Search Results (945)

Approximately 1 million tons of green tides decompose naturally in the Yellow Sea of China every year, releasing large quantities of nutrients that disrupt the marine ecological balance and cause significant environmental consequences. Currently, the identification of areas affected by green tides primarily relies on certain methods, such as ground sampling and biochemical analysis, which limit the ability to quickly and dynamically identify decomposition regions at large spatial and temporal scales. While multi-source remote sensing data can monitor the extent of green tides, accurately identifying areas of algal decomposition remains a challenge. Therefore, satellite data were integrated with key biochemical parameters, such as the carbon-to-nitrogen ratio (C/N), to develop a method for identifying green tide decomposition regions (DRIM). The DRIM shows a high accuracy in identifying green tide decomposition areas, validated through regional repetition rates and UAV measurements. Results indicate that the annual C/N threshold for green tide decomposition regions is 1.2. The method identified the primary decomposition areas in the Yellow Sea from 2015 to 2020, concentrated mainly in the southeastern region of the Shandong Peninsula, covering an area of approximately 1909.4 km². In 2015, 2016, and 2017, the decomposition areas were the largest, with an average annual duration of approximately 35 days. Our method provides a more detailed classification of the dissipation phase, offering reliable scientific support for accurate and detailed monitoring and management of green tide disasters. Full article

Soil nutrient distribution in desert grasslands is predominantly influenced by climatic factors, particularly precipitation and temperature. Siziwang Banner, situated within the desert grassland belt of Inner Mongolia, represents a typical arid zone where soil nutrient dynamics are shaped by the interplay of precipitation, temperature, and topography. This study aims to investigate the spatial distribution of soil nutrients and assess the dominant role of climatic factors in this region, using geostatistical analyses and GIS techniques. The results reveal that soil nutrients exhibit higher concentrations in surface layers, gradually decreasing with depth. Horizontally, a pronounced gradient can be observed, with nutrient levels being higher in the southern regions and lower in the northern regions. Precipitation and temperature emerge as decisive factors driving these patterns; increased precipitation enhances the accumulation of soil organic matter and nitrogen, whereas elevated temperatures accelerate decomposition of organic matter, leading to nutrient losses. These findings underscore the critical role of climatic factors in governing soil nutrient distribution, offering valuable insights for soil management and ecological restoration efforts in arid ecosystems. Full article

Once the rolling bearing fails, it will threaten the normal operation of the whole rotating machinery. Therefore, it is very necessary to conduct research on rolling bearing fault diagnosis. This paper proposes a rolling bearing fault diagnosis method combining MSSSA-VMD (variational mode decomposition optimized by the improved salp swarm algorithm based on mixed strategy) with the parallel network of GASF-CNN (convolutional neural network based on Gramian angular summation field) and bi-directional long short-term memory (BiLSTM) to solve the problem of poor diagnostic performance for the rolling bearing faults caused by the respective limitations of existing fault diagnosis methods based on signal processing and deep learning. Firstly, MSSSA-VMD is proposed to solve the problem where the decomposition effect of VMD is not ideal due to improper parameter selection. Then, MSSSA-VMD is employed to preprocess and extract characteristics. Finally, the extracted characteristics are input into the parallel network of GASF-CNN and BiLSTM for diagnosis. In one channel of the parallel network, GASF is used to convert the characteristic vectors into a two-dimensional image, which is then fed into CNN for spatial characteristic extraction. In the other channel of the parallel network, the characteristic vectors are directly input into BiLSTM for temporal characteristic extraction. Experimental results demonstrate that the proposed method has good performance in terms of fault diagnosis performance under constant operating conditions, generalization ability under variable operating conditions and noise resistance. Full article

