Partial Derivative Method Research Articles

Quantitative assessment of the relative impacts of climate change and human activities on net primary productivity of vegetation in the Beijing-Tianjin-Hebei region

IntroductionAs an important part of terrestrial ecosystem, vegetation plays a vital role in the terrestrial carbon cycle. As one of China’s three major urban clusters, the Beijing-Tianjin-Hebei region has experienced significant changes in vegetation due to climate change and large-scale ecological restoration efforts. Climate change and human activities are two factors that have important effects on the carbon cycles of terrestrial ecosystems. Quantitative assessment of the relative effects of climate change and human activities on vegetation dynamics is of paramount importance for regional sustainable development and ecological security.MethodsIn this study, based on ground meteorological data and remote sensing data including NDVI, an improved Carnegie-Ames-Stanford Approach model was employed to estimate the net primary productivity (NPP) of vegetation in the region from 2000 to 2020. Utilizing methods such as trend analysis, Mann-Kendall significance test, and correlation analysis, we investigated the spatiotemporal patterns of vegetation NPP and its correlations with precipitation, temperature, and solar radiation. Furthermore, the partial derivative trend residuals method was adopted to separate and quantify the impacts of climate change and human activities on vegetation NPP in different years.ResultsThe results show that: (1) from 2000 to 2020, the overall trend of vegetation NPP in the Beijing-Tianjin-Hebei region exhibited an increasing trend, with a spatial distribution pattern showing a step-like distribution from low to high to low from northwest to southeast. The multi-year average NPP was 384.90 g Cm−2, with an annual average growth rate of 3.00 g Cm−2. The NPP values of different vegetation types from large to small were: forests (578.40 g C m−2), shrubland (386.57 g C m−2), grassland (380.17 g C m−2), and cropland (324.91 g C m−2). (2) The proportion of regions with a positive correlation coefficient between average temperature and vegetation NPP in the Beijing-Tianjin-Hebei region from 2000 to 2020 was 69.85%, while the proportions of regions with positive correlations between precipitation and solar radiation with vegetation NPP were 92.1% and 89.19%, respectively. This indicates that vegetation NPP in most regions is positively correlated with precipitation, solar radiation, and temperature, with precipitation exerting a greater influence on vegetation NPP than temperature and solar radiation. (3) Vegetation improvement in the Beijing-Tianjin-Hebei region is mainly driven by both climatic factors and human activities, while vegetation degradation is primarily caused by human factors. The relative contributions of climate change and human activities to changes in vegetation NPP in the study area are 45.11% and 54.89%, respectively. The areas where they positively contribute account for 48.64% and 73.95% of the total study area, respectively, with the proportions of areas with relative contribution rates >60% being 45.86% and 28.86%, respectively.DiscussionThe findings of this study can provide scientific basis for vegetation restoration, low-carbon development, and ecosystem management decision-making in the Beijing-Tianjin-Hebei region.

Long-term variations in ecosystem water use efficiency in the Tibetan Plateau: Vegetation types, attribution methods and main drivers

Ecosystem water use efficiency (WUE) is a key indicator for understanding the response of carbon–water processes to environmental changes. However, as affected by uncertainty of WUE estimation and attribution, the individual contributions of environmental drivers to WUE changes and the underlying mechanisms remain unclear on the Tibetan Plateau (TP). Here, a theory-based analytical WUE model was modified by introducing an optimal stomatal behavior model, and used to estimate monthly WUE and their drivers during 1982–2018 for main vegetation types on the Tibetan Plateau (TP). Rationality of three mainstream analytical attribution methods—analytical-based partial derivative method (APDM), regression-based partial derivative method (NPDM), and climate elasticity method (CEM)—was examined. Furtherly, the contributions of various environmental drivers to WUE variation trend were estimated for various vegetation types. Results indicate that: (1) The modified model performed well in estimating WUE against with observed WUE at five eddy-covariance (EC) flux stations, which decreased the uncertainty of WUE estimation. WUE increased significantly in the TP with a slope of 0.49 × 10-2 g C kg−1 H2O yr−1 during 1982–2018 with forests increased most rapidly; (2) Only APDM captured both the variation and magnitude of WUE trends, and both NPDM and CEM failed to reproduce the magnitude of WUE trends, though CEM was better than NPDM benefited from its nonlinear structure. Through comparing these methods, we found that the nonlinear direct effects of these forcing variables on WUE change could be higher than their indirect effects caused by their interactions. (3) There are some differences in the main drivers of WUE trends for various vegetation types. Increase in WUE was mainly driven by the increase in leaf area index (LAI) for both steppes and meadows, while decreased air water vapor pressure deficit (VPD) and elevated CO2 showed higher positive effects for broad-leaf forests. The distinct response and extremely scarce EC observations of forests suggest that more attention should be paid to forests in the TP. This study could help in selection of attribution methods and understanding the mechanisms of carbon–water coupling of alpine ecosystems under a changing climate.

