A self-consistent multi-component model of plasma turbulence and kinetic neutral dynamics for the simulation of the tokamak boundary

A self-consistent model is presented for the simulation of a multi-component plasma in the tokamak boundary. A deuterium plasma is considered, with the plasma species that include electrons, deuterium atomic ions and deuterium molecular ions, while the deuterium atoms and molecules constitute the neutral species. The plasma and neutral models are coupled via a number of collisional interactions, which include dissociation, ionization, charge-exchange and recombination processes. The derivation of the three-fluid drift-reduced Braginskii equations used to describe the turbulent plasma dynamics is presented, including its boundary conditions. The kinetic advection equations for the neutral species are also derived, and their numerical implementation discussed. The first results of multi-component plasma simulations carried out by using the GBS code are then presented and analyzed, being compared with results obtained with the single-component plasma model.

Charged stellar model with three layers

We establish new charged stellar models from the Einstein-Maxwell field equations for relativistic superdense objects outfitted with three layers. The core layer is described by a linear equation of state (EoS) describing quark matter, while the intermediate layer is described by a Bose-Einstein condensate EoS for Bose-Einstein condensate matter and the envelope layers satisfying a quadratic EoS for the neutron fluid. We have specified a new choice of the electric field and one of the metric potentials. It is interesting to note that the choice of electric field in this model can be set to vanish and we can regain earlier neutral models. Plots generated depict that the matter variables, gravitational potentials and other physical conditions are consistent with astrophysical studies. The interior layers and exterior boundary are also matched.

A pragmatic approach for producing theoretical syntheses in ecology

It has been proposed that ecological theory develops in a pragmatic way. This implies that ecologists are free to decide what, from the knowledge available to them, they will use to build models and learn about phenomena. Because in fields that develop pragmatically knowledge generation is based on the decisions of individuals and not on a set of predefined axioms, the best way to produce theoretical synthesis in such fields is to assess what individuals are using to support scientific studies. Here, we present an approach for producing theoretical syntheses based on the propositions most frequently used to learn about a defined phenomenon. The approach consists of (i) defining a phenomenon of interest; (ii) defining a collective of scientists studying the phenomenon; (iii) surveying the scientific studies about the phenomenon published by this collective; (iv) identifying the most referred publications used in these studies; (v) identifying how the studies use the most referred publications to give support to their studies and learn about the phenomena; (vi) and from this, identifying general propositions on how the phenomenon is approached, viewed and described by the collective. We implemented the approach in a case study on the phenomenon of ecological succession, defining the collective as the scientists currently studying succession. We identified three propositions that synthesize the views of the defined collective about succession. The theoretical synthesis revealed that there is no clear division between “classical’’ and “contemporary’’ succession models, and that neutral models are being used to explain successional patterns alongside models based on niche assumptions. By implementing the pragmatic approach in a case study, we show that it can be successfully used to produce syntheses based on the actual activity of the scientific community studying the phenomenon. The connection between the resulting synthesis and research activity can be traced back through the methodological steps of the approach. This result can be used to understand how knowledge is being used in a field of study and can guide better informed decisions for future studies.

Genomic architecture controls spatial structuring in Amazonian birds

Large rivers are ubiquitously invoked to explain the distributional limits and speciation of the Amazon Basin's mega-diversity. However, inferences on the spatial and temporal origins of Amazonian species have narrowly focused on evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known to produce heterogeneity in differentiation across the genome. To test how these factors may influence evolutionary inferences across multiple taxa, we sequenced whole genomes of populations for three bird species that co-occur in southeastern Amazonian and exhibit different life histories linked to their propensity to maintain gene flow across the landscape. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with the species tree topology weight. Gene flow was less pervasive in regions of low recombination, making these windows more suitable for commonly used phylogenetic methods that assume a bifurcating-branching model. To corroborate that these associations were attributable to selection, we modeled the signature of adaptive alleles across the genome taking demographic history into account, and found that on average 31.6 percent of the genome showed high probability for patterns consistent with selective sweeps and linked selection directly affecting the estimation of evolutionary parameters. By implementing a comparative genomic approach we were able to disentangle the effects of intrinsic genomic characteristics and selection from the neutral processes and show how speciation hypotheses are sensitive to genomic architecture.

Option Pricing Under GARCH Models Applied to the SET50 Index of Thailand

Variance changes over time and depends on historical data and previous variances; as a result, it is useful to use a GARCH process to model it. In this paper, we use the notion of Conditional Esscher transform to GARCH models to find the GARCH, EGARCH and GJR risk-neutral models. Subsequently, we apply these three models to obtain option prices for the Stock Exchange of Thailand and compare to the well-known Black-Scholes model. Findings suggest that most of the pricing options under GARCH model are the nearest to the actual prices for SET50 option contracts with both times to maturity of 30 days and 60 days.

Downstream Computer-Aided Design, Engineering, and Manufacturing Integration Using Exchangeable Persistent Identifiers in Neutral Re-imported Computer-Aided Design Models

Computer-aided design, engineering, and manufacturing (CAx) have improved product design and development. The associativity of revised design in native files for downstream applications, assembly design, finite element analysis, and numerical control (NC) manufacturing has been supported by homogeneous CAx systems. However, heterogeneous CAx environments have issues when neutral models are revised and re-imported because the required identification information is missing. This results in a problem of the associativity of neutral models. In this study, different computer-aided design (CAD) applications have been analyzed with respect to their capabilities for import and export of neutral models with identification information. A tool has been selected to demonstrate the key findings. Moreover, future directions have been discussed to solve associativity issues in design re-import based on neutral models.

Lagging strand encoding promotes adaptive evolution

Cells may be able to promote adaptive evolution in a gene-specific and temporally-controlled manner. Genes encoded on the lagging strand have a higher mutation rate and evolve faster than genes on the leading strand. This effect is likely driven by head-on replication-transcription conflicts, which occur when lagging strand genes are transcribed during DNA replication. We previously suggested that the ability to selectively increase mutagenesis in a subset of genes may provide an adaptive advantage for cells. However, it is also possible that this effect could be neutral or even highly deleterious. Distinguishing between these models is important because, if the adaptive model is correct, it would indicate that 1) head-on conflicts, which are generally deleterious, can also provide a benefit to cells, and 2) cells possess the remarkable ability to fine-tune adaptive evolution. Furthermore, investigating these models may address the long-standing debate regarding whether accelerated evolution through conflicts can be adaptive. To distinguish between the adaptive and neutral models, we conducted single nucleotide polymorphism (SNP) analyses on wild strains of bacteria, from divergent phyla. To test the adaptive hypothesis, we analyzed convergent mutation patterns. As a simple test of the neutral hypothesis, we performed in silico modeling. Our results show that convergent mutations are enriched in lagging strand genes and that these mutations are unlikely to have arisen by chance. Additionally, we observe that convergent mutation frequency has a stronger positive correlation with gene-length in lagging strand genes. This effect strongly suggests that head-on conflicts between the DNA replication and transcription machineries are a key mechanism driving the formation of convergent mutations. Together, our data indicate that head-on replication-transcription conflicts can promote adaptive evolution in a variety of bacterial species, and potentially other organisms.