Ice crystal orientation strongly influences cloud radiative properties and remote sensing retrievals, but long-term, high-resolution quantitative observations remain scarce. This study presents comprehensive case studies and statistical analyses of horizontally oriented ice crystals (HOICs) based on full-year (2022) synergistic observations in Beijing, China, combining a zenith-pointing micropulse lidar, a collocated 15° off-zenith polarization lidar, and a Ka-band cloud Doppler radar. Applying a novel height-resolved classification method based on dual-angle polarization lidars, HOICs are identified with high spatiotemporal resolution. HOICs are found to be common, accounting for 15.0 % of all ice-containing cloud data points annually with a peak of 24.6 % in summer, and showing maximum occurrence at temperatures from −20 to −10 °C. Macroscopically, typical HOIC layers exhibit horizontal extents of 10–100 km and durations of several hours within their optimal formation temperature ranges. Furthermore, the Euclidean-distance analysis between HOICs and past overlying cloud layers reveals a strong linkage of HOIC occurrence to supercooled liquid water clouds (SWCs), being much closer to HOIC events than randomly oriented ice crystals (ROICs). Dynamically, cloud radar observations further reveal that HOICs preferentially occur in stable environments with turbulent eddy dissipation rates below 10−2 m2 s−3 and exhibit lower fall velocities than ROICs. Estimates based on radar-observed vertical velocity indicate typical HOIC equivalent diameters of approximately 1200 µm with Reynolds numbers predominantly below 100. These findings provide key observational constraints for improving ice cloud microphysics and orientation parameterizations in numerical models.
Organic coatings on mineral surfaces are ubiquitous organic–liquid interfaces in soil and sediment systems, and their functional groups have been reported to control heterogeneous iron (Fe) (oxyhydr)oxide precipitate amounts and sizes. However, the role of these organic–liquid interfaces as controls on Fe oxyhydroxide transformation kinetics and pathways remain poorly constrained, particularly regarding the role of individual functional groups during this process. Here, self-assembled organosilane films terminated with carboxyl, thiol or amine functional groups on fused silica (SiO2) substrates were used as idealised organic-functionalised mineral surfaces to mechanistically study the phases of homogeneously and heterogeneously precipitated Fe (oxyhydr)oxides and their Fe(II)-catalysed transformation at short and long timescales (4 and 79 days, pH 6.0). Transformation was analysed using synchrotron scattering techniques, transmission electron microscopy, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy and stable Fe isotope (57Fe) tracer measurements. Ferrihydrite was the initial phase for all precipitates, but the subsequent kinetics and pathways of heterogeneous precipitate transformation varied substantially with organic functional group identity and were distinct from those of the homogeneous precipitates. Precipitate-aqueous Fe isotopic exchange, and hence the reductive dissolution of ferrihydrite, was complete within four days of transformation for homogeneous precipitates but incomplete for heterogeneous precipitates, contributing to slower transformation kinetics for the latter. Transformation pathways were additionally controlled by surface functional group interactions with early-stage transformation products. After four days, strong carboxylate-Fe binding hindered crystallisation, forming only trace lepidocrocite at a lower surface carboxyl density. Comparatively weaker interactions with SiO2 and amine groups resulted in trace amounts of lepidocrocite, goethite and magnetite. However, much weaker interactions with thiol groups resulted in large amounts of lepidocrocite and goethite, similar to transformation of the homogeneous precipitates. After 79 days, large amounts of lepidocrocite, goethite and magnetite formed for both the homogeneous precipitates and heterogeneous precipitates on SiO2. In comparison, carboxylate-Fe binding almost entirely limited crystalline phase formation. Interactions with amine groups limited ferrihydrite/lepidocrocite transformation into goethite/magnetite, whilst the oxidation of thiol groups led to interactions with Fe species that limited magnetite formation. These results emphasise the importance of organic coatings as microenvironments for controlling the kinetics and pathways of ferrihydrite transformation.
