Radiation from Wavepackets

The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher information matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. We demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an active learning (AL) loop for materials science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly AL in large machine-learning models.

Reports of audible sonic booms along the south-central California coast during SpaceX Falcon 9 launch ascents prompted measurements in Ventura County during summer 2024. A total of 132 measurements were made over six launches, with 16–25 measurements per launch. The maximum overpressure measured was 1.90 psf (133 dB), but most measured booms had an overpressure below 0.5 psf and durations of several seconds. Two launches had appreciably lower overpressures and smaller terrestrial footprint, indicating that both meteorology and launch azimuth are important factors in terrestrial boom audibility. Agreement between this dataset and environmental assessment predictions was marginal.

The quantum dimer magnet, with antiferromagnetic intradimer and interdimer Heisenberg exchange between spin-1/2 moments, is known to host an $$(\left|\uparrow \downarrow \right\rangle -\left|\downarrow \uparrow \right\rangle )/\sqrt{2}$$singlet ground state when the intradimer exchange is dominant. Rare-earth-based quantum dimer systems with strong spin-orbit coupling offer the opportunity for tuning their magnetic properties by using magnetic anisotropy as a control knob. Here, we present bulk characterization and neutron scattering measurements of the quantum dimer magnet Yb2Be2SiO7. We find that the Yb3+ ions can be described by an effective spin-1/2 model at low temperatures and the system does not show signs of magnetic order down to 50 mK. The magnetization, heat capacity, and neutron spectroscopy data can be well-described by an isolated dimer model with highly anisotropic exchange that stabilizes a singlet ground state with a wavefunction $$(\left|\uparrow \uparrow \right\rangle -\left|\downarrow \downarrow \right\rangle )/\sqrt{2}$$or $$(\left|\uparrow \uparrow \right\rangle+\left|\downarrow \downarrow \right\rangle )/\sqrt{2}$$. Our results show that strong spin-orbit coupling can induce unusual entangled states of matter in quantum dimer magnets.

This paper presents ambient | global, an ambient soundscape model developed to predict global ambient sound levels from all anthropogenic, biological, and geophysical sources. The soundscape model adopts a geospatial approach by modeling the ambient sound level as a function of geospatial features at a location. The soundscape model consists of an ensemble of four machine learning regression models fitted at acoustic measurement sites where both the geospatial features and ambient sound levels are known. The fitted model is then applied to predict ambient sound levels at any location where the geospatial features are known. The results quantify the spatial, temporal, and spectral patterns of ambient sound levels across the world under various scenarios. This paper presents maps of the existing ambient sound levels across the world in terms of the daytime overall A-weighted L50, or median sound level, and partitions the existing sound levels into their natural and anthropogenic constituents. Ultimately, the soundscape model will enable research into the impacts of humans and nature on the ambient soundscape and the impacts of ambient sound levels on humans and nature across the world.

This study describes a carrier having submicron, uniform and non-aggregated poly lactic acid (PLA) spheres loaded with the anticancer drug 5-fluorouracil (5FU) and with 9 nm superparamagnetic iron oxide nanoparticles (SPIONs) for magnetically guided drug delivery and local controlled release. Using a water/organic/water (w/o/w) doubleemulsion process, we produced uniformly spherical microparticles smaller than 2 µm in diameter with well-dispersed SPIONs that retained superparamagnetic behavior after encapsulation. 5FU loading efficiency was determined to be 94%. Biological activity and chemical integrity was confirmed for the 5FU released from the product. Drug release kinetics showed faster release within the first day followed by sustained, slower release over 63 days with a cumulative release reaching 70% of loaded drug. Drug release was faster at 37°C compared to 21°C. PBS at pH 7.4 and 5.4 promoted faster release than did distilled water at pH 7.0. Release was prolonged from these PLA systems compared to other systems employing PLGA. This research introduces a rigorously optimized microcarrier system distinguished by sub-2-µm superparamagnetic PLA or PLGA microspheres of uniform morphology containing phase-dispersed SPIONs and exhibiting long-term controlled release, offering a transformative framework for magnetically directed drug delivery using high-gradient systems such as Halbach arrays.