Railways play an irreplaceable role in the long-distance transportation of bulk energy materials such as coal. In the context of uncertainty becoming the new normal in transportation operations, it has far-reaching strategic significance of exploring the reliability of railway transportation networks in energy-rich regions to enhance the resilience of railway transportation networks and ensure national energy security.On the basis, the railway network in Shanxi-Shaanxi-Inner Mongolia energy-rich region, one of China’s most important energy and chemical bases, is selected as the research object.A railway network model of Shanxi-Shaanxi-Inner Mongolia energy-rich region is constructed by using the Space-L method, the reliability of the railway transportation network is analyzed systematically by using two attack strategies: random attack and deliberate attack, and key transportation corridors are identified.The research results indicate that the railway network in Shanxi-Shaanxi-Inner Mongolia energy-rich region is relatively sparse and exhibits scale-free network characteristics. Small-scale random attacks render the network more reliable, while the network becomes more robust under high-betweenness attacks but exhibits vulnerability under high-degree attacks. Under different attack strategies, the cumulative effect of multiple node failures leads to a phased and sharp decline in network performance. The key corridors of the railway network in Shanxi-Shaanxi-Inner Mongolia energy-rich region are highly concentrated in small areas, mainly located at the intersections of coal transportation dedicated lines and external transportation railways. In future energy transportation planning, efforts of protecting these key corridors must be strengthened to ensure their continuous and stable operation, maintaining the safe operation of the energy transportation network.

With the rapid development of information and communication technology and transportation infrastructure, inter-city traffic is a vital foundation for regional integration. Based on multi-source data such as mobile phone signaling and social and economic development, this paper employs the rank-scale method and MRQAP regression to investigate the characteristics and influencing factors of inter-city flow network structure in Hunan province counties during the week and weekend. The results show that: the scale polarization effect of daily inter-city mobility in each county is significant. There are more counties with low-order and low-floating populations, and the main source of population mobility is high-order counties. The daily inter-city flow network of Hunan province takes Changsha city as the absolute center, and the prefecture-level cities take the main urban area of the city as the center, forming an inter-city flow network of “core-hinterland”. Changsha city is the hub of the province on weekdays and weekends. Regarding the inter-city flow group structure, the network aggregation on weekdays and weekends shows the characteristics of grade and group interaction. The inter-city group has a high degree of coupling with the Changsha-Zhuzhou-Xiangtan intercity railway, Shaolin expressway, and Beijing-Hong Kong-Macao expressway. The inter-city flow within the city group is greatly affected by distance. The spatial agglomeration effect of population will generate more demand for daily intercity mobility, and the population size will strengthen intercity mobility between counties. The smaller the gap between wage income and housing price levels between adjacent counties, within the same city area, and the shorter the travel time between counties, the more daily intercity mobility there will be. Among them, the population agglomeration effect and administrative resource control during weekdays are stronger.Economically developed counties may intercept potential migrant populations through the “income highland effect”.The study of daily intercity flow at the county level is helpful in formulating targeted facility allocation strategies based on regional differentiated needs, and provides reference significance for the high-quality integrated development layout of Hunan province.

The ecological environment of small watersheds in arid sandy lands is fragile, and research on microplastic(MPs) pollution remains insufficient.To investigate soil MPs pollution, the Hailiutu River Basin, an upper reach of the Wuding River and a typical small watershed in the Mu Us Sandy Land was selected as the study object.MPs were qualitatively and quantitatively analyzed using stereomicroscopy and confocal laser Raman microscopy, and the risks were assessed using the pollution load index (PLI) and potential ecological risk index (PERI). The results showed that soil MPs had an average abundance of 818.18±295.45 n/kg, dominated by transparent film-shaped particles smaller than 50 μm. Polyethylene(PE) and polybutene (PB) were the major polymers, primarily sourced from agricultural activities and domestic inputs.Land use types significantly influenced MPs distribution: sandy land showed the highest abundance, attributed to enhanced photodegradation due to low vegetation coverage (<30%) and limited migration caused by low human disturbance. Vegetation restoration projects effectively reduced MPs abundance over time, linked to tree species selection, increased coverage, and improved soil structure. The PLI revealed that the overall pollution level was grade Ⅰ(low risk), while PERI highlighted extremely high risks (grade Ⅴ) in sites with toxic polymers like polyvinyl chloride(PVC). The study clarified MPs distribution patterns and their coupling mechanisms with land use and vegetation restoration, providing scientific foundations for arid-region pollution control and ecological risk management.

In ecologically fragile areas, road expansion exacerbates issues such as landscape fragmentation and biodiversity loss.The study takes the Qinba Mountain region of Southern Shaanxi, a typical ecologically fragile region, as the research subject, constructs a five-dimensional landscape fragmentation evaluation system encompassing quantity, shape, area, aggregation, and connectivity.The spatial heterogeneity effects of different road structure characteristics on multi-dimensional landscape fragmentation are further investigated based on multi-scale geographically weighted regression (MGWR). The results show that: regional landscape fragmentation exhibits significant spatial differentiation, with Baoji, Xi’an, and Weinan forming a low-fragmentation core area, Shangluo serving as a moderate fragmentation transition zone, and central Hanzhong and Ankang constituting high fragmentation clustering areas. Sensitivity to road disturbance varies across multi-dimensional fragmentation features, with quantity and area characteristics being the most sensitive, followed by shape and connectivity features, while aggregation features exhibit the weakest sensitivity.Road density and distance from roads have the strongest explanatory power for the number, area, and connectivity of ecological patches, with lower-grade roads exerting a greater influence than higher-grade roads. Road curvature shows a significant negative correlation with the shape of ecological patches, whereas network degree centrality correlates with both patch shape and area.The study can provide a reference basis for future road construction and ecological conservation in the Qinba Mountain region of Southern Shaanxi.