Increased air pollution is driven by anthropogenic pollution emissions and climate change, which pose great challenges to environmental governance. Strengthening the monitoring of regional air quality levels and analyzing the causes of regional pollution is conducive to the management and sustainable development of the regional atmosphere. Functional data obtained on a wavelet basis were used in the fitting of air quality data of Fujian Province, and wavelet decomposition was performed to obtain low-frequency and high-frequency information. While the Fourier basis cannot adaptively adjust the time–frequency window, resulting in the loss of location information of special frequencies, the wavelet basis solves this problem. Functional analysis of variance was utilized for analyzing spatial differences in air pollution characteristics. Furthermore, the study established a multivariate functional linear regression model to explore the impact of meteorological factors and ozone precursor factors. The results indicated that the overall air quality was gradually improving in Fujian Province, but the concentration of ozone was progressively increasing. Air pollution in coastal areas was higher than that in inland areas. The p-values of the functional analysis of variance for energy values and crest values were less than 0.05. Moreover, the energy entropy and kurtosis values were greater than 0.05. There were significant differences of AQI in the fluctuation amplitude and variation characteristics of different cities. The total squared multiple correlation of regression model was above 50% on average. Ozone is currently the most serious pollution factor, mainly affected by wind speed, temperature, NO₂, and CO. In summer, it was principally influenced by VOCs. The findings of this study could act as a reference in exploring the time–frequency characteristics of air quality data and support of air pollution control. Full article

The mixing of poplar and robinia in coastal saline land is a useful attempt at difficult site afforestation. Investigating the long–term mixing effects of nitrogen–fixing and non–nitrogen–fixing tree species on the spatial heterogeneity of N and P nutrients and their ecological stoichiometric characteristics in the coastal saline–alkali soil can provide a scientific basis for soil improvement and plantation management in the coastal saline–alkali soil. By replacing time with space, poplar and robinia mixed forests and corresponding pure forests with the ages of 3, 7 and 18 years were selected, and soil profiles of 0–20 cm, 20–40 cm and 40–60 cm were dug up to determine the contents of total nitrogen, hydrolyzed nitrogen, total phosphorus and available phosphorus, the activities of soil urease and phosphatase and the number of soil bacteria, fungi and actinomycetes in rhizosphere soil. The mixture of poplar and robinia and the increase in planting years led to the heterogeneity of soil N and P in a coastal saline–alkali forest, which could significantly increase the contents of soil total nitrogen, hydrolyzed nitrogen, total phosphorus and available phosphorus between soil layers. Compared with the pure forest of poplar and robinia at the same age, the soil urease activity in the 0–20 cm soil layer of an 18a poplar and robinia mixed forest increased by 94.75% and 73.36%, and the soil phosphatase activity increased by 30.36% and 70.27%. The mix of poplar and robinia significantly increased the abundance of soil microorganisms in saline–alkali soil. The number of bacteria, fungi and actinomycetes in the 0–20 cm soil layer of the 18a poplar and robinia mixed forest was the highest, which were 703,200, 31,297 and 1903, respectively. Redundancy analysis showed that there was a significant positive correlation between soil N and P nutrient contents, soil enzyme activities and microbial abundance. The soil depth of N and P nutrient decomposition and transformation in the mixed poplar and robinia plantation was expanded. The soil N and P nutrient contents, enzyme activities and microbial abundance in the 40–60 cm soil layer of the mixed forest were higher than those of the pure forest. With the increase in plantation years, the depth of soil that can be used in the forest land is increasing. The mixture of poplar and robinia plantation is an excellent choice for the construction of coastal saline–alkali land plantation, which has a significant mixed gain for the decomposition and transformation of N and P nutrients and increases the depth of the available soil layer in the forest land in coastal saline–alkali land. However, the coastal saline–alkali land soil N/P is < 14 and is still restricted by nitrogen, so the application of nitrogen fertilizer can be increased during the afforestation process. Full article

Rural infrastructure is an important foundation for achieving sustainable rural development. To effectively formulate policies for rural infrastructure, it is crucial to evaluate the benefits of rural infrastructure investment (RII) using a systematic method. This study aims to conduct a systematic analysis of the income-increasing effect of RII from a multidimensional perspective, and provide a reference for developing countries to adjust and improve rural infrastructure policies. For this purpose, this study has utilized 15 years of data in China to analyze the income-increasing effect of RII from three dimensions: structure, spatiality, and heterogeneity. The results indicate that (1) in terms of structure, both living infrastructure investment (LII) and production infrastructure investment (PII) promote wage income. PII has an increasing effect on non-wage income, but the increasing effect of LII on non-wage income is not evident. Meanwhile, the income-increasing effect of RII for high-income groups is larger than that for low-income groups. (2) In terms of spatiality, RII has a spatial spillover effect, which increases villagers’ income in neighboring areas. From the perspective of spatial effect decomposition, the indirect effect of RII even exceeds the direct effect. (3) In terms of heterogeneity, the increase in the level of job-related migration inhibits the income-increasing effect of LII but promotes the income-increasing effect of PII; the improvement of the education level promotes the income-increasing effect of LII but inhibits the income-increasing effect of PII. Full article