Driving factors analysis of spatial-temporal evolution of vegetation ecosystem in rocky desertification restoration area of Guizhou Province, China.

The investigation of the temporal-spatial characteristics and driving factors of vegetation ecosystem (VE) alterations held significant practical implications for the evaluation of the efficacy of rocky desertification management initiatives and safeguarding the ecological environment in the rocky desertification restoration region of Guizhou. We computed the comprehensive ecological quality index (Q) of vegetation based on the normalized difference vegetation index (NDVI) and net primary productivity (NPP). Combined with temperature, precipitation, sunshine duration, rocky desertification grade, land use, and the time series of various regions being included in national ecological functional zones, we analyzed the spatial-temporal distribution characteristics of VE changes and their response to climate change (CC) and ecological engineering (EE) by using partial derivative analysis method and scenario setting method in rocky desertification restoration areas in Guizhou. Results demonstrated that (1) the average values of NDVI, NPP, and Q all showed a fluctuating upward trend since 2000. Although the VE status of rocky desertification area was obviously worse than that of no rocky desertification area, it has a higher growth rate, especially the growth rates of NDVI, NPP, and Q in severe rocky desertification area were as high as 0.0050 year-1, 9.0733 g C m-2 year-1, and 0.7829 year-1, and the area with high recovery degree accounted for 93.19%, followed by the middle rocky desertification area. (2) CC was the main driving factor for NDVI and Q recovery, and EE was the main driving factor for NPP recovery. The contribution of EE to NPP and Q recovery increased with the increase of rocky desertification, as high as 82.13% and 30.31% in severe rocky desertification area. (3) The more serious the rocky desertification was, the more dependent the vegetation restoration was on ecological engineering, and the more difficult the restoration was. It was urgent to solve the ecological environmental problems. (4) EE played a greater role in the restoration of VE in the early stage of implementation. Its role gradually decreased in the later stages of implementation, while the role of CC increased. We provide a scientific basis for the follow-up treatment of rocky desertification, ecological environment restoration, and ecological protection effectiveness evaluation in Guizhou.

Observed decreasing trend in pan evaporation in a tropical rainforest region during 1959–2021

Abstract Pan evaporation (Epan) is a critical measure of the atmospheric evaporation demand. Analyzing meteorological data from the Tropical Rainforest Comprehensive Meteorological Observation Field in the Xishuangbanna Tropical Botanical Garden (XTBG Meteorological Observing Station) based on physical models is helpful to improve our understanding of the state of the hydrological cycle in the Xishuangbanna tropical rainforest region. In this study, we investigated the long-term trend in Epan using the observation data from 1959 to 2021. Moreover, correlation analyses of Epan were performed, such as trend test, assessment of periodic properties and abrupt change analysis. Then, D20 Epan data and related meteorological data from 1979 to 2008 were used to drive Penman‒Monteith and PenPan models for simulating Epan. The partial derivative attribution method was used to analyze the dominant factors affecting Epan. The results showed that Epan exhibits obvious periodic changes, the 19a is the first primary period. In addition, there was a clear ‘evaporation paradox’ phenomenon in Xishuangbanna. Epan showed a decreasing trend during both 1959–2008 and 2009–2018, and the decreasing trend reached a significant level with a rate of −3.404 mm·a−2 during 1959–2008. Through comparative analysis, the PenPan model was considered more suitable for simulating Epan in Xishuangbanna. In order to identify the main meteorological factors influencing Epan, complete data from the D20 pan monitoring period, namely, 1979–2008, were selected for attribution calculations. The variations in the net radiation and saturated vapor pressure deficit are the main triggers that explain the ‘evaporation paradox’ phenomenon in Xishuangbanna.

An adaptive divided-difference perturbation method for solving stochastic problems

Most stochastic problems are based on the independent and symmetrically distributed random variables. Using this principle, in this study, we propose an efficient calculation scheme to obtain the statistical moments based on high-order stochastic perturbation (SP) expansion. This solves the long-existing problem of the SP method, namely, the low calculation efficiency of statistical moments using high-order perturbation expansion, and the complexity of the calculation format. The scheme involves adopting the divided-difference method to approximate the partial derivative items of the SP method. The proposed method, which is similar to the collocation point method, only requires the deterministic calculation result for each node in the divided-difference method. In addition, as the adoption of high-order perturbation expansion is limited in multidimensional random problems due to the curse of dimensionality, an adaptive partial derivative items selection method is proposed in this paper to efficiently and reasonably ignore the expansion items that have an insignificant influence on statistical moments. This considerably improves the calculation efficiency of the statistical moments. Finally, the adaptive divided-difference perturbation (ADDP) method proposed in this paper is compared with the quasi-Monte Carlo method and the adaptive sparse grid methods using three numerical examples. The results prove that the ADDP method not only has high efficiency but also leads to significant improvements in the accuracy of statistical moments.

Transient analysis for offset-elliptical journal bearings under misalignment and dynamic loading

Although the offset-elliptical journal bearings (OEJBs) are widely used in industry, there are few researches on the OEJBs, especially under the conditions of journal misalignment and sudden load. To overcome such deficiency, the dynamic response of misaligned OEJBs subjected to the dynamic load has been studied in this paper. Based on the Reynolds equation, Euler equation, and kinetic equations, a hydrodynamic model is developed and validated to conduct the transient analysis for the OEJBs. Moreover, the modified film thickness of the misaligned OEJB is given. The partial derivative method and the finite difference method are adopted to solve the lubrication equations. The correlations of bearing characteristics such as the minimum film thickness, maximum film pressure, journal orbits, and time-varying dynamic coefficients with the misalignment and dynamic load are studied. In addition, the effects of the misalignment, rotating speed, and clearance ratio on the transient performance of OEJBs under the dynamic load are discussed in detail. The results indicate that the journal misalignment and dynamic load have a significant impact on the transient performance of OEJBs. The decrease of the film thickness caused by the journal misalignment can be aggravated by the suddenly applied load. The proposed model can provide theoretical fundament in the design and monitoring of the dynamically loaded bearing-rotor system.

Machine Learning Based Diagnosis for Diabetic Retinopathy for SKPD-PSC

The study aimed to apply to Machine Learning (ML) researchers working in image processing and biomedical analysis who play an extensive role in comprehending and performing on complex medical data, eventually improving patient care. Developing a novel ML algorithm specific to Diabetic Retinopathy (DR) is a challenge and need of the hour. Biomedical images include several challenges, including relevant feature selection, class variations, and robust classification. Although the current research in DR has yielded favourable results, several research issues need to be explored. There is a requirement to look at novel pre-processing methods to discard irrelevant features, balance the obtained relevant features, and obtain a robust classification. This is performed using the Steerable Kernalized Partial Derivative and Platt Scale Classifier (SKPD-PSC) method. The novelty of this method relies on the appropriate non-linear classification of exclusive image processing models in harmony with the Platt Scale Classifier (PSC) to improve the accuracy of DR detection. First, a Steerable Filter Kernel Pre-processing (SFKP) model is applied to the Retinal Images (RI) to remove irrelevant and redundant features and extract more meaningful pathological features through Directional Derivatives of Gaussians (DDG). Next, the Partial Derivative Image Localization (PDIL) model is applied to the extracted features to localize candidate features and suppress the background noise. Finally, a Platt Scale Classifier (PSC) is applied to the localized features for robust classification. For the experiments, we used the publicly available DR detection database provided by Standard Diabetic Retinopathy (SDR), called DIARETDB0. A database of 130 image samples has been collected to train and test the ML-based classifiers. Experimental results show that the proposed method that combines the image processing and ML models can attain good detection performance with a high DR detection accuracy rate with minimum time and complexity compared to the state-of-the-art methods. The accuracy and speed of DR detection for numerous types of images will be tested through experimental evaluation. Compared to state-of-the-art methods, the method increases DR detection accuracy by 24% and DR detection time by 37.

