科研成果

Coastal sediment cores provide important records of land-based antibiotics' deposition. This study examined sediment cores from the Hangzhou Bay, East China Sea, dating back to 1980–2020 using 210Pbex. The 40-year analysis revealed a mismatch between sediment depth and age. Wastewater treatment facilities have significantly reduced antibiotics discharge into the sea. We identified 27 antibiotics, with enrofloxacin (ERFX) and nadifloxacin (NDFX) exhibiting the highest average concentrations of 84.9 and 83.4 ng/g, respectively. Quinolones (QNs) were prominent, displaying strong co-occurrence and similar distribution patterns shaped by comparable soil-water distribution coefficient (Kd). QNs correlated positively with total antibiotic concentration, serving as indicators. We proposed a multi-dimensional risk assessment of antibiotics, encompassing ecological and antimicrobial resistance (AMR) risks, complementing each other. The assessment revealed antibiotics with distinct risks: sulfacetamide (SCM) and clindamycin (CLIN) exhibited high ecological risks, while ERFX, ciprofloxacin (CFX), norfloxacin (NFX), gatifloxacin (GTFX), moxifloxacin (MXFX), and marbofloxacin (MBFX) presented high AMR risks.

The digital economy has become a driving force for global economic development, resulting in high demand for balanced regional development. Using surname distance as a proxy variable for cultural distance, this study examined the impact of cultural differences on the development of a regional digital economy. The results of the analysis of panel data from 31 Chinese provinces from 2011 to 2019 indicated that the development of a region's digital economy positively contributes to the development of the digital economy in areas of cultural proximity. Further analysis of the mechanisms of cultural differences in the digital economy showed that cultural distance affects the development of the digital economy in a province through three mechanisms: birth rate, divorce rate, and the share of small families. Moreover, the findings suggest regional, divorce, and demographic heterogeneity in the impact of cultural distance on the digital economy.

Conventional room geometry blind inference techniques with acoustic signals often rely on prior knowledge, such as source signals or source positions, limiting their applicability when the sound source is unknown. To solve this problem, the authors propose a novel multitask deep neural network (DNN) model that jointly estimates sound source localization and room geometry using signals captured by a spherical microphone array. Considering the coupling between sound source content and environmental parameters in reverberation signals, extracted early reflection direction and delay information as network inputs to estimate spatial parameters is used, ensuring independence from the sound source signal. The proposed model employs a hierarchical architecturewith dedicated subnetworks to process direction-of-arrival (DOA) andtime-difference-of-arrival features, followed by a shared fusion module that exploits geometricconstraints between source and boundary positions. Compared with traditional methods, thismodel requires less prior environmental information and performs sound source localizationand room geometry inference with single-position sound field measurements. Experimentalresults from simulations and real measurements demonstrate the method’s effectiveness andprecision compared with conventional approaches across various scenarios.