No effect of invasive tree species on aboveground biomass increments of oaks and pines in temperate forests

Prunus serotina and Robinia pseudoacacia are the most widespread invasive trees in Central Europe.In addition,according to climate models,decreased growth of many economically and ecologically important native trees will likely be observed in the future.We aimed to assess the impact of these two neophytes,which differ in the biomass range and nitrogen-fixing abilities observed in Central European conditions,on the relative aboveground biomass increments of native oaks Qucrcus robur and Q.petraea and Scots pine Pinus sylvestris.We aimed to increase our understanding of the relationship between facilitation and competition between woody alien species and overstory native trees.We established 72 circular plots(0.05 ha)in two different forest habitat types and stands varying in age in western Poland.We chose plots with different abundances of the studied neophytes to determine how effects scaled along the quantitative invasion gradient.Furthermore,we collected growth cores of the studied native species,and we calculated aboveground biomass increments at the tree and stand levels.Then,we used generalized linear mixed-effects models to assess the impact of invasive species abundances on relative aboveground biomass increments of native tree species.We did not find a biologically or statistically significant impact of invasive R.pseudoacacia or P.serotina on the relative aboveground,biomass increments of native oaks and pines along the quantitative gradient of invader biomass or on the proportion of total stand biomass accounted for by invaders.The neophytes did not act as native tree growth stimulators but also did not compete with them for resources,which would escalate the negative impact of climate change on pines and oaks.The neophytes should not significantly modify the carbon sequestration capacity of the native species.Our work combines elements of the per capita effect of invasion with research on mixed forest management.

A Hybrid Optimization Approach of Single Point Incremental Sheet Forming of AISI 316L Stainless Steel Using Grey Relation Analysis Coupled with Principal Component Analysiss

We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.

Efficient,rapid and incremental extraction of bioactive compounds from the flowers of Hibiscus manihot L.

Flavonoids are the primary functional components in the flowers of Hibiscus manihot L.(HMLF).In this study,an efficient and green ionic liquid-high-speed homogenization coupled with microwave-assisted extraction(IL-HSH-MAE)technique was firstly established and implemented to extract seven target flavonoids from HMLF.Single-factor experiments and Box-Behnken design(BBD)were utilized to maximize the extraction conditions of IL-HSH-MAE,which were as follows:0.1 M of[C4mim]Br,homogenate speed of 7,000 rpm,homogenate time of 120 s,liquid/solid ratio of 24 mL/g,extraction temperature of 62℃and extraction time of 15 min.The maximal total extraction yield of seven target flavonoids attained 22.04 mg/g,which was considerably greater than the yields obtained by IL-HSH,IL-MAE,60%ethanol-HSH-MAE and 60%ethanol-MAE.These findings suggested that the IL-HSH-MAE method can be exploited as a rapid and efficient approach for extracting natural products from plants.The process also possesses outstanding superiority in being environmentally friendly and having a high extraction efficiency and is expected to be a luciferous prospect extraction technology.

Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering analysis.To address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering analysis.Firstly,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing efficiency.Then,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary results.When new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in data.Finally,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic data.Through experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most scenarios.Therefore,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering techniques.It not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through experiments.This method shows great potential in real-world applications where dynamic data growth is common,and it is of significant importance for advancing the development of data analysis technology.

DR-IS:Dynamic Response Incremental Scheduling in Time-Sensitive Network

Time-Sensitive Network(TSN)with deterministic transmission capability is increasingly used in many emerging fields.It mainly guarantees the Quality of Service(QoS)of applications with strict requirements on time and security.One of the core features of TSN is traffic scheduling with bounded low delay in the network.However,traffic scheduling schemes in TSN are usually synthesized offline and lack dynamism.To implement incremental scheduling of newly arrived traffic in TSN,we propose a Dynamic Response Incremental Scheduling(DR-IS)method for time-sensitive traffic and deploy it on a software-defined time-sensitive network architecture.Under the premise of meeting the traffic scheduling requirements,we adopt two modes,traffic shift and traffic exchange,to dynamically adjust the time slot injection position of the traffic in the original scheme,and determine the sending offset time of the new timesensitive traffic to minimize the global traffic transmission jitter.The evaluation results show that DRIS method can effectively control the large increase of traffic transmission jitter in incremental scheduling without affecting the transmission delay,thus realizing the dynamic incremental scheduling of time-sensitive traffic in TSN.

