Search Results (18)

In recent years, with the development of sensor technology, the volume of spatial data has grown exponentially. However, this data is often unevenly distributed, and traditional indexing methods cannot predict the overall data distribution when data are continuously inserted into the database. This makes them inefficient for indexing large-scale, unevenly distributed spatial data. This paper proposes a hybrid indexing method based on the grid-indexing and R-tree methods, called R-MLGTI (R-Multi-Level Grid–Tree Index). The method first divides the two-dimensional space using the Z-curve to form multiple sub-grid regions. When incrementally inserting data, R-MLGTI calculates the grid encoding of the data and computes the $c\left(G\right)$ of the corresponding grid G to measure the sparsity or density within the grid region, where $c\left(G\right)$ is a metric that quantifies the data density within grid G. All data in sparse grids are indexed by R-trees associated with grid encodings. In dense grid areas, a finer-grained space-filling curve is recursively applied for further spatial division. This process forms multiple sub-grids until the data within all sub-grids becomes sparse, at which point the original data is re-indexed according to the sparse grids. Finally, this paper presents a prototype system of the in-memory R-MLGTI and conducts benchmark tests for incremental data import and range queries. The incremental data insertion performance of R-MLGTI is lower than that of the grid-indexing and R-tree methods; however, on various unevenly distributed simulated datasets, the average query time for different query regions in R-MLGTI is about 6.49% faster than that of the grid-indexing method and about 51.78% faster than that of the R-tree method. On a real dataset, Landsat 7 EMT, which contains 2,585,203 records, the average query time for various query ranges is 61.39% faster than that of the grid-indexing method and 17.01% faster than that of the R-tree method. Experiments show that R-MLGTI performs better than the traditional R-tree and grid-indexing methods in large-scale, unevenly distributed spatial data query requests. Full article

Hongqujiu, one of the three principal varieties of yellow wine, is a traditional fermented beverage originating from China that employs Hongqu as the fermentation agent. In this study, an untargeted metabolomics approach based on gas chromatography–mass spectrometry (GC-MS) and liquid chromatography–mass spectrometry (LC-MS) was applied to systematically analyze the volatile compounds (VOCs) and non-volatile compounds (NVCs) in Hongqujiu across different aging years for the first time. The analysis identified a total of 262 VOCs and 2564 NVCs in samples of Hongqujiu aged for six distinct years. Based on metabolic differences, the samples were categorized into two groups: the low-year group (5-year, 6-year) and the high-year group (8-year, 10-year, 15-year, 20-year). Nineteen VOCs (e.g., 4-amino-butyric acid and diethanolamine) and thirty NVCs (e.g., palmitoylethanolamide and erinacine D) were identified as key differential metabolites distinguishing the low-year group from the high-year group. The higher-year group is enriched with a variety of substances with different flavors or biological activities, such as sugar derivatives, amino acids and their complexes, organic acids and their intermediate metabolites, steroids and terpenoid compounds, lipids and their derivatives, nitrogen-containing heterocycles, and aromatic compounds. The accumulation of these substances not only shapes the unique and rich flavor characteristics of aged red rice wine (such as the caramel aroma and umami peptide flavor), but also endows red rice wine with potential health benefits due to the physiological regulatory functions of some active ingredients. This study contributes to a deeper understanding of the composition and dynamic variations in metabolites in Hongqujiu, offering a scientific foundation for identifying aged Hongqujiu and conducting further research to enhance its quality. Full article

Litter decomposition is important for soil nutrient cycling, but how the quality of litter links to nutrient dynamics is still unclear. In this work, high-quality Populus alba × P. Berolinensis, widely planted in Northeast China, and low-quality maize straw were selected as samples for a comparative study. In a short-term controlled litter decomposition incubation experiment, we applied different concentrations (25 u/g and 50 u/g) of biocatalyst (cellulase) to accelerate litter decomposition. Destructive sampling was conducted at 3, 7, 14, and 28 days to examine the influence of exogenous cellulase and litter with varying C:N ratios on the stoichiometric balance of soil carbon and nitrogen contents, as well as associated enzymatic activity. Litter addition significantly increased soil nutrients. Low nitrogen limited nutrient release during the decomposition of maize straw. After treatment, the soil organic carbon (SOC), total nitrogen (TN), microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) in maize straw were 11.7%, 11.35%, 24.49%, and 39.7% lower than those in high-quality Populus alba × P. Berolinensis, respectively. Exogenous cellulase addition increased microbial biomass and β-D-glucosidase activity. The promotion of soil urease (S-UE), sucrase (S-SC), and β-D-glucosidase (S-β-GC) activities was more significant in combination with litter and exogenous cellulase. In addition, soil nutrients were directly affected by the litter C:N ratio and indirectly affected by cellulase. Overall, these results suggest that high-quality litter better facilitates soil nutrient cycling and accumulation due to its higher carbon and nitrogen release. Full article

