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Detecting and analyzing the local environment is crucial for investigating the dynamical processes of crystal nucleation and shape colloidal particle self-assembly. Recent developments in machine learning provide a promising avenue for better order parameters in complex systems that are challenging to study using traditional approaches. However, the application of machine learning to self-assembly on systems of particle shapes is still underexplored. To address this gap, we propose a simple, physics-agnostic, yet powerful approach that involves training a multilayer perceptron (MLP) as a local environment classifier for systems of particle shapes, using input features such as particle distances and orientations. Our MLP classifier is trained in a supervised manner with a shape symmetry-encoded data augmentation technique without the need for any conventional roto-translations invariant symmetry functions. We evaluate the performance of our classifiers on four different scenarios involving self-assembly of cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with varying aspect ratios, and truncated shapes with different degrees of truncation. The proposed training process and data augmentation technique are both straightforward and flexible, enabling easy application of the classifier to other processes involving particle orientations. Our work thus presents a valuable tool for investigating self-assembly processes on systems of particle shapes, with potential applications in structure identification of any particle-based or molecular system where orientations can be defined. 

Urban surface and near-surface air temperatures are known to be often higher than their rural counterparts, a phenomenon now labeled as the urban heat island effect. However, whether the elevated urban temperatures are more persistent than rural temperatures at timescales commensurate to heat waves has not been addressed despite its importance for human health. Combining numerical simulations by a global climate model with a surface energy balance theory, it is demonstrated here that urban surface and near-surface air temperatures are significantly more persistent than their rural counterparts in cities dominated by impervious materials with large thermal inertia. Further use of these materials will result in even stronger urban temperature persistence, especially for tropical cities. The present findings help pinpoint mitigation strategies that can simultaneously ameliorate the larger magnitude and stronger persistence of urban temperatures. 

Abstract The sensitivity of urban canopy air temperature ( $T_a$) to anthropogenic heat flux ( $Q_{AH}$) is known to vary with space and time, but the key factors controlling such spatiotemporal variabilities remain elusive. To quantify the contributions of different physical processes to the magnitude and variability of $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$(where $\mathrm{\Delta }$represents a change), we develop a forcing-feedback framework based on the energy budget of air within the urban canopy layer and apply it to diagnosing $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$simulated by the Community Land Model Urban over the contiguous United States (CONUS). In summer, the median $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$is around 0.01 $\text{K\hspace{0.17em}}{\left(\text{W\hspace{0.17em}}{\text{m}}^{-\text{2}}\right)}^{-1}$over the CONUS. Besides the direct effect of $Q_{AH}$on $T_a$, there are important feedbacks through changes in the surface temperature, the atmosphere–canopy air heat conductance ( $c_a$), and the surface–canopy air heat conductance. The positive and negative feedbacks nearly cancel each other out and $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$is mostly controlled by the direct effect in summer. In winter, $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$becomes stronger, with the median value increased by about 20% due to weakened negative feedback associated with $c_a$. The spatial and temporal (both seasonal and diurnal) variability of $\mathrm{\Delta }{T}_a/\mathrm{\Delta }{Q}_{AH}$as well as the nonlinear response of $\mathrm{\Delta }{T}_a$to $\mathrm{\Delta }{Q}_{AH}$are strongly related to the variability of $c_a$, highlighting the importance of correctly parameterizing convective heat transfer in urban canopy models. 