Responses of carbon and water use efficiencies to climate and land use changes in China's karst areas

It is crucial to determine the factors affecting the carbon use efficiency (CUE) and water use efficiency (WUE) of vegetation in ecosystems. However, the relationships between climate changes and CUE and WUE by karst vegetation in China are still unclear. The response of the CUE and WUE to regional climate change was studied by using trend analysis and partial derivative method. In addition, the distribution characteristics of the CUE and WUE of different land use types were analyzed. The following results were found. (1). From 2000 to 2018, the average of CUE in karst areas of China was 0.557, and the average value of WUE was 1.237g C kg−1 H2O. (2) The CUE of grassland was higher than that of forest land (0.051), whereas the WUE value of grassland was far lower than that of forest land (1.415 g C kg−1 H2O). (3) CUE was affected by precipitation (P), exhibiting an increasing trend (5.5 × 10−3 yr−1), and the most obvious increase occurred in the grassland (7.2 × 10−3 yr−1). Under the influence of solar radiation (SR), the WUE decreased (−1.5 × 10−3 g C kg−1 H2O yr−1), and the largest decrease occurred in the shrub land (−8.4 × 10−3 g C kg−1 H2O yr−1). (4) 90.31 % CUE increase depended on P and SR, while 78.32 % of the decrease in the WUE was due to P and SR. This study makes important contributions to clarifying the responses of the CUE and WUE of ecosystems to climate changes in karst areas, optimizing the management of regional water and soil resources, and promoting healthy development of the ecological environment.

The Partial Derivative Method for Dynamic Stiffness and Damping Coefficients of Supercritical CO2 Foil Bearings

Supercritical CO2 foil bearings are promising bearing technology for supercritical CO2 high-speed turbomachinery. The partial derivative method including complete variable perturbation of the compressible turbulent lubrication Reynolds equation is effective to predict the frequency dependent dynamic stiffness and damping coefficients of supercritical CO2 bearings. In this research, the structural perturbation of foil dynamic model was introduced into this method and then the dynamic coefficients of supercritical CO2 foil bearings were calculated. The results of parametric analysis show that the structural loss factor has little influence on the trend of dynamic coefficients changing with the dimensionless support stiffness but mainly affects the value of stiffness coefficients as well as damping coefficients. Due to the turbulence effect, the bearing number is not able to directly determine the characteristics of supercritical CO2 foil bearings, which is different from air bearings. Compared to the bearing number, the influence of the average Reynolds number on the change of dynamic coefficients with dimensionless support stiffness is more obvious.

Climate Drivers Contribute in Vegetation Greening Stalls of Arid Xinjiang, China: An Atmospheric Water Drying Effect

Xinjiang, an arid region of China, has experienced a substantial warming–wetting trend over the past five decades. However, climate change has affected vegetation growth/greening in arid Central Asia in unexpected ways due to complex ecological effects. We found a significant greening trend (consistent increase in the normalized difference vegetation index or NDVI) from 1982 to 1996, during the growing season; however, the NDVI consequently decreased and plateaued from 1997 to 2015, especially in naturally vegetated regions. Atmospheric vapor pressure deficit (VPD) is a critical driver of vegetation growth, is a direct measure of atmospheric aridity, and has increased sharply in recent decades. A partial correlation analysis indicated a significant relationship between growing season NDVI and VPD from 1997 to 2015. This implies that decreased VPD corresponds to increasing NDVI, and increasing VPD corresponds to a decrease and plateauing in the NDVI trend. Using the partial derivative equation method, our results suggest that the trend in growing season NDVI was affected primarily by increasing VPD (contributing 87.57%) from 1997 to 2015, especially in the grassland and desert biomes. Rising temperatures lead to a greater VPD, resulting in exacerbated evaporative water loss. Soil drought and atmospheric aridity limit plant stomatal conductance and could effectively lead to a decrease in the greening trend and increased vegetation mortality in arid Xinjiang. Our results emphasize the importance of VPD as a limiting factor of greening trends in arid regions. The influence of VPD on vegetation growth should be considered when evaluating arid ecosystem functioning under global warming.

