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.
The effects of religiosity on organizational behavior have become a topic of interest in the fields of management and religion. However, whether religiosity is associated with green-related innovation has not been explored, and the underlying economic channels remain unclear. This study addresses this gap by conceptualizing both regional religiosity and industry peer effects as distinct dimensions of informal institutions that jointly shape corporate green innovation. Using Chinese listed companies in 2020 as research subjects and employing a spatial econometric model, this study systematically tests these relationships. A positive link between religiosity and green-related innovation in firms is observed, and a positive peer effect on green innovation is also identified. To reveal the economic channel, the mediating role of business performance is tested. The results clearly indicate that business performance mediates the link between religiosity and green innovation in firms: regional religiosity fosters a pro-social business environment that enhances business performance, which in turn provides the resources necessary for green innovation. Furthermore, this association is shown to be heterogeneous, with the effect being pronounced in non-state-owned enterprises but muted in state-owned enterprises, where managerial atheism may offset religious norms. Evidence is provided by this study that religion, as an informal institution, is conducive to firms’ green innovation, and the understanding of the role of informal institutions in shaping corporate environmental strategy in transitional economies is deepened.
Global climate change is an increasing challenge to healthy aging because extreme heat, air pollution, sleep disruption, and ecological instability can weaken physiological homeostasis in older adults. As aging reduces thermoregulatory, cardiovascular, immune, endocrine, and autonomic reserves, repeated climate stress may lead to delayed recovery and a biological resilience cascade that increases the risk of frailty, cognitive decline, cardiovascular events, hospitalization, and reduced healthspan. This Perspective proposes an integrative framework that situates biological resilience within psychosocial and cultural contexts. We argue that cultural ethics do not directly alter physiological biomarkers, but may shape upstream social and psychological conditions, including stress appraisal, help seeking, community care, adaptive behavior, and recovery after exposure. Drawing on planetary health and cultural ethics, we examine how Zhi Wei Bing, Ren, Yi, and Tian-Ren-He-Yi can inform climate adaptation for older adults. These concepts support preventive action, care for vulnerable groups, fair distribution of resources, resilience literacy, and ecological planning. By linking physiological mechanisms with psychosocial resources and cultural values, this article offers a hypothesis-generating framework for climate-resilient aging and for policies that protect vulnerable older adults in a warming world.
We fabricated self-aligned quasi-vertical trench p-NiO/GaN merged p-n Schottky diodes with p-NiO guard-rings and investigated the effects of trench depth (dt) and n-region width (Wn) on the electrical characteristics. The MPS diode with Wn of 2 μm and dt of 1 μm exhibited a high forward current density of 1 kA cm−2 and a low differential Ron,sp of 1.4 mΩ·cm2. Guard-rings edge termination was introduced to improve the breakdown voltage from 330 V to 430 V. The average breakdown electric field was calculated to be 1.1 MV cm−1 for the MPS diode on a sapphire substrate.
Ensuring equitable access to healthcare services safeguards individual wellbeing and enhances society’s overall happiness. This study investigates the complex relationships between spatial hospital accessibility, spatial inequality, and urban wellbeing, focusing on the physical dimension of access measured by travel time. Using geospatial and economic data from 13,776 hospitals, this study reveals that inequality in hospital accessibility, as measured by the Gini coefficient, significantly and negatively impacts urban happiness. Additionally, the results reveal a nonlinear, inverted U-shaped relationship between hospital accessibility and city-level happiness, indicating an optimal threshold beyond which marginal benefits decline. Additionally, the results indicate a key mediating mechanism: unequal access drives population out-migration and reduces the permanent resident population. This outcome, in turn, partially transmits adverse effects to city-level wellbeing. These findings demonstrate substantial spatial and contextual heterogeneity, underscoring the need for policymakers to tailor urban health policies that prioritize enhancing accessibility and ensure equitable distribution to foster sustainable demographic stability and overall urban wellbeing.
Hydrogen peroxide (H2O2) photosynthesis from H2O and O2 using covalent organic frameworks (COFs) is a sustainable approach, yet its efficiency is restricted by a sluggish water oxidation reaction (WOR) due to insufficient water adsorption and charge separation. Herein, we propose a facile and universal polar center spatial-manipulation strategy to enable efficient H2O2 photosynthesis by COFs via converting high-polarity C═N linkages into 4-carboxyl-quinolyl linkages with weakened-polarity quinoline backbones and ultra-polar carboxyl side chains (forming COF-TBC). This polar-center side-shifting strategy concurrently enhances water adsorption (via the polar carboxyl side chain) and water activation (enabled by efficient exciton formation and separation along the low-polarity quinoline backbone) by COF-TBC, lowering the energy barrier of the rate-determining WOR and achieving outstanding and stable H2O2 photosynthesis from O2 and H2O without sacrificial agents (5624 µmol g−1 h−1, accumulating to 41 mM, solar-to-chemical efficiency of 0.72%). The polar-center side-shifting strategy can be extended to modify other COFs for enhancing H2O2 photosynthesis, indicating its universality. COF-TBC maintains high H2O2 yield in complex real-water matrices and can be integrated into membrane-based and continuous-flow reactors for successive H2O2 generation under natural sunlight. COF-TBC also exhibits efficient photocatalytic performance toward organic contaminant degradation and microorganism inactivation, highlighting its broad potential for water purification.
Implementing Higher-Order Ambisonics (HOA) on consumer devices is hindered by their sparse, irregular microphone arrays, which challenge conventional methods with issues like spatial aliasing and ill-conditioning. This paper proposes a unified Spherical Harmonic Beamforming (SHB-AE) framework that recasts HOA encoding as a spatial filtering problem, enabling robust, signal-independent solutions. We develop two approaches: a frequency-domain (FD) method with compensation for high-frequency artifacts, and a time-domain (TD) methodthat holistically optimizes broadband FIR filters for enhanced stability. The framework is inherently scalable, allowing on-demand order expansion. Using a measured smartphone array, comprehensive objective and subjective evaluations demonstrate the clear superiority of theTD method. It excels in signal fidelity, spatial accuracy, and temporal consistency, outperforming baseline and FD approaches. The TD method also maintains its advantage in adverse conditions, showing remarkable robustness against noise, reverberation, and multi-source environments.It provides a practical, high-performance pathway for enabling high-fidelity spatial audio capture on ubiquitous consumer devices without requiring complex signal analysis or large datasets.
