Machine-learning fluid flow Quantifying fluid flow is relevant to disciplines ranging from geophysics to medicine. Flow can be experimentally visualized using, for example, smoke or contrast agents, but extracting velocity and pressure fields from this information is tricky. Raissi et al. developed a machine-learning approach to tackle this problem. Their method exploits the knowledge of Navier-Stokes equations, which govern the dynamics of fluid flow in many scientifically relevant situations. The authors illustrate their approach using examples such as blood flow in an aneurysm. Science, this issue p. 1026 A machine learning approach exploiting the knowledge of Navier-Stokes equations can extract detailed fluid flow information. For centuries, flow visualization has been the art of making fluid motion visible in physical and biological systems. Although such flow patterns can be, in principle, described by the Navier-Stokes equations, extracting the velocity and pressure fields directly from the images is challenging. We addressed this problem by developing hidden fluid mechanics (HFM), a physics-informed deep-learning framework capable of encoding the Navier-Stokes equations into the neural networks while being agnostic to the geometry or the initial and boundary conditions. We demonstrate HFM for several physical and biomedical problems by extracting quantitative information for which direct measurements may not be possible. HFM is robust to low resolution and substantial noise in the observation data, which is important for potential applications.
Fundamental constants are a cornerstone of our physical laws. Any constant varying in space and/or time would reflect the existence of an almost massless field that couples to matter. This will induce a violation of the universality of free fall. Thus, it is of utmost importance for our understanding of gravity and of the domain of validity of general relativity to test for their constancy. We detail the relations between the constants, the tests of the local position invariance and of the universality of free fall. We then review the main experimental and observational constraints that have been obtained from atomic clocks, the Oklo phenomenon, solar system observations, meteorite dating, quasar absorption spectra, stellar physics, pulsar timing, the cosmic microwave background and big bang nucleosynthesis. At each step we describe the basics of each system, its dependence with respect to the constants, the known systematic effects and the most recent constraints that have been obtained. We then describe the main theoretical frameworks in which the low-energy constants may actually be varying and we focus on the unification mechanisms and the relations between the variation of different constants. To finish, we discuss the more speculative possibility of understanding their numerical values and the apparent fine-tuning that they confront us with.
Abstract Surface and near‐surface wind speeds, critical factors for dust emission, are often overestimated in desert regions by models, leading to exaggerated predictions of sandstorm extent and dust concentration. This study implements a new turbulent orographic form drag (TOFD) parameterization scheme using 30‐m‐resolution terrain data in the WRF‐Chem model. Over a 1‐month simulation, this scheme reduced the overestimated surface wind speed by 45%. Compared with observations from 41 meteorological stations and one radio‐sounding station in the Taklimakan Desert, it also decreased the root mean square errors for surface and near‐surface winds by about 20% and 5%, respectively. Furthermore, the 30‐day simulation showed a 31% reduction in PM10 RMSE and a better‐matched aerosol optical depth distribution. The results demonstrate that the novel TOFD scheme, which utilizes high‐resolution terrain data, effectively resolves dunes and accurately accounts for the drag of small dunes on the near‐surface atmosphere in deserts.
The two pillars of modern physics, relativity and quantum mechanics, are incompatible with each other, creating a divide between macroscopic and microscopic physical theories. This research proposed dark particle hypothesis, filled gap on minimum existence quantity particle; discovered new equivalence principle, bridged differences in foundation of physics; and establishes the relative continuum, constructing an ideal model of cosmic continuum. In this model, regardless of scale—macro or micro—any cosmic system is a continuum relative to the wavelength of bosonic energy waves, and the cosmic and material structure have a unified physical mechanism. The study revealed deep essence of cosmic and material structure, provides new perspectives on the fundamental problems of physics and cosmology. Firstly, it elucidated the physical mechanism of bosons in fundamental interactions. Secondly, it reconstructed the understanding of the basic unit of the cosmic and material structure. Thirdly, it updated the inherent concepts about the existence form and existence dimension. Fourthly, it restored the causality truth of wave function collapse in quantum mechanics.
