Chongqing Academy of Science and Technology

ABSTRACT Tight sandstone gas (TSG), an unconventional oil–gas resource, has heterogeneous reservoirs. Traditional classification and evaluation methods fail to fully reveal reservoir characteristics and complex reservoir‐productivity links. As a key source of tight sandstone, the Taiyuan Formation in the Ordos Basin exhibits significant variations in reservoir quality due to its marine–terrestrial transitional sedimentary setting, necessitating the establishment of an effective classification and evaluation system. This study focuses on Block A's Taiyuan Formation tight sandstone reservoirs in the Ordos Basin, using core observations, thin‐section petrography, scanning electron microscopy, and high‐pressure mercury intrusion experiments to build a machine learning (ML) model for reservoir evaluation and improve the accuracy of reservoir evaluation. Results demonstrate that the K ‐means algorithm has the optimal classification results based on a variety of internal evaluation metrics. The tight sandstone reservoirs in the study area are classified into three distinct types: Type I reservoirs are mainly lithic quartz sandstone/lithic feldspathic sandstone, showing significant compaction, with large pore throats and low displacement pressure. Type II, mostly feldspathic lithic sandstone, acts as a transitional reservoir. Type III, predominantly lithic feldspathic sandstone, is most affected by cementation, featuring fine‐grained pore throats and high displacement pressure. Furthermore, evaluation results based on confusion matrices demonstrate that the Bayesian‐optimized Random Forest model achieves a classification accuracy of 92.76% in log interpretation, significantly outperforming other models. Through gray relational analysis, the nonlinear correlations between productivity and reservoir parameters are clarified. The prediction results demonstrate over 80% spatial consistency with actual gas reservoir distributions, effectively reflecting the production capacity of the tight sandstone reservoirs in the study area. The ML‐driven evaluation approach proposed in this study not only provides robust technical support for the exploration and development of TSG reservoirs and effectively delineates the distribution of favorable zones, but also offers practical guidance for the efficient exploration of hydrocarbon resources in geologically analogous settings.

This study examines how digital economic development affects income distribution in China, using panel data from 31 provinces between 2011 and 2021. Employing a two-way fixed effects model and robustness tests, it finds that the digital economy significantly increases household income, primarily through wage growth. However, the effects are uneven across different groups. Urban residents benefit more than rural ones, widening the urban–rural income gap. Regionally, the eastern provinces experience greater income gains than central and western areas. Industry-wise, high-digital sectors such as mining, finance, and energy see stronger effects, while traditional sectors like agriculture and public services show limited impact. Non-state-owned enterprises also gain more than state-owned ones, due to their flexibility and adaptability. These findings suggest the digital economy brings both opportunities and challenges—enhancing income overall but also contributing to inequality. Policy recommendations include improving digital infrastructure in less-developed areas, supporting digital upskilling, and strengthening regulations to ensure inclusive and equitable digital development.

Background: Steel box girders are widely employed in bridge engineering due to their excellent mechanical properties and construction convenience, yet their modular design still encounters bottlenecks such as knowledge reuse difficulties and information silos. This study proposes a BIM-driven framework based on knowledge graphs and data fusion. By constructing a professional knowledge graph comprising 85 core entity types and 150 semantic relationships (integrated with over 15,000 knowledge units), systematic management of design knowledge is achieved. The developed BIM reverse modeling technology improves parametric modeling efficiency by 30–40%, while the data fusion mechanism supports over 90% accuracy in design conflict detection. The intelligent decision-making system built upon this framework meets 75% of business scenario requirements while effectively assisting critical decisions such as module selection. Results demonstrate that this framework significantly enhances design collaboration efficiency and intelligence through knowledge structuring and deep data integration. Although some achievements were validated via simulation due to limited field measurement data, the approach demonstrates strong engineering applicability and provides novel technical pathways and methodological support for advancing digital transformation in bridge engineering.