To understand the public’s behavior, perception and attitude towards the use of takeaway plastic tableware in China, explore its influencing factors, and provide references for improving the current situation of the use of takeaway plastic tableware, a statistical analysis was done based on 23 087 questionnaires from 34 provinces in China. The results show that the public in China generally has a low awareness level of the hidden dangers of takeaway plastic tableware.However,usage is frequent,there is a high level of concern regarding the environmental impact of such tableware,and the majority are willing to pay extra for eco-friendly alternatives.Groups with underlying medical conditions use plastic takeaway tableware less frequently and reuse the tableware less frequently. About 2/3 of the public believe that plastic takeaway tableware does not have a significant impact on human health. There is a difference between the public’s self-perceived knowledge of the hazards of takeaway plastic tableware and their true knowledge, which is reflected in the fact that the deeper the public’s knowledge of the health hazards of takeaway plastic tableware is, the less frequently they consider the health hazards of takeaway plastic tableware, and the less hazardous substances released by takeaway plastic tableware are perceived to be. The majority of respondents were willing to pay an additional price to use environmentally friendly tableware, but 91.16% of the public accepted a price increase in the range of less than 2 RMB.The study suggests strengthening the popularization of plastic tableware for students, farmers and other groups with weak knowledge of the hazards of plastic tableware, using video software to provide targeted guidance in the form of popular science videos, and optimizing the tableware recycling system.

While the development of high-speed rail (HSR) has notably reduced travel time, its implications for equitable accessibility within urban clusters and spatial equity remain critical concerns. The study divides the medium-term and long-term intercity railway initiatives in the Beijing-Tianjin-Hebei urban into three phases from 2016 to 2030. Using road network datasets and socio-economic indicators, accessibility is analyzed through the average travel time, while spatial equity is evaluated using the accessibility dispersion index and the territorial cohesion index. The results indicate that the intercity HSR network substantially improves accessibility and strengthens regional connectivity at the municipal level. From 2016 to 2023, average intercity travel time decreases by 33.1% at the municipal level and 20.7% at the county level. From 2023 to 2030, accessibility increases by 9.5% at the municipal level and 10.2% at the county level, with a primary emphasis on improving the city connectivity in the capital region. Spatial equity and cohesion within the region are enhanced at the municipal level, whereas equity at the county level tends to polarization. The study recommends that both national and local governments should consider policies and coordination measures at different planning levels to promote spatial development and regional equity.

Based on social sensing theory, multi-source data from the online toll collection system (OTC) are used and methods such as graph theory modeling, complex networks, and function analysis are employed to explore the spatial interaction patterns of expressway traffic flow from the “section-station-city” scale. The results indicate that: 1)Section flow as a “flow process” which provides a micro perspective on the decay process of perceived distance and the spatial organization of traffic. At the section scale, densely trafficked sections of expressways in Guangdong province belong to the network area of “two loops, four horizontals and seven radials”, particularly concentrated on national expressways. Power functions have a high degree of explanatory power for the distance decay law of passenger and freight flows on expressways, and the aggregation level of motor vehicles in the Pearl River Delta urban agglomeration decreases with increasing vehicle size, with small vehicles (cars, light trucks) having the highest degree of aggregation. 2)Compared to section flow, as a “flow result”,station flow intuitively exhibits the characteristics of expressways serving short-distance transportation. Core station flows exhibit spatial proximity and primarily appear in short-cluster forms. Boundary station flows can be categorized into three interaction relationships: Guangdong-Guangxi, Guangdong-Hunan and Guangdong-Jiangxi,and Guangdong-Fujian. 3)In contrast to station flow, city flow offers a macro perspective on perceived urban spatial interactions. Based on city flow, the study examines the current development status of the “five major urban agglomerations” in Guangdong province. It reveals that internal traffic connections within the urban agglomeration on the west bank of the Pearl River are relatively loose. Conversely, the core urban agglomerations of Guangzhou and Shenzhen exhibit a trend of integrated traffic development. This study elucidates the methods and model correlations for mining multi-scale traffic flow big data, summarizes the perceptual differences in socio-economic phenomena under different scales of interaction patterns, and extends the methods and theoretical research of social perception from the perspective of scale differences.

The ecological environment quality of Fujian province remains excellent and stable. It is helpful to reveal the effects of the provincial expressway network evolution on the landscape ecology by studying the expressway network landscape and its impact on ecological quality. This study selects 3 km on both sides of the expressways in Fujian province as the research area, utilizes GIS technology, Fragstats software with landscape pattern indices, the remote sensing ecological index (RSEI), and the InVEST model, to investigate the evolution of landscape patterns and ecological quality of expressway corridor in Fujian province from 1994 to 2022. The results show that: the 30-90 m range constitutes the primary scale domain for the expressway corridor scale effect analysis, with 40 m being identified as the optimal grain size for landscape pattern analysis of the expressway corridor. The fragmentation index of cultivated land, forestland, grassland, water area and unused land have increased in varying degrees, while the fragmentation index of construction land has continued to decline. The overall fragmentation of the expressway corridor has intensified, the dispersion of various types has increased, and the stability of the ecosystem structure has declined. Carbon storage of the expressway corridor, forest land, and grassland has declined, while cultivated land, water area, unused land, and construction land increased in varying degrees. Areas with greater changes in carbon storage are mainly located in coastal cities with faster development. The average RSEI value of the expressway corridor is higher than 0.6, and the ecological environment quality is good. The areas with poor and relatively poor RSEI levels in the expressway corridor are mainly distributed in the southeastern coastal cities of Fujian province.The ecological environment quality of the expressway corridor improved and deteriorated are roughly the same over the 28 years. The fragmentation index and RSEI value within 1 000 m of the expressway corridor are most significantly affected, and the impact beyond 1 000 m gradually weakens.

Enhancing the transportation and tourism compatibility and promoting the integration of transportation and tourism are important levers for achieving rural revitalization and urban-rural integration in the context of high-quality development. Based on the framework of cognition-experience perspective for rural transportation-tourism coupling coordination measurement, this paper integrates the elements of visitor experience and perception satisfaction in transportation and tourism, and uses the statistical analysis and barrier model method to scientifically measure the level of rural transportation-tourism coupling coordination in the research area of Nanbaozhai village, Fangyan village and Liujiagou village in the suburbs of Xi’an city,identify the influencing factors of coordinated development of transportation and tourism coupling. The results show that: the coordination degree of transportation-tourism coupling in the research villages shows significant heterogeneity in both cognition and experience perspectives, and the order of coordination degree between different perspectives forms a strong contrast. Liujiagou village, Nanbaozhai village and Fangyan village show different misalignment directions and increasing misalignment degrees in coordination of transportation-tourism coupling. The main reason for the cognition-experience compatibility of rural transportation-tourism coupling coordination is the interaction between the objective conditions of rural transportation and tourism and the visitor’s transportation and tourism experience. The cognition dimension is mainly determined by the level of development of transportation and tourism and their synchronized development level, while the experience dimension is mainly affected by the tourism characteristics and the traffic and tourism environment pressure caused by the number of tourists. The study provides scientific basis for optimizing the measurement method of transportation-tourism coupling coordination level, promoting the continuous improvement of transportation and tourism functions in suburban areas of large cities, and driving the construction of beautiful rural areas that are suitable for tourism and industry.

