Inner Mongolia Normal University

ABSTRACT Wave energy is a vital component of sustainable ocean resources, yet current energy harvesting technologies struggle with the intrinsic coupling effect of low‐frequency resonance and low‐amplitude viscosity in fluid‐solid interaction, thereby constraining the overall energy harvesting performance. Here, we present an amplitude‑amplified triboelectric nanogenerator (AA‑TENG) enabled by resonance‐enhanced fluid‐structure coupling to address these bottlenecks. AA‑TENG employs an elastic prestress modulation to deliver a transient mechanical release mechanism that increases the amplitude by up to 300%, achieving an energy conversion efficiency of 51.2%. A unit outputs a peak power of 5.2 mW at a wave frequency of 1 Hz, while scaling the volume fivefold raises the peak power to 52.48 mW, sufficient to power low‑power marine monitoring devices. This work introduces a wave resonance‐enhanced energy harvesting strategy, promising research into sustainable energy harvesting in the fluid‐structure coupling, advancing the self‐constructed marine microgrids.

Astragalus membranaceus suffers severe yield and quality losses due to root rot caused by Fusarium solani. To address this, we analyzed the root-associated microbial communities of healthy and diseased plants in northwest China using high-throughput sequencing. Combining community analysis with pot experiments and transcriptomic profiling, we elucidated the molecular mechanisms by which the biocontrol fungus Purpureocilliu lilacinum BP2-7 suppresses root rot. Root rot reshaped root-associated microbial structure, affecting fungal diversity more than bacterial diversity. The antagonistic effect of P. lilacinum BP2-7 against F. solani reached 71.43% in plate assays and 63.7% control efficacy in pot experiments, representing the first report of P. lilacinum application for managing root rot in A. membranaceus. Transcriptomic analysis revealed that P. lilacinum BP2-7 promotes the transition of plants from a damaged to a recovering state by modulating translation and metabolic processes, and enhancing protein homeostasis, while moderately downregulating defense-related responses to alleviate pathogen-induced excessive defense mechanisms. Additionally, twenty candidate genes involved in the direct inhibition of F. solani were identified, suggesting a role in enhancing host resistance. This study supports eco-friendly biocontrol strategies and advances understanding of plant-microbe interactions for managing soil-borne diseases in other important crops.

Wind erosion is a multidimensional, dynamic process driven by natural and anthropogenic factors, but existing statistical methods struggle to capture its complex nonlinear relationships, resulting in incomplete quantification of drivers and their spatial variability. To address this, we integrate the Revised Wind Erosion Equation (RWEQ)model with explainable artificial intelligence to disentangle the spatiotemporal positive and negative effects of dominant drivers and their synergistic interactions in Inner Mongolia. Results show that, from 2000–2022, wind erosion has been decreasing on average by 1.1 t·ha−1·yr−1, mainly in the western deserts and locally in Hulunbuir sandy land. Severe erosion is mostly due to nature (78.7%) rather than anthropogenic (21.3%). Normalized difference vegetation index (NDVI), clay content (CL), windy days (WD), precipitation (PRE), temperature (TEM), and sand content (SA) were found to be the most important drivers of wind erosion. Critical threshold conditions for severe wind erosion are NDVI < 0.14, CL < 12%, GD > 26, PRE < 73.15 mm, and SA > 66%. When there is a certain combination of variables, wind erosion risk is greatly increased, which mainly happens in the western part of Alxa, Bayannur, and the area near the desert edge. Wind erosion control should shift toward region-specific precision management, including engineering protection, optimized grazing management, and vegetation restoration.

