KELT-9 as an Eclipsing Double-lined Spectroscopic Binary: A Unique and Self-consistent Solution to the System

Transiting hot Jupiters present a unique opportunity to measure absolute planetary masses due to the magnitude of their radial velocity signals and known orbital inclination. Measuring planet mass is critical to understanding atmospheric dynamics and escape under extreme stellar irradiation. Here we present the ultrahot Jupiter system KELT-9 as a double-lined spectroscopic binary. This allows us to directly and empirically constrain the mass of the star and its planetary companion without reference to any theoretical stellar evolutionary models or empirical stellar scaling relations. Using data from the PEPSI, HARPS-N, and TRES spectrographs across multiple epochs, we apply least-squares deconvolution to measure out-of-transit stellar radial velocities. With the PEPSI and HARPS-N data sets, we measure in-transit planet radial velocities using transmission spectroscopy. By fitting the circular orbital solution that captures these Keplerian motions, we recover a planetary dynamical mass of 2.17 ± 0.56 M J and stellar dynamical mass of 2.11 ± 0.78 M ⊙, both of which agree with the discovery paper. Furthermore, we argue that this system, as well as systems like it, are highly overconstrained, providing multiple independent avenues for empirically cross-validating model-independent solutions to the system parameters. We also discuss the implications of this revised mass for studies of atmospheric escape.

Meta-Heuristic Parameter Optimization for ANN and Real-Time Applications of ANN

Artificial neural networks (ANN) are often more suitable for classification problems. Even then, training of ANN is a surviving challenge task for large and high dimensional natured search space problems. These hitches are more for applications that involves process of fine tuning of ANN control parameters: weights and bias. There is no single search and optimization method that suits the weights and bias of ANN for all the problems. The traditional heuristic approach fails because of their poorer convergence speed and chances of ending up with local optima. In this connection, the meta-heuristic algorithms prove to provide consistent solution for optimizing ANN training parameters. This chapter will provide critics on both heuristics and meta-heuristic existing literature for training neural networks algorithms, applicability, and reliability on parameter optimization. In addition, the real-time applications of ANN will be presented. Finally, future directions to be explored in the field of ANN are presented which will of potential interest for upcoming researchers.

Galactic Chemical Evolution of Radioactive Isotopes with an s-process Contribution

Analysis of inclusions in primitive meteorites reveals that several short-lived radionuclides (SLRs) with half-lives of 0.1–100 Myr existed in the early solar system (ESS). We investigate the ESS origin of 107Pd, 135Cs, and 182Hf, which are produced by slow neutron captures (the s-process) in asymptotic giant branch (AGB) stars. We modeled the Galactic abundances of these SLRs using the OMEGA+ galactic chemical evolution (GCE) code and two sets of mass- and metallicity-dependent AGB nucleosynthesis yields (Monash and FRUITY). Depending on the ratio of the mean-life τ of the SLR to the average length of time between the formations of AGB progenitors γ, we calculate timescales relevant for the birth of the Sun. If τ/γ ≳ 2, we predict self-consistent isolation times between 9 and 26 Myr by decaying the GCE predicted 107Pd/108Pd, 135Cs/133Cs, and 182Hf/180Hf ratios to their respective ESS ratios. The predicted 107Pd/182Hf ratio indicates that our GCE models are missing 9%–73% of 107Pd and 108Pd in the ESS. This missing component may have come from AGB stars of higher metallicity than those that contributed to the ESS in our GCE code. If τ/γ ≲ 0.3, we calculate instead the time (T LE) from the last nucleosynthesis event that added the SLRs into the presolar matter to the formation of the oldest solids in the ESS. For the 2 M ⊙, Z = 0.01 Monash model we find a self-consistent solution of T LE = 25.5 Myr.

