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Magnetic flux density (B) is traditionally interpreted as a continuous field whose flux lines form closed loops, as prescribed by Maxwell’s equations. This description is well justified in macroscopic systems, where large ensembles of magnetic dipoles produce statistically smooth fields through spatial averaging. At the nanoscale, however, where only a limited number of dipoles may contribute, the conditions underlying this continuum interpretation become less clear. Here, we reexamine the meaning of magnetic flux density from a classical-statistical perspective, focusing on finite ensembles and isolated magnetic particles. We show that as the number of contributing dipoles decreases, ensemble averaging becomes insufficient to support a statistically stable, coarse-grained field description, even though the underlying electromagnetic fields remain well defined and fully consistent with Maxwell’s equations. In this regime, magnetic flux density retains its formal definition, but its interpretation as a robust macroscopic observable becomes strongly dependent on fluctuations and specific dipole configurations. This framework introduces a quantitative criterion based on a critical particle number and provides a consistent description of the transition from ensemble-averaged magnetostatics to discrete dipole behavior. The results clarify the limits of continuum field interpretations at the nanoscale and offer a unified perspective for understanding isolated nanoparticles, small dipole ensembles, and the emergence of classical magnetic behavior from discrete microscopic sources. 

In origins-of-life research, a key challenge is to explain the emergence of polymers of sufficient length to confer the complex functions needed for genetic inheritance. Previous studies have demonstrated that prebiotic environments enabling multilevel selection can facilitate the survival of cooperating polymers such as ribozymes, which allows that complex functions undertaken today by long polymers might have originated from multiple simpler functions undertaken by shorter polymers. To further investigate this possibility, we developed a new computational model of cooperative catalytic and replicating polymer systems that avoids tracking all possible polymer sequences. This approach scales well computationally, avoiding the need to set an a priori cap on polymer length. We first validated this model by replicating key conclusions of previous studies, for example that the persistence of cooperative synthetase-ligase systems is facilitated by both intrinsic factors (shorter length, higher catalytic efficiency) and by factors that promote multilevel-selection (compartmentalization, slower diffusion). We then explored the effects of introducing a mutation inhibitor into a cooperative synthetase-ligase system. The results support the possibility that mutation inhibition could have arisen, not through the appearance of a single proofreading polymerase, but through the emergence of distinct, mutation inhibiting catalysts. 

The global policy landscape has coalesced around “Trustworthy AI” as a normative goal for AI governance and development. However, despite its ubiquity, there is no widespread agreement on the definition of the term. Based on a convened discourse of interdisciplinary institute leaders at The George Washington University, this paper argues that the failure to define “Trustworthy AI” is not a technical gap, but a consequence of different epistemologies associated with different worldviews. We argue that “trust” is not a singular property, but a “boundary object” (Star and Griesemer in Soc Stud Sci 19:387–420, 1989) interpreted through four different worldviews: the Technocratic (trust as metrology), the Relational (trust as agency and social contract), the Critical (trust vs. power), and the Pragmatic (trust as pedestrian verification). This paper delineates these paradigms, explores the friction between them, and argues that a rigorous framework for trustworthy AI requires a pluralistic approach that acknowledges these inherent contradictions. 