This study explores the application of generative artificial intelligence technology in the design of cultural and creative products, using kumis packaging design as a case study. It establishes and validates a “human + AI” collaborative design framework oriented by user demands, driven by cultural resources, and supported by artificially intelligent technological tools. As an important part of ethnic cultural heritage, the Mongolian saddle, with its profound cultural connotations and unique visual characteristics, provides abundant inspiration for kumis packaging design. Through semi-structured interviews, this research identifies design requirement indicators for kumis packaging. The Analytic Hierarchy Process (AHP) is applied to construct a hierarchical model of requirements and calculate indicator weights to identify key design criteria. Subsequently, samples of Mongolian saddle cultural resources are collected, deconstructed, and categorized using an expert panel method, resulting in a structured image set. These elements are then translated into input prompts for AIGC tools to generate multiple kumis packaging designs rich in the cultural essence of the Mongolian saddle. The designs are scientifically evaluated using the Fuzzy Comprehensive Evaluation (FCE) method. The results indicate that the integration of AIGC technology not only enables the effective transformation and expression of cultural elements but also significantly enhances the efficiency and diversity of design generation. This validates the feasibility and practical value of AIGC as a “co-creator” in cultural and creative design. This study provides theoretical guidance and practical reference for the innovative integration of ethnic culture and modern design, expands the application scenarios of generative AI technology in cultural and creative design, and contributes to promoting cultural sustainability.

Accurate extraction of crop planting structures is important for crop area and yield estimation, but complex and fragmented cropping patterns with overlapping phenology in the Hetao Irrigation District hinder reliable crop discrimination. This study proposes a hierarchical workflow that integrates vegetation masking with multi-source feature optimization for crop mapping. First, dual-temporal Sentinel-2 imagery (May and August) is used to generate a vegetation region-of-interest(ROI) mask via Otsu thresholding applied to the Normalized Difference Vegetation Index (NDVI), combined with pixel-wise maximum-value fusion to reduce phenology-driven omissions and background interference. Second, within the vegetation mask, Sentinel-2 spectral, vegetation-index, and texture features are combined with Sentinel-1 synthetic aperture radar (SAR) backscatter and SAR texture features to construct a multi-source feature set. Random Forest(RF) feature-importance ranking is used to select an effective feature subset, and four classifiers (RF, support vector machine (SVM), eXtreme Gradient Boosting (XGBoost), and convolutional neural network (CNN)) are compared under the same training/validation setting. The vegetation extraction achieves an overall accuracy of 91% (Kappa = 0.80). Using Sentinel-2 features only, the optimized subset with CNN attains the best performance (overall accuracy = 95%, Kappa = 0.93). Adding Sentinel-1 SAR texture features provides an additional improvement (overall accuracy = 96%, Kappa = 0.94), particularly for classes prone to confusion in fragmented plots. Area proportions derived from the final map are consistent with statistical yearbook data (percentage errors: maize 3.45%, sunflower 2.66%, wheat 0.11%, tomato 0.92%) under the study conditions. This workflow supports practical crop-structure monitoring in complex irrigation districts.

ABSTRACT Nonaromatic organic small‐molecule luminescent materials have garnered significant attention in optoelectronic devices and biomedical applications due to their simple structures and excellent biocompatibility. However, steric hindrance and a large bandgap often constrain their efficient luminescence. Herein, we employ pressure treatment to induce highly efficient room‐temperature phosphorescence, achieving a significant emission enhancement in weakly emitting nonaromatic barbituric acid crystals. The absolute photoluminescence quantum yield of the treated samples increases from an initial 2.22% to 12.53% through 20.0 GPa pressure treatment by the Walker‐type large‐volume press. Additionally, after different pressure treatments, the emission changes from blue light with CIE coordinates (0.17, 0.10) to blue‐white light with CIE coordinates (0.23, 0.34). Detailed experimental and theoretical analyses reveal that pressure treatment selectively reconfigures the hydrogen‐bond network, generating free hydroxyl groups. The engineered electronic structure introduces new n‐π * transitions, which allow an intersystem crossing pathway from the 1 (n, π * ) state to the 3 (π, π * ) state, enabling efficient occupation of triplet excitons. This work provides novel insights and approaches for regulating exciton behavior through hydrogen bond engineering in nonaromatic organic systems, thereby facilitating the development of efficient luminescent materials.