Comment on "Energies of the ground state and first excited 0+0^{+} in an exactly solvable pairing model"

We comment on a recent application of the RPA method and its extensions to the case of the two-level pairing model by N. Dinh Dang [1].Comment: 5 pages, 1 figure, submitted to EPJ

Dense stellar matter with trapped neutrinos under strong magnetic fields

We investigate the effects of strong magnetic fields on the equation of state of dense stellar neutrino-free and neutrino-trapped matter. Relativistic nuclear models both with constant couplings (NLW) and with density dependent parameters (DDRH) and including hyperons are considered . It is shown that at low densities neutrinos are suppressed in the presence of the magnetic field. The magnetic field reduces the strangeness fraction of neutrino-free matter and increases the strangeness fraction of neutrino-trapped matter. The mass-radius relation of stars described by these equations of state are determined. The magnetic field makes the overall equation of state stiffer and the stronger the field the larger the mass of maximum mass star and the smaller the baryon density at the center of the star. As a consequence in the presence of strong magnetic fields the possibility that a protoneutron star evolves to a blackhole is smaller.Comment: 18 pages, 13 figures, 5 tables, submitted to J. Phys.

Effect of the δ\delta-meson on the instabilities of nuclear matter under strong magnetic fields

We study the influence of the isovector-scalar meson on the spinodal instabilities and the distillation effect in asymmetric non-homogenous nuclear matter under strong magnetic fields, of the order of $10^{18}-10^{19}$ G. Relativistic nuclear models both with constant couplings (NLW) and with density dependent parameters (DDRH) are considered. A strong magnetic field can have large effects on the instability regions giving rise to bands of instability and wider unstable regions. It is shown that for neutron rich matter the inclusion of the $\delta$ meson increases the size of the instability region for NLW models and decreases it for the DDRH models. The effect of the $\delta$ meson on the transition density to homogeneous $\beta$-equilibrium matter is discussed. The DDRH$\delta$ model predicts the smallest transition pressures, about half the values obtained for NL$\delta$.Comment: 6 pages, 5 figues, 3 tables, accepted for publication in Phys. Rev.

Modelling Exploratory Analysis Processes for eResearch

Financial markets produce high-frequency data and analysing it involves transforming the data, detecting patterns and testing financial models. These actions or steps form an exploratory analysis process (EAP). ADAGE is an open SOA incorporating a BPMS that allows users to model EAPs by composing analysis services. A typical application scenario is used to evaluate ADAGE’ s ability to express an EAP as a business process. It is shown that current BPMS technology cannot satisfactorily represent EAPs as fully executable business processes. Using the theory of situation awareness, EAPs are shown to be dynamic in nature. Hence three extensions based on the late composition technique are proposed: (1) a dynamic process representation of EAPs; (2) a process execution model; and (3) process templates to automate repetitive steps of EAPs

Stability Condition of a Retrial Queueing System with Abandoned and Feedback Customers

This paper deals with the stability of a retrial queueing system with two orbits, abandoned and feedback customers. Two independent Poisson streams of customers arrive to the system, and flow into a single-server service system. An arriving one of type i; i = 1; 2, is handled by the server if it is free; otherwise, it is blocked and routed to a separate type-i retrial (orbit) queue that attempts to re-dispatch its jobs at its specific Poisson rate. The customer in the orbit either attempts service again after a random time or gives up receiving service and leaves the system after a random time. After the customer is served completely, the customer will decide either to join the retrial group again for another service or leave the system forever with some probability

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources

PV array Control for MPPT Power Generation Under Partial Shading

The aim of this paper is to use an intelligent method for the Maximum Power Point Tracking (MPPT) of a photovoltaic (PV) generation system under partial shading conditions. In fact, the MPPT is achieved by using the intelligent algorithm for finding the global maximum power point. This algorithm combines a traditional MPPT technique (perturb and observe (P&O)) with an artificial neural network (ANN) method. The main goal of this algorithm is to predict the global maximum power point. simulation results show the effectiveness of the algorithm under partial shading conditions.Le but de cet article est d'utiliser une méthode intelligente pour le suivi du Point à puissance maximale (MPPT) d’un système photovoltaïque (PV) soumis à des conditions d'ombrage partiel. Effectivement, Le MPPT est achevé en utilisant l'algorithme intelligent pour trouver le point de puissance maximale global. Cet algorithme combine une traditionnelle technique pour le fonctionnement en MPPT (P & O) avec un réseau neuronal artificiel (ANN). Le but principal de cet algorithme est la prédiction du point à maximum de puissance. Les résultats de simulation obtenus montrent l’efficacité de cet algorithme

Warm and dense stellar matter under strong magnetic fields

We investigate the effects of strong magnetic fields on the equation of state of warm stellar matter as it may occur in a protoneutron star. Both neutrino free and neutrino trapped matter at a fixed entropy per baryon are analyzed. A relativistic mean field nuclear model, including the possibility of hyperon formation, is considered. A density dependent magnetic field with the magnitude $10^{15}$ G at the surface and not more than $3\times 10^{18}$ G at the center is considered. The magnetic field gives rise to a neutrino suppression, mainly at low densities, in matter with trapped neutrinos. It is shown that an hybrid protoneutron star will not evolve to a low mass blackhole if the magnetic field is strong enough and the magnetic field does not decay. However, the decay of the magnetic field after cooling may give rise to the formation of a low mass blackhole.Comment: 17 pages, 10 figures, 3 tables, submitted to Phys. Rev.

Credit risk prediction in an imbalanced social lending environment

© 2018, the Authors. Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets