Data as a Factor of Production Promoting the Deep Integration of the Digital Economy and the Real Economy:Theoretical Logic and Analysis Framework

Data is the primary factor of production in the digital economy,playing a role in promoting the deep integration of the digital economy and the real economy and smoothing the national economic cycle.After entering the circulation system of the real economy,data is rapidly integrated into production,circulation,consumption,distribution,and other links.It optimizes resource allocation,unblocks circulation channels,promotes accurate matching of supply and demand,stimulates emerging demand,and forms a virtuous circle of digital technology application,traditional physical enterprise transformation,and technological innovation.Integrated development is an important feature of the digital economy.Data promotes the integration of factors of production,products,enterprises,industries,and markets,which fosters a circular system with deep integration of the digital economy and the real economy.To promote the deep integration of the digital economy and the real economy,the government and business entities should take measures to improve the circular efficiency of the digital economy and the real economy.These measures include attaching importance to the role of data-driven development,improving data capacity,data development,and utilization in enterprises,exploring diverse circulation models of enterprise data,and creating typical application scenarios and industrial data spaces.

Study of inter-well interference in shale gas reservoirs by a robust production data analysis method based on deconvolution

In order to overcome the defects that the analysis of multi-well typical curves of shale gas reservoirs is rarely applied to engineering,this study proposes a robust production data analysis method based on deconvolution,which is used for multi-well inter-well interference research.In this study,a multi-well conceptual trilinear seepage model for multi-stage fractured horizontal wells was established,and its Laplace solutions under two different outer boundary conditions were obtained.Then,an improved pressure deconvolution algorithm was used to normalize the scattered production data.Furthermore,the typical curve fitting was carried out using the production data and the seepage model solution.Finally,some reservoir parameters and fracturing parameters were interpreted,and the intensity of inter-well interference was compared.The effectiveness of the method was verified by analyzing the production dynamic data of six shale gas wells in Duvernay area.The results showed that the fitting effect of typical curves was greatly improved due to the mutual restriction between deconvolution calculation parameter debugging and seepage model parameter debugging.Besides,by using the morphological characteristics of the log-log typical curves and the time corresponding to the intersection point of the log-log typical curves of two models under different outer boundary conditions,the strength of the interference between wells on the same well platform was well judged.This work can provide a reference for the optimization of well spacing and hydraulic fracturing measures for shale gas wells.

High glucose reduces Nrf2-dependent cRAGE release and enhances inflammasome-dependent IL-1βproduction in monocytes:the modulatory effects of EGCG

Soluble receptor for advanced glycation end products(sRAGE)acts as a decoy sequestering of RAGE ligands,thus preventing the activation of the ligand-RAGE axis linking human diseases.However,the molecular mechanisms underlying sRAGE remain unclear.In this study,THP-1 monocytes were cultured in normal glucose(NG,5.5 mmol/L)and high glucose(HG,15 mmol/L)to investigate the effects of diabetesrelevant glucose concentrations on sRAGE and interleukin-1β(IL-1β)secretion.The modulatory effects of epigallocatechin gallate(EGCG)in response to HG challenge were also evaluated.HG enhanced intracellular reactive oxygen species(ROS)generation and RAGE expression.The secretion of sRAGE,including esRAGE and cRAGE,was reduced under HG conditions,together with the downregulation of a disintegrin and metallopeptidase 10(ADAM10)and nuclear factor erythroid 2-related factor 2(Nrf2)nuclear translocation.Mechanistically,the HG effects were counteracted by siRAGE and exacerbated by siNrf2.Chromatin immunoprecipitation results showed that Nrf2 binding to the ADAM10 promoter and HG interfered with this binding.Our data reinforce the notion that RAGE and Nrf2 might be sRAGE-regulating factors.Under HG conditions,the treatment of EGCG reduced ROS generation and RAGE activation.EGCG-stimulated cRAGE release was likely caused by the upregulation of the Nrf2-ADAM10 pathway.EGCG inhibited HG-mediated NLRP3 inflammasome activation at least partly by stimulating sRAGE,thereby reducing IL-1βrelease.

Gas-Water Production of a Continental Tight-Sandstone Gas Reservoir under Different Fracturing Conditions

A numerical model of hydraulic fracture propagation is introduced for a representative reservoir(Yuanba continental tight sandstone gas reservoir in Northeast Sichuan).Different parameters are considered,i.e.,the interlayer stress difference,the fracturing discharge rate and the fracturing fluid viscosity.The results show that these factors affect the gas and water production by influencing the fracture size.The interlayer stress difference can effectively control the fracture height.The greater the stress difference,the smaller the dimensionless reconstruction volume of the reservoir,while the flowback rate and gas production are lower.A large displacement fracturing construction increases the fracture-forming efficiency and expands the fracture size.The larger the displacement of fracturing construction,the larger the dimensionless reconstruction volume of the reservoir,and the higher the fracture-forming efficiency of fracturing fluid,the flowback rate,and the gas production.Low viscosity fracturing fluid is suitable for long fractures,while high viscosity fracturing fluid is suitable for wide fractures.With an increase in the fracturing fluid viscosity,the dimensionless reconstruction volume and flowback rate of the reservoir display a non-monotonic behavior,however,their changes are relatively small.

Analysis of the Spatiotemporal Variation Characteristics and Driving Factors of Land Vegetation GPP in a Certain Region of Asia

Gross primary productivity (GPP) of vegetation is a critical indicator of ecosystem growth and carbon sequestration. The spatiotemporal variation characteristics of land vegetation GPP trends in a specific region of Asia from 2001 to 2020 were analyzed by Sen and MK trend analysis methods in this study .Moreover , a GPP change attribution model was established to explore the driving influences of factors such as Leaf Area Index (LAI), Land Surface Temperature (LST), Vapor Pressure Deficit (VPD), Soil Moisture, Solar Radiation and Wind Speed on GPP. The results indicate that summer GPP values are significantly higher than those in other months, accounting for 60.8% of the annual total GPP;spring and autumn contribute 18.91% and 13.04%, respectively. In winter, due to vegetation being nearly dormant, the contribution is minimal at 7.19%. Spatially, GPP shows a decreasing trend from southeast to northwest. LAI primarily drives the spatial and seasonal variations of regional GPP, while VPD, surface temperature, solar radiation, and soil moisture have varying impacts on GPP across different dimensions. Additionally, wind speed exhibits a minor contribution to GPP across different dimensions.

Exploring the Forest Cover Changes and Influential Factors of Dongsithouane National Production Forest Area, Savannakhet Province, Lao PDR

The Dongsithouane National Production Forest (DNPF) is one of the largest natural forest areas in Savannakhet, Lao PDR, which has been a vital support for the local community’s livelihood, Recently, significant changes in land use and land cover (LULC) have been observed in this area, leading to a reduction of natural forests. There were two separate methods of this study: firstly, to identify LULC changes across three different periods, spectral imagery from the Landsat 5 Thematic Mapper (TM) for the years 2001 and 2011, and the Landsat 8 Operational Land Imager (OLI) for 2021 were used as the primary data sources. The satellite images were preprocessed for various forest classes, including pretreatment of the top of atmosphere reflectance by using QGIS software’s semi-automatic classification plug-in (SCP), and ArcGIS was used for post-classification. A supervised classification approach was applied to the satellite images from 2001, 2011, and 2021 to generate diverse maps of LULC. Secondly, a household survey dataset was used to investigate influential factors. Approximately 220 households were interviewed in order to collect socio-economic information (including data on population growth, increased business activities, location of the area, agriculture land expansion, and need for settlement land). Household survey data was analyzed by using SPSS. Descriptive statistics, including frequency distributions and percentages, were applied to observe characteristics. Additionally, a binary logistic regression model was used to analyze the socioeconomic factors related to LULC change in DNPF. Key findings indicated a decline in natural forest areas within the study site. Specifically, both dry dipterocarp forest (−11.35%) and mixed deciduous forest (−0.18%) decreased from 2001 to 2021. The overall accuracy of the LULC maps was 94%, 86%, and 89% for the years 2001, 2011, and 2021 respectively. In contrast, agricultural land increased significantly by 155.70%, while built-up land, and water bodies increased by 65.54% and 35.33%, respectively. The results also highlighted a significant increase in construction land, up to 65.54%. Furthermore, the study found a correlation between agricultural expansion and a reduction of forest areas, along with an increase in built-up land along the forest areas’ boundaries. Timber exploitation and charcoal production also contributed to the decline in forest cover. The logistic regression model identified significant determinants of LULC change, including the area’s location, agricultural land expansion, increased business activity, and the need for settlement land. These factors have influenced the management of DNPF. Urgent sustainable management practices and actions, including forest ecosystem protection, village agricultural zoning, water source and watershed protection and public awareness, are required to preserve the forest areas of DNPF.

Research on the Impact of China’s Foreign Trade Technology Spillover on Green Total Factor Productivity of Countries Along the“Belt and Road”:Based on the Institutional Quality Threshold Effect

China’s growing trade with countries along the“Belt and Road”Initiative is accompanied by a focus on green development.Based on the panel data from 2007 to 2018,this paper establishes a threshold regression model to empirically analyze the institutional quality threshold effect of China’s foreign trade technology spillover on the GTFP of countries along the“Belt and Road.”The results show that China’s foreign trade technology spillover has a significant institutional quality double threshold effect on the green total factor productivity of the countries along the“Belt and Road.”As the institutional quality of the countries along the“Belt and Road”crosses a specific threshold value,the impact of China’s foreign trade technology spillover on the green total factor productivity of the countries along the“Belt and Road”has a significant positive promoting effect,and corresponding suggestions are put forward.

Data cleaning method for the process of acid production with flue gas based on improved random forest

Acid production with flue gas is a complex nonlinear process with multiple variables and strong coupling.The operation data is an important basis for state monitoring,optimal control,and fault diagnosis.However,the operating environment of acid production with flue gas is complex and there is much equipment.The data obtained by the detection equipment is seriously polluted and prone to abnormal phenomena such as data loss and outliers.Therefore,to solve the problem of abnormal data in the process of acid production with flue gas,a data cleaning method based on improved random forest is proposed.Firstly,an outlier data recognition model based on isolation forest is designed to identify and eliminate the outliers in the dataset.Secondly,an improved random forest regression model is established.Genetic algorithm is used to optimize the hyperparameters of the random forest regression model.Then the optimal parameter combination is found in the search space and the trend of data is predicted.Finally,the improved random forest data cleaning method is used to compensate for the missing data after eliminating abnormal data and the data cleaning is realized.Results show that the proposed method can accurately eliminate and compensate for the abnormal data in the process of acid production with flue gas.The method improves the accuracy of compensation for missing data.With the data after cleaning,a more accurate model can be established,which is significant to the subsequent temperature control.The conversion rate of SO_(2) can be further improved,thereby improving the yield of sulfuric acid and economic benefits.

Research Progress and Prospects of Total Factor Productivity in the Construction Industry

The high-quality development of the construction industry fundamentally stems from the significant improvement of total factor productivity.Therefore,it is of crucial significance for promoting the development of the construction industry to a higher level by scientifically and accurately measuring the total factor productivity of the construction industry and deeply analyzing the influencing factors behind it.Based on a comprehensive consideration of research methods and influencing factors,this paper systematically reviews the existing relevant literature on total factor productivity in the construction industry,aiming to reveal the current research development trend in this field and point out potential problems.This effort aims to provide a solid theoretical foundation and valuable reference for further in-depth research,and jointly promote the continuous progress and development of total factor productivity research in the construction industry.

Block Incremental Dense Tucker Decomposition with Application to Spatial and Temporal Analysis of Air Quality Data

How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.

A Data-Oriented Method to Optimize Hydraulic Fracturing Parameters of Tight Sandstone Reservoirs

Based on the actual data collected from the tight sandstone development zone, correlation analysis using theSpearman method was conducted to determine the main factors influencing the gas production rate of tightsandstone fracturing. An integrated model combining geological engineering and numerical simulation of fracturepropagation and production was completed. Based on data analysis, the hydraulic fracture parameters wereoptimized to develop a differentiated fracturing treatment adjustment plan. The results indicate that the influenceof geological and engineering factors in the X1 and X2 development zones in the study area differs significantly.Therefore, it is challenging to adopt a uniform development strategy to achieve rapid production increase. Thedata analysis reveals that the variation in gas production rate is primarily affected by the reservoir thickness andpermeability parameters as geological factors. On the other hand, the amount of treatment fluid and proppantaddition significantly impact the gas production rate as engineering factors. Among these factors, the influence ofgeological factors is more pronounced in block X1. Therefore, the main focus should be on further optimizing thefracturing interval and adjusting the geological development well location. Given the existing well location, thereis limited potential for further optimizing fracture parameters to increase production. For block X2, the fracturingparameters should be optimized. Data screening was conducted to identify outliers in the entire dataset, and adata-driven fracturing parameter optimization method was employed to determine the basic adjustment directionfor reservoir stimulation in the target block. This approach provides insights into the influence of geological,stimulation, and completion parameters on gas production rate. Consequently, the subsequent fracturing parameteroptimization design can significantly reduce the modeling and simulation workload and guide field operations toimprove and optimize hydraulic fracturing efficiency.