In the context of water scarcity, understanding the mechanisms influencing and altering agricultural water consumption can offer valuable insights into the scientific management of limited water resources. Using Henan Province as a case study, this research applies the Mann–Kendall test method, the spatial Markov transfer chain model, the optimal parameter geo-detector model, and the Logarithmic Mean Divisia Index (LMDI) decomposition method to investigate the evolution characteristics of agricultural water consumption in Henan Province and its key influencing factors. The findings revealed the following: (1) Agricultural water consumption has shown a significant decline from 1999 to 2022. (2) According to observations, the stability of agricultural water consumption exceeds the spillover effect, and cross-border grade transfer is challenging. Moreover, this phenomenon is influenced by the neighboring regions. (3) The key influencing factors of added agricultural value are the sown area of food crops, total sown area, irrigated area, and average annual air temperature. (4) Among the decomposition effects on agricultural water consumption, the contribution of each decomposition effect to changes in agricultural water consumption and the role of spatial distribution exhibit notable differences. Overall, these findings provide theoretical references for the efficient use of agricultural water resources and sustainable development in the region. Full article

The Five-Hundred-Meter Aperture Spherical Radio Telescope (FAST) faces challenges in establishing high-precision rigid connections between the receiver and the reflective surface due to its vast spatial span. Innovatively, FAST suspends the feed cabin in mid-air using six supporting cables. The precise positioning of the feed focal point is achieved through the coordinated control of cable extension and retraction, along with the A-B axis and the Stewart platform within the cabin. The cables and the feed cabin form a large parallel mechanism. Since the cables are flexible, and the feed cabin remains at a high altitude during observations, it is inevitably subject to internal and external disturbances. To quickly dissipate these disturbances, the system requires a certain level of damping, which directly affects the pointing and tracking accuracy of FAST. During the 2022–2023 operational period, there were multiple instances where the pulleys of the curtain mechanism on the supporting cables became stuck and were carried to the top of the towers by the cables. This also led to the phenomenon where the pulleys, after being stuck, would rapidly slide down the cables due to accumulation. At such moments, the cabin-cable system would experience instantaneous excitation, causing vibrations. This study uses the intrinsic time-scale decomposition (ITD) method to analyze the inertial navigation data installed in the cabin during these events, identifying modal frequencies and damping ratios. The analysis results show that the lowest primary vibration frequency of the FAST cabin-cable suspension system ranges from approximately 0.12 to 0.2 Hz, with a damping ratio of no less than 0.004. These data indicate that the current structure of FAST has a strong energy dissipation capability, providing important reference points for improving the control accuracy of FAST and for the upgrade of the feed support system. Full article

Finding ways to improve regional energy efficiency is important for the Chinese government to achieve its dual carbon target. This paper aims to explore ways to improve regional energy efficiency by studying the spatial–temporal dynamic evolution of energy efficiency. To scientifically study the evolution trend in regional energy efficiency in China, this study uses convergence analysis, a spatial Gini coefficient decomposition model (no spatial consideration), and a spatial Markov chain model and spatial measurement model (spatial consideration). The results show the following: from 2008 to 2019, the mean value of regional single-factor energy efficiency (RS) showed an obvious trend of continuous increase, while the mean value of regional green total-factor energy efficiency (RT) changed from a trend of continuous decline to a relatively stable trend. The overall Gini coefficient of RS showed a trend of “steady–rising–steady”, and the overall Gini coefficient of RT showed a trend of “steady–small increase–sharp increase–fall”. There was club convergence in the two types of regional energy efficiency, and both of them achieved certain “leapfrog” changes. The factors that had a significant impact on RS include human capital, industrialization, openness, urbanization, financial development, and innovation environment. The significant factors for RT included governance structure, industrialization, openness, policy support, and financial development. The limitation of this paper is that only provincial data were used. In the future, city-level data can be mined and more detailed policy suggestions can be put forward for city-level differences. The research method used in this paper to study regional energy efficiency evolution trends is also applicable to other countries. Full article