We developed a tree ring width chronology from 1797 to 2020 (224 years) for the northwestern foothills of the Greater Khingan Mountains (GKMs) in northeastern China using 51 tree ring sample cores from 24 Pinus sylvestris var. mongolica (PSM). Pearson’s correlation analysis was used to analyze the relationship between tree ring width and regional climate factors. The standardized chronology was positively associated with the minimum temperature (T_min) in the previous May (r = 0.721, p < 0.01), indicating that this parameter was the main climatic factor limiting PSM growth in the region. We established a secure reconstruction equation for the May T_min from 1797 to 2020. There were 31 warm and 43 cold years in the 224-year reconstructed temperature series, accounting for 13.8% and 19.2% of the total years, respectively. Warm periods were observed in 1820–1829, 1877–1898, 1947–1958, and 1991–2020, whereas cold periods occurred in 1820, 1829–1870, 1899–1927, 1934–1947, and 1960–1988. The observed temperature sequence was highly consistent with the reconstructed sequence from the tree rings, which verified the reliability of the reconstructed results. The spatial correlation analysis indicated that the reconstructed temperature sequence accurately represented the temperature changes in the northwestern foothills of the GKM and surrounding areas. Multi-window spectral analysis and wavelet analysis revealed significant periodic fluctuations from 2 to 6 years, 21.2 years, 48.5 years, and 102.2 years. These periodic variations may be related to the El Niño–Southern Oscillation (ENSO), the Atlantic Multi-Year Intergenerational Oscillation (AMO), the Pacific Decadal Oscillation (PDO), and solar activity. This study expands the existing climate records in the region and provides valuable data support for understanding climate change patterns in the GKM and the scientific predictions of future climate changes. Full article

In northeastern China, simultaneous reconstruction of temperature and precipitation changes in the same region using tree ring data has not yet been reported, limiting our understanding of the historical climate. Using tree ring samples from the Greater Khingan Mountains, it was established that there are five standardized tree ring width chronologies of Pinus sylvestris var. mongolica at five elevations. Correlation analyses revealed significant relationships between the tree ring chronologies and climate data for multiple months. Specifically, the correlation coefficient between the average minimum temperature from May to July and the composite chronologies of mid–high and mid-elevations was 0.726, whereas that between the total precipitation from August to July and the low-elevation chronology was 0.648 (p < 0.01). Based on these findings, we reconstructed two series: the average minimum temperature from May to July over the past 211 years and the total precipitation from August to July over the past 214 years. The reconstructed sequences revealed changes in the average minimum temperature from 1812 to 2022 and precipitation from 1809 to 2022 in the northern part of the Greater Khingan Mountains. The variances explained by the reconstruction equations were 0.528 and 0.421 (adjusted R-squared: 0.520 and 0.411), with F-test values of 65.896 and 42.850, respectively, exceeding the significance level of 0.01. The reliability of the reconstructed sequences was validated by historical records of meteorological disasters and the reconstruction results in the surrounding area. The reconstructed temperature and precipitation sequences exhibited distinct patterns of temperature fluctuations, dry–wet changes, and periodic oscillations. The region experienced two warm periods (1896–1909 and 2006–2020), two cold periods (1882–1888 and 1961–1987), a wet period (1928–1938), a drought period (1912–1914), and a period prone to severe drought events (1893–1919) during the past 210 years. The temperature series showed periodicities of 2–2.5 years, 3.9 years, 5.2 years, and 68 years, while the precipitation series exhibited periodicities of 2.1 years, 2.5 years, and 2.8 years, possibly related to El Niño–Southern Oscillation (ENSO) events, quasi-biennial oscillation, and Pacific Decadal Oscillation (PDO). Spatial correlation analysis indicated that the reconstructed temperature and precipitation sequences accurately represented the hydrothermal changes in the study area. Full article

