Solutions to many partial differential equations (PDEs) display coexisting smooth global transport and localized sharp features within a single trajectory: shock fronts, thin interfaces, and concentrated high-frequency content sit on top of slowly varying backgrounds. This poses a challenge for neural operators: Fourier-based architectures mix nonlocal interactions efficiently but tend to under-resolve localized non-smooth features, whereas spatially local architectures recover fine detail at the cost of long-range propagation and rollout stability. Existing hybrid operators paper over this tension with a fixed, spatially uniform fusion that forces the same trade-off everywhere. We propose U-HNO, a U-shaped hybrid neural operator whose central design is Sparse-Point Adaptive Routing (SPAR): at every spatial location, a per-pixel hard mask selects whether the global Fourier branch or the local multi-scale Gaussian branch should dominate, and the sparsity ratio is a function of the local contrast of the routing signal, so smooth and shock-aligned regions receive different mixtures of global and local computation. SPAR is embedded in a hierarchical encoder-bottleneck-decoder backbone
The astrochemistry of the HnO+ (n=1..3) ions is important as the main gas-phase formation route for water, and as tracer of the interstellar ionization rate by cosmic rays and other processes. While interstellar H3O+ has been known since the early 1990's, interstellar OH+ and H2O+ have only recently been detected using the Herschel space observatory and also from the ground. This paper reviews detections of HnO+ toward external galaxies and compares with ground-based work. The similarities and differences of the HnO+ chemistry within the Galaxy and beyond are discussed. Special attention is given to the low H2O/H3O+ ratio in M82 of only 3.3, suggesting rapid H2O photodissociation, and the high apparent OH+ and H2O+ abundances in Mrk 231, suggesting radiative excitation and/or formation pumping. Photodissociation rates for H3O+ and collisional cross-sections for OH+ and H2O+ with H, He and electrons are needed to test these interpretations.
General-sum differential games can approximate values solved by Hamilton-Jacobi-Isaacs (HJI) equations for efficient inference when information is incomplete. However, solving such games through conventional methods encounters the curse of dimensionality (CoD). Physics-informed neural networks (PINNs) offer a scalable approach to alleviate the CoD and approximate values, but there exist convergence issues for value approximations through vanilla PINNs when state constraints lead to values with large Lipschitz constants, particularly in safety-critical applications. In addition to addressing CoD, it is necessary to learn a generalizable value across a parametric space of games, rather than training multiple ones for each specific player-type configuration. To overcome these challenges, we propose a Hybrid Neural Operator (HNO), which is an operator that can map parameter functions for games to value functions. HNO leverages informative supervised data and samples PDE-driven data across entire spatial-temporal space for model refinement. We evaluate HNO on 9D and 13D scenarios with nonlinear dynamics and state constraints, comparing it against a Supervised Neural Operator (a variant
The NH2 + O reaction represents a critical oxidation pathway in ammonia and hydrazine combustion, yet significant discrepancies persist in reported kinetics. Here, we generate a full-dimensional ground-state potential energy surface (PES) for NH2O using high-level internally contracted multi-reference configuration interaction (ic-MRCI) calculations and the permutation invariant polynomial-neural network (PIP-NN) method. The PES encompasses all energetically accessible channels, including HNO + H, NH + OH, NO + H2, and HON + H. Quasi-classical trajectory calculations on this surface yield thermal rate coefficients and branching ratios over a wide temperature range, particularly extending into the high-temperature regime relevant to combustion. The results provide accurate first principles kinetic data essential for refining combustion models of nitrogen containing fuels.