Scientific identification of the intrinsic connection between digital infrastructure construction and tourism economic resilience is of great significance for driving the transformation of the digitalization of the Yangtze River Economic Belt and improving the modern tourism system. The entropy method is used to assess the degree of development of the two systems during 2011-2022, and the characteristics of the spatio-temporal coupling between digital infrastructure construction and tourism economic resilience in 108 prefectural-level and above cities in the Yangtze River Economic Belt.As well as the influencing factors are profoundly revealed through the coupling coordination degree model, kernel density estimation, exploratory spatial analysis (ESDA) and geographic detector. Research shows that: during the study period, the composite index of digital infrastructure construction and tourism economic resilience of the Yangtze River Economic Belt showed a fluctuating upward trend, and presented multi-level “core-edge” characteristics, and the phenomenon of polarization is becoming more and more obvious. The coupling and coordination degree between the construction of digital infrastructure and the resilience of the tourism economy in the Yangtze River Economic Belt has been increasing year by year, with significant spatial differences between cities. There is a spatial clustering phenomenon in the coupling coordination degree of the two systems, but the spatial clustering characteristics are weakened. The level of economic growth, human capita and travel resources endowment are the main factors affecting the degree of coupling coordination. The impact of human capital on factor interaction is the strongest. The interaction between economic development level and human capital, human capital and transportation accessibility, and human capital and tourism resource endowment has the strongest explanatory power for the coupling and coordination level.

Taking the urban agglomeration in the middle reaches of the Yangtze River as the research object, the RMP theory is introduced into the theoretical analysis framework and an appraisal index system for the integration efficiency of the culture and tourism industry is constructed.DEA-Malmquist index, center of gravity and standard deviation ellipse, and geodetector models are used to explore the time-space development landscape of the integration efficiency of the culture and tourism industry for the urban agglomeration in the middle reaches of the Yangtze River and its influencing factors from 2009 to 2021. The findings suggested that: in terms of time-series evolution, the general degree of integration efficiency of cultural and tourism industry is comparatively high during the study period, with a tendency of increasing firstly and then reducing secondly. The Malmquist index is rising at an average annual rate of 11.9%, and technological progress and scale effect are the primary reasons for sustained growth in total factor productivity.From the perspective of spatial distribution, the integration efficiency of cultural and tourism industry shows the “core-edge”structure with “Wuhan-Changsha-Nanchang”as the core and other cities as the edges. The integration efficiency of culture and tourism industry in the three regions has been improving, and the advantages of the “core” region have been highlighting, with significant regional differences.In an evolutionary space-time dimension, the integration efficiency of the cultural and tourism industry as a whole shows a spatial distribution pattern dominated by the “southeast-northwest” direction. The trends of the gravity center show in the direction of “northeast-southwest-southeast”, and the spatial pattern shows the characteristics of agglomeration.The results of geographic exploration show that the integration efficiency of cultural and tourism industry is the result of a combination of factors, such as consumer demand, industrial structure, digitalization level, government regulation, transportation level and human capital.

Cities are the spatial carriers of corporate activities, and the cross-regional investment behavior of tourism enterprises represents and maps the interregional tourism economic competition relationship. Based on the construction of the theoretical framework, using the off-site investment data of 265 tourism enterprises, we conduct an in-depth analysis of the complexity characteristics and driving mechanism of the off-site investment network in the three time periods of 2008-2012, 2013-2017, and 2018-2023, with the help of spatial analysis, social network analysis and weighted index random graph model. The study finds that: the spatio-temporal complexity of Chinese tourism enterprises’ cross-regional investment network is significant. The network has shifted from Beijing and Shanghai as a single center to a triangular structure with “Beijing-Shanghai-Guangzhou”,“Shanghai-Xi’an-Haikou”,“Shanghai-Shenzhen-Kunming” as the core nodes. Beijing, Shanghai and Haikou were the major outward investment cities during the study period, Xi’an, Shenzhen and Kunming were important inward investment cities. The cross-regional investment network of Chinese tourism enterprises has developed a typical “core-edge” circle structure, showing a trend of evolution from “dominated by a single core city” to “interconnected by multiple core cities”.At the same time, it is characterized by “small world” and scalelessness.The formation of tourism enterprises’ cross-regional investment network is the result of endogenous structural effect, actor-relationship effect and multi-dimensional neighboring mechanism. Among them, tourism resource endowment, tourism market size, government support level, factor agglomeration and diffusion level, degree of openness to the outside world, and the convergence and transferability of investment have a significant contribution to the formation of the investment network. Tourism enterprises tend to establish investment links between cities with strong economic ties, geographical proximity, fewer cultural differences and similar institutions.

Ecosystem carbon storage is an important index to measure regional carbon cycle and ecosystem service function. How to accurately quantify the ecosystem carbon storage of the ecological restoration project on the Loess Plateau has become a key issue in scientifically evaluating the effectiveness of regional ecological restoration and serving the country’s “double carbon” strategy. In the study, to provide a scientific basis for the evaluation of carbon storage of ecological restoration projects,the Yanhe River Basin was used as the research area, and three machine learning algorithms, support vector regression (SVR), random forest(RF) and extreme gradient boosting(XGBoost), were used to analyze the pixel-by-pixel inversion of ecosystem carbon storage from 2000 to 2020, and the driving factors combined with field sampling data and multi-source remote sensing data. The results show that: the XGBoost model can effectively capture the spatial heterogeneity of ecosystem carbon storage under complex terrain conditions, and its verification accuracy R² of vegetation and soil reaches 0.80 and 0.74, respectively. In terms of spatial pattern, the distribution of carbon density showed a trend of diffusion from the middle to the southeast and northwest. From 2000 to 2020, the ecosystem carbon storage in the study area showed an overall increasing trend, with an average annual increase of 0.077 kg C/(m²∙a), and the positive growth area accounted for as high as 98.4%. Geodetector analysis showed that NDVI was the dominant driving force for the spatial differentiation of carbon storage (q=0.42), and there was a significant interaction between multiple factors. The research can provide an evaluation paradigm for carbon neutrality targets in the Loess Plateau.