Effect of external magnetic field on the instability of THz plasma waves in nanoscale graphene field-effect transistors

The instability of plasma waves in the channel of field-effect transistors will cause the electromagnetic waves with THz frequency.Based on a self-consistent quantum hydrodynamic model,the instability of THz plasmas waves in the channel of graphene field-effect transistors has been investigated with external magnetic field and quantum effects.We analyzed the influence of weak magnetic fields,quantum effects,device size,and temperature on the instability of plasma waves under asymmetric boundary conditions numerically.The results show that the magnetic fields,quantum effects,and the thickness of the dielectric layer between the gate and the channel can increase the radiation frequency.Additionally,we observed that increase in temperature leads to a decrease in both oscillation frequency and instability increment.The numerical results and accompanying images obtained from our simulations provide support for the above conclusions.

Effects of baffle position in serpentine flow channel on the performance of proton exchange membrane fuel cells

This study used a three-dimensional numerical model of a proton exchange membrane fuel cell with five types of channels:a smooth channel(Case 1);eight rectangular baffles were arranged in the upstream(Case 2),midstream(Case 3),downstream(Case 4),and the entire cathode flow channel(Case 5)to study the effects of baffle position on mass transport,power density,net power,etc.Moreover,the effects of back pressure and humidity on the voltage were investigated.Results showed that compared to smooth channels,the oxygen and water transport facilitation at the diffusion layer-channel interface were added 11.53%-20.60%and 7.81%-9.80%at 1.68 A·cm^(-2)by adding baffles.The closer the baffles were to upstream,the higher the total oxygen flux,but the lower the flux uniformity the worse the water removal.The oxygen flux of upstream baffles was 8.14%higher than that of downstream baffles,but oxygen flux uniformity decreased by 18.96%at 1.68 A·cm^(-2).The order of water removal and voltage improvement was Case 4>Case 5>Case 3>Case 2>Case 1.Net power of Case 4 was 9.87%higher than that of the smooth channel.To the Case 4,when the cell worked under low back pressure or high humidity,the voltage increments were higher.The potential increment for the back pressure of 0 atm was 0.9%higher than that of 2 atm(1 atm=101.325 kPa).The potential increment for the humidity of 100%was 7.89%higher than that of 50%.

Improving Network Availability through Optimized Multipath Routing and Incremental Deployment Strategies

Currently,distributed routing protocols are constrained by offering a single path between any pair of nodes,thereby limiting the potential throughput and overall network performance.This approach not only restricts the flow of data but also makes the network susceptible to failures in case the primary path is disrupted.In contrast,routing protocols that leverage multiple paths within the network offer a more resilient and efficient solution.Multipath routing,as a fundamental concept,surpasses the limitations of traditional shortest path first protocols.It not only redirects traffic to unused resources,effectively mitigating network congestion,but also ensures load balancing across the network.This optimization significantly improves network utilization and boosts the overall performance,making it a widely recognized efficient method for enhancing network reliability.To further strengthen network resilience against failures,we introduce a routing scheme known as Multiple Nodes with at least Two Choices(MNTC).This innovative approach aims to significantly enhance network availability by providing each node with at least two routing choices.By doing so,it not only reduces the dependency on a single path but also creates redundant paths that can be utilized in case of failures,thereby enhancing the overall resilience of the network.To ensure the optimal placement of nodes,we propose three incremental deployment algorithms.These algorithms carefully select the most suitable set of nodes for deployment,taking into account various factors such as node connectivity,traffic patterns,and network topology.By deployingMNTCon a carefully chosen set of nodes,we can significantly enhance network reliability without the need for a complete overhaul of the existing infrastructure.We have conducted extensive evaluations of MNTC in diverse topological spaces,demonstrating its effectiveness in maintaining high network availability with minimal path stretch.The results are impressive,showing that even when implemented on just 60%of nodes,our incremental deployment method significantly boosts network availability.This underscores the potential of MNTC in enhancing network resilience and performance,making it a viable solution for modern networks facing increasing demands and complexities.The algorithms OSPF,TBFH,DC and LFC perform fast rerouting based on strict conditions,while MNTC is not restricted by these conditions.In five real network topologies,the average network availability ofMNTCis improved by 14.68%,6.28%,4.76%and 2.84%,respectively,compared with OSPF,TBFH,DC and LFC.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

