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.
Digital-driven scaling poses significant problems to analog circuits because scaling severely deteriorates transistor current saturation, significantly degrading the intrinsic gain. Special material properties of emerging low-dimensional semiconductors trigger the possibility of providing solutions. We report complementary carbon nanotube thin-film transistors with negative differential resistance-induced current super-saturation for high, exponentially variable intrinsic gain with immunity against degradation during scaling. Current super-saturation at the negative-to-positive differential resistance transition boundary provides intrinsic gain singularities. The large-window, gate-modulated negative differential resistance behavior derived from carbon nanotube’s characteristics enables its practical utilization in circuits. When approaching the singularity, we record that the intrinsic gain varies by orders of magnitude, ranging from 102 to 106 at different operation points. We further demonstrate high and exponentially variable gain in an operational amplifier, showing a tunable single-stage gain ranging from 35 to 60 decibels.
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.