David Breitenmoser, Alberto Stabilini, M. Kasprzak
et al.
Airborne Gamma-Ray Spectrometry (AGRS) is a critical tool for radiological emergency response, enabling the rapid identification and quantification of hazardous terrestrial radionuclides over large areas. However, existing calibration methods are limited to a few gamma-ray sources, excluding most radionuclides released in severe nuclear accidents and nuclear weapon detonations, compromising effective response and risk assessment. Here, we present a high-fidelity Monte Carlo model that overcomes these limitations, offering full-spectrum calibration for any gamma-ray source. Unlike previous approaches, our model integrates a detailed mass model of the aircraft and a calibrated non-proportional scintillation model, enabling accurate event-by-event predictions of the spectrometer's response to arbitrarily complex gamma-ray fields. Validation in near-, mid-, and far-field scenarios demonstrates that the model not only addresses major deficiencies of previous approaches but also achieves the accuracy required to supersede empirical calibration methods. This advancement enables high-fidelity spectral signature generation for any gamma-ray source, reduces calibration time and costs, minimizes reliance on high-intensity sources, and eliminates related radioactive waste. The approach presented here is a critical step toward integrating advanced full-spectrum data reduction methods for AGRS, unlocking new capabilities beyond emergency response, such as atmospheric cosmic-ray flux quantification for geophysics and trace-level airborne radionuclide identification for nuclear security.
Subsurface geometries, such as faults and subducting slab interfaces, are often poorly constrained, yet they exert first‐order control on key geophysical processes, including subduction zone thermal structure and earthquake rupture dynamics. Quantifying model sensitivity to geometric variability remains challenging for high‐fidelity simulations that require generated meshes, due to the manual effort of mesh generation and the computational cost of exploring high‐dimensional parameter spaces. We present a mesh morphing approach that deforms a reference mesh into geometrically varying configurations while preserving mesh connectivity. This enables the automated generation of large ensembles of geometrically variable meshes with minimal user input. Importantly, the preserved connectivity allows for the application of data‐driven, non‐intrusive reduced‐order models (ROMs) to perform robust sensitivity analysis and uncertainty quantification. We demonstrate mesh morphing in two geophysical applications: (a) 3D dynamic rupture simulations with fault dip angles varying across a 40° range, and (b) 2D thermal models of subduction zones incorporating realistic slab interface curvature and depth uncertainties. The morphed meshes retain high quality and lead to accurate simulation results that closely match those obtained using generated meshes. For the dynamic rupture case, we construct ROMs that efficiently predict surface displacement and velocity time series as functions of fault geometry, achieving speedups of up to 109 $1{0}^{9}$ times relative to full simulations. Our results show that mesh morphing can be a powerful and generalizable tool for incorporating geometric uncertainty into physics‐based modeling. The method supports efficient ensemble modeling for rigorous sensitivity studies applicable across a range of problems in computational geophysics.
Abstract The Earth’s Critical Zone (CZ) is a dynamic and essential layer extending from the canopy top to the unweathered bedrock, where interactions among the atmosphere, lithosphere, hydrosphere, and biosphere sustain ecosystems. This study focuses on mapping the critical zone thickness in northeastern India. To achieve this, the CZ was delineated into two key components: canopy height and depth to bedrock. Canopy height was estimated using the ALT08 data product of the ICESat-2 satellite using the laser altimetry principle, while depth-to-bedrock modelling incorporated environmental factors such as temperature, precipitation, humidity, canopy height, groundwater levels, rock type and soil type. An artificial neural network (ANN) was employed to predict the spatial variation of bedrock depth using publicly available soil profiles, borehole data, and remote sensing-derived environmental covariates. The estimated depth to bedrock (DTB) varies from 12.75 to 460.12 m across Northeast India. CZ thickness (CZT) was determined as the sum of canopy height and bedrock depth, ranging from 20.08 to 485.78 m with an increase northward from the Bramhaputra River and decreases southward, indicating a general trend of increase in DTB from south to north direction across the Kamrup district. The findings of this research provide an assessment of how human activities, land use changes, and climate shifts impact the CZ and the ecosystems it sustains. This knowledge can be instrumental in improving CZ management and contributing to the sustainability of ecosystems in a rapidly changing world.