Methylcyclohexane (MCH, C₇H₁₄) is a typical component in hydrocarbon fuels and is frequently utilized in surrogate fuel mixtures as a typical representative of alkylated cycloalkanes. However, comprehensive experimental studies on speciation during its combustion remain limited. This research investigates for the first time the chemical structure of laminar premixed flames of lean and stoichiometric mixtures (φ = 0.8 and 1.0) of MCH/O₂/Ar under atmospheric pressure. Using probe-sampling molecular-beam mass spectrometry (MBMS), the spatial distribution of 18 compounds, including reactants, products, and intermediates, in the flame front was measured. The obtained results were compared with numerical simulations based on three established chemical–kinetic models of MCH combustion. The comparative analysis demonstrated that while the models effectively describe the profiles of reactants, primary products and key intermediates, significant discrepancies were observed for various C₂–C₆ compounds. To indicate the roots of the discrepancies, a rate of production (ROP) analysis was performed in each simulation. ROP analyses revealed that the primary cause for the discrepancies could be attributed to the overprediction of the rates of initial stages during MCH decomposition. Particularly, the role of non-elementary reactions was emphasized, indicating the need for refinement of the mechanisms based on new experimental data. Full article

The rapid growth of the global population, the acceleration of the urbanization process, and the demands of economic development, place enormous pressure on scarce land resources. Cultivated land use presents a series of problems, hindering its socioeconomic and ecological sustainability. The sustainable intensification of cultivated land use (SICLU) is a development model designed to maximize land use efficiency, while minimizing environmental pollution. It is considered to be an efficient method to achieve three aspects of sustainable goals, namely in regard to society, the economy, and ecology, simultaneously. This approach has significant theoretical and practical implications for China’s food security and ecological safety. This study incorporates the “agricultural carbon emissions” indicator into the indicator evaluation system. Using the super-efficiency SBM model, we estimate the SICLU levels in China from 2001 to 2020. ArcGIS and the Dagum Gini coefficient decomposition model are employed to explore the temporal and spatial evolution characteristics and non-equilibrium spatial dynamics of SICLU in China. Finally, the Tobit regression model is used to reveal the driving factors. The results show the following: (1) Since 2003, China’s SICLU levels demonstrate an overall ascent amid fluctuations, sustaining a relatively high average annual level of 0.945. (2) In terms of spatial evolution patterns, China’s SICLU levels demonstrate significant spatial disparities, with distinct differences among the four major regions. Regions with similar SICLU levels show a certain degree of spatial adjacency. (3) There are significant regional disparities in China’s SICLU levels, which overall exhibit a declining trend. The differences between regions are the primary source of spatial variation, followed by hypervariable density and intra-regional disparities. (4) The regional industrial structure, the level of agricultural modernization, the agricultural cropping structure, and the per capita sown area, positively influence the enhancement of SICLU levels in China. Throughout the study period, the SICLU levels in China continuously improved and the overall regional disparities diminished. However, significant inter-regional imbalances persist, necessitating tailored optimization measures, based on local conditions. Establishing a coordinated mechanism for orderly and synergistic regional development is crucial, in order to provide references to decision-makers to promote the rational use of arable land in China. Full article

Under heavy load conditions, bearings are subjected to non-uniform and frequently changing loads, which leads to randomness in the spatial distribution of bearing degradation characteristics. Aiming at the problem that the traditional degradation index cannot accurately reflect the degradation state of heavy-duty bearings in the whole life cycle, a new degradation evaluation method based on multi-domain features is proposed in this paper, which aims to capture the early degradation point of heavy-duty bearings and characterize their degradation trend. Firstly, the energy entropy feature is obtained by improving the wavelet packet decomposition, and the original multi-domain feature set is constructed by combining the time domain and frequency domain features. Then, the optimal feature matrix is formed by using the comprehensive evaluation index. Finally, integrating probability and distance information, a comprehensive degradation index was constructed to evaluate the degradation, determine the initial degradation time, and quantitatively analyze the bearing degradation state. The validity of the proposed method is verified in two datasets. The proposed method can accurately identify the early degradation of bearings and track the state of bearing degradation, so as to realize the degradation assessment. Full article

