Crustacean Genomics arises from the TCS 2017 conference held in Barcelona, Spain, between 19 and 22 June, 2017. Applications of next generation sequencing have expanded rapidly over recent years, unveiling many new and exciting areas of genomic research. This includes novel insights into physiology, reproductive biology, response to environmental challenges, and their evolutionary history. Despite their importance, crustaceans still lack genomic resources compared with other widely-studied groups such as insects and vertebrates. The present volume integrates a collection of contributions from the speakers, and compiles the main results presented during the symposium. Following the structure of the Crustacean Genomics workshop, contributed papers have been arranged in two main blocks, a first set of studies focused on the use of transcriptomics to investigate crustacean physiology and reproduction while the second set focused on molecular systematics and evolutionary studies. [ABSTRACT FROM AUTHOR]

In the book The Essential Tension (1979) Thomas Kuhn described the conflict between tradition and innovation in scientific research—i.e., the desire to explore new promising areas, counterposed to the need to capitalize on the work done in the past. While it is probable that along their careers many scientists felt this tension, only few works have tried to quantify it. Here, we address this question by analyzing a large-scale dataset, containing all the papers published by the American Physical Society (APS) in 26 years, which allows for a better understanding of scientists' careers evolution in Physics. We employ the Physics and Astronomy Classification Scheme (PACS) present in each paper to map the scientific interests of 103,246 authors and their evolution along the years. Our results indeed confirm the existence of the "essential tension" with scientists balancing between exploring the boundaries of their area and exploiting previous work. In particular, we found that although the majority of physicists change the topics of their research, they stay within the same broader area thus exploring with caution new scientific endeavors. Furthermore, we quantify the flows of authors moving between different subfields and pinpoint which areas are more likely to attract or donate researchers to the other ones. Overall, our results depict a very distinctive portrait of the evolution of research interests in Physics and can help in designing specific policies for the future. [ABSTRACT FROM AUTHOR]

Evolutionary robotics (ER) has emerged as a fast growing field in the last two decades and has earned the attention of a number of researchers. Principles of biological evolution are applied in the form of evolutionary techniques for solving the complicated problems in the areas of robotic design and control. The diversity and the intensity of this growing field is presented in this paper through the contributions made by several researchers in the categories of robot controller design, robot body design, co-evolution of body and brain and in transforming the evolved robots in physical reality. The paper discusses some of the recent achievements in each of these fields along with some expected applications which are likely to motivate the future research. For the quick reference of the readers, a digest of all the works is presented in the paper, spanning the years and the areas of the research contributions. [ABSTRACT FROM AUTHOR]

The homological scaffold leverages persistent homology to construct a topologically sound summary of a weighted network. However, its crucial dependency on the choice of representative cycles hinders the ability to trace back global features onto individual network components, unless one provides a principled way to make such a choice. In this paper, we apply recent advances in the computation of minimal homology bases to introduce a quasi-canonical version of the scaffold, called minimal, and employ it to analyze data both real and in silico. At the same time, we verify that, statistically, the standard scaffold is a good proxy of the minimal one for sufficiently complex networks. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid co-evolutionary genetic algorithm (HCGA) has been presented for determining the optimal moving paths of several nanoparticles in a complex environment. In the proposed approach, an artificial potential field (APF) has been used to determine the feasible initial paths for moving the nanoparticles. The proposed APF prepares a potential map of the environment using the initial positions of the nanoparticles and positions of the obstacles and surface roughness. The cost function used in this paper includes the area under the critical force-time diagram, surface roughness as well as path smoothness. The performed investigations indicate the importance of each of these parameters in determining the optimal paths for displacing the nanoparticles. Also, the dynamic application of crossover and mutation operators has been used to avoid premature convergence. Using the information of the potential map, two new operators have been introduced to improve the feasible and infeasible paths. Furthermore, a novel co-evolutionary mechanism for solving the multiple nanoparticle path planning problems has been presented. The proposed co-evolutionary mechanism is able to determine the best destination for each particle, optimal sequence of moves for several particles, and also the optimal path for moving each particle. Finally, the performance of the proposed HCGA has been compared with the conventional genetic algorithm (CGA) and the ant colony optimization algorithm. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

The Baldwin effect is a process by which learnt traits become gradually incorporated into the genome through a Darwinian mechanism. From its inception, the Baldwin effect has been regarded with skepticism. The objective of this paper is to relativize this assessment. Our contribution is two-fold. To begin with, we provide a taxonomy of the different arguments that have been advocated in its defense, and distinguish between three justificatory dimensions-feasibility, explanatory relevance and likelihood-that have been unduly conflated. Second, we sharpen the debate by providing an evolutionary game theoretic perspective that is able to generalize previous results. The upshot of this paper is that the mechanism envisaged by Baldwin is less puzzling than commonly thought. [ABSTRACT FROM AUTHOR]

