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We consider multiple senders with informational advantage signaling to convince a single selfinterested actor to take certain actions. Generalizing the seminal Bayesian Persuasion framework, such settings are ubiquitous in computational economics, multi-agent learning, and machine learning with multiple objectives. The core solution concept here is the Nash equilibrium of senders’ signaling policies. Theoretically, we prove that finding an equilibrium in general is PPAD-Hard; in fact, even computing a sender’s best response is NP-Hard. Given these intrinsic difficulties, we turn to finding local Nash equilibria. We propose a novel differentiable neural network to approximate this game’s non-linear and discontinuous utilities. Complementing this with the extra-gradient algorithm, we discover local equilibria that Pareto dominates full-revelation equilibria and those found by existing neural networks. Broadly, our theoretical and empirical contributions are of interest to a large class of economic problems. 

Diabetes is a major risk factor for Alzheimer’s disease (AD). Amino acid compound 2 (AAC2) improves glycemic and cognitive functions in diabetic mouse models through mechanisms distinct from insulin. Our goal was to compare the effects of AAC2, insulin, and their nanofiber-forming combination on early asymptomatic AD pathogenesis in APP/PS1 mice. Insulin, but not AAC2 or the combination treatment (administered intraperitoneally every 48 h for 120 days), increased seizure-related mortality, altered the brain fat-to-lean mass ratio, and improved specific cognitive functions in APP/PS1 mice. NanoString and pathway analysis of cerebral gene expression revealed dysregulated synaptic mechanisms, with upregulation of Bdnf and downregulation of Slc1a6 in insulin-treated mice, correlating with insulin-induced seizures. In contrast, AAC2 promoted the expression of Syn2 and Syp synaptic genes, preserved brain composition, and improved survival. The combination of AAC2 and insulin counteracted free insulin’s effects. None of the treatments influenced canonical amyloidogenic pathways. This study highlights AAC2’s potential in regulating synaptic gene expression in AD and insulin-induced contexts related to seizure activity. 

In this article, we consider a phase field model with different densities and viscosities for the coupled two-phase porous media flow and two-phase free flow, as well as the corresponding numerical simulation. This model consists of three parts: a Cahn–Hilliard–Darcy system with different densities/viscosities describing the porous media flow in matrix, a Cahn–Hilliard–Navier–Stokes system with different densities/viscosities describing the free fluid in conduit, and seven interface conditions coupling the flows in the matrix and the conduit. Based on the separate Cahn–Hilliard equations in the porous media region and the free flow region, a weak formulation is proposed to incorporate the two-phase systems of the two regions and the seven interface conditions between them, and the corresponding energy law is proved for the model. A fully decoupled numerical scheme, including the novel decoupling of the Cahn–Hilliard equations through the four phase interface conditions, is developed to solve this coupled nonlinear phase field model. An energy-law preservation is analyzed for the temporal semi-discretization scheme. Furthermore, a fully discretized Galerkin finite element method is proposed. Six numerical examples are provided to demonstrate the accuracy, discrete energy law, and applicability of the proposed fully decoupled scheme. 

Abstract There is a growing recognition that responding to climate change necessitates urban adaptation. We sketch a transdisciplinary research effort, arguing that actionable research on urban adaptation needs to recognize the nature of cities as social networks embedded in physical space. Given the pace, scale and socioeconomic outcomes of urbanization in the Global South, the specificities and history of its cities must be central to the study of how well-known agglomeration effects can facilitate adaptation. The proposed effort calls for the co-creation of knowledge involving scientists and stakeholders, especially those historically excluded from the design and implementation of urban development policies. 

Strategies to create organized multicomponent nanostructures composed of discrete, self-sorted domains are important for developing materials that mimic the complexity and multifunctionality found in biological systems. These structures can be challenging to achieve due to the required balance of molecular self-recognition and supramolecular attraction needed between the components. Herein, we report a strategy to construct a two-component nanostructure via a hierarchical assembly process whereby two monomeric building blocks undergo self-sorting assembly at the molecular level followed by a supramolecular association to form a nanofiber-wrapped nanotube. The two molecules self-sorted into respective nanofiber and nanotube assemblies, yet assembly of the nanofibers in the presence of the nanotube template allowed for directed integration into a hierarchical multilayer structure via electrostatic interactions. The fiber-wrapped nanotube co-assembly was characterized using transmission electron microscopy (TEM), atomic force microscopy (AFM) and Förster resonance energy transfer (FRET) between the components. Strategies to co-assemble multicomponent nanostructures composed of discrete, spatially sorted domains with controllable higher level interactions will be critical for the development of novel, functionally competent nanomaterials.