While Neural Audio Codecs (NAC) have revolutionized monaural audio compression, achieving high-fidelity dual-channel coding at low bitrates remains a significant challenge. Existing approaches often rely on naive independent channel quantization, leading to phase incoherence, or entangled latent modeling, which sacrifices spatial precision for spectral energy. This paper proposes a novel dual-channel coding framework based on contentspatial disentanglement. Reframing spatial reconstruction as an informed source separation task, our architecturesynergizes a frozen, pre-trained DAC encoder for robust mono content preservation with a parameter-efficient side information encoder that predicts fine-grained time-frequency masks. To ensure precise spatial imaging, we introduce explicit physical constraints into the end-to-end training. Experimental results indicate that at low bitrates of 9 and 11 kbps, the proposed method outperforms state-of-the-art dual-mono neural baselines and industry standards in both objective spatial metrics and subjective MUSHRA evaluations.
Binaural rendering is typically assessed via timbre and localization accuracy, while its intrinsic spatial resolution remains rarely quantified. This paper proposes a perceptual evaluation method based on Minimum Audible Angle (MAA) measurements to estimate the azimuthal just-noticeable difference (JND) introduced by binaural rendering algorithms. We systematically compared several rendering algorithms across eight reference azimuths using two participant-allocation paradigms. The results show that spatial resolution is significantly influencedby Ambisonic order and choice of the rendering algorithm, with MAA thresholds systematically decreasing as the truncation order increases. Furthermore, the proposed method successfully captures physiological spatial characteristics and identifies resolution limits imposed by reference angles. While both participant-allocation paradigms yield consistent qualitative trends, the repeated-measures design provides superior data stability. These findings demonstrate that the proposed MAA-based method is an effective tool for quantifying the spatial resolutionof binaural rendering algorithms.
The oxidation of organic compounds by nitrate radicals (NO3) in the atmospheric aqueous phase makes significant contributions to the production of secondary organic aerosols (SOA) and brown carbon (BrC). However, relevant kinetic parameters remain scarce, particularly in aerosol liquid water (ALW), where high concentrations of inorganic ions coexist. In this study, we developed a predictive model to estimate the aqueous-phase reaction rate constants between NO3 and organic compounds (kNO3) using a novel machine learning (ML)-based approach. By simultaneously considering the chemical properties of the reactants and experimental conditions, this model enables an accurate prediction of kNO3 across ionic strength (I) ranges of 0–6 M, while also accounting for the influence of different ionic species. The model's predictive accuracy, generalization ability, and applicability domain (AD) are evaluated, followed by a mechanistic interpretation via Shapley additive explanation (SHAP) analysis. In summary, this study provides a valuable supplementary tool for estimating kinetic parameters in both cloud droplets and ALW. As an application, the model is employed to predict kNO3 values for phenolic compounds emitted from biomass burning (BB), extending the currently available data set for atmospheric modeling.
We propose a three-stage framework named as Recovery-Informed Strategy Enhancement (RISE) to forecast the recovery of Chinese outbound tourism following the coronavirus disease 2019 pandemic. The framework decomposes the forecasts into three parts: the initial forecasts, the terminal forecasts and the recovery curve forecasts that connect the two points. We integrate multiple sources of information and employ forecast combination techniques in all stages, enhancing both the accuracy and robustness of recovery forecasts. Compared with conventional forecasting approaches, our framework provides a structured and transparent pipeline to integrate model-based forecasts with expert-informed judgment under structural breaks and high uncertainty. Our findings demonstrate the effectiveness of this framework, offering an adaptable tool for recovery trajectory forecasting in post-crisis contexts.
Sound source localization and identity tracking are fundamental tasks in acoustic scene analysis, enabling machines to determine what, where, and when sound events occur. While deep attractor-based networks have demonstrated improved performance under an unknown number of sources, maintaining continuous source tracking over longform audio remains challenging due to memory limitations and permutation ambiguities across adjacent segments. In this paper, we propose a Recursive Attractor Network (RANet) for long-form sound source localizationand identity tracking with a variable number of sources. RANet explicitly represents attractors as transferable embeddings and recursively propagates them across adjacent audio segments using a LSTM-based model, thereby preserving source identity continuity over time. Experimental results on simulated datasets demonstrate that RANet achieves robust long-form localization and consistent source identity tracking, outperforming baseline approaches.