Using the Burgess Shale controversies as a case-study, this paper argues that controversies within different domains may interact as to create a situation of 'com- plicated intricacies,' where the practicing scientist has to navigate through a context of multiple thought collectives. To some extent each of these collectives has its own dynamic complete with fairly negotiated standards for investigation and explanation, theoretical background assumptions and certain peculiarities of practice. But the intellectual development in one of these collectives may 'spill over' having far reaching consequences for the treatment of apparently independent epistemic problems that are subject of investigation in other thought collectives. For the practicing scientist it is necessary to take this complex web of interactions into account in order to be able to navigate in such a situation. So far most studies of academic science have had a tendency to treat the practicing scientist as members of a single (enclosed) thought collective that stands intellectually isolated from other similar entities unless the discipline was in a state of crisis of paradigmatic proportions. The richness and complexity of Burgess Shale debate shows that this encapsulated kind of analysis is not enough. [ABSTRACT FROM AUTHOR]

This special issue of Evolutionary Ecology provides ten papers that have been presented at a conference on Evolutionary Ecology of Fishes in 2009. In addition to briefly summarizing the main content of the papers which is related to adaptive radiations, processes of ecological divergence, and fisheries-induced evolution, we review and synthesize in short the recent advance in studies on evolutionary ecology of fishes. We conclude that fishes are excellent model systems to study evolutionary ecology of animals, and suggest three promising new research avenues; (1) the contribution of behavioural processes to evolution, in particular the consideration of animal personalities and predator-prey interactions, (2) metabolic physiology and parasite-host interactions as new niche dimensions to be considered for adaptive diversification, and (3) the opportunities for mechanistic understanding of adaptation and speciation emerging from new genetic tools. [ABSTRACT FROM AUTHOR]

Topic and sentiment detection has been considered as an effective method to reveal the facts and sentiments in a massive volume of information. Existing works mainly focus on separate topic and sentiment extraction or static topic-sentiment associations, neglecting topic-sentiment dynamics and missing the opportunity to provide a in-depth analysis of online news. Actually, sentiment orientations are highly dependent on topic content and thus detecting topic-sentiment associations and their evolution over time is very important. This paper proposes a manifold learning-based model to explore the topic-sentiment associations and their evolution over time in the online news domain. The proposed model can visualize the hidden sentiment dynamics of topics in a low-dimensional space. Extensive experiments are conducted on online news crawled from the American Cable News Networks (CNN) website. The experimental results show that the proposed model outperforms the KL distance-based and the Similarity-based methods and improves the accuracy of topic classification by 12%. [ABSTRACT FROM AUTHOR]

In this paper, we consider the nonlocal controllability of α ∈ (1 , 2) -order fractional evolution systems of Sobolev type in abstract spaces. By utilizing fixed point theorems and the theory of resolvent operators we establish some sufficient conditions for the nonlocal controllability of Sobolev-type fractional evolution systems. [ABSTRACT FROM AUTHOR]

The abnormal erosion of hearth refractories is widely recognized as the main limiting factor for a long blast furnace life. In this paper, the embrittlement phenomenon of the hearth lining found in the blast furnace dissection has been described. Combining the migration figure of the thermocouple temperature in the blast furnace hearth, the evolution process of the carbon brick erosion and the heat transfer were analyzed. The results show that the embrittlement layer contains a large amount of ZnO, which accounts for more than 40%, and the thermal conductivity of the embrittled carbon brick is about 4.9 W/(m K). Moreover, due to the detachment of the embrittlement layer, the erosion evolution process of the hearth carbon brick exists as a cyclically periodic rising phenomenon together with the thermocouple temperature. Thereafter, the residual thickness of the carbon bricks and the temperature of the thermocouples during the formation of the embrittlement layer and the protective layer were calculated quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, a new spectral variables selection method, induced mutation genetic algorithm (IMGA), is proposed for near-infrared (NIR) spectroscopy. Based on the idea of genetic algorithm (GA), the IMGA greatly simplifies the process of biological evolution, which not only inherits the advantages of global optimization of the GA, but also effectively improves the convergence speed. In this study, the IMGA is applied to the selection of characteristic spectral variables for green tea origin identification. After five times of genetic evolutions, 11 characteristic spectral variables are selected from 156 spectral variables. Based on the 11 characteristic spectral variables, the classification model is built by partial least squares (PLS), and both the sensitivity and specificity of classification model are raised to 1 for prediction set. The overall results indicate that the IMGA can be well applied to the selection of characteristic spectral variables and effectively improve the prediction accuracy and calculation speed of the near-infrared model. [ABSTRACT FROM AUTHOR]

Recently, a new strategy to the reconstruction of f (R) -gravity models based on the Raychaudhuri equation has been suggested by Choudhury et al. (MNRAS 485:5693, 2019). In this paper, utilizing this method, the reconstruction of anisotropic Einstein–Maxwell equation in the 1 + 3 covariant formalism of f (R) -gravity is investigated in four modes: (i) Reconstruction from a negative constant deceleration parameter refereeing to an ever-accelerating universe; (ii) Reconstruction from a constant jerk parameter j = 1 which recovers celebrated Λ CDM mode of evolution; (iii) Reconstruction from a variable jerk parameter j = Q (t) ; and (iv) Reconstruction from a slowly varying jerk parameter. Furthermore, two suggestions for enhancing the method are proposed. [ABSTRACT FROM AUTHOR]

