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Tectonic plate convergence is accommodated across the continental lithosphere via discrete lithospheric subduction or distributed shortening and thickening. These end-member deformation modes control intra-plate mountain building, but their selection mechanism remains unclear. The variable composition of the continental crust and lithospheric mantle, which impacts its density and rheology, can be inferred by the distribution of magnetic-indicated crustal iron. Here we demonstrate that vertically coherent pure-shear shortening dominated the active Tian Shan orogen, central Asia, based on high-resolution aeromagnetic imaging and geophysical-geodetic observations. Integrating these findings with thermomechanical collisional models reveals that the mode of intracontinental deformation depends on contrasts in lower crust composition and mantle lithosphere depletion between the converging continents and central orogenic region. Distributed shortening prevails when the converging continents have a more iron-enriched mafic crust and iron-depleted mantle lithosphere when compared to the intervening orogenic region. Conversely, continental subduction occurs without such lithospheric contrasts. This result explains how the Tian Shan orogen formed via distributed lithospheric thickening without continental subduction or underthrusting. Our interpretations imply that iron distribution in the crust correlates with lithospheric compositional, density, and rheological structure, which impacts the preservation and destruction of Earth’s continents, including long-lived cratons, during intracontinental orogeny. 

Abstract Taking place naturally in a gas subject to a given wall temperature distribution, the “ghost effect” exhibits a rare kinetic effect beyond the prediction of classical fluid theory and Fourier law in such a classical problem in physics. As the Knudsen number goes to zero, the finite variation of temperature in the bulk is determined by an infinitesimal, ghost‐like velocity field, created by a givenfinitevariation of the tangential wall temperature as predicted by Maxwell's slip boundary condition. Mathematically, such a finite variation leads to the presence of a severe singularity and a Knudsen layer approximation in the fundamental energy estimate. Neither difficulty is within the reach of any existing PDE theory on the steady Boltzmann equation in a general 3D bounded domain. Consequently, in spite of the discovery of such a ghost effect from temperature variation in as early as 1960s, its mathematical validity has been a challenging and intriguing open question, causing confusion and suspicion. We settle this open question in affirmative if the temperature variation is small but finite, by developing a new framework with four major innovations as follows: (1) a key ‐Hodge decomposition and its corresponding local ‐conservation law eliminate the severe bulk singularity, leading to a reduced energy estimate; (2) a surprising gain in via momentum conservation and a dual Stokes solution; (3) the ‐conservation, energy conservation, and a coupled dual Stokes–Poisson solution reduces to an boundary singularity; (4) a crucial construction of ‐cutoff boundary layer eliminates such boundary singularity via new Hardy's and BV estimates. 

Abstract As the United States phases out traditional fossil fuels in favor of renewable energy sources, it is important to capitalize on all available avenues to increase renewable penetration. In the last decade, the costs associated with residential solar photovoltaic (PV) installations have decreased significantly, providing more homeowners with the opportunity to generate their own clean electricity. Research has found that the decision to invest in a residential solar PV system is guided by economic, social, and personal factors. Accounting for such complexities, the joint power of agent-based modeling and social network analysis is leveraged in this study to evaluate the effect of social influence on solar PV adoption. Featuring residential consumer agents with data-driven attributes, a logistic regression function to predict solar adoption, and random and small-world social network implementations, this work simulates residential solar PV adoption in New Jersey. Results indicate that including social influence in an agent-based electricity system model leads to increased installed residential solar capacity, but not necessarily higher adoption rates. These findings suggest that, with an understanding of the intricacies of consumer social networks, there are potential opportunities to bolster residential solar installations through low-cost social campaigns that motivate individuals to adopt home solar through their social ties.