Toward a research framework to conceptualize data as a factor of production:The data marketplace perspective

The widespread use of machine learning techniques and artificial intelligence algorithms has highlighted the strategic role of data.To acquire data for training algorithms and eventually empowering the digital transformation,data marketplaces are often required to support and coordinate cross-organizational data transactions.However,the prior industry practices have suggested that the transaction costs in the data marketplaces are severely high,and the supporting infrastructure is far from mature.This paper proposes a data attributes-affected data exchange(DADE)conceptual model to understand the challenges and directions for developing data marketplaces.Specifically,our model framework is built upon two dimensions,data lifecycle maturity and data asset specificity.Based on the DADE model,we propose four approaches for developing data marketplaces and discuss future research directions with an overview of computational methods as potential technical solutions.

Tea Production Response to Seasonal Rainfall Variability: Evidence from Rwanda

Tea is a very important cash crop in Rwanda, as it provides crucial income and employment for farmers in poor rural areas. From 2017 to 2020, this study was intended to determine the impact of seasonal rainfall on tea output in Rwanda while still considering temperature, plot size (land), and fertiliser for tea plantations in three of Rwanda’s western, southern, and northern provinces, western province with “Gisovu” and “Nyabihu”, southern with “Kitabi”, and northern with “Mulindi” tea company. The study tested the level of statistical significance of all considered variables in different formulation of panel data models to assess individual behaviour of independent variables that would affect tea production. According to this study, a positive change in rainfall of 1 mm will increase tea production by 0.215 percentage points of tons of fresh leaves. Rainfall is a statistically significant variable among all variables with a positive impact on tea output Qitin Rwanda’s Western, Southern, and Northern provinces. Rainfall availability favourably affects tea output and supports our claim. Therefore, there is a need for collaboration efforts towards developing sustainable adaptation and mitigation options against climate change, targeting tea farming and the government to ensure that tea policy reforms are targeted towards raising the competitiveness of Rwandan tea at local and global market.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

A Novel CCA-NMF Whitening Method for Practical Machine Learning Based Underwater Direction of Arrival Estimation

Underwater direction of arrival(DOA)estimation has always been a very challenging theoretical and practical problem.Due to the serious non-stationary,non-linear,and non-Gaussian characteristics,machine learning based DOA estimation methods trained on simulated Gaussian noised array data cannot be directly applied to actual underwater DOA estimation tasks.In order to deal with this problem,environmental data with no target echoes can be employed to analyze the non-Gaussian components.Then,the obtained information about non-Gaussian components can be used to whiten the array data.Based on these considerations,a novel practical sonar array whitening method was proposed.Specifically,based on a weak assumption that the non-Gaussian components in adjacent patches with and without target echoes are almost the same,canonical cor-relation analysis(CCA)and non-negative matrix factorization(NMF)techniques are employed for whitening the array data.With the whitened array data,machine learning based DOA estimation models trained on simulated Gaussian noised datasets can be used to perform underwater DOA estimation tasks.Experimental results illustrated that,using actual underwater datasets for testing with known machine learning based DOA estimation models,accurate and robust DOA estimation performance can be achieved by using the proposed whitening method in different underwater con-ditions.

Evaluation of Ecological Sustainable Development in the Yangtze River Delta Region Based on Ecological Footprint Theory

The ecological footprint was employed as a quantitative indicator of resource inputs,enabling a detailed account of the structure of biological resources and energy occupancy,as well as the variation of resource productivity in the Yangtze River Delta(YRD)Region.From 2004 to 2018,there were notable variations in the ecological productivity of different types of land on basis of China’s equilibrium factor across the three provinces and one city in the YRD region.Jiangsu Province exhibited the highest ecological productivity of arable land,while Anhui Province exhibited the highest ecological productivity of forest land.Shanghai City exhibited the highest ecological productivity of pasture land,while Zhejiang Province exhibited the highest ecological productivity of water area.In 2018,the proportion of arable land within the total ecological carrying capacity of the YRD region reached 74.35%.Furthermore,the contribution of Jiangsu and Anhui provinces to the YRD’s total ecological carrying capacity was 41.36%and 41.26%,respectively.In the construction of a new development pattern in the YRD region,which is dominated by the domestic cycle as the main body and mutually reinforced by domestic and international double-cycle,the YRD region should combine the utilization of natural forces with innovation in science,technology and cooperation mechanisms.Furthermore,the government should guide the concentration of social capital towards green industries.It is also recommended that the moderate reduction of ecological footprints should be encouraged,and that the security of biological resources and energy,the leadership in the field of cutting-edge science and technology should be ensured in YRD region.This will facilitate the formation of a new development pattern of higher-quality integration at the national level firstly.