Background: Diaphorase (DI) has received wide attention as the key anodic enzyme mediating the electron transfer and electric energy generation in enzymatic biofuel cells (EBFCs). Lowering the anodic pH may be a useful strategy for constructing high-performance in EBFCs. However, most DI suffered from the poor activity at low pHs. Therefore, it is necessary to modify the activity and its acidic tolerance to further improve the performance of the EBFC. Results: This paper attempts to improve the enzyme activity of DI originated from Geobacillus stearothermophilus under acidic conditions through directed evolution. Three rounds of random mutagenesis by error-prone PCR of the GsDI gene followed by high-throughput screening allowed the identification of the mutant 3–8 (H37Q, S73T, F105L, S68T, G61S, D74V) exhibiting a 4- or 7-fold increase in the catalytic activity at pH 5.4 or 4.5 compared to that of the wild type. And the pH stability of mutant 3–8 was significantly better than that of wild type and showed a 1.3 times higher in the stability at pH 5.4. The EBFC anode equipped with 0.5 mg of mutant 3–8 achieved a maximum current of 40 μA at pH 5.4, much higher than that with the same loading of the wild type enzyme. Conclusion: The GsDI has been improved in the specific activity and pH stability by directed evolution which leads to the improvement of the EBFC performance. Also, the enlarged catalytic channel of mutant and decreased B-factor may be beneficial for the activity and stability. These results suggest that this engineered DI will be a useful candidate for the construction of enhanced EBFCs. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark datasets based on some fixed metrics. The algorithm(s) with the smallest value of this metric are chosen to be the 'best performing'. We offer a new approach to flip this paradigm. We instead aim to gain a richer picture of the performance of an algorithm by generating artificial data through genetic evolution, the purpose of which is to create populations of datasets for which a particular algorithm performs well on a given metric. These datasets can be studied so as to learn what attributes lead to a particular progression of a given algorithm. Following a detailed description of the algorithm as well as a brief description of an open source implementation, a case study in clustering is presented. This case study demonstrates the performance and nuances of the method which we call Evolutionary Dataset Optimisation. In this study, a number of known properties about preferable datasets for the clustering algorithms known as k-means and DBSCAN are realised in the generated datasets. [ABSTRACT FROM AUTHOR]

This paper addresses the Phanerozoic tectonic evolution of the western Tarim Basin based on an integrated stratigraphic, structural and tectonic analysis. P-wave velocity data show that the basin has a stable and rigid basement. The western Tarim Basin experienced a complex tectonic evolutionary history, and this evolution can be divided into six stages: Neoproterozoic to Early Ordovician, Middle Ordovician to Middle Devonian, Late Devonian to Permian, Triassic, Jurassic to Cretaceous and Paleogene to Quaternary. The western Tarim Basin was a rift basin in the Neoproterozoic to Early Ordovician. From the Middle Ordovician to Middle Devonian, the basin consisted of a flexural depression in the south and a depression that changed from a rift depression to a flexural depression in the north during each period, i.e., the Middle–Late Ordovician and the Silurian to Middle Devonian. During the Late Devonian to Permian, the basin was a depression basin early and then changed into a flexural basin late in each period, i.e., the Late Devonian to Carboniferous and the Permian. In the Triassic, the basin was a foreland basin, and from the Jurassic to Cretaceous, it was a downwarped basin. After the Paleogene, the basin became a rejuvenated foreland basin. Based on two cross sections, we conclude that the extension and shortening in the profile reflect the tectonic evolution of the Tarim Basin. The Tarim Basin has become a composite and superimposed sedimentary basin because of its long-term and complicated tectonic evolutionary history, highly rigid and stable basement and large size. [ABSTRACT FROM AUTHOR]

In this paper, we consider the finite approximate controllability of some Hilfer fractional evolution systems. Using a variational approach and Schauder's fixed point theorem, we give sufficient conditions for finite approximate controllability of semilinear controlled systems. An example is given to illustrate our theory. [ABSTRACT FROM AUTHOR]

Over the last few years, user modeling scenery is changing. With the recent advancements in ubiquitous and wearables technologies, the amount and type of data that can be gathered about users and used to build user models is expanding. User Model can now be enriched with data regarding different aspects of people's everyday lives. All these changes bring forth new research questions about the kinds of services which could be provided, the ways for effectively conveying new forms of personalisation and recommendation, and how traditional user modeling should change to exploit ubiquitous and wearable technology to provide these services. In this paper we follow the evolution of user modeling process, starting from the traditional User Model and progressing to RWUM - Real World User Model, which contains data from a person's everyday life. We tried to answer the above questions and to present a conceptual framework that represents the RWUM process, which might be used as a reference model for designing RWUM-based systems. Finally, we propose some inspiring usage scenarios and design directions that can guide researchers in designing novel, robust and versatile services based on RWUM. [ABSTRACT FROM AUTHOR]