Calculations of cooling rate by CO2 15 μm band in the earth's upper mesosphere and lower thermosphere be-come very difficult because of the non-LTE. This is primarily due to the nonlinear vibration-vibrational (V...Calculations of cooling rate by CO2 15 μm band in the earth's upper mesosphere and lower thermosphere be-come very difficult because of the non-LTE. This is primarily due to the nonlinear vibration-vibrational (VV) transition processes between CO, molecules in different states. This paper suggests that the non-LTE source function be parameterized as a linear combination of two limiting source functions. One limiting source function neglects the VV transitions while the other limiting source function assumes VV transitions being dominant. These two limiting source functions can be derived by linear models. The parameterization schemes proposed here can be applied to the general circulation models including those non-LTE regions.展开更多
Objective To evaluate the effect of the aluminum hydroxide （Al-OH） adjuvant on the 2009 pandemic influenza A/H1N1 （pH1N1） vaccine. Methods In a multicenter, double-blind, randomized, placebo-controlled trial, part...Objective To evaluate the effect of the aluminum hydroxide （Al-OH） adjuvant on the 2009 pandemic influenza A/H1N1 （pH1N1） vaccine. Methods In a multicenter, double-blind, randomized, placebo-controlled trial, participants received two doses of split-virion formulation containing 15 ug hemagglutinin antigen, with or without aluminum hydroxide （N-OH）. We classified the participants into six age categories （〉61 years, 41-60 years, 19-40 years, 13-18 years, 8-12 years, and 3-7 years） and obtained four blood samples from each participant on days 0, 21, 35, and 42 following the first dose of immunization. We assessed vaccine immunogenicity by measuring the geometric mean titer （GMT） of hemagglutination inhibiting antibody. We used a two-level model to evaluate the fixed effect of aluminum Al-OH and other factors, accounting for repeated measures. Results The predictions of repeated measurement on GMTs of formulations with or without Al-OH, were 80.35 and 112.72, respectively. Al-OH significantly reduced immunogenicity after controlling for time post immunization, age-group and gender. Conclusion The Al-OH adjuvant does not increase but actually reduces the immunogenicity of the split-virion pH1N1 vaccine.展开更多
The paper presents a novel multi-level model for quasi-brittle cracking analysis.Based on the partition of unity and information transmission technology,it provides a new non-re-meshing way to describe the cracking ph...The paper presents a novel multi-level model for quasi-brittle cracking analysis.Based on the partition of unity and information transmission technology,it provides a new non-re-meshing way to describe the cracking phenomenon in structures constructed from materials with complex microstructures.In the global model,the concept of the material particle is defined and the basic unknowns are the boundary displacements of these particles,which is different from the concept of the traditional displacement field.A series of enrichment functions with continuous steps is proposed,describing the boundary displacement affected by crack bands and allowing the intersections of crack bands with particle boundaries a priori unknown.Simultaneously,additional equations are introduced to determine element status and make the degrees of freedom of the global model remain at a stable level.Compared with previous research by our group,where the local description is equal to the global description on the boundary of a material particle,the introduced enrichment functions enable more accurate capture of the characteristics of the crack band.The model avoids the complex and dynamic model adjustments due to the activation and exit of representative volume elements(RVEs)and the accuracy of the description of the crack pattern can be ensured.The RVEs are activated at first,but then many of them exit the computation due to the unloading which reduces many of the degrees of freedom.Two examples of concrete specimens are analyzed,and the concrete fracture experiment and the digital image correlation(DIC)test are conducted.Compared with the reference solutions and the experimental data,even though the microstructure of concrete is very complex,the cracking process and crack pattern can be obtained accurately.展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is empl...Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.展开更多
The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-bas...The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.展开更多
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(...Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].展开更多
To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the stre...To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.展开更多
Kinetic impact is the most practical planetary-defense technique,with momentum-transfer efficiency central to deflection design.We present a Monte Carlo photometric framework that couples ejecta sampling,dynamical evo...Kinetic impact is the most practical planetary-defense technique,with momentum-transfer efficiency central to deflection design.We present a Monte Carlo photometric framework that couples ejecta sampling,dynamical evolution,and image synthesis to compare directly with HST,LICIACube,ground-based and Lucy observations of the DART impact.Decomposing ejecta into(1)a highvelocity(~1600 m/s)plume exhibiting Na/K resonance,(2)a low-velocity(~1 m/s)conical component shaped by binary gravity and solar radiation pressure,and(3)meter-scale boulders,we quantify each component’s mass and momentum.Fitting photometric decay curves and morphological evolution yields size-velocity distributions and,via scaling laws,estimates of Dimorphos’bulk density,cratering parameters,and cohesive strength that agree with dynamical constraints.Photometric ejecta modeling therefore provides a robust route to constrain momentum enhancement and target properties,improving predictive capability for kinetic-deflection missions.展开更多
Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use...This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.展开更多
Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy...Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.展开更多
Vegetation plays an important role in the environmental transport behavior of organic pollutants,however,the different roles of crops and natural vegetation have been ignored in most previous studies.In this study,we ...Vegetation plays an important role in the environmental transport behavior of organic pollutants,however,the different roles of crops and natural vegetation have been ignored in most previous studies.In this study,we developed the BETR-Urban-Rural-Veg model to quantitatively evaluate the influences of both natural vegetation and crops on the multimedia transport processes of Phenanthrene(PHE)and Benzo(a)pyrene(BaP)in mainland of China.The geographic distribution of polycyclic aromatic hydrocarbon(PAH)emissions and concentrations were consistent,displaying higher levels in northern China while lower levels in southern China.Under seasonal simulations,for both natural vegetation and crops,PAH concentrations in winter and spring were 1.5 to 27-fold higher than in summer and autumn,especially for PHE.Owing to the higher leaf area index(LAI)of natural vegetation and harvesting of crops,the filter and sequestration effect of natural vegetation was stronger than crops,while the seasonal changes of PAH concentrations in crops were more significant than natural vegetation.Temperature,precipitation rates and LAI might have important influences on seasonal concentrations and overall persistence of PAHs.PHE was more sensitive to the impacts of seasonal environmental parameters.Under different landscape scenarios,average annual PAH concentrations in natural vegetation were always a little higher than those in crops,and the overall persistence of BaP was greatly affected increasing by 15.15%-16.47%.This improved model provides a useful tool for environmental management.The results of this study are expected to support land use plans and decision-making in China's mainland.展开更多
Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiote...Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.展开更多
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys...Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.展开更多
文摘Calculations of cooling rate by CO2 15 μm band in the earth's upper mesosphere and lower thermosphere be-come very difficult because of the non-LTE. This is primarily due to the nonlinear vibration-vibrational (VV) transition processes between CO, molecules in different states. This paper suggests that the non-LTE source function be parameterized as a linear combination of two limiting source functions. One limiting source function neglects the VV transitions while the other limiting source function assumes VV transitions being dominant. These two limiting source functions can be derived by linear models. The parameterization schemes proposed here can be applied to the general circulation models including those non-LTE regions.
基金supported by the Infectious Disease Prevention and Control Major Research plan from the Ministry of Science and Technology of China-the Platform of Construction of Clinical Trial of Vaccine. (Project number 2009ZX0004-806)
文摘Objective To evaluate the effect of the aluminum hydroxide （Al-OH） adjuvant on the 2009 pandemic influenza A/H1N1 （pH1N1） vaccine. Methods In a multicenter, double-blind, randomized, placebo-controlled trial, participants received two doses of split-virion formulation containing 15 ug hemagglutinin antigen, with or without aluminum hydroxide （N-OH）. We classified the participants into six age categories （〉61 years, 41-60 years, 19-40 years, 13-18 years, 8-12 years, and 3-7 years） and obtained four blood samples from each participant on days 0, 21, 35, and 42 following the first dose of immunization. We assessed vaccine immunogenicity by measuring the geometric mean titer （GMT） of hemagglutination inhibiting antibody. We used a two-level model to evaluate the fixed effect of aluminum Al-OH and other factors, accounting for repeated measures. Results The predictions of repeated measurement on GMTs of formulations with or without Al-OH, were 80.35 and 112.72, respectively. Al-OH significantly reduced immunogenicity after controlling for time post immunization, age-group and gender. Conclusion The Al-OH adjuvant does not increase but actually reduces the immunogenicity of the split-virion pH1N1 vaccine.
基金supported by the National Natural Science Foundation of China(No.51878154)。
文摘The paper presents a novel multi-level model for quasi-brittle cracking analysis.Based on the partition of unity and information transmission technology,it provides a new non-re-meshing way to describe the cracking phenomenon in structures constructed from materials with complex microstructures.In the global model,the concept of the material particle is defined and the basic unknowns are the boundary displacements of these particles,which is different from the concept of the traditional displacement field.A series of enrichment functions with continuous steps is proposed,describing the boundary displacement affected by crack bands and allowing the intersections of crack bands with particle boundaries a priori unknown.Simultaneously,additional equations are introduced to determine element status and make the degrees of freedom of the global model remain at a stable level.Compared with previous research by our group,where the local description is equal to the global description on the boundary of a material particle,the introduced enrichment functions enable more accurate capture of the characteristics of the crack band.The model avoids the complex and dynamic model adjustments due to the activation and exit of representative volume elements(RVEs)and the accuracy of the description of the crack pattern can be ensured.The RVEs are activated at first,but then many of them exit the computation due to the unloading which reduces many of the degrees of freedom.Two examples of concrete specimens are analyzed,and the concrete fracture experiment and the digital image correlation(DIC)test are conducted.Compared with the reference solutions and the experimental data,even though the microstructure of concrete is very complex,the cracking process and crack pattern can be obtained accurately.