Metal–organic frameworks (MOFs) have drawn intensive attention as a class of highly porous, crystalline materials with significant potential in various applications due to their tunable porosity, large internal surface areas, and high crystallinity. This paper comprehensively reviews the fabrication methods of pure MOF membranes and films, including in situ solvothermal synthesis, secondary growth, electrochemical deposition, counter diffusion growth, liquid phase epitaxy and solvent-free synthesis in the category of different MOF families with specific metal species, including Zn-based, Cu-based, Zr-based, Al-based, Ni-based, and Ti-based MOFs. Full article

To enhance the accuracy of identifying fresh meat varieties using laser-induced breakdown spectroscopy (LIBS), we utilized the LightGBM model in combination with the Optuna algorithm. The procedure involved flattening fresh meat slices with glass slides and collecting spectral data of the plasma from the surfaces of the fresh meat tissues (pork, beef, and chicken) using LIBS technology. A total of 900 spectra were collected. Initially, we established LightGBM and SVM (support vector machine) models for the collected spectra. Subsequently, we applied information gain and peak extraction algorithms to select the features for each model. We then employed Optuna to optimize the hyperparameters of the LightGBM model, while a 10-fold cross-validation was conducted to determine the optimal parameters for SVM. Ultimately, the LightGBM model achieved higher accuracy, macro-F1, and Cohen’s kappa coefficient (kappa coefficient) values of 0.9370, 0.9364, and 0.9244, respectively, compared to the SVM model’s values of 0.8888, 0.8881, and 0.8666. This study provides a novel method for the rapid classification of fresh meat varieties using LIBS. Full article

Nitrogen is an important nutrient element for crop growth. Rapid and accurate acquisition of nitrogen content in cultivation substrate is the key to precise fertilization. In this study, laser-induced breakdown spectroscopy (LIBS) was used to detect the total nitrogen (TN) of coco-peat substrate. A LIBS spectrum acquisition system was established to collect the spectral line signal of samples with wavelengths ranging from 200 nm to 860 nm. Synergy interval partial least squares (Si-PLS) algorithm and elimination of uninformative variables (UVE) algorithm were used to select the spectral data of TN characteristic lines in coco-peat substrate. Univariate calibration curve and partial least squares regression (PLSR) were used to build mathematical models for the relationship between the spectral data of univariate characteristic spectral lines, full variables and screened multi-variable characteristic spectral lines of samples and reference measurement values of TN. By comparing the detection performance of calibration curves and multivariate spectral prediction models, it was concluded that UVE was used to simplify the number of spectral input variables for the model and PLSR was applied to construct the simplest multivariate model for the measurement of TN in the substrate samples. The model provided the best measurement performance, with the calibration set determination coefficient ($R_C^2$) and calibration set root mean square error (RMSEC) values of 0.9944 and 0.0382%, respectively; the prediction set determination coefficient ($R_P^2$) and prediction set root mean square error (RMSEP) had values of 0.9902 and 0.0513%, respectively. These results indicated that the combination of UVE and PLSR could make full use of the variable information related to TN detection in the LIBS spectrum and realize the rapid and high-performance measurement of TN in coco-peat substrate. It would provide a reference for the rapid and quantitative assessment of nutrient elements in other substrate and soil. Full article

To study the radial growth of Mongolian pine (Pinus sylvestris var. mongholica, MP) trees in response to climatic factors against the global warming background in the northeast part of the Greater Khingan Mountains (GKM), 101 tree cores were collected at contrasting altitudes (1100 and 650 m) in the Mordoga area, a tree-ring width chronology of MP was established for that region at both altitudes, and the relation between climatic factors and ring width trends at different time scales was investigated. The results revealed four major findings. (1) The ring width chronology of MP in the low-altitude area has better quality. (2) The growth of MP at high (low) altitude was mainly influenced by temperature (precipitation) factors. (3) Before a sudden change in temperature, there was a decreasing trend in the annual indices of MPs at higher altitudes. The chronological coefficients of MPs at both altitudes showed a significant upward trend after the increase in temperature. (4) The sliding analysis results showed that the stability of the relationship between MP growth and its response to climatic factors at both altitudes was also mostly similar. MP growth is relatively stable and sensitive and to climatic factors as temperatures increase. Full article