Detection of brain networks based on functional magnetic resonance imaging data is crucial for understanding cognitive and functional aspects of the brain, as well as exploring brain disorders. With the development of deep learning techniques, an increasing number of researchers have applied them in the field of brain network detection. The main research achievements and advancements in this field are summarized. Firstly, it introduces the basic principles of brain network detection based on fMRI. Then, it discusses the deep learning models and their applications in brain network detection, analyzing their strengths and limitations. Finally, it summarizes the challenges and future research directions in applying deep learning methods to brain network detection. An important reference for further promoting research and applications in brain network detection using deep learning and fMRI imaging is provided.

The rapid development of the takeaway industry has brought convenience, but also brought problems such as food safety. The health problem of takeaway plastic tableware has gradually become the focus. Based on the evaluation of scientific experiments,‘research+publicity’, Pearson correlation coefficient and integrated Dagum Gini coefficient, this research studied the cognitive status and spatial heterogeneity of health risks of takeaway plastic tableware in 128 cities (counties, districts, autonomous prefectures) and 645 counties (cities, districts, banners) of 33 provinces (municipalities and autonomous regions) in China, involving 531 streets and 543 townships. A total of 23 088 people from various groups contributed their cognitive status to health risks of takeaway plastic tableware and the spatial heterogeneity of influencing factors, of which 21 876 valid questionnaires were collected. Because the number of respondents in Shaanxi Province is far more than those in other provinces, a random sampling method was used to randomly sample the valid questionnaires in Shaanxi Province. Correspondingly, a total of 4 893 valid questionnaires were determined nationwide. The results found that: 1) Polypropylene (PP) released 4 363 microplastics/L when used once and 12 880 microplastics/L when used twice. After using it once, 7 225 microplastics/L was released under high temperature condition, 21 991 microplastics/L was released under microwave condition, and 9 110 microplastics/L was released under freeze-thaw condition. Polystyrene (PS) released 19 792 microplastics/L at room temperature. 2) The public’s cognitive about the release of harmful substances from takeaway plastic tableware in 33 provinces reveals the unbalanced characteristics of their cognitive level, showing significant regional differences between the eastern and western regions as well as within the south China regions.The hypervariable density and inter-regional gap are the main reasons for the difference in the health risk perception level of takeaway plastic tableware between the eastern and western regions. 3) Education background is related to the health risk perception level of takeaway plastic tableware, and it is statistically significant (P<0.05). The difference in health risk perception of takeaway plastic tableware may be caused by education background and regional difference.The questionnaire designed according to the results of scientific experiments can accurately judge the health risk perception level of takeaway plastic tableware, and find out the main problems, so as to provide reference for food safety managers to formulate measures to improve consumers’ health risk awareness of takeaway plastic tableware, enhance health cognition, and reduce the use of one-time takeaway plastic tableware.

Children’s health is related to family happiness and the future of the nation, and is an important guarantee for sustainable social development. However, the accumulation of microplastics released during the use of plastic feeding utensils in the body can pose significant health risks to children. Therefore, this study adopted a combination of questionnaire surveys and scientific experiments, using random sampling, snowball sampling, and judgmental sampling methods to select samples and conduct a survey on 12 168 childcare families and key groups nationwide, analyzing the cognitive and behavioral status of relevant childcare subjects on microplastics and their influencing factors. Meanwhile, we did experiment and risk assessment and proposed scientific feeding countermeasures. The results showed that the release of microplastics from plastic feeding utensils poses a certain ingestion risk to children and there is a significant mismatch between the risk of microplastic ingestion in plastic feeding utensils and blind use, and the scientific feeding and health awareness of childcare subjects is generally weak, failing to use feeding utensils scientifically. Based on this, this study proposed thinking and solutions from three levels of government,society and industry,providing scientific basis for reducing the risk of microplastic intake in children’s plastic feeding utensils and supporting the implementation of the Healthy China strategy.

Exercise ameliorates obesity-induced fat accumulation, insulin resistance and inflammatory response through AMPK/SIRT1/PPARs signaling pathways, yet its efficacy may be modulated by dietary factors. Notably, the excessive consumption of sugar adversely affects host health. The substitution of high-calorie sucrose with low-calorie functional sweeteners has gained widespread popularity among sports enthusiasts and people seeking for weight reduction. Nevertheless, the intricate relationship between sweeteners and host energy metabolism, fat metabolism, and immune health remains largely unexplored. Given the pivotal roles played by exercise and functional sweetener intake in the physiology of obesity and its related complications, we systematically reviewed the impacts of exercise on mitigating obesity-induced dysfunctions across multiple organs(e.g., cardiovascular, cerebral vascular, hepatic, renal, intestinal, skeletal muscle, and adipose tissues). Moreover, impacts of distinct sweeteners on core regulating targets(e.g., AMPK/SIRT1/PPARs, STAT3/NF-κB/Smad, PI3K/Akt signaling pathways) of exercise for combating obesity and its related metabolic diseases were then summarized to illustrate potential synergistic or antagonistic effects of sweeteners on the health effects of exercise. This review furnishes theoretical basis and practical guidance aiming for optimization of exercise strategies that could successfully prevent obesity and improve weight maintenance.

To address the challenge of non-destructively characterizing deep three-dimensional residual stress fields in critical load-bearing hot-section components of domestically developed large wide-body aircraft engines, this study investigates ultrasonic-based methods for non-destructive residual stress characterization and imaging. An ultrasonic acoustoelastic theoretical model capable of describing the influence of arbitrary three-dimensional stresses is established, revealing a quantitative relationship between the relative velocity variations of ultrasonic bulk longitudinal waves and triaxial stresses. By developing an ultrasonic wave-propagation simulation framework for components containing residual stresses, multi-angle ultrasonic transmission data are designed and acquired for representative high-temperature alloy forgings. Two imaging approaches are proposed: a tomographic imaging method based on iterative reconstruction algorithms and an inversion imaging method based on neural networks. The results demonstrate that the tomographic approach can sensitively capture the characteristic “tensile core-compressive surface” stress distribution within the forging and exhibits high sensitivity to process-induced asymmetric distributions. The neural network method, in contrast, shows strong nonlinear fitting capability for complex patterns and yields smaller average errors in high-stress regions. The two methods are complementary, their inversion results remain at the same stress level as the ground truth and effectively reflect the magnitudes and distributions of radial, circumferential, and axial residual stresses within the actual forging. The proposed methodology provides important data support for ensuring the dimensional stability, reliability, and in-service safety of critical load-bearing hot-section components in aero-engines.

