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Physical Sciences Forum

Physical Sciences Forum is an open access journal dedicated to publishing findings resulting from academic conferences, workshops and similar events in the area of physical sciences.
Each conference proceeding can be individually indexed, is citable via a digital object identifier (DOI) and freely available under an open access license. The conference organizers and proceedings editors are responsible for managing the peer-review process and selecting papers for conference proceedings.

All Articles (363)

  • Proceeding Paper
  • Open Access

Since its inception in 2004, nested sampling has been used in acoustics applications. This work applies nested sampling within a Bayesian framework to the detection and localization of sound sources using a spherical microphone array. Beyond an existing work, this source localization task relies on spherical harmonics to establish parametric models that distinguish the background sound environment from the presence of sound sources. Upon a positive detection, the parametric models are also involved to estimate an unknown number of potentially multiple sound sources. For the purpose of source detection, a no-source scenario needs to be considered in addition to the presence of at least one sound source. Specifically, the spherical microphone array senses the sound environment. The acoustic data are analyzed via spherical Fourier transforms using a Bayesian model comparison of two different models accounting for the absence and presence of sound sources for the source detection. Upon a positive detection, potentially multiple source models are involved to analyze direction of arrivals (DoAs) using Bayesian model selection and parameter estimation for the sound source enumeration and localization. These are two levels (enumeration and localization) of inferential estimations necessary to correctly localize potentially multiple sound sources. This paper discusses an efficient implementation of the nested sampling algorithm applied to the sound source detection and localization within the Bayesian framework.

20 May 2024

Spherical microphone array of radius 
  
    a
    =
    3.5
  
 cm. Altogether, 32 microphones are nearly uniformly flush-mounted over the rigid spherical surface.
  • Proceeding Paper
  • Open Access

Manifold-Based Geometric Exploration of Optimization Solutions

  • Guillaume Lebonvallet,
  • Faicel Hnaien and
  • Hichem Snoussi

This work introduces a new method for the exploration of solutions space in complex problems. This method consists of the build of a latent space which gives a new encoding of the solution space. We map the objective function on the latent space using a manifold, i.e., a mathematical object defined by an equations system. The latent space is built with some knowledge of the objective function to make the mapping of the manifold easier. In this work, we introduce a new encoding for the Travelling Salesman Problem (TSP) and we give a new method for finding the optimal round.

16 May 2024

  • Proceeding Paper
  • Open Access

NuMI Beam Monitoring Simulation and Data Analysis

  • Yiding Yu,
  • Thomas Joseph Carroll and
  • Sudeshna Ganguly
  • + 6 authors

Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis νμ Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the Main Injector (NuMI) beam. This study endeavors to ensure the monitor signal quality and to correlate them with the Neutrino beam profile. Leveraging muon monitor simulations, we systematically explore the monitor responses to variations in proton-beam and lattice parameters. Through the amalgamation of individual pixel data from muon monitors, pattern-recognition algorithms, simulations, and measured data, we devise machine-learning-based models to predict muon monitor responses and Neutrino flux.

22 April 2024

  • Proceeding Paper
  • Open Access

Analysis of Ecological Networks: Linear Inverse Modeling and Information Theory Tools

  • Valérie Girardin,
  • Théo Grente and
  • Nathalie Niquil
  • + 1 author

In marine ecology, the most studied interactions are trophic and are in networks called food webs. Trophic modeling is mainly based on weighted networks, where each weighted edge corresponds to a flow of organic matter between two trophic compartments, containing individuals of similar feeding behaviors and metabolisms and with the same predators. To take into account the unknown flow values within food webs, a class of methods called Linear Inverse Modeling was developed. The total linear constraints, equations and inequations defines a multidimensional convex-bounded polyhedron, called a polytope, within which lie all realistic solutions to the problem. To describe this polytope, a possible method is to calculate a representative sample of solutions by using the Monte Carlo Markov Chain approach. In order to extract a unique solution from the simulated sample, several goal (cost) functions—also called Ecological Network Analysis indices—have been introduced in the literature as criteria of fitness to the ecosystems. These tools are all related to information theory. Here we introduce new functions that potentially provide a better fit of the estimated model to the ecosystem.

20 February 2024

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Phys. Sci. Forum - ISSN 2673-9984