Neural operators have emerged as a powerful, data-driven paradigm for learning solution operators of partial differential equations (PDEs). State-of-the-art architectures, such as the Fourier Neural Operator (FNO), have achieved remarkable success by performing convolutions in the frequency domain, making them highly effective for a wide range of problems. However, this method has some limitations, including the periodicity assumption of the Fourier transform. In addition, there are other methods of analysing a signal, beyond phase and amplitude perspective, and provide us with other useful information to learn an effective network. We introduce the \textbf{Hilbert Neural Operator (HNO)}, a new neural operator architecture to address some advantages by incorporating a strong inductive bias from signal processing. HNO operates by first mapping the input signal to its analytic representation via the Hilbert transform, thereby making instantaneous amplitude and phase information explicit features for the learning process. The core learnable operation -- a spectral convolution -- is then applied to this Hilbert-transformed representation. We hypothesize that this architecture enables H
The development of ammonia-methane (NH3-CH4) combustion as a hydrogen-carrier energy source faces major challenges such as significant NOx emissions, hindering its practical implementation. This paper examines how ethanol (C2H6O) and methanol (CH4O) additives influence formation pathways of NOx using ReaxFF molecular dynamics (MD) simulations at temperatures of 2,000 K and 3,000 K. Ten carefully designed fuel mixtures (C1-C10) were evaluated across 0%, 5%, and 10% alcohol concentrations. The findings show that adding alcohol can effectively suppress NOx production, especially at elevated temperatures. At 3,000 K, 10% ethanol addition and 10% methanol addition reduced the production of NOx by approximately 39.6% and 30.1%, respectively, compared with the base fuel. This suppression is attributed to the charge redistribution and the redirection of nitrogen intermediates through stabilising pathways such as HNO, HNO2, and N2O. Simulation-derived descriptors served as the training data for machine learning (ML) models, including Random Forest Regression (RFR), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), and Fully Connected Neural Networks (FCNN). RFR achieved s
When reading lips, many people benefit from additional visual information from the lip movements of the speaker, which is, however, very error prone. Algorithms for lip reading with artificial intelligence based on artificial neural networks significantly improve word recognition but are not available for the German language. A total of 1806 video clips with only one German-speaking person each were selected, split into word segments, and assigned to word classes using speech-recognition software. In 38,391 video segments with 32 speakers, 18 polysyllabic, visually distinguishable words were used to train and validate a neural network. The 3D Convolutional Neural Network and Gated Recurrent Units models and a combination of both models (GRUConv) were compared, as were different image sections and color spaces of the videos. The accuracy was determined in 5000 training epochs. Comparison of the color spaces did not reveal any relevant different correct classification rates in the range from 69% to 72%. With a cut to the lips, a significantly higher accuracy of 70% was achieved than when cut to the entire speaker's face (34%). With the GRUConv model, the maximum accuracies were 87% w
This study investigates the characteristics of nitrogen oxide (NO) formation in two-dimensional (2D) laminar premixed ammonia/hydrogen/air flames and the impact of thermodiffusively driven intrinsic flame instabilities (IFIs). To this end, a set of three highly resolved direct numerical simulations (DNS) at lean ambient conditions and varying hydrogen fraction in the fuel blend are conducted. The analysis of these DNS reveals a significant increase of NO formation in positively curved regions of the flame, particularly for lower hydrogen fuel fractions, while negatively curved areas exhibit reduced NO concentrations. However, despite the strong variations of local mass fractions of NO in the flame sheet, the mean mass fraction in the post-flame region remains close to the solution from a one-dimensional flame. Through a representative flame segment analysis of positively curved, negatively curved, and flat regions, key reactions contributing to NO formation are determined, with the HNO pathway being the predominant production and the deNOx pathway being the predominant consumption pathway across all cases. Thermal NO plays no significant role in the considered cases. Generally, the
