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Simulations of chemical reaction probabilities in gas surface dynamics require the calculation of ensemble averages over many tens of thousands of reaction events to predict dynamical observables that can be compared to experiments. At the same time, the energy landscapes need to be accurately mapped, as small errors in barriers can lead to large deviations in reaction probabilities. This brings a particularly interesting challenge for machine learning interatomic potentials, which are becoming well-established tools to accelerate molecular dynamics simulations. We compare state-of-the-art machine learning interatomic potentials with a particular focus on their inference performance on CPUs and suitability for high throughput simulation of reactive chemistry at surfaces. The considered models include polarizable atom interaction neural networks (PaiNN), recursively embedded atom neural networks (REANN), the MACE equivariant graph neural network, and atomic cluster expansion potentials (ACE). The models are applied to a dataset on reactive molecular hydrogen scattering on low-index surface facets of copper. All models are assessed for their accuracy, time-to-solution, and ability to simulate reactive sticking probabilities as a function of the rovibrational initial state and kinetic incidence energy of the molecule. REANN and MACE models provide the best balance between accuracy and time-to-solution and can be considered the current state-of-the-art in gas-surface dynamics. PaiNN models require many features for the best accuracy, which causes significant losses in computational efficiency. ACE models provide the fastest time-to-solution, however, models trained on the existing dataset were not able to achieve sufficiently accurate predictions in all cases.

The fast dynamic properties of the surface of metallic glasses (MGs) play a critical role in determining their potential applications. However, due to the significant difference in thermal history between atomic simulation models and laboratory-made samples, the atomic-scale behaviors of the fast surface dynamics of MGs in experiments remain uncertain. Herein, we prepared model MG films with notable variations in thermal stability using a recently developed efficient annealing protocol, and investigated their atomic-scale dynamics systematically. We found that the dynamics of surface atoms remain invariant, whereas the difference in dynamical heterogeneity between surface and interior regions increases with the improvement of thermal stability. This can be associated with the more pronounced correlation between atomic activation energy spectra and depth from the surface in samples with higher thermal stability. In addition, dynamic anisotropy appears for surface atoms, and their transverse dynamics are faster than normal components, which can also be interpreted by activation energy spectra. Our results reveal the presence of strong liquid-like atomic dynamics confined to the surface of laboratory-made MGs, illuminating the underlying mechanisms for surface engineering design, such as cold joining by ultrasonic vibrations and superlattice growth.

Computational modeling occupies a unique niche in Earth and environmental sciences. Models serve not just as scientific technology and infrastructure but also as digital containers of the scientific community's understanding of the natural world. As this understanding improves, so too must the associated software. This dual nature – models as both infrastructure and hypotheses – means that modeling software must be designed to evolve continually as geoscientific knowledge itself evolves. Here we describe design principles, protocols, and tools developed by the Community Surface Dynamics Modeling System (CSDMS) to promote a flexible, interoperable, and ever-improving research software ecosystem. These include a community repository for model sharing and metadata, interface and ontology standards for model interoperability, language-bridging tools, a modular programming library for model construction, modular software components for data access, and a Python-based execution and model-coupling framework. Methods of community support and engagement that help create a community-centered software ecosystem are also discussed.

Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.

Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

A disease is non-communicable when it is not transferred from one person to another. Typical examples include all types of cancer, diabetes, stroke, or allergies, as well as mental diseases. Non-communicable diseases have at least two things in common—environmental impact and chronicity. These diseases are often associated with reduced quality of life, a higher rate of premature deaths, and negative impacts on a countries’ economy due to healthcare costs and missing work force. Additionally, they affect the individual’s immune system, which increases susceptibility toward communicable diseases, such as the flu or other viral and bacterial infections. Thus, mitigating the effects of non-communicable diseases is one of the most pressing issues of modern medicine, healthcare, and governments in general. Apart from the predisposition toward such diseases (the genome), their occurrence is associated with environmental parameters that people are exposed to (the exposome). Exposure to stressors such as bad air or water quality, noise, extreme heat, or an overall unnatural surrounding all impact the susceptibility to non-communicable diseases. In the identification of such environmental parameters, geoinformation products derived from Earth Observation data acquired by satellites play an increasingly important role. In this paper, we present a review on the joint use of Earth Observation data and public health data for research on non-communicable diseases. We analyzed 146 articles from peer-reviewed journals (Impact Factor ≥ 2) from all over the world that included Earth Observation data and public health data for their assessments. Our results show that this field of synergistic geohealth analyses is still relatively young, with most studies published within the last five years and within national boundaries. While the contribution of Earth Observation, and especially remote sensing-derived geoinformation products on land surface dynamics is on the rise, there is still a huge potential for transdisciplinary integration into studies. We see the necessity for future research and advocate for the increased incorporation of thematically profound remote sensing products with high spatial and temporal resolution into the mapping of exposomes and thus the vulnerability and resilience assessment of a population regarding non-communicable diseases.

