arXiv Physics @arxiv_physics@qoto.org

In this paper, we introduce a kinetic model which describes a learning process leading individuals to build personal awareness about fake news. Next, we embed the results of this model into another kinetic model, which describes the popularity gained by news on social media conditioned to the reliability of the disseminated information. Both models are formulated in terms of linear inelastic Boltzmann-type equations, of which we investigate the main analytical properties - existence and uniqueness of solutions, trend to equilibrium, identification of the equilibrium distributions - by employing extensively Fourier methods for kinetic equations. We also provide evidence of the analytical results by means of Monte Carlo numerical simulations.

We demonstrate the existence of a complex Hilbert Space with Hermitian operators for calculations in classical electromagnetism. This approach lets us derive a variety of fundamental expressions for electromagnetism using minimal mathematics and a calculation sequence well-known for traditional quantum mechanics. The purpose of this Hilbert Space is not to calculate the expectation values of known observables, however, like in Koopman-von Neumann-Sudarshan (KvNS) mechanics (for classical point particles) and quantum mechanics (quantum waves or fields). We also demonstrate the existence of the wave commutation relationship $[\hat{x},\hat{k}]=i$, which is the never seen before classical analogue to the canonical commutator $[\hat{x},\hat{p}]=i\hbar$. The difference between classical and quantum mechanics lies in the presence of $\hbar$. This is the first report of noncommutativity of observables for a classical theory. Further comparisons between electromagnetism, KvNS classical mechanics, and quantum mechanics are made. Finally, supplementing the analysis presented, we additionally demonstrate for the first time a completely relativistic version of Feynman's proof of Maxwell's equations \citep{Dyson}. Unlike what \citet{Dyson} indicated, there is no need for Galilean relativity for the proof to work. This fits parsimoniously with our usage of classical commutators for electromagnetism.

The micro-canonical phase-space volume for the three-body problem is a topic of intrinsic interest. Within the flux-based statistical theory, it provides a means to predict the scale of disintegration times for non-hierarchical systems. While the bare phase-volume diverges, arXiv:2205.04294 (Paper I) showed that a regularized version can be defined. Building on Paper I, which determined the regularized phase-volume for a given energy $\barσ(E)$, this paper extends the analysis to its distribution over angular momentum, $\barσ(E,L)$. Through analytical integrations, we reduce the problem to a 3d numerical integration, a step up in complexity from the 2d integration required for $\barσ(E)$. We provide regularized phase-volume values for several mass sets across a range of $E$ and $L$, validated through an $L$-integration test. Notably, the values remain positive for all tested parameters, lending further support to the validity of the chosen regularization procedure. For high values of $L$ at fixed masses and $E$, we observe a strong suppression of $\barσ(E,L)$.

The role of magnetic moments in electrodynamics is examined in this work. The effects are described in the context of conventional quantum electrodynamics expressed in terms of the electromagnetic fields or in the context of an extended Poynting theorem and extended Maxwell equations. These extensions take into account the energetics of interaction of magnetic moments with inhomogeneous magnetic fields. We show how magnetic moment effects are included in either version of electrodynamics and that these apparently different formulations can give consistent results. In either case, we express the interactions in terms of electromagnetic fields only, avoiding use of a vector potential.

We employ a novel computational modeling framework to perform high-fidelity direct numerical simulations of aero-structural interactions in bat-inspired membrane wings. The wing of a bat consists of an elastic membrane supported by a highly articulated skeleton, enabling localized control over wing movement and deformation during flight. By modeling these complex deformations, along with realistic wing movements and interactions with the surrounding airflow, we expect to gain new insights into the performance of these unique wings. Our model achieves a high degree of realism by incorporating experimental measurements of the skeleton's joint movements to guide the fluid-structure interaction simulations. The simulations reveal that different segments of the wing undergo distinct aeroelastic deformations, impacting flow dynamics and aerodynamic loads. Specifically, the simulations show significant variations in the effectiveness of the wing in generating lift, drag, and thrust forces across different segments and regions of the wing. We employ a force partitioning method to analyze the causality of pressure loads over the wing, demonstrating that vortex-induced pressure forces are dominant while added mass contributions to aerodynamic loads are minimal. This approach also elucidates the role of various flow structures in shaping pressure distributions. Finally, we compare the fully articulated, flexible bat wing to equivalent stiff wings derived from the same kinematics, demonstrating the critical impact of wing articulation and deformation on aerodynamic efficiency.

Background: The MAJORANA DEMONSTRATOR , a modular array of isotopically enriched high-purity germanium (HPGe) detectors, was constructed to demonstrate backgrounds low enough to justify building a tonne-scale experiment to search for the neutrinoless double-beta decay ($ββ(0ν)$) of $^{76}\mathrm{Ge}$. Purpose: This paper presents a description of the instrument, its commissioning, and operations. It covers the electroforming, underground infrastructure, enrichment, detector fabrication, low-background and construction techniques, electronics, data acquisition, databases, and data processing of the MAJORANA DEMONSTRATOR. Method: The MAJORANA DEMONSTRATOR operated inside an ultra-low radioactivity passive shield at the 4850-foot~level of the Sanford Underground Research Facility (SURF) from 2015-2021. Results and Conclusions: The MAJORANA DEMONSTRATOR achieved the best energy resolution and second-best background level of any $ββ(0ν)$ search. This enabled it to achieve an ultimate half-life limit on $ββ(0ν)$ in $^{76}\mathrm{Ge}$ of $8.3\times 10^{25}$~yr (90\% C.L.) and perform a rich set of searches for other physics beyond the Standard Model.