Deep learning-based image compressive sensing (CS) methods often suffer from high computational complexity and significant loss of image details in reconstructions. A non-local prior dense feature distillation network (NPDFD-Net) is proposed for image CS. First, the non-local priors of images are leveraged to enhance high-frequency information in the measurements. Second, a discrete wavelet decomposition learning module and an inverse discrete wavelet reconstruction module are designed to reduce information loss and significantly lower computational complexity. Third, a feature distillation mechanism is incorporated into residual dense blocks to improve feature transmission efficiency. Finally, a multi-scale enhanced spatial attention module is proposed to strengthen feature diversity. Experimental results indicate that compared to MRCS_GAN, OCTUF, and DPC-DUN, the proposed method achieves an average PSNR improvement of 1.52%, 2.35%, and 0.93%, respectively, on the Set5 dataset. The image reconstruction running time is enhanced by 93.93%, 71.76%, and 40.74%, respectively. Furthermore, the proposed method exhibits significant advantages in restoring fine texture details in the reconstructed images. Full article

Extreme rainfall and soil moisture play important roles in the survival, community composition, and ecosystem function of desert plants. This study focused on arid desert riparian forests ecosystems in the Ebinur Lake Basin of Xinjiang, China. We analyzed the effects of rainfall and soil moisture on species composition, indicator species, β diversity, species turnover, and nestedness using three consecutive years of community surveys. A zero-model combined with a Bayesian framework was used to explore the response of species turnover and nestedness to soil moisture and rainfall, and variance decomposition was used to quantify the relative importance of spatial distance, rainfall, and soil factors in determining species composition. The results indicated the following: (1) when rainfall was high, the richness and abundance of annual herbaceous plants increased. The proportion of the community based on richness (32%) and abundance (58.1%) of annual herbaceous plants in 2016 was higher than that in 2015 and 2017. The Jaccard, Bray–Curtis, and Chao indexes of the community in years with higher rainfall were significantly higher than in years with lower rainfall; however, a lag effect was also observed. (2) Soil factors explained 5% of the changes in community composition, rainfall explained 12% of the changes in community composition, and spatial distance, soil factors, and rainfall jointly explained 32% of the changes in community composition. (3) We also showed that high soil moisture leads to greater β diversity than low soil moisture. Rainfall had the greatest explanatory power on the measured values of β diversity (19.6%) and species turnover (38%), and the factor with the greatest explanatory power for species nestedness was the interaction between rainfall and soil moisture (26.2%). Our findings indicate that drought and rainfall drive differences in plant community composition, with rainfall playing a dominant role. These results provide a basis for understanding the impact of extreme rainfall events on arid ecosystem functions. Full article

The Belt and Road Initiative holds significant importance for achieving the United Nations’ Sustainable Development Goals, particularly Goals 9 and 17. Drawing on data from the Web of Science database, the BRI database, and the World Bank database, this study explores the potential carbon emission reduction effects that cooperative innovations may bring to participating countries under the Belt and Road Initiative. The study constructs variable endogenous spatio-temporal weight matrices based on initial spatial weight matrices and, drawing on trends in co-authored publications, innovatively establishes time dummy variables and event dummy variables in a difference-in-differences (DID) model. This approach fully considers the interconnected, shared model of the Belt and Road Initiative and the spatial effects of policy implementation. A spatial DID model was established for 106 BRI participating countries and regions from 2005 to 2021. The results reveal the following: first, cooperative innovation under the BRI significantly reduces carbon emission intensity in participating countries. Second, the BRI primarily achieves carbon reduction through investment, innovation, and trade mechanisms. Third, the results of the global SDID model indicate that cooperative innovation with China negatively impacts carbon emission intensity. Also, this effect exhibits spatial spillover, suggesting that there is a policy spillover effect. Fourth, the decomposition of local policy shock effects indicates that the influences of cooperative innovation exhibit spatial heterogeneity, with varying degrees of direct and indirect effects on carbon emission intensity across different countries. Full article