Hyperspectral Image Super-Resolution Network Based on Reinforcing Inter-Spectral Incremental Information

Hyperspectral images typically have high spectral resolution but low spatial resolution,which impacts the reliability and accuracy of subsequent applications,for example,remote sensingclassification and mineral identification.But in traditional methods via deep convolution neural net-works,indiscriminately extracting and fusing spectral and spatial features makes it challenging toutilize the differentiated information across adjacent spectral channels.Thus,we proposed a multi-branch interleaved iterative upsampling hyperspectral image super-resolution reconstruction net-work(MIIUSR)to address the above problems.We reinforce spatial feature extraction by integrat-ing detailed features from different receptive fields across adjacent channels.Furthermore,we pro-pose an interleaved iterative upsampling process during the reconstruction stage,which progres-sively fuses incremental information among adjacent frequency bands.Additionally,we add twoparallel three dimensional(3D)feature extraction branches to the backbone network to extractspectral and spatial features of varying granularity.We further enhance the backbone network’sconstruction results by leveraging the difference between two dimensional(2D)channel-groupingspatial features and 3D multi-granularity features.The results obtained by applying the proposednetwork model to the CAVE test set show that,at a scaling factor of×4,the peak signal to noiseratio,spectral angle mapping,and structural similarity are 37.310 dB,3.525 and 0.9438,respec-tively.Besides,extensive experiments conducted on the Harvard and Foster datasets demonstratethe superior potential of the proposed model in hyperspectral super-resolution reconstruction.

CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction

To improve the prediction accuracy of chaotic time series and reconstruct a more reasonable phase space structure of the prediction network,we propose a convolutional neural network-long short-term memory(CNN-LSTM)prediction model based on the incremental attention mechanism.Firstly,a traversal search is conducted through the traversal layer for finite parameters in the phase space.Then,an incremental attention layer is utilized for parameter judgment based on the dimension weight criteria(DWC).The phase space parameters that best meet DWC are selected and fed into the input layer.Finally,the constructed CNN-LSTM network extracts spatio-temporal features and provides the final prediction results.The model is verified using Logistic,Lorenz,and sunspot chaotic time series,and the performance is compared from the two dimensions of prediction accuracy and network phase space structure.Additionally,the CNN-LSTM network based on incremental attention is compared with long short-term memory(LSTM),convolutional neural network(CNN),recurrent neural network(RNN),and support vector regression(SVR)for prediction accuracy.The experiment results indicate that the proposed composite network model possesses enhanced capability in extracting temporal features and achieves higher prediction accuracy.Also,the algorithm to estimate the phase space parameter is compared with the traditional CAO,false nearest neighbor,and C-C,three typical methods for determining the chaotic phase space parameters.The experiments reveal that the phase space parameter estimation algorithm based on the incremental attention mechanism is superior in prediction accuracy compared with the traditional phase space reconstruction method in five networks,including CNN-LSTM,LSTM,CNN,RNN,and SVR.

Evolution of mechanical parameters of Shuangjiangkou granite under different loading cycles and stress paths

Surrounding rocks at different locations are generally subjected to different stress paths during the process of deep hard rock excavation.In this study,to reveal the mechanical parameters of deep surrounding rock under different stress paths,a new cyclic loading and unloading test method for controlled true triaxial loading and unloading and principal stress direction interchange was proposed,and the evolution of mechanical parameters of Shuangjiangkou granite under different stress paths was studied,including the deformation modulus,elastic deformation increment ratios,fracture degree,cohesion and internal friction angle.Additionally,stress path coefficient was defined to characterize different stress paths,and the functional relationships among the stress path coefficient,rock fracture degree difference coefficient,cohesion and internal friction angle were obtained.The results show that during the true triaxial cyclic loading and unloading process,the deformation modulus and cohesion gradually decrease,while the internal friction angle gradually increases with increasing equivalent crack strain.The stress path coefficient is exponentially related to the rock fracture degree difference coefficient.As the stress path coefficient increases,the degrees of cohesion weakening and internal friction angle strengthening decrease linearly.During cyclic loading and unloading under true triaxial principal stress direction interchange,the direction of crack development changes,and the deformation modulus increases,while the cohesion and internal friction angle decrease slightly,indicating that the principal stress direction interchange has a strengthening effect on the surrounding rocks.Finally,the influences of the principal stress interchange direction on the stabilities of deep engineering excavation projects are discussed.

