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Hierarchical Plant Protein Microcapsules for Hydrophilic and Hydrophobic Cargo Molecules arxiv.org/abs/2501.01962

Robustness of chaotic-light correlation plenoptic imaging against turbulence arxiv.org/abs/2501.01967

Nondipole interaction between two uniformly magnetized spheres and its relation to superconducting levitation arxiv.org/abs/2501.01978

A Complex Hilbert Space for Classical Electromagnetic Potentials arxiv.org/abs/2501.01995

A Complex Hilbert Space for Classical Electromagnetic Potentials

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.

arXiv.org

Computational Modeling and Analysis of the Coupled Aero Structural Dynamics in Bat Inspired Wings arxiv.org/abs/2501.02034

Computational Modeling and Analysis of the Coupled Aero Structural Dynamics in Bat Inspired Wings

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.

arXiv.org

Innovative Approaches to Teaching Quantum Computer Programming and Quantum Software Engineering arxiv.org/abs/2501.01446

The Effects of Anthropogenic Air and Light Pollution on Astrophysics and Society arxiv.org/abs/2501.01452

Sparse identification of evolution equations via Bayesian model selection arxiv.org/abs/2501.01476

Sparse identification of evolution equations via Bayesian model selection

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.

arXiv.org

Garbage in Garbage out: Impacts of data quality on criminal network intervention arxiv.org/abs/2501.01508

Garbage in Garbage out: Impacts of data quality on criminal network intervention

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.

arXiv.org

Dynamic realization of emergent high-dimensional optical vortices arxiv.org/abs/2501.01550

Dynamic realization of emergent high-dimensional optical vortices

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.

arXiv.org
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