With the rapid development of virtual reality (VR) and augmented reality (AR), spatial audio recording and reproductionhave gained increasing research interest. Higher Order Ambisonics (HOA) stands out for its adaptabilityto various playback devices and its ability to integrate head orientation. However, current HOA recordings oftenrely on bulky spherical microphone arrays (SMA), and portable devices like smartphones are limited by arrayconfiguration and number of microphones. We propose SHB-AE, a spherical harmonic beamforming based methodfor Ambisonics encoding using a smartphone microphone array (SPMA). By designing beamformers for eachorder of spherical harmonic functions based on the array manifold, the method enables Ambisonics encoding andup-scaling. Validation on a real SPMA and its simulated free-field counterpart in noisy and reverberant conditionsshowed that the method successfully encodes and up-scales Ambisonics up to the fourth order with just fourirregularly arranged microphones.
This paper investigates whether a location's growth benefits or suffers from proximity to a big city and explores the underlying mechanisms. Using county-level data from China for 1990–2020, we find that an area's being close to a big city (in the 150–250 km range) reduces its decadal population growth rate by 2.9–3.6 percentage points relative to areas beyond 250 km, which we call the urban growth shadow effect. Initial agricultural employment share has the strongest power to explain whether the negative effect exists. The mechanism is consistent with lower opportunity costs of migration for people employed in agriculture, yet contrasts with core–periphery models that give transport costs a central role. Notably, this effect exhibits a temporal trend. Over time, being proximate to a big city becomes increasingly beneficial.
In response to the growing prevalence of online second language learning and the burgeoning field of international Chinese language education, this study examines the impact of multimodal inputs (MMI) on vocabulary acquisition within online environments among learners of Chinese as a second language (CSL). A teaching intervention was conducted with 90 Mongolian CSL learners, who were grouped into audiovisual, audio, and visual groups. The findings indicate that the audiovisual condition significantly improved vocabulary retention compared to the single-modality conditions in a delayed post-test. Nevertheless, the efficacy of the MMI treatment was observed to vary with learners’ proficiency levels, with beginner-level CSL learners deriving greater benefit from MMI than intermediate-level learners. Furthermore, participants expressed both favorable and critical perspectives regarding the application of MMI in vocabulary instruction. These results highlight the potential of MMI interventions to enhance vocabulary learning in online second-language education, while also underscoring the necessity of considering learners’ target language proficiency and their attitudes when developing MMI-based instructional approaches.
Awe, a self-transcendent emotion, has been theoretically posited as a precursor to wise reasoning. However, direct empirical evidence supporting this relationship and the underlying mechanism has been limited. In four studies (N = 3700), we examined the relationship between awe and wise reasoning, as well as the mediating effect of self-transcendence, employing cross-sectional, longitudinal, and experimental designs. We consistently found that awe had a lagged effect on (Study 1), enhanced (Studies 2 & 3), and was associated with (Study 4) wise reasoning. Furthermore, self-transcendence mediated this relationship (Studies 3 & 4). The impact of awe on wise reasoning and mediating effect of self-transcendence could not solely be attributed to awe’s predominantly positive nature, and the mediation model was established beyond the influence of self-smallness (Studies 3–4). These findings contribute to understanding the emotional trigger of wise reasoning, the cognitive implications of awe, and its role in promoting wise conflict resolution.
As artificial intelligence-generated content (AIGC) continues to evolve, video-to-audio (V2A) generation has emerged as a key area with promising applications in multimedia editing, augmented reality, and automated content creation. While Transformer and Diffusion models have advanced audio generation, a significant challenge persists in extracting precise semantic information from videos, as current models often lose sequential context by relying solely on frame-based features. To address this, we present TA-V2A, a method that integrates language, audio, and video features to improve semantic representation in latent space. By incorporating large language models for enhanced video comprehension, our approach leverages text guidance to enrich semantic expression. Our diffusion model-based system utilizes automated text modulation to enhance inference quality and efficiency, providing personalized control through text-guided interfaces. This integration enhances semantic expression while ensuring temporal alignment, leading to more accurate and coherent video-to-audio generation.
The asymmetrical global higher education and knowledge systems ordered by Euro–American hegemony have been increasingly interrogated, especially by scholars in the humanities and social sciences (HSS). With gathering awareness, growing HSS scholars from non-Western backgrounds have called for global intellectual pluriversality. Responding to such a trend, this article sheds new light on the status quo of East Asian and other non-Euro–American intellectual traditions by taking Chinese intellectual traditions as a case. Since the nineteenth century, generations of Chinese intellectuals have strived to transform their intellectual traditions into modern resources. This historical mission has been carried on by contemporary scholars and become even more complex in the current global era. By unpacking the real perceptions and recent experiences of Chinese HSS scholars, this study demonstrates that Chinese intellectual traditions deeply influence today’s knowledge production and have been transformed into three kinds of academic resources: approaches, methodologies/paradigms, and theories. However, the transformation process has never been smooth. Domestically, the great endeavours of Chinese HSS scholars are often impeded by the dominant intellectual extraversion and coercive audit culture; internationally, they feel constrained by epistemic injustice. This article proposes an empirical approach to examining and presenting intellectual traditions in the individual experiences of scholars. It reveals the high complexities of navigating through asymmetrical globalisation to achieve intellectual pluriversality.
This paper develops a unified theory integrating the three pillars of the pension system—public, occupational, and private pensions—within a heterogeneous-agent overlapping generations (OLG) model. By incorporating income heterogeneity and institutional features unique to each pillar, the model captures how individuals across the income distribution participate in the pension system and derive utility. We characterize the distinct yet interactive roles of each pillar in providing risk sharing and retirement security and identify fundamental trade-offs in pension design. Our model provides a laboratory for analyzing the coordination of the three pillars that aims at enhancing equity and fiscal sustainability.
This study aims to identify the associations between teacher mental health and student mental health. Cross-sectional data were collected from 127,877 students aged 9–20 years and 2,759 teachers across 31 provinces in China. The mental health of students and teachers were assessed by well-being (life satisfaction and positive mental health), and psychological distress (depression and anxiety). Controlling for demographic variables, multilevel regression analyses suggest that higher teacher positive mental health was linked to higher student positive mental health and lower student depression; higher teacher depression were correlated with higher student depression; and teacher life satisfaction and anxiety were not correlated with any indicators of student mental health. The study highlights the significant association between teacher mental health and student mental health.
Trust in artificial intelligence (AI) has become a central issue due to the opacity and unpredictability of AI decision-making processes. However, existing studies often produce inconsistent results and fail to provide a unified understanding of the underlying factors, making a comprehensive review necessary. To address this gap, we conducted a systematic review of 562 empirical studies to explore the antecedents and consequences of human trust in AI. The review identified key antecedents of trust, including AI capability, anthropomorphism, individual factors, and explainability, and associated consequences, such as behavioral intention, attitude, and acceptance. A cross-cultural analysis revealed significant differences in how cultural contexts influence the perception and prioritization of factors, such as capability, transparency, and anthropomorphism. These findings emphasize the need for a multidimensional approach to developing trustworthy AI systems, highlighting the importance of cultural sensitivity in AI design. The review also suggests several promising avenues for future research, including dynamic trust formation, reciprocal trust relationships, and the evolution of trust over time. Addressing these areas will enhance our understanding of trust in AI and contribute to the development of culturally adapted and ethically sound AI technologies.