To explore the enrichment characteristics and risks of soil heavy metals along highways and in key areas of aeolian sandy regions, the Wubu-Zizhou-Jingbian section of the Qingdao-Yinchuan Expressway was selected as the research object, and the pollution risks of eight heavy metals (Cd, Pb, Cr, Cu, As, Zn, Ni, and Mn) in the dust along the highway, service areas, toll stations, and tunnels were analyzed systematically. Through the detection of heavy metal concentrations, it was found that Pb and Cd in the study area were the most severely exceeded, with mean values being 2.84 times and 2.23 times the background values, respectively. The coefficient of variation for Cd was as high as 96.63%, indicating significant localized enrichment hotspots. Systematic analysis of the spatial distribution characteristics revealed that the heavy metal content in the study area generally increased from west to east and was significantly regulated by land use types and the intensity of human activities. The index of geo-accumulation (Igeo) was used to assess the pollution degree and level of the exceeding elements, showing that the pollution levels of Cd and Pb in all functional areas were higher than those of other elements, and Zn pollution was significantly enhanced inside tunnels. Meanwhile, the potential ecological risk index and human health risk model were introduced. The results indicated a potential trend for the comprehensive ecological risk index in the study area to evolve toward moderate risk, with Cd being the key contributing element to the ecological risk. Furthermore, based on the absolute principal component scores-multiple linear regression (APCS-MLR) receptor model, the source contributions of soil heavy metals were quantitatively identified. The results reflected that industrial-transport mixed sources (39%), coal combustion-transport emission sources (25%), and agricultural sources (15%) were the main pollution sources. This study systematically reveals the differentiated characteristics and potential risks of soil heavy metal pollution in different functional areas of highways in aeolian sandy regions, and suggests implementing zoned management and control while strengthening health protection for occupational groups.

The identification of crack tips in total focusing method (TFM) is easily affected by diffraction waves. In this study, the ultrasonic TFM based on Otsu thresholding is proposed for crack characterization. The full-matrix amplitude map is first constructed from the full matrix capture (FMC) data. Then, the adaptive algorithm of Otsu threshold is applied to automatically segment the reflection and diffraction regions. Only the reflection signals are used for delay-and-sum process and imaging to avoid diffraction interference and improve efficiency. Detection results of internal cracks in aluminum alloy show that the proposed method outperforms the conventional TFM using complete FMC data in crack measurement and imaging efficiency. The error of crack length is less than 0.3 mm, the error of angular is within 2.0°, and the required imaging time is reduced by more than 60%.

As a core fundamental component in various large-scale mechanical systems, bearings play a critical role in ensuring the safe and reliable operation of equipment, making high-precision detection and imaging of internal defects of significant theoretical and engineering importance. To address the challenges associated with bearings featuring complex curved surfaces, this study proposes a high signal-to-noise ratio ultrasonic imaging method based on the delay multiply and sum (DMAS) algorithm. In the experiments, a wedge in combination with an ultrasonic phased-array transducer was employed to inspect internal flat-bottom hole defects in bearings using oblique ultrasonic incidence. Signal-to-noise ratio (SNR), array performance index (API), and other evaluation metrics were introduced to quantitatively assess the imaging performance for different types of damage. The experimental results demonstrate that the surface-corrected p-DMAS imaging algorithm exhibits superior performance across all image quality metrics, achieving a maximum SNR improvement of 25.8 dB and an API increase of up to 8.4, thereby significantly enhancing defect detection capability. This method provides a novel approach and practical solution for in situ nondestructive testing of bearing-like curved structures.

As a distinctive historical and cultural heritage, rock art faces significant challenges in image classification due to its vast quantity, wide distribution and complex overlapping elements, which hinder efficient and accurate identification. Recent advances in deep learning offer new opportunities for rock art conservation research. A dual-branch attention fusion network (DBAFN) that integrates the local feature extraction capability of ResNet50 with the global semantic modeling strength of ViT(vision Transformer) is proposed. Using Helan Mountain rock art as a case study, our method employs a gated attention mechanism to dynamically weight features, significantly enhancing classification accuracy. Evaluated on a dataset containing 1 200 Helan Mountain rock art images (human faces,animals,hunting scenes), DBAFN achieves a weighted average classification accuracy of 85.62%, outperforming standalone ResNet50 (81.46%) and ViT (80.02%) models. Notably, the F₁-score for hunting scenes reaches 82.35%. Experiments demonstrate that DBAFN effectively resolves misclassification caused by interleaved elements, providing an innovative technical pathway for semantic analysis of cultural heritage and interdisciplinary research while advancing the application of artificial intelligence in digital conservation of cultural relics.

As an important ecological security barrier and key economic development belt in China, the vulnerability research of human settlements in the Yellow River Basin holds critical significance for analyzing the risks of the human-land system and promoting high-quality development in the basin.An evaluation system for human settlement vulnerability is constructed based on the “pressure-state-response”(PSR) model, the urban human settlement vulnerability in the Yellow River Basin from 2010 to 2023 is assessed by using the entropy method, and then the dynamic changes in the urban human settlement vulnerability index and the spatiotemporal heterogeneity of its influencing factors are analyzed by using methods such as natural breaks, Markov chains, and geographically and temporally weighted regression(GTWR). The results show that: the overall human settlement vulnerability index in the Yellow River Basin exhibits a good trend of fluctuating decline, with a distribution characteristic of upstream>midstream>downstream.The spatial differentiation characteristics of the human settlement vulnerability index in the Yellow River Basin are significant, with high-value areas mainly distributed in a spotty manner in the upstream and midstream regions of the basin, and low-value areas clustered in a patchy manner in the downstream region. The type evolution demonstrates a development trend of “overall stability with gradual, stepwise convergence towards lower values.”The human settlement vulnerability index in the Yellow River Basin is the result of multi-dimensional factor interactions, where urbanization rate, industrial agglomeration degree, environmental regulation intensity, and technological innovation capability have a negative impact, while land use intensity has a positive impact. In terms of space, the spatial heterogeneity of each influencing factor is significant, specifically manifested as a clustered or strip-like distribution pattern.