Lessons of Descartes: Metaphysicity of Man and Poetry

Purpose. To consider the uniqueness of Descartes’ way of interpreting poetry as a type of philosophizing that makes it possible to comprehend the metaphysical nature of man. Its implementation involves the consistent solution of the following tasks: a) understanding methodological changes in the philosophy of the 20th century in the process of actualization of anthropological interest; b) argumentation of the importance of poetic thinking for early Descartes in the process of addressing modern historians of philosophy and the thinker’s texts. Theoretical basis. I rely on the conceptual positions of phenomenology, existentialism and hermeneutics. Originality. Finding of the study is that poetic thinking is the most authentic way of meaningful comprehension of the metaphysicity of man. The paper outlines the nature of the expression of this correlation in the philosophizing of the 20th-21th centuries and substantiates the thesis about the importance of the poetic principle for understanding the phenomenon of man in early works by Descartes. Conclusions. The paper examined the methodological shifts in anthropologically oriented philosophizing of the 20th-21th centuries and focused on the manifestations of related moments in the philosophical legacy of Descartes. The latter demonstrates the existence of a still underestimated version of interpreting the metaphysical foundations of human existence, the form of understanding of which is poetic thinking. It is a form of caring for the humane in man.

A Closed-Form Analytical Solution to the Complete Three-Dimensional Unsteady Compressible Navier-Stokes Equations

The three main approaches in fluid dynamics are actual experiments, numerical simulations, and theoretical solutions. Numerical simulations and theoretical solutions are based on the continuity equation and Navier-Stokes equations (NSE) that govern experimental observations of fluid dynamics.Theoretical solutions can offer huge advantages over numerical solutions and experiments in the understanding of fluid flows and design. These advantages are in terms of cost and time consumption. However, theoretical solutions have been limited by the prized NSE problem that seeks a physically consistent solution than what classical potential theory (CPT) offers. Therefore, the current author refined CPT. He introduced refined potential theory (RPT) that provides a viscous potential/stream function as a physically consistent solution to the NSE problem. This function captures observable unsteady flow features including separation, wake, vortex shedding, compressibility, turbulence, and Reynolds-number-dependence. It appropriately combines the properties of a three-dimensional potential function that satisfy the inertia terms of NSE and the features of a stream function that satisfy the continuity equation, the viscous vorticity equation, and the viscous terms of NSE. RPT has been verified and validated against experimental and numerical results of incompressible unsteady sub-critical Reynolds number flows on stationary finite circular cylinder, sphere, and spheroid.

A New Integrated FUCOM–CODAS Framework with Fermatean Fuzzy Information for Multi-Criteria Group Decision-Making

Smartphones have become an inevitable part of every facet of modern society. The selection of a particular smartphone brand from multiple options that are available is a complex and dynamic decision-making problem, involving multiple conflicting criteria that are associated with imprecise asymmetric information imposed by the uncertainty of the consumers. In this paper, we propose a novel hybrid full consistency method (FUCOM) and a combinative distance based assessment (CODAS) based on the multi-criteria group decision-making (MAGDM) framework in the Fermatean fuzzy (FF) domain for smartphone brand selection. We derive the criteria using the UTAUT2 (unified theory of acceptance and ese of technology) model. A group of 15 decision makers (DMs) participated in our study. We compare 14 leading smartphone brands in India and find that the brands having superior features of a good quality and selling a brand image at a affordable price outperform other smartphones. To check the validity of our framework, we compare the results using extant multi-criteria decision-making (MCDM) models. We observe our model provides a consistent solution. Furthermore, we carry out a sensitivity analysis for ascertaining the robustness and stability of the results generated by our model. The results of the sensitivity analysis show that our proposed framework delivers a stable and robust solution.

The Copyrightability of Opera Direction

A judgment of the Italian Court of Cassation – No. 17565 of 18 June 2021 – offers an opportunity to investigate the legal protection options applicable to the direction of opera. As this issue is not addressed by EU law, EU Member States have adopted different approaches, from awarding copyright, to neighboring rights, to a mixture of the two. This opinion aims at finding the most consistent solution within the Italian legal framework. After an assessment of the alternative legal options, it argues that opera direction should be eligible for copyright protection via an analogical application of Art. 44 of the Italian copyright law, which indirectly includes cinematic direction within the area of copyrightable works. This conclusion is based on the fact that there are no substantial differences between the two types of direction justifying a diverse treatment. Otherwise, it would result in blatant and unsubstantiated discrimination because both categories of directors set out to convert a text – the dramatic text or the film script – to the medium of theatre or film respectively. Finally, this opinion suggests that even if the other creative roles involved in opera making are not addressed by the law, they too should qualify as co-authors if they make a creative contribution.