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
文摘Utilizing finite element analysis,the ballistic protection provided by a combination of perforated D-shaped and base armor plates,collectively referred to as radiator armor,is evaluated.ANSYS Explicit Dynamics is employed to simulate the ballistic impact of 7.62 mm armor-piercing projectiles on Aluminum AA5083-H116 and Steel Secure 500 armors,focusing on the evaluation of material deformation and penetration resistance at varying impact points.While the D-shaped armor plate is penetrated by the armor-piercing projectiles,the combination of the perforated D-shaped and base armor plates successfully halts penetration.A numerical model based on the finite element method is developed using software such as SolidWorks and ANSYS to analyze the interaction between radiator armor and bullet.The perforated design of radiator armor is to maintain airflow for radiator function,with hole sizes smaller than the bullet core diameter to protect radiator assemblies.Predictions are made regarding the brittle fracture resulting from the projectile core′s bending due to asymmetric impact,and the resulting fragments failed to penetrate the perforated base armor plate.Craters are formed on the surface of the perforated D-shaped armor plate due to the impact of projectile fragments.The numerical model accurately predicts hole growth and projectile penetration upon impact with the armor,demonstrating effective protection of the radiator assemblies by the radiator armor.
基金supported by the CAS Pioneer Hundred Talents Program and Second Tibetan Plateau Scientific Expedition Research Program(2019QZKK0708)as well as the Basic Research Program of Qinghai Province:Lithospheric Geomagnetic Field of the Qinghai‒Tibet Plateau and the Relationship with Strong Earthquakes(2021-ZJ-969Q).
文摘The National Geophysical Data Center(NGDC)of the United States has collected aeromagnetic data for input into a series of geomagnetic models to improve model resolution;however,in the Tibetan Plateau region,ground-based observations remain insufficient to clearly reflect the characteristics of the region’s lithospheric magnetism.In this study,we evaluate the lithospheric magnetism of the Tibetan Plateau by using a 3D surface spline model based on observations from>200 newly constructed repeat stations(portable stations)to determine the spatial distribution of plateau geomagnetism,as well as its correlation with the tectonic features of the region.We analyze the relationships between M≥5 earthquakes and lithospheric magnetic field variations on the Tibetan Plateau and identify regions susceptible to strong earthquakes.We compare the geomagnetic results with those from an enhanced magnetic model(EMM2015)developed by the NGDC and provide insights into improving lithospheric magnetic field calculations in the Tibetan Plateau region.Further research reveals that these magnetic anomalies exhibit distinct differences from the magnetic-seismic correlation mechanisms observed in other tectonic settings;here,they are governed primarily by the combined effects of compressional magnetism,thermal magnetism,and deep thermal stress.This study provides new evidence of geomagnetic anomalies on the Tibetan Plateau,interprets them physically,and demonstrates their potential for identifying seismic hazard zones on the Plateau.
文摘Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)].
基金Funded by State Railway Administration Research Project(No.2023JS007)National Natural Science Foundation of China(No.52438002)+1 种基金Research and Development Programs for Science and Technology of China Railways Corporation(No.J2023G003)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.
基金supported by the National Natural Science Foundation of China(Grant No.12272018)the National Key Basic Research Project(2022JCJQZD20600).
文摘Kinetic impact is the most practical planetary-defense technique,with momentum-transfer efficiency central to deflection design.We present a Monte Carlo photometric framework that couples ejecta sampling,dynamical evolution,and image synthesis to compare directly with HST,LICIACube,ground-based and Lucy observations of the DART impact.Decomposing ejecta into(1)a highvelocity(~1600 m/s)plume exhibiting Na/K resonance,(2)a low-velocity(~1 m/s)conical component shaped by binary gravity and solar radiation pressure,and(3)meter-scale boulders,we quantify each component’s mass and momentum.Fitting photometric decay curves and morphological evolution yields size-velocity distributions and,via scaling laws,estimates of Dimorphos’bulk density,cratering parameters,and cohesive strength that agree with dynamical constraints.Photometric ejecta modeling therefore provides a robust route to constrain momentum enhancement and target properties,improving predictive capability for kinetic-deflection missions.
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
基金funded by the Office of the Vice-President for Research and Development of Cebu Technological University.
文摘This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities.