Constructing an effective evaluation model for tourism resource competitiveness and revealing its spatiotemporal differentiation patterns and regional transmission mechanisms are key pathways to address the common challenges of coordinated resource development in river basin economic zones.Taking the nine provinces (or regions) in the Yellow River Basin as the study object, the tourism resource competitiveness and effectiveness levels in 2010, 2014, 2018, and 2022 are examined quantitatively by using the entropy-weight TOPSIS method and effectiveness model. Additionally, Kernel density estimation and Theil index decomposition are employed to explore the spatiotemporal evolution and underlying causes of differences in effectiveness. The results show that: during the observation period, 77.8% of the provinces (regions) along the Yellow River Basin experienced negative fluctuations in tourism resource competitiveness, with the overall mean competitiveness declining by 7.26%. Spatially, a “weak-moderate-strong” three-level gradient was observed from the upper to the lower reaches, forming a spatially locked pattern characterized by “higher in the east and lower in the west”. The competitiveness effectiveness of tourism resources in the nine provinces (regions) exhibited a steady upward trend, although the overall effectiveness value remained below 1, indicating room for improvement in resource conversion efficiency. Spatially, effectiveness levels showed a “low-moderate-high” distribution from the upper to the lower reaches. Although inter-segment differences were significant, they tended to narrow over time, while intra-segment disparities remained the primary source of overall variation, particularly in the upper reaches. During the observation period, the spatiotemporal evolution of tourism resource competitiveness effectiveness exhibited a trend of initial convergence followed by divergence, with a multipolar development pattern emerging by 2022. Overall, the Yellow River Basin has entered a stage of deepening differentiation in resource development, highlighting the urgent need to strengthen regional resource integration and coordination mechanisms to promote high-quality development across the basin.

To address the challenge of extracting subtle ultrasonic defect features in low signal-to-noise ratio environments, this study proposes a gated residual and dual-attention collaborative enhancement network for low SNR ultrasonic signals. Based on convolutional neural network, the model adopts a ‘residual block squeeze-excitation(SE) module-pooling’ cascaded structure: a standard SE module is embedded in the residual block for initial channel screening, a locally enhanced SE module is used at the end of network stages to focus on peak signals, and gated residual connections are employed to dynamically preserve original subtle features, thus realizing collaborative optimization of noise suppression and feature enhancement. Experimental results show that the improved model achieves a mean root mean square error (RMSE) of 0.068 3 and a mean absolute error (MAE) of 0.047 1, which are 49.7% and 41.7% lower than those of the baseline CNN, respectively. It also outperforms models with only a single attention mechanism or residual blocks, verifying the superiority of dual-mechanism collaboration, while exhibiting excellent training stability and maintaining high accuracy in low SNR environments. In conclusion, the proposed model effectively overcomes the bottlenecks of noise interference and subtle feature learning. Its prediction accuracy, anti-interference capability, and stability are significantly superior to traditional methods and existing models, providing an efficient technical solution for ultrasonic non-destructive testing of steel pipes with important industrial application value.

Based on the GPU-parallelized algorithm of the time-domain spectral element method (SEM), this study investigates the S₀ mode guided waves in anisotropic plates. Parallelization is achieved by integrating high-order spectral element discretization with the CUDA computing platform, thereby establishing a numerical model for guided wave propagation in composite plates. The proposed model accurately simulates the excitation and propagation processes of guided waves in composite plates, extracts the characteristics of the S₀ mode, calculates its wave velocity, and subsequently plots the distribution curves of S₀ mode wave velocity. To validate the model, an experimental system was set up using piezoelectric sensors as excitation units to conduct S₀ mode guided wave propagation tests on T300 composite plates. By incorporating element-level parallel computation and a matrix-free assembly strategy, the proposed method significantly improves computational efficiency and remarkably reduces memory consumption. Numerical verification demonstrates that the method achieves high accuracy while offering superior computational performance and resource efficiency compared with traditional SEM. The simulation results show excellent agreement with the experimental measurements and accurately capture the wave-velocity curve of the S₀ mode, thereby verifying the accuracy and feasibility of the proposed parallel time-domain spectral element method. This study offers an effective technical route for the simulation of guided wave propagation in composite plates and demonstrates broad prospects for application in the field of health monitoring of composite plates.

The aim of this study is to optimize the preparation process of enteric plant dropping pills and to enhance their comprehensive properties in order to promote their application in food and biomedical fields.Single factor experiments and response surface methodology were employed to optimize the optimal formula: pullulan 2.875%, sodium alginate 0.702%,m_{κ-carrageenan}∶m_{gellan gum}=3∶1,glycerol 2.5%,KCl 0.20%. The results of the verification experiments showed that the tensile strength of the resulting film was 10.98±0.42 MPa and the elongation at break was 55.31%±0.65%, which were similar to the theoretical predictions and showed good mechanical properties. Moreover, the enteric plant dropping pills made from this formula were tested in simulated gastric fluid and simulated intestinal fluid for 2 h and 3 h, respectively, and showed no signs of disintegration or softening. Whereas the dropping pills completely disintegrated in simulated colonic fluid within 11±3 min, meeting the requirements of the Pharmacopoeia of the People’s Republic of China(2020 edition). Meanwhile, the dissolution rates of the dropping pills in gastric and intestinal fluids were 38.32%±4.63% and 294.92%±7.33%, respectively, and there was no erosion or deformation, showing good dissolution properties. This study provides a theoretical basis and technical support for the preparation of enteric plant dropping pills with excellent mechanical properties, and strongly promotes their application expansion in the fields of food and medicine.

Ancient murals carry profound historical, cultural, and artistic value. A network based on 3D hyperspectral Transformer for the classification of style features of Chinese Central Plains style and Western Regions style murals is proposed. By collecting hyperspectral images of murals and constructing corresponding datasets. On this basis, the proposed network is trained using transfer learning methods. The experimental results show that compared with other traditional methods, the accuracy of this study has increased by 0.92%, the precision has increased by 0.12%, the recall has increased by 1.4%, and the F₁ score has increased by 0.75% compared to the optimal algorithm. In addition, compared with the deep learning classification method based on color images, the accuracy improved by 4.5%, the precision improved by 1.9%, the recall improved by 5.4%, and the F₁ score improved by 3.7% compared to the method with the best results.The study verified the unique advantages of hyperspectral information in improving classification accuracy.

The propagation behavior of Lamb waves in variable thickness plates is complex and exhibits significant dispersion effects, which leads to the difficulty of defect localization and imaging. A defect imaging method for slowly varying-thickness plates based on a Lamb wave propagation model is proposed in this paper. A Lamb wave propagation model for variable thickness plates is established, enabling the prediction of response signals through segmented constant thickness approximation and dispersion curve interpolation. A backward propagation phase compensation algorithm is introduced to eliminate signal distortion induced by dispersion effects. Combined with a delay-and-sum imaging algorithm, this approach achieves high-resolution defect imaging in the slowly varying-thickness plates. Numerical simulations and experimental studies are conducted to validate the proposed method. Numerical results demonstrate a positioning error of approximately 5.8 mm for an 8 mm diameter circular hole defect in a 500 mm×500 mm aluminum plate with thickness varying linearly from 2 mm to 4 mm. Experimental results confirm clear defect visualization with a positioning error of about 10 mm, verifying the method’s applicability in defect detection for slowly varying-thickness plates.