Hybrid data-driven framework for shale gas production performance analysis via game theory, machine learning, and optimization approaches

A comprehensive and precise analysis of shale gas production performance is crucial for evaluating resource potential,designing a field development plan,and making investment decisions.However,quantitative analysis can be challenging because production performance is dominated by the complex interaction among a series of geological and engineering factors.In fact,each factor can be viewed as a player who makes cooperative contributions to the production payoff within the constraints of physical laws and models.Inspired by the idea,we propose a hybrid data-driven analysis framework in this study,where the contributions of dominant factors are quantitatively evaluated,the productions are precisely forecasted,and the development optimization suggestions are comprehensively generated.More specifically,game theory and machine learning models are coupled to determine the dominating geological and engineering factors.The Shapley value with definite physical meaning is employed to quantitatively measure the effects of individual factors.A multi-model-fused stacked model is trained for production forecast,which provides the basis for derivative-free optimization algorithms to optimize the development plan.The complete workflow is validated with actual production data collected from the Fuling shale gas field,Sichuan Basin,China.The validation results show that the proposed procedure can draw rigorous conclusions with quantified evidence and thereby provide specific and reliable suggestions for development plan optimization.Comparing with traditional and experience-based approaches,the hybrid data-driven procedure is advanced in terms of both efficiency and accuracy.

A Data-Driven Oil Production Prediction Method Based on the Gradient Boosting Decision Tree Regression

Accurate prediction ofmonthly oil and gas production is essential for oil enterprises tomake reasonable production plans,avoid blind investment and realize sustainable development.Traditional oil well production trend prediction methods are based on years of oil field production experience and expertise,and the application conditions are very demanding.With the rapid development of artificial intelligence technology,big data analysis methods are gradually applied in various sub-fields of the oil and gas reservoir development.Based on the data-driven artificial intelligence algorithmGradient BoostingDecision Tree(GBDT),this paper predicts the initial single-layer production by considering geological data,fluid PVT data and well data.The results show that the GBDT algorithm prediction model has great accuracy,significantly improving efficiency and strong universal applicability.The GBDTmethod trained in this paper can predict production,which is helpful for well site optimization,perforation layer optimization and engineering parameter optimization and has guiding significance for oilfield development.

Comparison of multiple salinity datasets:upper ocean salinity and stratification in the tropical Pacific during the Argo period

Ocean salinity is an important variable that affects the ocean stratification.We compared the salinity and ocean stratification in the tropical Pacific derived from the Argo(Array for Real-time Geostrophic Oceanography data),EN4(Ensemble 4 analysis),SODA(the Simple Ocean Data Assimilation reanalysis),IAP(Institute of Atmospheric Physics data),and ORAS4(Ocean Reanalysis System 4)over 2005–2017.Results show that the spatial distribution of climatological mean of sea surface salinity(SSS)in all the products is consistent,and the low salinity region showed large deviation and strong dispersion.The Argo has the smallest RMSE and the highest correlation with the ensemble mean,while the IAP shows a high-salinity deviations relative to other datasets.All the products show high positive correlations between the sea surface density(SSD)and SSS with respect to the deviations of climatological mean from ensemble mean,suggesting that the SSD deviation may be mainly influenced by the SSS deviation.In the aspect of the ocean stratification,the mixed layer depth(MLD)climatological mean in the Argo shows the highest correlation with the ensemble mean,followed by EN4,IAP,ORAS4,and SODA.The Argo and EN4 show thicker barrier layer(BL)relative to the ensemble mean while the SODA displays the largest negative deviation in the tropical western Pacific.Furthermore,the EN4,ORAS4,and IAP underestimate the stability in the upper ocean at the depths of 20–140 m,while Argo overestimates ocean stability.The salinity fronts in the western-central equatorial Pacific from Argo,EN4,and ORAS4 are consistent,while those from SODA and IAP show large deviations with a westward position in amplitude of 0°–6°and 0°–10°,respectively.The SSS trend patterns from all the products are consistent in having ensemble mean with high spatial correlations of 0.95–0.97.

Measuring moisture content of dead fine fuels based on the fusion of spectrum meteorological data

Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire risk.In order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is proposed.The near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data set.The surface fine dead fuel of Mongolian oak(Quercus mongolica Fisch.ex Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were investigated.We used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion methods.The results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological method.The spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.