Hydroxylamine, NH2OH, is one of the already detected interstellar molecules with the highest prebiotic potential. Yet, the abundance of this molecule found by astronomical observations is rather low for a relatively simple molecule, $\sim$ 10$^{-10}$ relative to H2. This seemingly low abundance can be rationalized by destruction routes operating on interstellar dust grains. In this work, we tested the viability of this hypothesis under several prisms, finding that the origin of a lower abundance of \ce{NH2OH} can be explained by two chemical processes, one operating at low temperature (10 K) and the other at intermediate temperature (20 K). At low temperatures, enabling the hydrogen abstraction reaction HNO + H -> NO + H2, even in small amounts, partially inhibits the formation of NH2OH through successive hydrogenation of NO, and reduces its abundance on the grains. We found that enabling a 15--30 % of binding sites for this reaction results in reductions of \ce{NH2OH} abundance of $\sim$ 1-2 orders of magnitude. At warmer temperatures (20 K, in our study), the reaction NH2OH + H -> HNOH + H2, which was found to be fast (k$\sim$10$^{6}$ s$^{-1}$) in this work, followed by fur
In the absence of laboratory data, state-of-the-art quantum chemical approaches can provide estimates of the binding energy (BE) of interstellar species with grains. Without BE values, contemporary astrochemical models are compelled to utilize wild guesses, often delivering misleading information. Here, we employed a fully quantum chemical approach to estimate the BE of seven diatomic radicals - CH, NH, OH, SH, CN, NS, and NO - that play a crucial role in shaping the interstellar chemical composition, using a suitable amorphous solid water model as a substrate since water is the principal constituent of interstellar ice in dense and shielded regions. While the BEs are compatible with physisorption, the binding of CH in some sites shows chemisorption, in which a chemical bond to an oxygen atom of a water molecule is formed. While no structural change has been observed for the CN radical, it is believed that the formation of a hemibonded system between the outer layer of the water cluster and the radical is the reason for the unusually large BE in one of the binding sites considered in our study. A significantly lower BE for NO, consistent with recent calculations, is obtained, which
Quantum computing algorithms have been shown to produce performant quantum kernels for machine-learning classification problems. Here, we examine the performance of quantum kernels for regression problems of practical interest. For an unbiased benchmarking of quantum kernels, it is necessary to construct the most optimal functional form of the classical kernels and the most optimal quantum kernels for each given data set. We develop an algorithm that uses an analog of the Bayesian information criterion to optimize the sequence of quantum gates used to estimate quantum kernels for Gaussian process models. The algorithm increases the complexity of the quantum circuits incrementally, while improving the performance of the resulting kernels, and is shown to yield much higher model accuracy with fewer quantum gates than a fixed quantum circuit ansatz. We demonstrate that quantum kernels thus obtained can be used to build accurate models of global potential energy surfaces (PES) for polyatomic molecules. The average interpolation error of the six-dimensional PES obtained with a random distribution of 2000 energy points is 16 cm$^{-1}$ for H$_3$O$^+$, 15 cm$^{-1}$ for H$_2$CO and 88 cm$^{
Atmospheric aerosols facilitate reactions between ambient gases and dissolved species. Here, we review our efforts to interrogate the uptake of these gases and the mechanisms of their reactions both theoretically and experimentally. We highlight the fascinating behavior of $\mathrm{N}_2\mathrm{O}_5$ in solutions ranging from pure water to complex mixtures, chosen because its aerosol-mediated reactions significantly impact global ozone, hydroxyl, and methane concentrations. As a hydrophobic, weakly soluble, and highly reactive species, $\mathrm{N}_2\mathrm{O}_5$ is a sensitive probe of the chemical and physical properties of aerosol interfaces. We employ contemporary theory to disentangle the fate of $\mathrm{N}_2\mathrm{O}_5$ as it approaches pure and salty water, starting with adsorption and ending with hydrolysis to HNO$_3$, chlorination to $\mathrm{ClNO}_2$, or evaporation. Flow reactor and gas-liquid scattering experiments probe even greater complexity as added ions, organic molecules, and surfactants alter interfacial composition and reaction rates. Together, we reveal a new perspective on multiphase chemistry in the atmosphere.