The research aims to examine the distribution of porosity and the combined heat and moisture movement while grains are being dried. This research concerns the porosity and flow of soybeans with different particle size ratios and the drying of soybeans with varying particle temperatures. Due to the similarity in shape between soybeans and balls, this article adopts a ball shape to study the heat and moisture transfer of soybean particles, which can also be used for the study of grains with similar shapes, such as mung beans and red beans. Random models of soybeans with varying proportions were created using modeling software Edem and UG. UDF programming was added to the preprocessing software Fluent to analyze the porosity, airstream allocation, and the interaction of temperature and moisture transfer in packed beds with various cylinder-to-particle size ratios and particle temperatures. A packed bed of soybeans was created, and the study examined the impact of cylinder-to-particle size ratios of 4.44, 5.6, and 6.25 on porosity. The results show that the radial porosity in the packed bed displays a fluctuating profile, with partial porosity increasing as the cylinder-to-particle size ratio increases. Increasing the ratio of cylinder size to particle size exacerbated the tortuosity of the flow paths within the packed bed. Simultaneously, the particle temperature increases, leading to a rise in the instantaneous heat transfer during the drying process, strengthening the ratio of moisture transfer within the packed bed. The method effectively models during convective heat and mass transfer in the liquid facies, as well as thermal and mass spread in the solid facies. The results of this study have been validated on physical models. The air temperature of 273 K is considered during the simulation process Full article

Global warming significantly affects forest ecosystems in the Northern Hemisphere’s mid-to-high latitudes, altering tree growth, productivity, and spatial distribution. Additionally, spatial and temporal heterogeneity exists in the responses of different tree species to climate change. This research focuses on two key species in China’s Greater Khingan Range: Larix gmelinii (Rupr.) Kuzen. (Pinaceae) and Quercus mongolica Fisch. ex Ledeb. (Fagaceae). We utilized a Maxent model optimized by the kuenm R package to predict the species’ potential habitats under various future climate scenarios (2050s and 2070s) considering three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5. We analyzed 313 distribution records and 15 environmental variables and employed geospatial analysis to assess habitat requirements and migration strategies. The Maxent model demonstrated high predictive accuracy, with Area Under the Curve (AUC) values of 0.921 for Quercus mongolica and 0.985 for Larix gmelinii. The high accuracy was achieved by adjusting the regularization multipliers and feature combinations. Key factors influencing the habitat of Larix gmelinii included the mean temperature of the coldest season (BIO11), mean temperature of the warmest season (BIO10), and precipitation of the driest quarter (BIO17). Conversely, Quercus mongolica’s habitat suitability was largely affected by annual mean temperature (BIO1), elevation, and annual precipitation (BIO12). These results indicate divergent adaptive responses to climate change. Quercus mongolica’s habitable area generally increased in all scenarios, especially under SSP5-8.5, whereas Larix gmelinii experienced more complex habitat changes. Both species’ distribution centroids are expected to shift northwestward. Our study provides insights into the divergent responses of coniferous and broadleaf species in the Greater Khingan Range to climate change, contributing scientific information vital to conserving and managing the area’s forest ecosystems. Full article