— Kinetic dependences have been determined for the surface tension, dilatational dynamic surface elasticity, and ellipsometric angles of solutions of styrene copolymers with 4-vinylbenzyl chloride modified with N , N -dimethyldodecylamine. The micromorphology of the adsorbed and spread layers of these polyelectrolytes has also been studied. All kinetic dependences of the dynamic surface elasticity have turned out to be monotonic in contrast to those observed for previously studied solutions of polyelectrolytes free of polystyrene fragments. The peculiarities of the surface properties of the studied solutions may be related to the formation of microaggregates in the surface layers, because these microaggregates suppress the formation of loops and tails of polymer chains at the interfaces, and, consequently, decrease the surface elasticity after the local maximum. Atomic force microscopy data also indicate the formation of aggregates with sizes in the Z direction of 1–4 nm in the surface layers. The obtained results confirm the conclusions previously made about the formation of aggregates in the surface layers of solutions of polyelectrolytes containing fragments of sodium poly(styrene sulfonate). A 2D phase transition to a denser surface phase and the formation of aggregates with a size of 40 nm in the Z direction have been observed at surface pressures of 25–30 mN/m for the layers of the studied styrene-free polyelectrolyte spread over an aqueous substrate.

In this article, the research team systematically developed a method to model the kinematics and dynamics of a 3-wheeled robot subjected to external disturbances and sideways wheel sliding. These models will be used to design control laws that compensate for wheel slippage, model uncertainties, and external disturbances. These control algorithms were developed based on dynamic surface control (DSC). An adaptive trajectory tracking DSC algorithm using a fuzzy logic system (AFDSC) and a radial neural network (RBFNN) with a fuzzy logic system were used to overcome the disadvantages of DSC and expand the application domain for non-holonomic wheeled mobile robots with lateral slip (WMR). However, this adaptive fuzzy neural network dynamic surface control (AFNNDSC) adaptive controller ensures the closed system is stable, follows the preset trajectory in the presence of wheel slippage model uncertainty, and is affected by significant amplitude disturbances. The stability and convergence of the closed-loop system are guaranteed based on the Lyapunov analysis. The AFNNDSC adaptive controller is evaluated by simulation on the Matlab/simulink software R2022b and in a steady state. The maximum position error on the right wheel and left wheel is 0.000572 (m) and 0.000523 (m), and the angular velocity tracking error in the right and left wheels of the control method is 0.000394 (rad/s). The experimental results show the theoretical analysis’ correctness, the proposed controller’s effectiveness, and the possibility of practical applications. Orbits are set as two periodic functions of period T as follows.

In this paper, we experimentally study the water entry of a set of buoyant bodies with a circular cylindrical shape, which is relevant for several engineering applications, being related to aircraft fuselages in sea landing, solid rocket boosters, and bulbous bows of large ships. Free fall experiments are conducted for three different specimen weights and four different falling heights. The hydrodynamics of the interaction of the buoyant cylinders with the water free surface upon impact is analyzed through an accelerometer, a high-frequency potentiometer and a high-speed camera. Results are presented in terms of impact velocity, kinematics of the cylinder penetration and free surface dynamics, comprising the pile-up and water jet dynamics. Further, the air cavity development is analyzed after the cylinder is completely immersed below the undisturbed free surface.

Isostasy is one of the oldest and most widely applied concepts in the geosciences, but the geoscientific community lacks a coherent, easy-to-use tool to simulate flexure of a realistic (i.e., laterally heterogeneous) lithosphere under an arbitrary set of surface loads. Such a model is needed for studies of mountain building, sedimentary basin formation, glaciation, sea-level change, and other tectonic, geodynamic, and surface processes. Here I present gFlex (for GNU flexure), an open-source model that can produce analytical and finite difference solutions for lithospheric flexure in one (profile) and two (map view) dimensions. To simulate the flexural isostatic response to an imposed load, it can be used by itself or within GRASS GIS for better integration with field data. gFlex is also a component with the Community Surface Dynamics Modeling System (CSDMS) and Landlab modeling frameworks for coupling with a wide range of Earth-surface-related models, and can be coupled to additional models within Python scripts. As an example of this in-script coupling, I simulate the effects of spatially variable lithospheric thickness on a modeled Iceland ice cap. Finite difference solutions in gFlex can use any of five types of boundary conditions: 0-displacement, 0-slope (i.e., clamped); 0-slope, 0-shear; 0-moment, 0-shear (i.e., broken plate); mirror symmetry; and periodic. Typical calculations with gFlex require << 1 s to ∼ 1 min on a personal laptop computer. These characteristics - multiple ways to run the model, multiple solution methods, multiple boundary conditions, and short compute time - make gFlex an effective tool for flexural isostatic modeling across the geosciences.