Nitrogen oxides are thought to play a significant role as a nitrogen reservoir and to potentially participate in the formation of more complex species. Until now, only NO, N$_2$O and HNO have been detected in the interstellar medium. We report the first interstellar detection of nitrous acid (HONO). Twelve lines were identified towards component B of the low-mass protostellar binary IRAS~16293--2422 with the Atacama Large Millimeter/submillimeter Array, at the position where NO and N$_2$O have previously been seen. A local thermodynamic equilibrium model was used to derive the column density ($\sim$ 9 $\times$ 10$^{14}$ cm$^{-2}$ in a 0.5'' beam) and excitation temperature ($\sim$ 100 K) of this molecule. HNO, NO$_2$, NO$^+$, and HNO$_3$ were also searched for in the data, but not detected. We simulated the HONO formation using an updated version of the chemical code Nautilus and compared the results with the observations. The chemical model is able to reproduce satisfactorily the HONO, N$_2$O, and NO$_2$ abundances, but not the NO, HNO, and NH$_2$OH abundances. This could be due to some thermal desorption mechanisms being destructive and therefore limiting the amount of HNO and NH
We report the tentative detection in space of the nitrosylium ion, NO$^+$. The observations were performed towards the cold dense core Barnard 1-b. The identification of the NO$^+$ $J$=2--1 line is supported by new laboratory measurements of NO$^+$ rotational lines up to the $J$=8--7 transition (953207.189\,MHz), which leads to an improved set of molecular constants: $B_0 = 59597.1379(62)$\,MHz, $D_0 = 169.428(65)$\,kHz, and $eQq_0(\textrm{N}) = -6.72(15)$\,MHz. The profile of the feature assigned to NO$^+$ exhibits two velocity components at 6.5 and 7.5 km s$^{-1}$, with column densities of $1.5 \times 10^{12}$ and $6.5\times10^{11}$ cm$^{-2}$, respectively. New observations of NO and HNO, also reported here, allow to estimate the following abundance ratios: $X$(NO)/$X$(NO$^+$)$\simeq511$, and $X$(HNO)/$X$(NO$^+$)$\simeq1$. This latter value provides important constraints on the formation and destruction processes of HNO. The chemistry of NO$^+$ and other related nitrogen-bearing species is investigated by the means of a time-dependent gas phase model which includes an updated chemical network according to recent experimental studies. The predicted abundance for NO$^+$ and NO is f
Due to increased activity in high-throughput structural genomics efforts around the globe, there has been an accumulation of experimental protein 3D structures lacking functional annotation, thus creating a need for structure-based protein function assignment methods. Computational prediction of ligand binding sites (LBS) is a well-established protein function assignment method. Here we apply the specific LBS detection algorithm we recently described (Reyes, V.M. & Sheth, V.N., 2011; Reyes, V.M., 2015a) to some 801 functionally unannotated experimental structures in the Protein Data Bank by screening for the binding sites (BS) of 6 biologically important ligands: GTP in small Ras-type G-proteins, ATP in ser/thr protein kinases, sialic acid (SIA), retinoic acid (REA), and heme-bound and unbound (free) nitric oxide (hNO, fNO). Validation of the algorithm for the GTP- and ATP-binding sites has been previously described in detail (ibid.); here, validation for the BSs of the 4 other ligands shows both good specificity and sensitivity. Of the 801 structures screened, 8 tested positive for GTP binding, 61 for ATP binding, 35 for SIA binding, 132 for REA binding, 33 for hNO binding, an
Nitric acid is a possible biomarker in the atmospheres of exoplanets. An accurate line list of rotational and rotational-vibrational transitions is computed for nitric acid (HNO$_3$). This line list covers wavelengths longer than 1.42 $μ$m (0 - 7000 cm$^{-1}$) and temperatures up to 500 K. The line list is computed using a hybrid variational -- perturbation theory and empirically tuned potential energy and dipole surfaces. It comprises almost 7 billion transitions involving rotations up to $J=100$. Comparisons with spectra from the HITRAN and PNNL databases demonstrate the accuracy of our calculations. Synthetic spectra of water - nitric acid mixturessuggest that nitric acid has features at 7.5 and 11.25 $μ$m that are capable of providing a clear signature for HNO$_3$; the feature at 11.25 $μ$m is particularly promising. Partition functions plus full line lists of transitions are made available in an electronic form as supplementary data to the article and at www.exomol.com.
A new nature-inspired membrane uses perfectly uniform one-nanometer pores to filter molecules with remarkable precision。 The technology could transform industries such as pharmaceuticals and textiles by reducing energy consumption, improving water reuse, and delivering separation performance far beyond current filters
Scientists have digitally preserved the world’s most endangered marine mammal by creating highly detailed 3D models of a vaquita skeleton using advanced imaging technology。 The virtual archive provides an unprecedented look at the species and could help inspire conservation efforts before the tiny porpoise disappears forever
Researchers at EPFL have developed a chip-scale ultrafast laser that performs on par with traditional tabletop femtosecond lasers。 The innovation could make advanced laser technologies far smaller, cheaper, and more accessible for applications ranging from medical diagnostics to atomic clocks