基金financially supported by the National Key Research and Development Program of China (No. 2023YFB3812601)the National Natural Science Foundation of China (No. 51925401)the Young Elite Scientists Sponsorship Program by CAST, China (No. 2022QNRC001)。
文摘Machine learning-assisted methods for rapid and accurate prediction of temperature field,mushy zone,and grain size were proposed for the heating−cooling combined mold(HCCM)horizontal continuous casting of C70250 alloy plates.First,finite element simulations of casting processes were carried out with various parameters to build a dataset.Subsequently,different machine learning algorithms were employed to achieve high precision in predicting temperature fields,mushy zone locations,mushy zone inclination angle,and billet grain size.Finally,the process parameters were quickly optimized using a strategy consisting of random generation,prediction,and screening,allowing the mushy zone to be controlled to the desired target.The optimized parameters are 1234℃for heating mold temperature,47 mm/min for casting speed,and 10 L/min for cooling water flow rate.The optimized mushy zone is located in the middle of the second heat insulation section and has an inclination angle of roughly 7°.
基金supported by the National Natural Science Foundation of China(Nos.42107420,U23A20157,and U1910207)Shanxi Province Science Foundation for Young Scholars(No.20210302124363).
文摘Vegetation plays an important role in the environmental transport behavior of organic pollutants,however,the different roles of crops and natural vegetation have been ignored in most previous studies.In this study,we developed the BETR-Urban-Rural-Veg model to quantitatively evaluate the influences of both natural vegetation and crops on the multimedia transport processes of Phenanthrene(PHE)and Benzo(a)pyrene(BaP)in mainland of China.The geographic distribution of polycyclic aromatic hydrocarbon(PAH)emissions and concentrations were consistent,displaying higher levels in northern China while lower levels in southern China.Under seasonal simulations,for both natural vegetation and crops,PAH concentrations in winter and spring were 1.5 to 27-fold higher than in summer and autumn,especially for PHE.Owing to the higher leaf area index(LAI)of natural vegetation and harvesting of crops,the filter and sequestration effect of natural vegetation was stronger than crops,while the seasonal changes of PAH concentrations in crops were more significant than natural vegetation.Temperature,precipitation rates and LAI might have important influences on seasonal concentrations and overall persistence of PAHs.PHE was more sensitive to the impacts of seasonal environmental parameters.Under different landscape scenarios,average annual PAH concentrations in natural vegetation were always a little higher than those in crops,and the overall persistence of BaP was greatly affected increasing by 15.15%-16.47%.This improved model provides a useful tool for environmental management.The results of this study are expected to support land use plans and decision-making in China's mainland.
基金supported by the National Key Research and Development Program of China[grant number 2022YFE0106800]an Innovation Group Project of the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number 311024001]+3 种基金a project supported by the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)[grant number SML2023SP209]a Research Council of Norway funded project(MAPARC)[grant number 328943]a Nansen Center´s basic institutional funding[grant number 342624]the high-performance computing support from the School of Atmospheric Science at Sun Yat-sen University。
文摘Current shipping,tourism,and resource development requirements call for more accurate predictions of the Arctic sea-ice concentration(SIC).However,due to the complex physical processes involved,predicting the spatiotemporal distribution of Arctic SIC is more challenging than predicting its total extent.In this study,spatiotemporal prediction models for monthly Arctic SIC at 1-to 3-month leads are developed based on U-Net-an effective convolutional deep-learning approach.Based on explicit Arctic sea-ice-atmosphere interactions,11 variables associated with Arctic sea-ice variations are selected as predictors,including observed Arctic SIC,atmospheric,oceanic,and heat flux variables at 1-to 3-month leads.The prediction skills for the monthly Arctic SIC of the test set(from January 2018 to December 2022)are evaluated by examining the mean absolute error(MAE)and binary accuracy(BA).Results showed that the U-Net model had lower MAE and higher BA for Arctic SIC compared to two dynamic climate prediction systems(CFSv2 and NorCPM).By analyzing the relative importance of each predictor,the prediction accuracy relies more on the SIC at the 1-month lead,but on the surface net solar radiation flux at 2-to 3-month leads.However,dynamic models show limited prediction skills for surface net solar radiation flux and other physical processes,especially in autumn.Therefore,the U-Net model can be used to capture the connections among these key physical processes associated with Arctic sea ice and thus offers a significant advantage in predicting Arctic SIC.
基金supported by the Central Government Guiding Local Science and Technology Development Fund Project(No.2024SZY0343)the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin(No.2022-YRUC-01-050205)+2 种基金the Higher Education Scientific Research Project of Inner Mongolia Autonomous Region(No.NJZZ23078)the project of Inner Mongolia"Prairie Talents"Engineering Innovation Entrepreneurship Talent Team,the Major Projects of Erdos Science and Technology(No.2022EEDSKJZDZX015)the Innovation Team of the Inner Mongolia Academy of Science and Technology(No.CXTD2023-01-016).
文摘Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.