Dynamic Coefficients of Tilting Pad Bearing by Perturbing the Turbulence Model

Tilting pad bearings are appropriate for the trend of high efficiency and reliability design of rotating machinery due to their high stability. The laminar and turbulent flow states exist in the lubricating oil film of high-speed and heavy-load tilting pad bearings simultaneously. By perturbing the multiple flow state lubrication model with a partial derivative method, together with the pad-pivot structural perturbations, the frequency-dependent stiffness and damping coefficients of tilting pad bearings, embracing the effect of dynamical variations of both turbulence and pressure-viscous, were numerically solved in this research. The importance of each perturbed variable was studied, and the results indicate that the perturbed film thickness included in turbulence coefficients perturbations is significant enough to be taken into account otherwise the equivalent stiffness coefficients will be obviously overestimated. Unlike the perturbed film thickness, the consideration of the perturbed viscosity is optional, because it makes the stiffness and damping coefficients larger at both laminar and turbulent flow states. For a simplified simulation and conservative prediction results, the perturbed viscosity can be neglected.

A State Space Modeling Method for Aero-Engine Based on AFOS-ELM

State space models (SSMs) are important for multi-variable performance analysis and controller design of aero-engines. In order to solve the problems of the traditional state space modeling methods that rely on component-level models (CLMs) and cannot be carried out in real time, an aero-engine state space modeling method based on adaptive forgetting factor online sequential extreme learning machine (AFOS-ELM) is proposed in this paper. The structure of the extreme learning machine (ELM) is determined according to the form of the state space model, and the inverse-free ELM algorithm is used to automatically select the appropriate number of hidden nodes to improve the efficiency of offline initialization. The focus of the ELM on current operation performance is enhanced by the adaptive renewed forgetting factor, which reduces the impact of aero-engine history and deviated data on the current output and improves the accuracy of the model. Then, according to the analytical equation of the ELM model, the state space model of an aero-engine at each sampling time is obtained by using the partial derivative method. The simulation results based on engine test data show that the real-time performance and accuracy of the state space model established online in this paper can meet the needs of aero-engine control system requirement.

Static and dynamic characteristics of externally pressurized gas bearing for high-speed hydrogen turbo-expander

The high-speed hydrogen turbo-expander, serves as a key component of the large-scale hydrogen liquefaction system, receives a lot of attention. Being an essential part of the turbo-expander, gas bearings are widely used for its pollution-free and low viscosity advantages. However, studies on characteristics of hydrogen bearing, especially on the static and dynamic characteristics of externally pressurized hydrogen journal bearing, have not been conducted by many. Here, the finite difference method (FDM) was employed to solve the Reynolds equation thus obtaining the pressure distribution and the static load capacity. The partial derivative method (PDM) was used to calculate the dynamic stiffness coefficients and damping coefficients of hydrogen bearing. The critical mass was used to analyze the linear instability of the hydrogen bearing-rotor system. Relationship between various design parameters, including eccentricities, orifice diameter, clearance, supply pressure and rotor speeds, and static and dynamic behaviours of hydrogen bearing has been established. The results indicate that load capacity increase and volume flow rate decrease is viable through parameter optimization. The stability of the hydrogen bearing-rotor system is evaluated quantitatively.

Predicting patient experience of Invisalign treatment: An analysis using artificial neural network

ObjectivePoor experience with Invisalign treatment affects patient compliance and, thus, treatment outcome. Knowing the potential discomfort level in advance can help orthodontists better prepare the patient to overcome the difficult stage. This study aimed to construct artificial neural networks (ANNs) to predict patient experience in the early stages of Invisalign treatment.MethodsIn total, 196 patients were enrolled. Data collection included questionnaires on pain, anxiety, and quality of life (QoL). A four-layer fully connected multilayer perception with three backpropagations was constructed to predict patient experience of the treatment. The input data comprised 17 clinical features. The partial derivative method was used to calculate the relative contributions of each input in the ANNs.ResultsThe predictive success rates for pain, anxiety, and QoL were 87.7%, 93.4%, and 92.4%, respectively. ANNs for predicting pain, anxiety, and QoL yielded areas under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons and upper incisors with attachments.ConclusionsThe constructed ANNs in this preliminary study show good accuracy in predicting patient experience (i.e., pain, anxiety, and QoL) of Invisalign treatment. Artificial intelligence system developed for predicting patient comfort has potential for clinical application to enhance patient compliance.