A Closed-Form Analytical Solution to the Complete Three-Dimensional Unsteady Compressible Navier-Stokes Equation

The three main approaches to exploring fluid dynamics are actual experiments, numerical simulations, and theoretical solutions. Numerical simulations and theoretical solutions are based on the continuity equation and Navier-Stokes equations (NSE) that govern experimental observations of fluid dynamics. Theoretical solutions can offer huge advantages over numerical solutions and experiments in the understanding of fluid flows and design. These advantages are in terms of cost and time consumption. However, theoretical solutions have been limited by the prized NSE problem that seeks a physically consistent solution than what classical potential theory (CPT) offers. Therefore, the current author embarked on a doctoral research on the refinement of CPT. He introduced the Refined Potential Theory (RPT) that provides the Kwasu function as a physically consistent solution to the NSE problem. The Kwasu function is a viscous scalar potential function that captures known and observable unsteady features of experimentally observed wall bounded flows including flow separation, wake formation, vortex shedding, compressibility effects, turbulence and Reynolds-number-dependence. It is appropriately defined to combine the properties of a three-dimensional potential function to satisfy the inertia terms of the NSE and the features of a stream function to satisfy the continuity equation, the viscous vorticity equation and the viscous terms of the NSE. RPT has been verified and validated against experimental and numerical results of incompressible unsteady sub-critical Reynolds number flows on stationary finite circular cylinder, sphere and spheroid. It is concluded that the Kwasu function is a physically consistent and closed-form analytical solution to the NSE problem.

Nature-Inspired Techniques For Dynamic Constraint Satisfaction Problems

Combinatorial applications such as configuration, transportation and resource allocation, often operate under highly dynamic and unpredictable environments. In this regard, one of the main challenges is to maintain a consistent solution anytime constraints are (dynamically) added. While many solvers have been developed to tackle these applications, they often work under idealized assumptions of environmental stability. In order to address limitation, we propose a methodology, relying on nature-inspired techniques, for solving constraint problems when constraints are added dynamically. The choice for nature-inspired techniques is motivated by the fact that these are iterative algorithms, capable of maintaining a set of promising solutions, at each iteration. Our methodology takes advantage of these two properties, as follows. We first solve the initial constraint problem and save the final state (and the related population) after obtaining a consistent solution. This saved context will then be used as a resume point for finding, in an incremental manner, new solutions to subsequent variants of the problem, anytime new constraints are added. More precisely, once a solution is found, we resume from the current state to search for a new one (if the old solution is no longer feasible), when new constraints are added. This can be seen as an optimization problem where we look for a new feasible solution satisfying old and new constraints, while minimizing the differences with the solution of the previous problem, in sequence. This latter objective ensures to find the least disruptive solution, as this is very important in many applications including scheduling, planning and timetabling. Following on our proposed methodology, we have developed the dynamic variant of several nature-inspired techniques to tackle dynamic constraint problems. Constraint problems are represented using the well-known Constraint Satisfaction Problem (CSP) paradigm. Dealing with constraint additions in a dynamic environment can then be expressed as a series of static CSPs, each resulting from a change in the previous one by adding new constraints. This sequence of CSPs is called the Dynamic CSP (DCSP). To assess the performance of our proposed methodology, we conducted several experiments on randomly generated DCSP instances, following the RB model. The results of the experiments are reported and discussed.

Coherent oscillations in balanced neural networks driven by endogenous fluctuations

We present a detailed analysis of the dynamical regimes observed in a balanced network of identical Quadratic Integrate-and-Fire (QIF) neurons with a sparse connectivity for homogeneous and heterogeneous in-degree distribution. Depending on the parameter values, either an asynchronous regime or periodic oscillations spontaneously emerge. Numerical simulations are compared with a mean field model based on a self-consistent Fokker-Planck equation (FPE). The FPE reproduces quite well the asynchronous dynamics in the homogeneous case by either assuming a Poissonian or renewal distribution for the incoming spike trains. An exact self consistent solution for the mean firing rate obtained in the limit of infinite in-degree allows identifying balanced regimes that can be either mean- or fluctuation-driven. A low-dimensional reduction of the FPE in terms of circular cumulants is also considered. Two cumulants suffice to reproduce the transition scenario observed in the network. The emergence of periodic collective oscillations is well captured both in the homogeneous and heterogeneous setups by the mean field models upon tuning either the connectivity, or the input DC current. In the heterogeneous situation we analyze also the role of structural heterogeneity.