Efficacy of a Diabetes Specific Nutritional Supplement (DSNS) on Glycemic Response in Prediabetic Adults: A Two-Armed, Open-Labelled Randomized Controlled Study

It is well known that Diabetes Specific Nutritional Supplements (DSNSs) are linked to improved glycemic control in individuals with diabetes. However, data on efficacy of DSNSs in prediabetics is limited. This was a two-armed, open-labelled, randomized controlled six-week study on 199 prediabetics [30 - 65 years;Glycosylated Hemoglobin (HbA1c) 5.7% - 6.4% and/or Fasting Blood Glucose (FBG) 100-125 mg/dl]. Two parallel phases were conducted: Acute Blood Glucose Response (ABGR) and Intervention phase. Prediabetic participants were randomized into test (n = 100) and control (n = 99). The primary objective was to assess the ABGR of DSNS versus an isocaloric snack, measured by incremental Area under the Curve (iAUC). Test and control received 60 g of DSNS and 56 g of isocaloric snack (cornflakes) respectively, both in 250 ml double-toned milk on visit days 1, 15, 29 and 43. Postprandial Blood Glucose (PPG) was estimated at 30, 60, 90, 120, 150 and 180 minutes. During the 4 weeks intervention phase, the test group received DSNS with lifestyle counselling (DSNS + LC) and was compared with the control receiving lifestyle counselling alone (LC alone). Impact was studied on FBG, HbA1C, anthropometry, body composition, blood pressure, nutrient intake, and physical activity. The impact of DSNS was also studied using CGM between two 14-day phases: CGM1 baseline (days 1 - 14) and CGM2 endline (days 28 - 42). DSNS showed significantly lower PPG versus isocaloric snack at 30 (p 12, and chromium were reported by DSNS + LC versus LC alone. No other significant changes were reported between groups. It may be concluded that DSNS may be considered as a snack for prediabetic or hyperglycemic individuals requiring nutritional support for improved glycemic control.

Effect of Different Nitrogen Application Amount on the Net Increment of Fresh Dry Weight in Hybrid Rape

[ Objective] The aim of the research was to provide reference for reasonable application of nitrogen fertilizer for high yield.cultivation of hybrid rape cuhivar Youyan 9 and Youyan 10. [ Method] The net increment changes of individual plant fresh weight and dry matter weight of Youyan 9 and Youyan 10 with different nitrogen application treatments were studied. [ Result] The differences among average fresh weight increments of individual plant and average dry matter weight increment of individual plant with different treatments reached 0. 01 extremely significant level. Fresh weight increment and dry matter weight net increment of individual plant declined gradually with the increase of nitrogen application. In growtheourse ,fresh weight net increment of individual plant increased firstly then decreased and the maximum was in beginning flowering stage, besides that dry matter net increment increased gradually and the maximum was in mature period. The correlations among fresh net increment, dry matter weight net increment and yield net increment were positive or extremely positive. [ Conclusion] Under experimental condition, when nitrogen application was 225 kg/hm^2, hybrid rape Yanyou 9 and Yanyou 10 with low erucic,low glucosinolate could obtain high yield.

Correlation of eco-hydrographic benefit and height increment of Robinia pseudoacacia stand with climatic environmental factors in Yellow River Delta Wetland of China