Abstract Coral skeletons exhibit a complex composition of mineral and organic components, with the water content playing a significant role in their structure. Water associated with organic matrices constitutes the major share, whereas nonstructural water (skeleton inclusion water) is present in much smaller amounts, typically less than 0.5 wt% of the coral skeleton. The isotopic composition of this water may reflect biomineralization processes or environmental conditions during skeleton formation. Here, we present fluid inclusion hydrogen and oxygen isotopic compositions of cold‐water coral skeletons from Angola and Iceland. We confirmed the kinetic isotope fractionation when coral skeletons were crushed at a temperature of 120°C using a cavity ring‐down spectroscopy analyzer. δ18O and δ2H values are not reproducible across analytical setups with varying temperature parameters. When coral fragments are encapsulated in a glass tube, the amount of released water and its isotopic signature are far more reproducible. δ18O and δ2H values of coral skeleton inclusion water from Angola show no significant differences between the Holocene and the last glacial period. The δ2H values are characterized by strong isotopic fractionation compared to seawater. Our study highlights that current methods do not adequately capture the variability in the initial δ18O and δ2H of the “quasi‐free” skeleton inclusion water in coral aragonite. It is also possible that there are varying degrees of exchange of skeleton inclusion water with seawater. A better understanding of the coral calcification process is still necessary to establish a clearer link between the isotopic compositions of seawater and skeleton inclusion water.
Felipe Queiroz Miano, César Augusto Moreira, Vânia Rosolen
et al.
Abstract Wetlands are highly complex and notorious importance in storing, supplying water and re-charging aquifers. Approximately 20% of the Brazilian territory is covered by wetlands and, de-spite their importance, the zoning of these areas is neglected, even after several water crisis events in the country. In this context, the present article aims to monitor and evaluate the surface dynamics of a closed wetland located in the Metropolitan Region of Piracicaba, São Paulo, which presents critical indices in water availability. For monitoring the wetland and the water table on the surface, monthly aerial surveys of high spatial resolution were carried out with Remotely Piloted Aircraft (RPAs) over a period of one year, and together, measurements of water levels were carried out in subsurface through wells implemented in the study area. As a result, the Irati Formation present in the region is decisive for the existence of the wetland due to the low permeability of the soil, as well as the rainfall rate (1,036.2 mm) and evapotranspiration (1,307.1 mm), which directly contribute to the evolution of the surface water table and regulate the height of the water level in the subsurface and on the slopes of the contribution basin throughout the year.
A variety of observations impose upper limits at the nano Gauss level on magnetic fields that are coherent on inter-galactic scales while blazar observations indicate a lower bound ∼10−16 G. Such magnetic fields can play an important astrophysical role, for example at cosmic recombination and during structure formation, and also provide crucial information for particle physics in the early Universe. Magnetic fields with significant energy density could have been produced at the electroweak phase transition. The evolution and survival of magnetic fields produced on sub-horizon scales in the early Universe, however, depends on the magnetic helicity which is related to violation of symmetries in fundamental particle interactions. The generation of magnetic helicity requires new CP violating interactions that can be tested by accelerator experiments via decay channels of the Higgs particle.
