Estimating Pan evolutionary history from nucleotide site patterns

Introgression appears increasingly ubiquitous in the evolutionary history of various taxa, including humans. However, accurately estimating introgression is difficult, particularly when 1) there are many parameters, 2) multiple models fit the data well, and 3) parameters are not simultaneously estimated. Here, we use the software Legofit to investigate the evolutionary history of bonobos (Pan paniscus) and chimpanzees (P. troglodytes) using whole genome sequences. This approach 1) ignores within-population variation, reducing the number of parameters requiring estimation, 2) allows for model selection, and 3) simultaneously estimates all parameters. We tabulated site patterns from the autosomes of 71 bonobos and chimpanzees representing all five extant Pan lineages. We then compared previously proposed demographic models and estimated parameters using a deterministic approach. We further considered sex bias in Pan evolutionary history by analyzing the site patterns from the X chromosome. Introgression from bonobos into the ancestor of eastern and central chimpanzees and from western into eastern chimpanzees best explained the autosomal site patterns. This second event was substantial with an estimated 0.21 admixture proportion. Estimates of effective population size and most divergence dates are consistent with previous findings; however, we observe a deeper divergence within chimpanzees at 987 ka. Finally, we identify male-biased reproduction in Pan evolutionary history and suggest that western to eastern chimpanzee introgression was driven by western males mating with eastern females.

The Influence of Variable CO2 Emission Tax Rate on Flexible Chemical Process Synthesis

The emission of greenhouse gasses is a major environmental problem, and efforts are being made worldwide in various ways to encourage producers to reduce their emissions. There is a need to incorporate environmental measures into process design and synthesis, as pollution prevention is a higher priority than waste management, and in this way, more sustainable solutions can also be achieved. One possibility is to introduce a CO2 tax, the value of which is very uncertain in the future. This paper demonstrates how the CO2 tax affects the optimal results of synthesizing chemical processes using mixed-integer nonlinear programming (MINLP). It was found that the tax increase promotes the use of better-quality raw materials and more efficient process units. Energy consumption and emissions are reduced and economic performance deteriorates. A multi-period, two-stage stochastic approach with recourse is suitable to incorporate the uncertainty of the CO2 tax in the MINLP process synthesis and gives better results than a simpler deterministic approach. In the case of the heat exchanger network synthesis, the costs obtained with the stochastic approach were 5% lower, and the emissions 7% lower than with a deterministic approach.

Distribution Grid Future Planning Under Uncertainty Conditions

The paper presents an approach for making decisions about the future development of a distribution grid under uncertainty conditions. The levels of a grid hosting capacity and adequacy are examined using probabilistic approach compared to the conventional deterministic fit-andforget approach. It is shown that the probabilistic approach according to the 99 % confidence probability saves significant costs in comparison with the deterministic approach. The probabilistic calculations prove the use of an equipment rated capacity downsized by 2 points of a typical IEC scale, and in some cases to refuse the construction of a parallel circuit. The main contribution of the paper is a method for choosing an effective rated voltage of a distribution grid in a probabilistic interpretation based on the conventional formulas of Still, Zalessky and Illarionov. The technique includes obtaining the probability of loads location at different distances from power supply centre and the probability of load power distribution in a given range of values. It is shown that the calculation using the developed method makes possible to prefer grid rated voltage at least 1 point downsized by IEC scale with sufficient savings due to the difference in the equipment price compared with the deterministic fit-and-forget approach.

Physics-Informed Neural Networks and Functional Interpolation for Data-Driven Parameters Discovery of Epidemiological Compartmental Models

In this work, we apply a novel and accurate Physics-Informed Neural Network Theory of Functional Connections (PINN-TFC) based framework, called Extreme Theory of Functional Connections (X-TFC), for data-physics-driven parameters’ discovery of problems modeled via Ordinary Differential Equations (ODEs). The proposed method merges the standard PINNs with a functional interpolation technique named Theory of Functional Connections (TFC). In particular, this work focuses on the capability of X-TFC in solving inverse problems to estimate the parameters governing the epidemiological compartmental models via a deterministic approach. The epidemiological compartmental models treated in this work are Susceptible-Infectious-Recovered (SIR), Susceptible-Exposed-Infectious-Recovered (SEIR), and Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS). The results show the low computational times, the high accuracy, and effectiveness of the X-TFC method in performing data-driven parameters’ discovery systems modeled via parametric ODEs using unperturbed and perturbed data.

Fuzzy logic power management for a PV/Wind microgrid with backup and storage systems

<span>This work introduces a power management scheme based on the fuzzy logic controller (FLC) to manage the power flows in a small and local distributed generation system. The stand-alone microgrid (MG) includes wind and PV generators as main power sources. The backup system includes a battery storage system (BSS) and a diesel generator (DG) combined with a supercapacitor (SC). The different energy sources are interconnected through the DC bus. The MG is modeled using MATLAB/Simulink Sim_Power System™. The SC is used to compensate for the shortage of power during the start-up of the DG and to compensate for the limits on the charging/discharging current of the BSS. The power balance of the system is the chief objective of the proposed management scheme. Some performance indexes are evaluated: the frequency-deviation, the stability of the DC bus voltage, and the AC voltage total harmonic distortion. The performance of the planned scheme is assessed by two 24-hours simulation sets. Simulation results confirm the effectiveness of FLC-based management. Moreover, the effectiveness of the FLC approach is compared with the deterministic approach. FLC approach has saved 18.7% from the daily load over the deterministic approach. The study shows that the quality of the power signal in the case of FLC is better than the deterministic approach.</span>