Prediction of nonlinear dynamic coefficients of tilting-pad journal bearings with pad perturbation

In this paper, a modified partial derivative method is developed to predict the linear and nonlinear dynamic coefficients of tilting-pad journal bearings with journal and pad perturbation. To this end, Reynolds equation and its boundary conditions along with equilibrium equations of the pad are used. Finite difference, partial derivative method, and perturbation technique have been employed simultaneously for solving these equations. The accuracy of the results is investigated by comparing the linear dynamic coefficients of three types of tilting-pad journal bearings with those published the literature. It is shown that the nonlinear dynamic coefficients depend on Sommerfeld number, eccentricity ratio, and length to diameter ratio. Similar to the case of linear dynamic coefficients of TPJB, it is observed that the eccentricity ratio effects on nonlinear dynamic coefficients are more notable when the eccentricity ratio is higher than 0.8 or less than 0.2.

Thermohydrodynamic investigation for supercritical carbon dioxide high speed tilting pad bearings considering turbulence and real gas effect

A global heat balance method for supercritical carbon dioxide (S-CO2) high speed tilting pad bearings was developed, in which the real gas effect, variable thermodynamic properties, and turbulence effect were considered. The bearing characteristics can be obtained by the partial derivative method embracing dynamic variations of complete variables. Then thermohydrodynamic lubrication mechanisms for S-CO2 bearings are studied, and results indicate that the real gas effect of thermal compressibility is significant. The influence of the thermal effect on the static and dynamic characteristics of bearings is caused by changes in density, specific heat, and thermal expansion coefficient rather than viscosity, which is totally different from oil bearings. In general, the influence of thermohydrodynamic lubrication on static characteristics of S-CO2 tilting pad bearings is more obvious than that on dynamic coefficients. For example, the maximum deviation of damping coefficients of a bearing with parameters in this research at a certain speed range is 8%, and that of the load capacity at the corresponding speed is 10.7%. However, the influence of the thermal effect on dynamic coefficients is varied at different rotating speeds.

Both climate and socioeconomic drivers contribute to vegetation greening of the Loess Plateau

Abstract Revegetation of the degraded ecosystems of the Loess Plateau (LP) of China is mainly attributed to Grain for Green (GFG) project introduced in 1999. This research aims to investigate the individual and collective impacts of climate and socioeconomic factors on variations and trends in satellite-based normalized difference vegetation index (NDVI) of the LP at the county level for the period of 2000 to 2018, and to identify the dominant driving factors for each county. The contributions of six driving factors (precipitation, temperature, solar radiation, gross domestic product, population, and livestock) are determined by applying the partial derivative equation method. Our results reveal increasing NDVI trends in 284 out of 294 counties of the LP, which are mostly dominated by the combined effect of climatic and socioeconomic factors covering 62% area of the LP. Among the six influencing factors, precipitation is the leading contributor factor to NDVI restoration in 152 counties. Our investigation found that climate variations, balanced implementation of environmental protection policies, and the economic incentives given to the local communities under the GFG program have made a remarkable contribution to vegetation greening in the LP. This research provides a theoretical reference for each county to facilitate policy-makers and local administrations to review and enhance the sustainable management of green infrastructure to solve evolving ecological challenges.