Abstract The interannual‐to‐decadal variability of sea surface temperature and height in the North Atlantic exhibits a tripolar pattern. Here, we explore the spatiotemporal structure, including the vertical, of the North Atlantic tripole using observations and reanalysis data in 1993–2021. For the first time, we demonstrate that the tripole's vertical structure across the Mid‐Atlantic Bight continental shelf and slope differs from that in the ocean interior. The tripole strongly projects in the Slope Water north of the Gulf Stream mean path, marked with temperature changes across the water column not maintained by air‐sea heat flux. Over the shelf, the tripole‐associated sea level, temperature, and ocean current are weak. In the ocean interior, the tripole temperature variability is apparent in the upper 100 m in the tropics and three times as deep in the subtropics. The tripole imprints resemble those of the North Atlantic Oscillation, peaking after the dominant atmospheric mode's winter maximum.
<p>In this paper, we are concerned with data pertinent to <i>transportation networks</i>, which model situations in which objects move along a graph-like structure. We assume that these networks are equipped with <i>sensors</i> that monitor the network and the objects moving along it. These sensors produce <i>time series data</i>, resulting in sensor networks. Examples are river, road, and electricity networks.</p>
<p>Geographical information systems are used to gather, store, and analyse data, and we focus on these tasks in the context of data emerging from transportation networks equipped with sensors. While tailored solutions exist for many contexts, they are limited for sensor-equipped networks at this moment. We view time series data as temporal properties of the network and approach the problem from the viewpoint of property graphs. In this paper, we adapt and extend the theory of the existing property graph databases to model spatial networks, where nodes and edges can contain temporal properties that are time series data originating from the sensors. We propose a language for querying these property graphs with time series, in which time series and measurement patterns may be combined with graph patterns to describe, retrieve, and analyse real-life situations. We demonstrate the model and language in practice by implementing both in Neo4j and explore questions hydrology researchers pose in the context of the Internet of Water, including salinity analysis in the Yser river basin.</p>
Abstract. Ionospheric sporadic E layers (Es) are intense plasma irregularities between 80 and 130 km in altitude and are generally unpredictable. Reconstructing the morphology of sporadic E layers is not only essential for understanding the nature of ionospheric irregularities and many other atmospheric coupling systems, but is also useful for solving a broad range of demands for reliable radio communication of many sectors reliant on ionosphere-dependent decision-making. Despite the efforts of many empirical and theoretical models, a predictive algorithm with both high accuracy and high efficiency is still lacking. Here we introduce a new approach for Sporadic E Layer Forecast using Artificial Neural Networks (SELF-ANN). The prediction engine is trained by fusing observational data from multiple sources, including a high-resolution ERA5 reanalysis dataset, Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) measurements, and integrated data from OMNIWeb. The results show that the model can effectively reconstruct the morphology of the ionospheric E layer with intraseasonal variability by learning complex patterns. The model obtains good performance and generalization capability by applying multiple evaluation criteria. The random forest algorithm used for preliminary processing shows that local time, altitude, longitude, and latitude are significantly essential for forecasting the E-layer region. Extensive evaluations based on ground-based observations demonstrate the superior utility of the model in dealing with unknown information. The presented framework will help us better understand the nature of the ionospheric irregularities, which is a fundamental challenge in upper-atmospheric and ionospheric physics. Moreover, the proposed SELF-ANN can make a significant contribution to the development of the prediction of ionospheric irregularities in the E layer, particularly when the formation mechanisms and evolution processes of the Es layer are not well understood.
Abstract Despite multiple advances in the understanding of the water cycle intensification in a warmer climate, climate models still diverge in their hydrological projections. Here we constrain annual runoff projections over individual and aggregated Arctic river basins. For this purpose, we use two ensembles of global climate models and two statistical methods: a regression scheme assuming similar runoff sensitivities at interannual versus climate change timescales, and a Bayesian method where models are used to derive a posterior runoff response conditioned on historical observations. While both techniques are shown to narrow model uncertainties, more or less substantially depending on rivers, the Bayesian method is less sensitive to the choice of the model ensemble and is more skillful when tested with synthetic observations. It has also been applied over the whole Arctic watershed, showing so far a limited narrowing of the inter‐model spread, but its skill will further improve with increasing climate change.
