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ABSTRACT In this paper, we present an interface‐filtering structural optimization method that optimizes structural shape and topology through successive interface movements. This interface filtering is achieved via the combination of the variable‐radius‐based partial‐differential‐equation (PDE) filtering and the Heaviside projection on a density representation. In the proposed method, designs are represented by a density field with sharp interface and no internal grey features, and a filter radius field is used as the design variable in the optimization process. With this method, any density distribution with sharp interfaces can be used as initial designs, and sharp density contrast in density distribution is preserved throughout the optimization process. An analytical relation between the maximum movements of interfaces and the maximum filter radius is given, so that the interface movement can be controlled during the optimization process. Sensitivities with respect to filter radius variables are derived. Two numerical treatments, involving the density update scheme and the radius re‐initialization scheme, are developed to achieve smooth successive shape updates and avoid artificial local minima. Numerical examples, including geometric deformation problem, structural compliance minimization, thermal compliance minimization, and negative Poisson ratio problem, are presented to demonstrate the capabilities of the proposed method. 

Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us tomap the natural image preferences of the neural population at 100-μmscale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4. 

Climate change mitigation measures are often projected to reduce anthropogenic carbon dioxide concentrations. Yet, it seems there is ample evidence suggesting that we have a limited understanding of the impacts of these measures and their combinations. For example, the Inflation Reduction Act (IRA) enacted in the U.S. in 2022 contains significant provisions, such as the electric vehicle (EV) tax credits, to reduce CO2 emissions. However, the impact of such provisions is not fully understood across the U.S., particularly in the context of their interactions with other macroeconomic systems. In this paper, we employ an Integrated Assessment Model (IAM), the Global Change Assessment Model (GCAM), to estimate the future CO2 emissions in the U.S. GCAM is equipped to comprehensively characterize the interactions among different systems, e.g., energy, water, land use, and transportation. Thus, the use of GCAM-USA that has U.S. state-level resolution allows the projection of the impacts and consequences of major provisions in the IRA, i.e., EV tax credits and clean energy incentives. To compare the performance of these incentives and credits, a policy effectiveness index is used to evaluate the strength of the relationship between the achieved total CO2 emissions and the overarching emission reduction costs. Our results show that the EV tax credits as stipulated in the IRA can only marginally reduce carbon emissions across the U.S. In fact, it may lead to negative impacts in some states. However, simultaneously combining the incentives and tax credits improves performance and outcomes better than the sum of the individual effects of the policies. This demonstrates that the whole is greater than the sum of the parts in this decarbonization approach. Our findings provide insights for policymakers with a recommendation that combining EV tax credits with clean energy incentives magnifies the intended impact of emission reduction. 

Abstract Stabilization of topological spin textures in layered magnets has the potential to drive the development of advanced low-dimensional spintronics devices. However, achieving reliable and flexible manipulation of the topological spin textures beyond skyrmion in a two-dimensional magnet system remains challenging. Here, we demonstrate the introduction of magnetic iron atoms between the van der Waals gap of a layered magnet, Fe₃GaTe₂, to modify local anisotropic magnetic interactions. Consequently, we present direct observations of the order-disorder skyrmion lattices transition. In addition, non-trivial topological solitons, such as skyrmioniums and skyrmion bags, are realized at room temperature. Our work highlights the influence of random spin control of non-trivial topological spin textures. 

While extant research explores the impact of Electric Vehicle (EV) incentives on EV market shares, less is known about how such policies and other socioeconomic factors interact that ultimately affect the goal of transportation emission reductions. The study summarized herein employed a sample of 510 state-year CO 2 emissions data sets in the transportation sector spanning a decade (2010-2019) in a multiple linear regression model. Going beyond earlier studies, we find that, while a higher number of EV incentives would significantly contribute to transportation emission reductions, this effect could be dampened by population growth. In addition, we find that, while higher electricity prices may weaken the effectiveness of EV incentives, a high count of EV incentives is more effective in reducing CO 2 emissions than a low count of EV incentives when the electricity price is low. This finding implies that having multiple EV incentives can be effective in reducing transportation carbon emissions even in the face of rising prices of electricity. The study also examines the effectiveness of promoting the density of charging stations and alternative fuel incentives in advancing carbon emission reductions. 

In this paper, we investigated the performance of cooperative adaptive cruise control (CACC) algorithms in mixed traffic environments featuring connected automated vehicles (CAVs) and unconnected vehicles. For CAVs, we tested the recently proposed linear feedback control approach (Linear- CACCu) and adaptive model predictive control approach (A- MPC-CACCu) which have been tailored to extend CACC to mixed traffic environments. In contrast to most literature where CACC design and evaluation are performed on freeways, we focused on urban arterial roads using the CACC Field Operation Test Dataset from the Netherlands. We compared the performances of Linear-CACCu and A-MPC-CACCu to regular adaptive cruise control (ACC), where automated vehicles do not rely on connectivity, as well as human drivers. Performance comparison was done in terms of ego vehicle’s spacing error, acceleration, and energy consumption which relate to safety, driving comfort, and energy efficiency, respectively. Simulation results showed that CACCu algorithms significantly outper- formed the ACC and human drivers in these metrics. Moreover, we found that the fluctuations of the lead vehicle’s behavior due to changes in traffic signal phase have a significant impact on which CACCu is optimal (i.e., A-MPC-CACCu or Linear- CACCu). Thus, the CACC mode could be switched based on the expectation of traffic signal phase changes to assure better performance.