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.
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.