Shushik Avagyan, Vladimir Katkovnik, Karen Egiazarian
A fast and shallow convolutional neural network is proposed for hyperspectral image super-resolution inspired by Spatial-Spectral Reconstruction Network (SSR-NET). The feature extraction ability is improved compared to SSR-NET and other state-of-the-art methods, while the proposed network is also shallow. Numerical experiments show both the visual and quantitative superiority of our method. Specifically, for the fusion setup with two inputs, obtained by 32× spatial downsampling for the low-resolution hyperspectral (LR HSI) input and 25× spectral downsampling for high-resolution multispectral (HR MSI) input, a significant improvement of the quality of super-resolved HR HSI over 4 dB is demonstrated as compared with SSR-NET. It is also shown that, in some cases, our method with a single input, HR MSI, can provide a comparable result with that achieved with two inputs, HR MSI and LR HSI.
Traditional target detection algorithms have difficulty to adapt complex environmental changes and have limited applicable scenarios. However, the deep-learning-based target detection model can automatically learn with strong generalization capability. In this article, we choose a single-stage deep-learning-based target detection model for research based on the model’s real-time processing requirements and to improve the accuracy and the robustness of target detection in remote sensing images. In addition, we improve the YOLOv4 network and present a new approach. First, we propose a classification setting of the nonmaximum suppression threshold to increase the accuracy without affecting the speed. Second, we study the anchor frame allocation problem in YOLOv4 and propose two allocation schemes. The proposed anchor frame scheme also improves the detection performance, and experimental results on the DOTA dataset validate their effectiveness.
During the last 20 years, fast urbanization activities have been highly concentrated in just few countries (e.g., China, India, and Nigeria) and have led to the emergence of large urban aggregations, with high population density. Still, very few researches have focused on this dynamic phenomenon with a global perspective using multisource remote sensing data. In this article, combining radar and spectral sensors of different spatial resolution, a novel approach based on a novel hierarchical biclustering technique is proposed and proved to be effective in discriminating the underlying change patterns without pre-estimating the number of clusters. To this aim, experimental results focused on newly emerging megalopolis in China, India, and Nigeria, as well as on the highly urbanized and stable Lombardy region in Italy, are presented. The analysis of the results allows us to understand, in a global and comparative perspective, the spatiotemporal differentiation of urban density and how cities are changing and evolving in the building volume and, to some extent, their economic level.
<p>From the perspective of the social history of science and transnational history, this paper reviewed the development of the Institute of Geophysics, Chinese Academy of Sciences (IGCAS), rather than focusing on its scientific achievements. Before the 1950s, the discipline of geophysics in China, except for the branch of meteorology, had a very weak foundation, and few researchers were engaged in it. The systematic development of geophysics began with the establishment of IGCAS. In this paper, the early development of IGCAS was researched thoroughly. At first, we briefly reviewed the establishment process for IGCAS. After being promoted by the desire of scientists to develop big geophysics, the Chinese Academy of Sciences (CAS) integrated scattered academic forces, which included geomagnetism and geophysical exploration, to establish the IGCAS. The IGCAS was based on the Institute of Meteorology of Academia Sinica in the Republic of China era. After that, we summarized work done by IGCAS in the development of geophysics from the 1950s to 1966, the year in which the Cultural Revolution began. We focused on policy support, adjustment of organizational structure, and scientific capacity building, when China was facing an isolated international diplomatic environment, continuous domestic political movements, and an austere social economy. Then, to bolster the development of geophysics in China, the slogan of “Missions Drive Disciplines”, which was instilled and implemented by the Chinese scientific community, was introduced briefly. The scientific development of the IGCAS and typical examples in several branches of geophysics, which included atmospheric science, seismology, space physics, and other fields, were systematically summarized and benchmarked to the international academic level. We then summarized the basic research on geophysics carried out by the institute in economic construction and national defense. Finally, the experience and lessons in the development of this institute and its effect on geophysics in China were explored.</p>