The quantitative formulation of evolution equations is the backbone for prediction, control, and understanding of dynamical systems across diverse scientific fields. Besides deriving differential equations for dynamical systems based on basic scientific reasoning or prior knowledge in recent times a growing interest emerged to infer these equations purely from data. In this article, we introduce a novel method for the sparse identification of nonlinear dynamical systems from observational data, based on the observation how the key challenges of the quality of time derivatives and sampling rates influence this problem. Our approach combines system identification based on thresholded least squares minimization with additional error measures that account for both the deviation between the model and the time derivative of the data, and the integrated performance of the model in forecasting dynamics. Specifically, we integrate a least squares error as well as the Wasserstein metric for estimated models and combine them within a Bayesian optimization framework to efficiently determine optimal hyperparameters for thresholding and weighting of the different error norms. Additionally, we employ distinct regularization parameters for each differential equation in the system, enhancing the method's precision and flexibility. We demonstrate the capabilities of our approach through applications to dynamical fMRI data and the prototypical example of a wake flow behind a cylinder. In the wake flow problem, our method identifies a sparse, accurate model that correctly captures transient dynamics, oscillation periods, and phase information, outperforming existing methods. In the fMRI example, we show how our approach extracts insights from a trained recurrent neural network, offering a novel avenue for explainable AI by inferring differential equations that capture potentially causal relationships.

Criminal networks such as human trafficking rings are threats to the rule of law, democracy and public safety in our global society. Network science provides invaluable tools to identify key players and design interventions for Law Enforcement Agencies (LEAs), e.g., to dismantle their organisation. However, poor data quality and the adaptiveness of criminal networks through self-organization make effective disruption extremely challenging. Although there exists a large body of work building and applying network scientific tools to attack criminal networks, these work often implicitly assume that the network measurements are accurate and complete. Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based approaches. Decentralization emerges as a major factor in network robustness, particularly under conditions of incomplete data, which renders attack strategies largely ineffective. Moreover, the robustness of centralized networks can be boosted using simple heuristics, making targeted attack more infeasible. Consequently, we advocate for a more cautious application of network science in disrupting criminal networks, the continuous development of an interoperable intelligence ecosystem, and the creation of novel network inference techniques to address data quality challenges.

Physics plays a fundamental role in advancing pharmacy education and research, providing theoretical underpinnings and practical tools necessary to address complex challenges in drug development, delivery, and diagnostics. This review explores the integration of physics into the pharmacy curriculum, highlighting how principles such as fluid dynamics, thermodynamics, and spectroscopy (Tokgoz and Sakalli, 2018) enhance students' critical thinking and problem-solving skills. Additionally, it examines the pivotal contributions of physics to pharmaceutical research, including molecular modeling, imaging technologies like MRI and PET, and nanotechnology-driven drug delivery systems. Despite challenges in interdisciplinary collaboration and resource allocation, innovative teaching strategies and laboratory based learning are shown significant promise. Looking forward, the convergence of artificial intelligence and physics, as highlighted by recent Nobel Prize achievements in attosecond physics and bioorthogonal chemistry, is set to revolutionize pharmaceutical sciences, offering unprecedented precision and efficiency in drug discovery and personalized medicine.

Ekman pumping is a phenomenon induced by no-slip boundary conditions in rotating fluids. In the context of Rayleigh-Bénard convection, Ekman pumping causes a significant change in the linear stability of the system compared to when it is not present (that is, stress-free). Motivated by numerical solutions to the marginal stability problem of the incompressible Navier-Stokes (iNSE) system, we seek analytical asymptotic solutions which describe the departure of the no-slip solution from the stress-free. The substitution of normal modes into a reduced asymptotic model yields a linear system for which we explore analytical solutions for various scalings of wavenumber. We find very good agreement between the analytical asymptotic solutions and the numerical solutions to the iNSE linear stability problem with no-slip boundary conditions.

We report on the performance evaluation of a prototype muon trigger detector for the PSI muEDM experiment, conducted as a proof-of-principle test at the $π$E1 beamline of the Paul Scherrer Institute (PSI) using \SI{27.5}{MeV/c} muons. The detector is designed to identify muons within the acceptance phase space of a compact storage solenoid and activate a pulsed magnetic kicker for muon storage; it was tested without the application of a magnetic field. It comprises a telescope made up of four scintillators in anticoincidence with a gate scintillator, all read out by silicon photomultipliers. The study focused on characterizing the detector's response to various muon trajectories and the light yield of its plastic scintillators. Experimental results demonstrated strong agreement with Geant4 Monte Carlo simulations that incorporate optical photon modeling, confirming the detector's concept and its potential for meeting the stringent requirements of the muEDM experiment.

The dimensionality of vortical structures has recently been extended beyond two dimensions, providing higher-order topological characteristics and robustness for high-capacity information processing and turbulence control. The generation of high-dimensional vortical structures has mostly been demonstrated in classical systems through the complex interference of fluidic, acoustic, or electromagnetic waves. However, natural materials rarely support three- or higher-dimensional vortical structures and their physical interactions. Here, we present a high-dimensional gradient thickness optical cavity (GTOC) in which the optical coupling of planar metal-dielectric multilayers implements topological interactions across multiple dimensions. Topological interactions in high-dimensional GTOC construct non-trivial topological phases, which induce high-dimensional vortical structures in generalized parameter space in three, four dimensions, and beyond. These emergent high-dimensional vortical structures are observed under electro-optic tomography as optical vortex dynamics in two-dimensional real-space, employing the optical thicknesses of the dielectric layers as synthetic dimensions. We experimentally demonstrate emergent vortical structures, optical vortex lines and vortex rings, in a three-dimensional generalized parameter space and their topological transitions. Furthermore, we explore four-dimensional vortical structures, termed optical vortex sheets, which provide the programmability of real-space optical vortex dynamics. Our findings hold significant promise for emulating high-dimensional physics and developing active topological photonic devices.