Spatial Externalities in Agriculture: Empirical Analysis, Statistical Identification, and Policy Implications

Spatial externalities can affect economic welfare and landscape pattern by linking farm returns on adjoining parcels of land. While policy can be informed by research that documents spatial externalities, statistically quantifying the presence of externalities from landscape pattern is insufficient for policy guidance unless the underlying cause of the externality can be identified as positive or negative. This article provides a springboard for empirical research by examining the underlying structure, social-environmental interactions, and statistical identification strategies for the analysis and quantification of agricultural spatial externalities that are derived from observations of landscape change. The potential for original policy treatments of agricultural spatial externalities in development and environment outcomes are highlighted.

Process and Drying Behavior Toward Higher Drying Rates of Hard Carbon Anodes for Sodium‐Ion Batteries with Different Particle Sizes: An Experimental Study in Comparison to Graphite for Lithium‐Ion‐Batteries

Sodium-ion batteries are considered to be one of the most promising postlithium batteries on the verge of commercialization. The electrode processing is expected to be similar to lithium-ion batteries. However, the producibility and material processing challenges of potential electrode materials for anodes and cathodes are poorly understood. For industrial electrode production, a deep understanding of the processing of electrode materials with different particle morphologies is of great importance. In particular, the correlation between the process conditions and the electrode properties needs to be investigated further to understand the complex interactions between the battery slurry materials, the binder system, the drying process, and the microstructure formation. One promising anode material is hard carbon. The water-based processing of hard carbon slurries presented in this article shows that the drying behavior is strongly interconnected with the particle size and particle interactions in the drying electrode. This study shows that all the hard carbons investigated do not exhibit binder migration at moderate drying rates. Even at very high drying rates (9 g m−2 s−1, 12 s drying time), an increase in adhesion force of up to 39% is observed for comparatively smaller particles compared to the adhesion force at lower drying rate

RecycleNet: Latent Feature Recycling Leads to Iterative Decision Refinement

Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initial guess from different angles, distilling relevant information, arriving at a better decision. Here, we propose RecycleNet, a latent feature recycling method, instilling the pondering capability for neural networks to refine initial decisions over a number of recycling steps, where outputs are fed back into earlier network layers in an iterative fashion. This approach makes minimal assumptions about the neural network architecture and thus can be implemented in a wide variety of contexts. Using medical image segmentation as the evaluation environment, we show that latent feature recycling enables the network to iteratively refine initial predictions even beyond the iterations seen during training, converging towards an improved decision. We evaluate this across a variety of segmentation benchmarks and show consistent improvements even compared with top-performing segmentation methods. This allows trading increased computation time for improved performance, which can be beneficial, especially for safety-critical applications.Comment: Accepted at 2024 Winter Conference on Applications of Computer Vision (WACV