The relationship between eco-hydrographic benefit of forest vegetation and climatic environmental factors is one of the focuses in the research on environmental protection and ecosystem countermeasures in Wetland. The runoff, sediment and soil moisture rate dynamics in Robinia pseudoacacia plantation and its clearcut area were investigated in the natural runoff experiment plots in Yellow River Delta Wet- land, Shandong Province, China. The correlation of height increment ofR. pseudoacacia with nine climate factors such as light, water, heat, etc. was analyzed by stepwise regression analysis. The results showed that the amounts of runoff and sediment in clearcut area of R. pseudoacacia were 53.9%-150.8% and 172.8%-387.1% higher than that in Robinia pseudoacacia plantation, respectively. The runoff peak value in R. pseudoacacia stand was obviously lower than that in clerarcut area, meantime, the occurrence of runoffpeak in R. pseudoacacia stand was 25 min later than in its clerarcut area. The soil moisture rates in R. pseudoacacia stand and its clearcut varied periodically with annual rainfall precipitation in both dry season and humid season. The annual mean soil moisture rate in R. pseudoacacia stand was 23.3%-25.6% higher than that in its clearcut area. Meanwhile, a regression model reflecting the correlation between the height increment of R. pseudoacacia and climatic factors was developed by stepwise regression procedure method. It showed that the light was the most important factor for the height increment ofR. pseudoacacia, followed by water and heat factors.

Effects of climate factors on the height increment of poplar protec-tion forest in the riverbank field

Based on the data of stand investigation and stem analysis, the effects of climate factors on the poplar protection forest increment in the riverbank field of the Dalinghe and Xiaolinghe rivers of Liaoning Province, China were studied by step-wise regression procedure and grey system theories and methods. A regression model reflecting the correlation between the height increment of poplar protection forest and climatic factors was developed. The order of grey relevance for the effect of climatic factors on the height increment of poplar protection forest is: light>water>heat, and it could be interpreted that the poplar increment was mainly influenced by light factor, water factor, and heat factor. This result will provide scientific basis for the in-tensive cultivation and regeneration of the poplar protection forest in riverbank field in similar regions in China.

Spatial and Temporal Characteristics of Rice Potential Productivity and Potential Yield Increment in Main Production Regions of China

The vast area and marked variation of China make it difficult to predict the impact of climate changes on rice productivity in different regions.Therefore,analyzing the spatial and temporal characteristics of rice potential productivity and predicting the possible yield increment in main rice production regions of China is important for guiding rice production and ensuring food security.Using meteorological data of main rice production regions from 1961 to 1970（the 1960s） and from 1996 to 2005（the 2000s） provided by 333 stations,the potential photosynthetic,photo-thermal and climatic productivities in rice crop of the 1960s and 2000s in main rice production regions of China were predicted,and differences in the spatial and temporal distribution characteristics between two decades were analyzed.Additionally,the potential yield increment based on the high yield target and actual yield of rice in the 2000s were predicted.Compared with the 1960s,the potential photosynthetic productivity of the 2000s was seen to have decreased by 5.40%,with rates in northeastern and southwestern China found to be lower than those in central and southern China.The potential photo-thermal productivity was generally seen to decrease（2.56%） throughout main rice production regions,decreasing most in central and southern China.However,an increase was seen in northeastern and southwestern China.The potential climatic productivity was observed to be lower（7.44%） in the 2000s compared to the 1960s,but increased in parts of central and southern China.The potential yield increment from the actual yield to high yield target in the 2000s were no more than 6×103 kg ha-1 and ranged from 6×103 to 12×103 kg ha-1 in most of the single-and double-cropping rice growing regions,respectively.The yield increasing potential from the high yield target to the potential photo-thermal productivity in 2000s were less than 10×103 kg ha-1 and ranged from 10×103 to 30×103 kg ha-1 in most of the single-and double-cropping rice growing regions,respectively.The potential yield increment contributed by irrigation was between 5×103 and 20×103 kg ha-1,and between 20×103 and 40×103 kg ha-1 in most of the single-and double-cropping rice growing regions,respectively.These findings suggested that the high yield could be optimized by making full use of climatic resources and through a reasonable management plan in rice crop.

Event-based incremental updating of spatio-temporal database

Based on the relationship among the geographic events, spatial changes and the database operations, a new automatic (semi-automatic) incremental updating approach of spatio-temporal database (STDB) named as (event-based) incremental updating (E-BIU) is proposed in this paper. At first, the relationship among the events, spatial changes and the database operations is analyzed, then a total architecture of E-BIU implementation is designed, which includes an event queue, three managers and two sets of rules, each component is presented in detail. The process of the E-BIU of master STDB is described successively. An example of building’s incremental updating is given to illustrate this approach at the end. The result shows that E-BIU is an efficient automatic updating approach for master STDB.