Accurate modeling of aqueous monovalent ions is essential for understanding the function of biomolecules, such as nucleic acid stability and the binding of charged drugs to protein targets. The 1D and 3D reference interaction site models (1D- and 3D-RISM) of molecular solvation, as implemented in the AmberTools molecular modeling suite, are well suited for modeling mixtures of ionic species around biomolecules across a wide range of concentrations. However, the available ion model parameters were optimized for molecular dynamics simulations, not for the RISM framework, which includes a closure approximation. To address this, we optimized the Lennard-Jones 12–6 model for monovalent ions for 1D-RISM with the partial series expansion of order 3 closure by fitting to experimental values of ion–oxygen distance (IOD), hydration free energy (HFE), partial molar volume (PMV), and mean activity coefficient. The new parameter set demonstrated significant improvement in HFE, IOD, and mean activity coefficients, whereas no overall change was observed for the PMV. A second optimization step, introducing non-bonded fix (NBFIX) parameters into the model, was necessary to account for the cation–anion interactions that affect the mean activity coefficients. The new parameters were validated at finite salt concentrations against experimental data for 16 ion pairs and showed improved accuracy for 12 of them, with predictions for CsI and LiI ranked second best, while those for CsF and LiCl ranked third best among the tested parameter sets. The isothermal compressibilities for NaCl, KCl, and LiCl were compared against experimental data. Although 1D-RISM overestimated the value for pure water by ∼40%, the relative change as a function of salt concentration was improved with the new parameter set for NaCl and KCl. 1D-RISM results obtained with the new NaCl parameters were used to calculate the preferential interaction parameter of the ions around the 24L B-DNA using 3D-RISM. The new parameters demonstrated better agreement with the experiment at physiological and higher concentrations. At lower concentrations, the results primarily depended on the closure with little effect from the ion parameters. Overall, the ion parameters specifically developed for RISM show improved accuracy at infinite dilution and finite concentrations. No difference was observed for the preferential interaction parameters and isothermal compressibility calculations when comparing NBFIX and non-NBFIX parameters. However, the NBFIX parameters are numerically more stable at higher concentrations. 

Abstract Cosmic voids may have novel effects on the propagation of high-energy photons. We consider the fraction of the line of sight that intersect voids (termed “voidiness”). A previous study showed that active galactic nuclei (AGN) detected by the Fermi Large Area Telescope (LAT) lie along voidier lines of sight than redshift-matched populations of Sloan Digital Sky Survey (SDSS) optically detected quasars in the redshift range from 0.4 ≤z < 0.7. We explore this difference and various astrophysical explanations for it. Weaker intergalactic magnetic fields in voids would naturally enhance the gamma-ray cascading flux within the Fermi-LAT point-spread function. We find that line-of-sight interactions increasing the flux in the Fermi-LAT energy band by ∼0.1% Mpc^–1of void traversed may be sufficient to result in the observed difference in voidiness distributions. Voidiness comparisons between SDSS QSOs and AGN detected by imaging atmospheric Cherenkov telescopes at very high energies (VHEs) do not yield any conclusive statement, likely because of the limited VHE sample size, and therefore are inconclusive about the role of possibly weaker extragalactic background light within voids. Finally, we measure that 28% ± 3% of gamma-ray-detected sources exist within a void (consistent with random mock populations) compared to 19.1% ± 0.3% of SDSS quasars. We do not find any significant local void effect for gamma-ray sources that would explain the voidiness difference between Fermi-LAT gamma-ray and SDSS QSO sources. These results suggest that the observed difference in voidiness distributions may be due to line-of-sight interactions rather than the local emission environment of gamma-ray AGN. 

The collective action of actively contractile units embedded in elastic biopolymer networks plays a crucial role in regulating the network's macroscopic mechanical response. Here, we investigate how the macroscopic boundary... 

Abstract Coral reefs are essential for the foundation of marine ecosystems. However, ocean acidification (OA), driven by rising atmospheric carbon dioxide (CO₂) threatens coral growth and biological homeostasis. This study examines two Hawaiian coral species—Montipora capitataandPocillopora acutato elevated pCO₂simulating OA. Utilizing pH and O₂microsensors under controlled light and dark conditions, this work characterized interspecific concentration boundary layer (CBL) traits and quantified material fluxes under ambient and elevated pCO₂. The results of this study revealed that under increased pCO₂,P. acutashowed a significant reduction in dark proton efflux, followed by an increase in light O₂flux, suggesting reduced calcification and enhanced photosynthesis. In contrast,M. capitatadid not show any robust evidence of changes in either flux parameters under similar increased pCO₂conditions. Statistical analyses using linear models revealed several significant interactions among species, treatment, and light conditions, identifying physical, chemical, and biological drivers of species responses to increased pCO₂. This study also presents several conceptual models that correlate the CBL dynamics measured here with calcification and metabolic processes, thereby justifying our findings. We indicate that elevated pCO₂exacerbates microchemical gradients in the CBL and may threaten calcification in vulnerable species such asP. acuta, while highlighting the resistance ofM. capitata. Therefore, this study advances our understanding of how interspecific microenvironmental processes could influence coral responses to changing ocean chemistry.