Wildfires, a recurring and persistent natural disaster, present direct threats to both ecological balance and human safety. Despite the northeastern region of China boasting abundant forest resources, it grapples with a significant wildfire issue. This study, focused on China’s northeastern region, employs sophisticated methodologies, including the Mann–Kendall (MK) mutation test, sliding t-test, and geographical heat maps, to unveil the spatial distribution and temporal trends of wildfires. Furthermore, a random forest model is utilized to develop a wildfire susceptibility map, enabling an in-depth analysis of the relationships between various potential factors and wildfires, along with an assessment of the significance of these driving factors. The research findings indicate that wildfires in the northeastern region exhibit distinct seasonality, with the highest occurrences in the autumn and spring and fewer incidents in the summer and winter. Apart from the spring season, historical wildfires show a decreasing trend during other seasons. Geographically, wildfires tend to cluster, with over half of the high-risk areas concentrated at the junction of the Greater Khingan Mountains and Lesser Khingan Mountains in the northeastern region. The random forest model assumes a pivotal role in the analysis, accurately identifying both natural and human-induced factors, including topography, climate, vegetation, and anthropogenic elements. This research further discloses that climate factors predominantly influence wildfires in the northeastern region, with sunshine duration being the most influential factor. In summary, this study highlights the variation in various wildfire-driving factors, providing the basis for tailored management strategies and region-specific fire prevention. Through a comprehensive analysis of the spatiotemporal patterns of wildfires and associated risk factors, this research offers valuable insights for mitigating wildfire risks and preserving the region’s ecological integrity. Full article

Piezoelectric accurate actuation plays an important role in industrial applications. The intrinsic frequency of previous actuators is invariable. However, variable frequency can approach the range near the low-intrinsic-frequency and realize a high actuation capability. The frequency-variable linear and rotary motion (FVLRM) principle is proposed for rotor-blade-based two-degree-of-freedom driving. Inertial force is generated by frequency-variable piezoelectric oscillators (FVPO), the base frequency and vibration modes of which are adjustable by the changeable mass and position of the mass block. The variable-frequency principle of FVPO and the FVLRM are recognized and verified by the simulations and experiments, respectively. The experiments show that the FVLRM prototype moves the fastest when the mass block is placed at the farthest position and the prototype is at the second-order intrinsic frequencies of 42 Hz and 43 Hz, achieving a linear motion of 3.52 mm/s and a rotary motion of 286.9 mrad/s. The actuator adopts a lower operating frequency of less than 60 Hz and has the function of adjusting the natural frequency. It can achieve linear and rotational motion with a larger working stroke with 140 mm linear movement and 360° rotation. Full article

The water pollution caused by the release of organic pollutants has attracted remarkable attention, and solutions for wastewater treatment are being developed. In particular, the photocatalytic removal of organic pollutants in water systems is a promising strategy to realize the self-cleaning of ecosystems under solar light irradiation. However, at present the semiconductor-based nanocatalysts can barely satisfy the industrial requirements because their wide bandgaps restrict the effective absorption of solar light, which needs an energy band modification to boost the visible light harvesting via surface engineering. As an innovative approach, pulsed laser heating in liquids has been utilized to fabricate the nanomaterials in catalysis; it demonstrates multi-controllable features, such as size, morphology, crystal structure, and even optical or electrical properties, with which photocatalytic performances can be precisely optimized. In this review, focusing on the powerful heating effect of pulsed laser irradiation in liquids, the functional nanomaterials fabricated by laser technology and their applications in the catalytic degradation of various organic pollutants are summarized. This review not only highlights the innovative works of pulsed laser-prepared nanomaterials for organic pollutant removal in water systems, such as the photocatalytic degradation of organic dyes and the catalytic reduction of toxic nitrophenol and nitrobenzene, it also critically discusses the specific challenges and outlooks of this field, including the weakness of the produced yields and the relevant automatic strategies for massive production. Full article

The purpose of photocatalysis is to realize the conversion between solar energy and chemical energy, and it is essential to develop a high-performance photocatalyst under visible-light irradiation. The conventional methods for photocatalyst preparation are mainly wet chemical routes, and abundant yields can be obtained. However, the products are not neat and accompanied by chemical groups and impurities, which are not beneficial for the enhancement of photocatalytic performance. In recent years, as a powerful tool for nanomaterial fabrication, pulsed laser heating in a liquid medium has been utilized to prepare a variety of nanocomposites. Products with synergistic effects and high crystallinity can be rapidly prepared under pulsed laser selective heating, which is beneficial for obtaining more effective photocatalytic performance. In this review, the typical characteristics of pulsed laser heating in liquids and their prepared nanocomposites for photocatalytic applications are summarized. This review not only highlights the innovative works of pulsed-laser-prepared nanocomposites in liquids for photocatalysis but also briefly introduces the specific challenges and prospects of this field. Full article