河西走廊植被净初级生产力时空变化及其影响因子研究

PDF HTML阅读 XML下载 导出引用 引用提醒 河西走廊植被净初级生产力时空变化及其影响因子研究 DOI: 10.5846/stxb202001130105 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41761083）；甘肃省自然科学基金（17JR5RA061） Temporal and spatial change of Net Primary Productivity of vegetation and its determinants in Hexi Corridor Author: Affiliation: Fund Project: National Natural Science Foundation of China;Natural Science Foundation of Gansu Province of China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:干旱半干旱区植被NPP变化对全球碳循环有重要影响，该区域NPP对气候变化的响应表现出较大的时空异质性，其驱动机制并不十分清楚。选择中国河西走廊，利用随机森林算法估算了2002-2018年的NPP，基于偏导数法计算了气候与人类活动对NPP的影响。结果表明：（1）随机森林算法能较好的适用于干旱半干旱区NPP估算。（2）2002-2018年间河西走廊年NPP的平均值为153.32 gC m-2 a-1，总量为37.468 Tg C/a，呈东南向西北递减的分布特征，研究期间NPP呈2.37 gC m-2 a-1（P=0.09）增长趋势。（3）河西走廊NPP变化52.51%由气候因子贡献，47.49%由人类活动贡献。（4）在气候变化对NPP的影响中，降水主导了该区72.21%的区域，温度对NPP变化量的贡献占73.71%，前者影响着NPP变化格局，后者主导NPP变化数量。升温和增湿均有利于该区NPP增加，随着西北地区气候暖湿化，河西走廊植被会持续改善，该研究有助于理解干旱半干旱区NPP对气候变化的响应机制，为适应气候变化政策制定提供理论依据。 Abstract:The variation of vegetation Net Primary Productivity (NPP) has an important impact on the global carbon cycle in arid and semi-arid regions. The response of NPP to climate change in this region shows great spatial and temporal heterogeneity, while its driving mechanism is not clear. In this study, the Hexi Corridor in China was selected as study area, and the NPP from 2002 to 2018 was estimated using a random forest algorithm. The effects of climate and human activities on NPP were calculated based on the partial derivative method. The results showed that (1) random forest algorithms presented good performance in the NPP estimation in arid and semi-arid areas. (2) The average annual NPP of Hexi Corridor from 2002 to 2018 was 153.32 gC m-2 a-1, and the total amount was 37.468 Tg C/a, which indicated a decreasing distribution pattern from southeast to northwest. During the study period, the NPP showed an increasing trend of 2.37 gC m-2 a-1 (P=0.09). (3) 52.51% of the NPP variation in the Hexi Corridor was contributed by climate factors, and 47.49% was contributed by human activities. (4) For the impact of climate change on the NPP, precipitation dominated 72.21% of the area, and temperature contributed 73.71% of the amount of NPP variation. The former affected the NPP change pattern and the latter dominated the amount of NPP variation. Both warming and humidification are conducive to the increase of NPP in this area. With the warm and humid climate in the Northwest China, the vegetation in the Hexi Corridor will continue to increase. This study is helpful for understanding the response mechanisms of the NPP to climate change in arid and semi-arid areas, and provides a theoretical basis for decision making in adaptation to climate change. 参考文献 相似文献 引证文献

Thermo-Elasto-Hydrodynamic Characteristics Analysis of Journal Microbearing Lubricated with Rarefied Gas.

Temperature rise and elastic deformation are unavoidable issues occurring in high-speed gas microbearings due to the dominant small-scale fluid dynamics in rarefied gas flow applications. In this paper, thermo-elasto-aerodynamic analysis requires simultaneously solving the modified Reynolds equation, modified energy equation, temperature–viscosity relationship and the elasticity equations for predicting the lubrication characteristics of microbearings. A thermo-elasto-aerodynamic lubrication is systematically investigated by using the partial derivative method, finite difference formulation and the finite element approach. The results indicate that, compared with rigid microbearing which has a constant viscosity gas lubricant, the temperature effect increases the load capacity, friction coefficient and stiffness coefficients, and it decreases the attitude angle and damping coefficients of the microbearing. The flexibility of the bearing pad also leads to the increase in load capacity and direct stiffness coefficients, while it remains to further decrease the direct damping coefficients on the basis of thermo-aerodynamic performance. The present study is conducive to accurately analyze the microscopic flow properties in a microbearing-rotor system.

Beat traveling wave principle and its verification on rotating ultrasonic motor

A beat traveling wave principle which combines beat vibration with a traveling wave is proposed. Based on the traveling wave principle and the beat vibration theory, a mathematical model is established to analyze the operation law of the beat traveling wave. Theoretical analyses and numerical simulations are used to study the amplitude variation of the beat traveling wave. Then, a spatial–temporal partial derivative method is developed to study the changing law of the beat traveling wave propagation direction. The research shows that the beat traveling wave is a waveform with periodic variation of both amplitude and propagation directions. An ultrasonic motor, as a potential application field of the beat wave traveling, is investigated deeply during the research. As a driving principle, the beat wave traveling can efficiently and conveniently realize the continuous reciprocating operation on ultrasonic motors. The beat traveling wave principle and its applicability are verified by finite element simulations and experiments. Meanwhile, a good prospect of its application is also given in this study.