Stochastic Approach for Optimal Positioning of Pumps As Turbines (PATs)

A generic water system consists of a series of works that allow the collection, conveyance, storage and finally the distribution of water in quantities and qualities such as to satisfy the needs of end users. In places characterized by high altitude differences between the intake works and inhabited centres, the potential energy of the water is very high. This energy is attributable to high pressures, which could compromise the functionality of the pipelines; it is therefore necessary to dissipate part of this energy. A common alternative to dissipation is the possibility of exploiting this energy by inserting a hydraulic turbine. The present study aims to evaluate the results obtained from a stochastic approach for the solution of the multi-objective optimization problem of PATs (Pumps As Turbines) in water systems. To this end, the Bayesian Monte Carlo optimisation method was chosen for the optimization of three objective functions relating to pressure, energy produced and plant costs. The case study chosen is the Net 3 literature network available in the EPANET software manual. The same problem was addressed using the NSGA-III (Nondominated Sorting Genetic Algorithm) to allow comparison of the results, since the latter is more commonly used. The two methods have different peculiarities and therefore perform better in different contexts.

The distribution of fitness effects of spontaneous mutations in Chlamydomonas reinhardtii inferred using frequency changes under experimental evolution

The distribution of fitness effects (DFE) for new mutations is fundamental for many aspects of population and quantitative genetics. In this study, we have inferred the DFE in the single-celled alga Chlamydomonas reinhardtii by estimating changes in the frequencies of 254 spontaneous mutations under experimental evolution and equating the frequency changes of linked mutations with their selection coefficients. We generated seven populations of recombinant haplotypes by crossing seven independently derived mutation accumulation lines carrying an average of 36 mutations in the homozygous state to a mutation-free strain of the same genotype. We then allowed the populations to evolve under natural selection in the laboratory by serial transfer in liquid culture. We observed substantial and repeatable changes in the frequencies of many groups of linked mutations, and, surprisingly, as many mutations were observed to increase as decrease in frequency. We developed a Bayesian Monte Carlo Markov Chain method to infer the DFE. This computes the likelihood of the observed distribution of changes of frequency, and obtains the posterior distribution of the selective effects of individual mutations, while assuming a two-sided gamma distribution of effects. We infer that the DFE is a highly leptokurtic distribution, and that approximately equal proportions of mutations have positive and negative effects on fitness. This result is consistent with what we have observed in previous work on a different C. reinhardtii strain, and suggests that a high fraction of new spontaneously arisen mutations are advantageous in a simple laboratory environment.

Job satisfaction, expected retirement financial insufficiency, and the expected retirement age of the entrepreneurs: an attraction-selection-attrition theory perspective

Purpose Underpinned by the attraction-selection-attrition theory, this paper aims to investigate the impact of entrepreneurship on an individual’s expected retirement age and explore how job satisfaction and expected retirement financial insufficiency (ERFI) as key factors can explain that. Design/methodology/approach A framework including direct and indirect relationships among key factors is empirically tested by using a pooled data sets consists of 13,420 individuals from the UK Household Longitudinal Survey, the analysis uses the entropy balance matching method and combined with quasi-bayesian monte Carlo method and hierarchy regressions to enhance the robustness of results. Findings The research finds entrepreneurs plan to retire later than organizational employees. In addition, a strong mediating impact of job satisfaction and moderating role of ERFI on the relationship between entrepreneurship and expected retirement age is verified. Originality/value The theoretical perspective and findings offer a novel insight into the research on entrepreneurs’ decision of retirement. The findings suggest entrepreneurs as crucial policy stakeholders contribute to retirement deferment should be valued. Effective interventions could be delicately designed in the future to unleash the potential of entrepreneurship in dealing with aging challenges.

Estimasi Jumlah Penghuni Ruangan Berdasarkan Konsentrasi CO2 Dengan Metode Bayesian MCMC

<span lang="PT-BR">The number of occupants in the building is important information for building management because it is related to security issues, evacuation, and energy saving. This article focuses on estimating the number of occupants using the Bayesian Monte Carlo Markov chain (MCMC) method based on indoor CO₂ levels. Probability theory underlies the Bayesian MCMC principle, where the mass balance equation of indoor CO₂ is used as a physical model of estimation calculations. Determination of the variables in the mass balance equation is investigated to obtain the effect on the accuracy of the estimated number of occupants. It found that the higher the standard deviation of the input variable on the physical model, the higher the error estimation produced. In addition, the Bayesian MCMC algorithm is tested in a real-time scheme of test</span><span lang="IN">-</span><span lang="PT-BR">chamber. The result shows an estimated error of 39%. Rapid changes influence estimation errors in actual occupants relative to the sample interval and the time delay of the estimation.</span>

A self-supervised, physics-aware, Bayesian neural network architecture for modelling galaxy emission-line kinematics

In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we blackexplore the use of a self-supervised, physics aware neural network capable of Bayesian kinematic modelling of galaxies. We demonstrate the network’s ability to model the kinematics of cold gas in galaxies with an emphasis on recovering physical parameters and accompanying modelling errors. The model is able to recover rotation curves, inclinations and disc scale lengths for both CO and H i data which match well with those found in the literature. The model is also able to provide modelling errors over learned parameters thanks to the application of quasi-Bayesian Monte-Carlo dropout. This work shows the promising use of machine learning, and in particular self-supervised neural networks, in the context of kinematically modelling galaxies. This work represents the first steps in applying such models for kinematic fitting and we propose that variants of our model would seem especially suitable for enabling emission-line science from upcoming surveys with e.g. the SKA, allowing fast exploitation of these large datasets.

High-resolution Bayesian spatial autocorrelation (SPAC) quasi-3-D Vs model of Utah FORGE site with a dense geophone array

SUMMARY We expand the application of spatial autocorrelation (SPAC) from typical 1-D Vs profiles to quasi-3-D imaging via Bayesian Monte Carlo inversion (BMCI) using a dense nodal array (49 nodes) located at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) site. Combinations of 4 and 9 geophones in subarrays provide for 36 and 25 1-D Vs profiles, respectively. Profiles with error bars are determined by calculating coherency functions that fit observations in a frequency range of 0.2–5 Hz. Thus, a high-resolution quasi-3-D Vs model from the surface to 2.0 km depth is derived and shows that surface-parallel sedimentary strata deepen to the west, consistent with a 3-D seismic reflection survey. Moreover, the resulting Vs profile is consistent with a Vs profile derived from distributed acoustic sensing (DAS) data located in a borehole at the FORGE site. The quasi-3-D velocity model shows that the base of the basin dips ∼22° to the west and topography on the basement interface coincident with the Mag Lee Wash suggests that the bedrock interface is an unconformity.