Habitat degradation and loss of genetic diversity are common threats faced by almost all of today’s wild cats. Big cats, such as tigers and lions, are of great concern and have received considerable conservation attention through policies and international actions. However, knowledge of and conservation actions for small wild cats are lagging considerably behind. The black-footed cat,Felis nigripes, one of the smallest felid species, is experiencing increasing threats with a rapid reduction in population size. However, there is a lack of genetic information to assist in developing effective conservation actions. A de novo assembly of a high-quality chromosome-level reference genome of the black-footed cat was made, and comparative genomics and population genomics analyses were carried out. These analyses revealed that the most significant genetic changes in the evolution of the black-footed cat are the rapid evolution of sensory and metabolic-related genes, reflecting genetic adaptations to its characteristic nocturnal hunting and a high metabolic rate. Genomes of the black-footed cat exhibit a high level of inbreeding, especially for signals of recent inbreeding events, which suggest that they may have experienced severe genetic isolation caused by habitat fragmentation. More importantly, inbreeding associated with two deleterious mutated genes may exacerbate the risk of amyloidosis, the dominant disease that causes mortality of about 70% of captive individuals. Our research provides comprehensive documentation of the evolutionary history of the black-footed cat and suggests that there is an urgent need to investigate genomic variations of small felids worldwide to support effective conservation actions. 

Clouded leopards (Neofelisspp.), a morphologically and ecologically distinct lineage of big cats, are severely threatened by habitat loss and fragmentation, targeted hunting, and other human activities. The long-held poor understanding of their genetics and evolution has undermined the effectiveness of conservation actions. Here, we report a comprehensive investigation of the whole genomes, population genetics, and adaptive evolution ofNeofelis. Our results indicate the genusNeofelisarose during the Pleistocene, coinciding with glacial-induced climate changes to the distributions of savannas and rainforests, and signatures of natural selection associated with genes functioning in tooth, pigmentation, and tail development, associated with clouded leopards’ unique adaptations. Our study highlights high-altitude adaptation as the main factor driving nontaxonomic population differentiation inNeofelis nebulosa. Population declines and inbreeding have led to reduced genetic diversity and the accumulation of deleterious variation that likely affect reproduction of clouded leopards, highlighting the urgent need for effective conservation efforts. 

Abstract Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these networks allows them to model arbitrarily long memories in the time series used in training, it makes it harder to impose prior knowledge or intuition through generic constraints. In this work, we present a path sampling approach based on principle of Maximum Caliber that allows us to include generic thermodynamic or kinetic constraints into recurrent neural networks. We show the method here for a widely used type of recurrent neural network known as long short-term memory network in the context of supplementing time series collected from different application domains. These include classical Molecular Dynamics of a protein and Monte Carlo simulations of an open quantum system continuously losing photons to the environment and displaying Rabi oscillations. Our method can be easily generalized to other generative artificial intelligence models and to generic time series in different areas of physical and social sciences, where one wishes to supplement limited data with intuition or theory based corrections. 

Dynamic variables of drop impact such as force, drag, pressure, and stress distributions are key to understanding a wide range of natural and industrial processes. While the study of drop impact kinematics has been in constant progress for decades thanks to high-speed photography and computational fluid dynamics, research on drop impact dynamics has only peaked in the last 10 years. Here, we review how recent coordinated efforts of experiments, simulations, and theories have led to new insights on drop impact dynamics. Particularly, we consider the temporal evolution of the impact force in the early- and late-impact regimes, as well as spatiotemporal features of the pressure and shear-stress distributions on solid surfaces. We also discuss other factors, including the presence of water layers, air cushioning, and nonspherical drop geometry, and briefly review granular impact cratering by liquid drops as an example demonstrating the distinct consequences of the stress distributions of drop impact. 

Abstract Drop impact causes severe surface erosion, dictating many important natural, environmental and engineering processes and calling for substantial prevention and preservation efforts. Nevertheless, despite extensive studies on the kinematic features of impacting drops over the last two decades, the dynamic process that leads to the drop-impact erosion is still far from clear. Here, we develop a method of high-speed stress microscopy, which measures the key dynamic properties of drop impact responsible for erosion, i.e., the shear stress and pressure distributions of impacting drops, with unprecedented spatiotemporal resolutions. Our experiments reveal the fast propagation of self-similar noncentral stress maxima underneath impacting drops and quantify the shear force on impacted substrates. Moreover, we examine the deformation of elastic substrates under impact and uncover impact-induced surface shock waves. Our study opens the door for quantitative measurements of the impact stress of liquid drops and sheds light on the origin of low-speed drop-impact erosion.