In recent years, the distribution and transport of microplastics (MPs) in aquatic environments have garnered significant research interest due to their interactions with sediments, which directly influence their migration pathways, deposition patterns, and ecological risks. This study reviews research on the co-migration of suspended sediments (SS) and MPs based on publications from the Web of Science and Engineering Village databases spanning 2011–2025. A co-occurrence network analysis of keywords was conducted using CiteSpace, and the literature was visualized accordingly. The study also investigates the distribution of MPs in sediments within Chinese waters as a case study. The spatiotemporal distribution of MPs is influenced by hydrological conditions (e.g., flow and runoff intensity) and MP properties (e.g., density, shape, polymer type). This paper provides a systematic overview of key physical processes in sedimentary environments, including MP aggregation, settling, burial, and resuspension. Hydrographic conditions, particle concentration, and material properties are identified as the primary factors governing their co-migration. At the microscopic level, interactions between MPs and SS are mainly controlled by van der Waals forces, electrostatic interactions, and covalent bonding. The co-migration of MPs and SS involves multi-mechanistic coupling governed by physical, chemical, and biological processes. This study offers a scientific basis for assessing pollution risks and developing effective management strategies.

<strong class="journal-contentHeaderColor">Abstract.</strong> South China, a densely populated region frequently affected by transported biomass burning aerosols (BBA), is in need of sensitive remote sensing observations to characterize these plumes. Laser-induced fluorescence (LIF) lidar is powerful tool for detecting fluorescence aerosols and has recently been demonstrated to identify transported BBA over Europe, while its applications in South China remain scarce. Here, we present LIF lidar observations of fluorescent aerosols conducted at Nanping, South China. The detected fluorescence layer exhibited relatively weak intensity (maximum fluorescence backscatter coefficient &asymp; 0.16&times;10^&minus;5 Mm^&minus;1 Sr^&minus;1 nm^&minus;1), more than two orders of magnitude lower than the N₂ Raman backscatter signal. Nevertheless, it showed a distinct spectral signature compared with typical urban aerosols. By integrating multi-source datasets, the fluorescence layer was attributed to long-range transported BBA originating from weak fire activity in the Indo-China Peninsula (ICP). Furthermore, the concurrent presence of BBA and enhanced water vapor indicated a humid environment favorable for aerosol processing. This study demonstrates that multi-channel LIF lidar provides a sensitive and promising approach for detecting and characterizing BBA layers over South China, thereby offering new insights into their transport mechanisms and potential environmental impacts.

Against the backdrop of the deep integration of the global film and television industry, character design has long transcended the mere pursuit of aesthetics and has become a key symbolic system that carries cultural information, builds identity recognition, and drives narrative. This article, by integrating semiotic theory and cultural dimension analysis, deeply dissects the core functions and common predicaments (stereotypes, cultural misinterpretation) of formative symbols in cross-cultural communication and systematically proposed four core design strategies: “extraction and translation of cultural symbols,” “visual mapping of cultural dimensions,” “symbolic support of narrative functions,” and “cultural decoding presuppositions for target audiences.” This article holds that successful cross-cultural modeling design should be committed to creative transformation on the basis of respecting the authenticity of culture, constructing a visual symbol system that combines cultural depth and universal appeal, and ultimately serving global narratives and in-depth cultural dialogues.

Diagnosis of dengue virus infections typically relies on RT-PCR-based methods, for which reliable positive controls are essential. Viral RNA is an ideal positive control, but its inherent instability poses a major challenge. Herein, we report a simple and effective method for stabilizing dengue virus RNA by immobilizing it onto silk fibroin films (RNA-SFFs). We evaluated various substrate surfaces for RNA-SFFs preparation and found that the inner surface of sealable bags is optimal for uniform film formation and easy harvesting. Screening different silk fibroin concentrations revealed that even low concentrations (2.8%) effectively preserved RNA well and kept Ct constant for up to 16 days at 25 °C, 37 °C, and even 45 °C (extreme weather for transportations). Due to its rapid film formation and ease of peeling, 7% silk fibroin was selected. Notably, the RNA-SFFs demonstrated robust resistance to UV irradiation, with no significant Ct value changes after 4 h of exposure. Long-term stability testing at −20 °C, 25 °C, and 37 °C showed that dengue serotype 1–4 RNA-SFFs remained stable for the entire duration of the study—up to 56 weeks (approximately 14 months)—at all tested temperatures. These results demonstrate that RNA-SFFs are highly stable, portable, and practical as positive controls for dengue diagnostics, with strong potential for use in on-site and resource-limited settings.