Segmenting the butterflies from ecological images will provide accurate butterfly masks, guaranteeing the accuracy of the automatic butterfly species identification using the ecological images of butterflies. Therefore, the segmentation study of butterfly ecological images is of great significance. However, existing butterfly ecological image dataset cannot train an excellent butterfly segmentation model with strong generalization due to the small number of samples in the dataset and the mimicries and wing folds of butterflies in the butterfly ecological images. To address these issues, a new enhanced SAM (segment anything model) with good and robust segmentation capability is proposed. This enhanced SAM is named as SABM (segment any butterfly model) for segmenting the butterfly ecological images. This SABM introduces two-way convolution module, butterfly token, and a 3-layer MLP (multi-layer perceptron) to enhance SAM to adapt to the ecological butterfly image segmentation task. The 2-fold cross validation experimental results on the available butterfly ecological image dataset containing 707 ecological butterfly images demonstrate that this proposed SABM obtains an excellent segmentation performance for the ecological butterfly images. It is superior to SAM and its variants, particularly the SOTA model of SAM variants. Additionally, the segmentation experiments on the entirely new 7 645 butterfly ecological images show that this SABM has strong generalization capability, and it can segment all these 7 645 ecological butterfly images efficiently. This segmentation results provide a 10 times larger dataset than the available one for future butterfly segmentation task utilizing the ecological images while providing a much better dataset for the automatic butterfly species identification task through ecological images of butterflies, and a very challenging dataset for testing the performance of a clustering algorithm. Furthermore, the robust of the proposed SABM is tested on medical image datasets.

To address the challenge of extracting weak nonlinear effects, a method for extracting mixed nonlinear components is proposed based on sliding correlation analysis. Finite element simulations are conducted to investigate the influence of time window width on the extraction results of mixed components. A sine signal modulated with a Hann window is selected as the reference signal, and when the time window width matches the excitation signal length, the extraction of mixed nonlinear components achieves the best results. Mixed nonlinear detection experiments are carried out on specimens with different bonding strengths, and the mixed components are extracted using three different methods. The results indicate that, compared with the three-excitation difference and polarity reversal methods, the sliding correlation analysis method simplifies the nonlinear detection process, allowing accurate extraction of the nonlinear mixed-frequency components using only a single detection signal. The normalized nonlinear coefficient obtained can effectively characterize the bond strength of the adhesive structure.

Although the results of the SASRec(self-attention sequential recommendation)model on both sparse and dense datasets are superior to various sequence recommendation models, in sequence recommendation, it also suffers from representation degradation, that is, frequently occurring Items are often concentrated in a small region of the representation space, degrading recommendation performance. In order to solve this problem, a comparative learning loss function is introduced. Gaussian noise was added in the embedding space for data enhancement, and the original item sequence and the item sequence after data enhancement were used to construct positive sample pairs to promote similar instances in the mapping the closer the distance in the space, and the distribution of different instances in the mapping space showed uniformity. As far as possible, the instance can retain its own personalized information after being mapped to embedding. A comprehensive experimental study on two benchmark datasets shows that, although it appears to be very simple, the proposed method can smoothly adjust the popularity bias of the learned representations.The contrastive learning is based on the graph recommendation model SGL(self-supervised graph learning for recommendation), which suffers from negative sampling bias in representation learning.The model proposed in this paper can effectively improve the recommendation performance.

The limited visible light absorption and relatively large bandgap of zeolitic imidazolate framework-8 (ZIF-8) substantially restrict its antibacterial efficacy under visible light irradiation. Herein, a novel visible-light-responsive antibacterial nanocomposite, EGCG@ZIF-8, was synthesized via post-synthetic modification through the etching of ZIF-8 with epigallocatechin gallate (EGCG). Under visible light irradiation, EGCG@ZIF-8 can efficiently generate reactive oxygen species (ROS), demonstrating a antibacterial rate of 99.999 9% against Staphylococcus aureus within 60 min, which significantly surpassed the performance of pristine ZIF-8. Furthermore, EGCG@ZIF-8 retained robust antibacterial activity even after five consecutive usage cycles. The incorporation of EGCG narrowed the bandgap of ZIF-8, promoting the separation and migration of photogenerated electron-hole pairs, which enhanced the visible-light photocatalytic performance of the composite and subsequently boosted ROS generation. In addition, in vitro cytotoxicity assays confirmed the outstanding biocompatibility of EGCG@ZIF-8. This study not only offers a straightforward and efficient approach for synthesizing high-performance antibacterial composites, but also paves the way for the development of innovative visible-light-driven antibacterial materials, underscoring its substantial scientific and practical significance.

To improve the “pitch-catch” detection mode in the method for micro-damage detection based on the static component of nonlinear ultrasound, a detection method based on the pulse-echo approach is proposed, which requires only a straight probe to achieve both transmission and reception of ultrasonic waves.Taking an aluminum plate specimen as an example, finite element simulations and experimental measurements were conducted.Nonlinear ultrasonic longitudinal waves were excited using a single straight probe, and their reflected signals were collected.The fundamental frequency and static component were extracted from these signals, and the relative nonlinear coefficient was calculated to evaluate the degree of plastic damage in the aluminum plate.The results show that when using an ultrasonic transducer with a center frequency of 5 MHz for excitation and reception, the static component of the reflected wave can be effectively obtained, verifying the feasibility of the single transducer pulse-echo method for acquiring the static component.The relative nonlinear coefficient of the static component of the reflected wave increases with the thickness of the aluminum plate and the degree of plastic damage, further demonstrating the effectiveness and applicability of this method for micro-damage detection.

