Evolution of the Rhynchotrema–Hiscobeccus lineage: implications for the diversification of the Late Ordovician epicontinental brachiopod fauna of Laurentia

Multivariate analysis based on nine biometric characters of 171 Late Ordovician rhynchonellide specimens from nine upper Sandbian–upper Katian localities in North America supports the hypothesis that one of the diagnostic taxa of the North American epicontinental brachiopod fauna, Hiscobeccus, evolved from Rhynchotrema, which lived predominantly in peri‐cratonic settings. The oldest known Hiscobeccus, H. mackenziensis of early Katian age, exhibits transitional characteristics between Rhynchotrema and Hiscobeccus, and it clusters more closely with Rhynchotrema than with younger species of Hiscobeccus of mid–late Katian (Maysvillian–Richmondian) age. Diversification of the Hiscobeccus lineage in epicontinental seas was characterized by drastic increase in shell size, globosity and lamellosity, especially in palaeoequatorially located inland basins. Such morphological trends are interpreted as adaptation to relatively shallow, muddy substrates, moderate water turbulence, relatively low oxygen content and unstable supply of nutrients in generally overheated epicontinental seas with sluggish circulation.     

Combined use of coral reefs optimization and multi-agent deep Q-network for energy-aware resource provisioning in cloud data centers using DVFS technique

Big data processing, scientific calculations, and multimedia operations are some applications that require very complex time-consuming computations which cannot be performed on personal computers. Utilizing powerful cloud resources is a common method to address this problem. The amount of energy consumption of cloud data centers is an important challenge in these complex calculations, and reducing the energy consumption of cloud data centers is one of the most important goals of the researches in this area. The proposed method of this paper, called multi-agent deep Q-network with coral reefs optimization (MDQ-CR), combines the coral reefs optimization algorithm and multi-agent deep Q-network to reduce the energy consumption of data centers and cloud resources using the dynamic voltage and frequency scaling (DVFS) technique. The MDQ-CR has two main phases. The first phase exploits coral reefs optimization algorithm with a short-term view, and the second phase uses deep Q-network with a long-term view. The Markov game model is used to lead the learning agents to converge to the global optimal solution. Since processors consume the highest amount of energy of cloud compared to the other resources, the proposed method focuses on reducing the processors’ energy consumption. Reducing the voltage and frequency of processors, considering expiration times of their tasks, can reduce their energy consumption significantly. The empirical experiments show that the proposed method can save energy about 89% compared to completely randomized methods, and about 20% compared to the two recent methods of the literature.

     

Self-assembly of cationic surfactants on the carbon nanotube surface: insights from molecular dynamics simulations

The insolubility of carbon nanotubes (CNTs) in aqueous media has been a limitation for the practical application of this unique material. Recent studies have demonstrated that the suspend ability of CNT can be substantially improved by employing appropriate surfactants. Although various surfactants have been tested, the exact mechanism by which carbon nanotubes and the different surfactants interact is not fully understood. To deepen the understanding of molecular interaction between CNT and surfactants, as well as to investigate the influence of the surfactant tail length on the adsorption process, we report here the first detailed large-scale all-atomistic molecular dynamics simulation study of the adsorption and morphology of aggregates of the cationic surfactants containing trimethylammonium headgroups (C₁₂TAB and C₁₆TAB) on single-walled carbon nanotube (SWNT) surfaces. We find that the aggregation morphology of both C₁₂TAB and C₁₆TAB on the SWNT is dependent upon the number of the surfactants in the simulation box. As the number of the surfactants increases the random monolayer structure gradually changes to the cylinder-like monolayer structure. Moreover, we make a comparison between the C₁₂TAB and C₁₆TAB adsorption onto SWNTs to clarify the role of the surfactant tail length on the adsorption process. This comparison indicates that by increasing the number of surfactant molecules, the larger number of the C₁₆TAB molecules tend to adsorb onto SWNTs. Further, our results show that a longer chain yields the higher packed aggregates in which the surfactant heads are extended far into the aqueous phase, which in turn may increase the SWNTs stabilization in aqueous suspensions.