A novel vortex ultrasonic hole-detection methodology is proposed to address the pressing need for rapid and cost-effective internal defect detection within materials, leveraging the unique properties of vortex ultrasound.A systematic analysis was conducted on the propagation characteristics of vortex ultrasound within titanium alloy materials. The research findings reveal that the second harmonic signal detected at the center of the vortex ultrasound field can effectively ascertain the presence of internal pores in the material. Furthermore, the signal amplitude diminishes as the radial distance and depth between the pore and the center position increase, thereby defining the effective radial and depth detection ranges of vortex ultrasonic inspection and enabling qualitative localization of the pore’s position. Concurrently, the amplitude at the vortex ultrasound center position escalates with the enlargement of the pore size, indicating its potential for quantitative assessment of pore dimensions. Numerical simulation results further corroborate that variations in the presence, position, and size of pores induce alterations in the amplitude at the vortex ultrasound center, thereby validating the efficacy and feasibility of vortex ultrasound for detecting pore defects in titanium alloys. This study is anticipated to furnish novel theoretical insights and methodologies for the swift detection of internal pores in materials.

To study the variations of phylogenetic diversity and species diversity of woody plant communities in Karst areas under different terrains and their influencing factors, woody plants in different terrains within the Maolan Dynamic Plot of Karst Forest Eco-system in South China were selected as the research objects. Utilizing the hierarchical partitioning (HP) method and Pearson correlation analysis, it examined the changing trends of diversity indices under different terrains (depression, valley, hillside, saddle, ridge and summit), the key terrain factors driving the diversity indices, and the interrelationship between species diversity and phylogenetic diversity. The results demonstrate that: 1)There is a significant difference in the community species richness index between depressions and saddles (P<0.05), while there is no significant difference in the community Pielou evenness index and Simpson dominance index among the six terrains. The Faith’s phylogenetic diversity index and the mean nearest taxon phylogenetic distance show significant differences between depressions and saddles. The mean pair phylogenetic distance shows a significant difference between hillsides and depressions,valleys. 2)The relative influence of aspect on the diversity indices is the largest and plays a leading role, followed by slope, and the influence of four topographic factors on the diversity indices is not significant.3)Species richness index is significantly positively correlated with Faith’s phylogenetic diversity index (P<0.000 1), significantly negatively correlated with mean nearest taxon phylogenetic distance and Simpson dominance index. Faith’s phylogenetic diversity index is significantly negatively correlated with mean nearest taxon phylogenetic distance. This study reveals that different terrains in the Maolan Karst area have different effects on species diversity and phylogenetic diversity, and the correlation between species diversity and phylogenetic diversity is not high. The aspect and slope jointly drive the variations of species diversity and phylogenetic diversity.

Aiming at the performance requirements of mobile robot path planning for shortest path length and smoothness, an improved A-star algorithm is proposed. This approach is combined with an enhanced dynamic window approach (DWA) algorithm to solve the problem of dynamic path planning for mobile robots. A new heuristic function of A-star algorithm is designed, along with dynamic weight allocation and the introduction of a new evaluation function, which realizes the shortest global planning path and reduces the inflection point and redundant nodes of the planning path. The distance function of static obstacle and dynamic obstacle is designed for DWA algorithm, along with the addition of a path deviation distance function. By incorporating guidance from the global path, the DWA algorithm generates a trajectory that closely adheres to the global path, enabling timely avoidance of unknown and dynamic obstacles while ensuring alignment with the globally optimal path. The simulation results show that the improved A-star algorithm reduces the path length by 34.4% and the inflection point by 53.5% compared with the traditional algorithm in complex environment.

In recent years, advances in semiconductor silicon wafer manufacturing have increased the demand for accurate detection of subsurface defects. When inspecting microcracks far smaller than the wavelength, nonlinear Lamb waves offer distinct advantages such as high efficiency, high sensitivity, and nondestructive evaluation. However, their application in silicon wafer inspection remains limited, partly due to the unclear relationship between nonlinear Lamb wave signal characteristics and subsurface microcrack features. In this work, finite element models of Lamb wave propagation in silicon wafers with subsurface microcracks are established. The Lamb wave mode pair S₀-s₀, satisfying phase-velocity matching, is employed to investigate how the acoustic nonlinearity parameter (ANP) of the second-harmonic correlates with propagation distance and microcrack characteristics. Simulation results indicate that the ANP increases with propagation distance, and the presence of subsurface microcracks significantly amplifies its amplitude. Moreover, the relative ANP increases with the number,length, density of subsurface microcracks, and subsurface damage layer thickness. Additionally, for a given density, the length of subsurface microcracks has a more significant influence on the relative ANP than their number. This study illustrates the potential of nonlinear Lamb waves for detecting subsurface microcracks in silicon wafers and provides simulation-based validation for its feasibility.

The dried ripe fruit of Schisandra sphenanthera (commonly known as Nanwuweizi) is a medicinal material officially recorded in the Pharmacopoeia of the People’s Republic of China, with significant pharmacological effects in hepatoprotection and antioxidation. The quality of its germplasm resources directly determines the medicinal yield and efficacy. In this study, 12 Schisandra sphenanthera germplasm accessions from the same nursery were evaluated based on fruit phenotypic traits, bioactive compound contents, and antioxidant activity to identify superior germplasm resources. The results demonstrated that accessions No.2 and No.4 exhibited superior phenotypic characteristics.Significant variations were observed among different accessions in terms of volatile oil yield, polysaccharide content, and DPPH radical scavenging capacity of both volatile oils and ethanolic extracts. A comprehensive assessment was conducted, considering aggregate fruit weight, berry pericarp thickness, total lignan content, polysaccharide content, volatile oil extraction rate, and the antioxidant activity of volatile oils and ethanolic extracts. The top three germplasm accessions were ranked as No. 2, No. 5, and No. 8, showing promising potential for further breeding and cultivar improvement of Schisandra sphenanthera.

Gold nanoclusters (AuNCs) have been widely used in the sensing of various medicines. However, they have not been reported in the sensing of glucocorticoids. Based on this, this study prepared an AuNCs nanocomposite CB[7]/AuNCs by combining cucurbit urea CB[7] with the surface ligand ATT of ATT-AuNCs. This CB[7]/AuNCs nanocomposite demonstrated excellent aggregation induced fluorescence enhancement effect. Compared with ATT-AuNCs, the fluorescence intensity of CB[7]/AuNCs was enhanced by 15 fold. Moreover, the CB[7]/AuNCs could induce disaggregation by binding CB[7] to dexamethasone, leading to fluorescence quenching of CB[7]/AuNCs. Based on this mechanism, a sensitive and rapid dexamethasone fluorescence sensing method was developed. The detection limit of the established method was 22 nmol/L and the linear range was 0.05~40 μmol/L. The method can be applied to the rapid detection of dexamethasone in skin care products, and has the advantages of high sensitivity and simple operation.