Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Effect of Product Differemiation on Business Model Performance in B2B Market

Focusing on B2B market, this paper studies the effect of product differentiation on business model perlbrmance. After interviewing several experts and analyzing numbers of distributing questionnaires. the author discovered that product differentiation has a positive effect on business model performance. Through SEM. it is revealed that product differentiation at each level have positive effect on business model, with different effect weight. The main contributions of this research are summarized as follows： Firstly, the proposed model of the product differentiation on business model performance lays the basis for scientific measurement of product differentiation and its effect on business model performance; Secondly, this research ranks the importance of product differentiation at different level in B2B market; Finally, this research provides a good reference for enterprises to update their product strategy for better business model performance.

Python Server Page Performance Analysis and Modeling

Today, in the field of computer networks, new services have been developed on the Internet or intranets, including the mail server, database management, sounds, videos and the web server itself Apache. The number of solutions for this server is therefore growing continuously, these services are becoming more and more complex and expensive, without being able to fulfill the needs of the users. The absence of benchmarks for websites with dynamic content is the major obstacle to research in this area. These users place high demands on the speed of access to information on the Internet. This is why the performance of the web server is critically important. Several factors influence performance, such as server execution speed, network saturation on the internet or intranet, increased response time, and throughputs. By measuring these factors, we propose a performance evaluation strategy for servers that allows us to determine the actual performance of different servers in terms of user satisfaction. Furthermore, we identified performance characteristics such as throughput, resource utilization, and response time of a system through measurement and modeling by simulation. Finally, we present a simple queue model of an Apache web server, which reasonably represents the behavior of a saturated web server using the Simulink model in Matlab (Matrix Laboratory) and also incorporates sporadic incoming traffic. We obtain server performance metrics such as average response time and throughput through simulations. Compared to other models, our model is conceptually straightforward. The model has been validated through measurements and simulations during the tests that we conducted.

Dominant woody plant species recognition with a hierarchical model based on multimodal geospatial data for subtropical forests

Since the launch of the Google Earth Engine(GEE)cloud platform in 2010,it has been widely used,leading to a wealth of valuable information.However,the potential of GEE for forest resource management has not been fully exploited.To extract dominant woody plant species,GEE combined Sen-tinel-1(S1)and Sentinel-2(S2)data with the addition of the National Forest Resources Inventory(NFRI)and topographic data,resulting in a 10 m resolution multimodal geospatial dataset for subtropical forests in southeast China.Spectral and texture features,red-edge bands,and vegetation indices of S1 and S2 data were computed.A hierarchical model obtained information on forest distribution and area and the dominant woody plant species.The results suggest that combining data sources from the S1 winter and S2 yearly ranges enhances accuracy in forest distribution and area extraction compared to using either data source independently.Similarly,for dominant woody species recognition,using S1 winter and S2 data across all four seasons was accurate.Including terrain factors and removing spatial correlation from NFRI sample points further improved the recognition accuracy.The optimal forest extraction achieved an overall accuracy(OA)of 97.4%and a maplevel image classification efficacy(MICE)of 96.7%.OA and MICE were 83.6%and 80.7%for dominant species extraction,respectively.The high accuracy and efficacy values indicate that the hierarchical recognition model based on multimodal remote sensing data performed extremely well for extracting information about dominant woody plant species.Visualizing the results using the GEE application allows for an intuitive display of forest and species distribution,offering significant convenience for forest resource monitoring.

Framework to Model User Request Access Patterns in the World Wide Web

In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.

The Performance of Atmospheric Component Model R42L9 of GOALS/LASG

This paper examines the performance of an atmospheric general circulation model (AGCM) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics (LASG/IAP). It is a spectral model truncated at R42(2.8125°long×1.66°lat) resolution and with nine vertical levels, and referred to as R42L9/LASG hereafter. It is also the new version of atmospheric component model R15L9 of the global ocean-atmosphere-land system (GOALS/LASG). A 40-year simulation in which the model is forced with the climatological monthly mean sea surface temperature is compared with the 40-year (1958-97) U.S. National Center for Environmental Prediction (NGEP) global reanalysis and the 22-year (1979-2000) Xie-Arkin monthly precipitation climatology. The mean DJF and JJA geographical distributions of precipitation, sea level pressure, 500-hPa geopotential height, 850-hPa and 200-hPa zonal wind, and other fields averaged for the last 30-year integration of the R42L9 model are analyzed. Results show that the model reproduces well the observed basic patterns, particularly precipitation over the East Asian region. Comparing the new model with R15L9/LASG, the old version with coarse resolution (nearly 7.5°long×4.5°lat), shows an obvious improvement in the simulation of regional climate, especially precipitation. The weaknesses in simulation and future improvements of the model are also discussed.

Multi-Model Ensemble Projection of Precipitation Changes over China under Global Warming of 1.5 and 2℃ with Consideration of Model Performance and Independence

A weighting scheme jointly considering model performance and independence(PI-based weighting scheme) is employed to deal with multi-model ensemble prediction of precipitation over China from 17 global climate models. Four precipitation metrics on mean and extremes are used to evaluate the model performance and independence. The PIbased scheme is also compared with a rank-based weighting scheme and the simple arithmetic mean(AM) scheme. It is shown that the PI-based scheme achieves notable improvements in western China, with biases decreasing for all parameters. However, improvements are small and almost insignificant in eastern China. After calibration and validation, the scheme is used for future precipitation projection under the 1.5 and 2℃ global warming targets(above preindustrial level). There is a general tendency to wetness for most regions in China, especially in terms of extreme precipitation. The PI scheme shows larger inhomogeneity in spatial distribution. For the total precipitation PRCPTOT(95 th percentile extreme precipitation R95 P), the land fraction for a change larger than 10%(20%) is 22.8%(53.4%)in PI, while 13.3%(36.8%) in AM, under 2℃ global warming. Most noticeable increase exists in central and east parts of western China.

Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments （data are non-continuous and fragmental in some catchments）, we used non-continuous calibration periods for more independent streamflow data for SIMHYD （simple hydrology） model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization （PSO） method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

A data assimilation-based forecast model of outer radiation belt electron fluxes

Because radiation belt electrons can pose a potential threat to the safety of satellites orbiting in space,it is of great importance to develop a reliable model that can predict the highly dynamic variations in outer radiation belt electron fluxes.In the present study,we develop a forecast model of radiation belt electron fluxes based on the data assimilation method,in terms of Van Allen Probe measurements combined with three-dimensional radiation belt numerical simulations.Our forecast model can cover the entire outer radiation belt with a high temporal resolution(1 hour)and a spatial resolution of 0.25 L over a wide range of both electron energy(0.1-5.0 MeV)and pitch angle(5°-90°).On the basis of this model,we forecast hourly electron fluxes for the next 1,2,and 3 days during an intense geomagnetic storm and evaluate the corresponding prediction performance.Our model can reasonably predict the stormtime evolution of radiation belt electrons with high prediction efficiency(up to~0.8-1).The best prediction performance is found for~0.3-3 MeV electrons at L=~3.25-4.5,which extends to higher L and lower energies with increasing pitch angle.Our results demonstrate that the forecast model developed can be a powerful tool to predict the spatiotemporal changes in outer radiation belt electron fluxes,and the model has both scientific significance and practical implications.

Application of the Bayesian Statistical Approach to Develop a Stone Mastic Asphalt(SMA)Pavement Performance Model

Stone mastic asphalt(SMA)has not been widely used in the pavement industry,and there are no detailed design specifications for this type of asphalt.Therefore,long-term behavior properties of this pavement type are not accessible widely,and no model has been established for SMA regarding its performance.The main purpose of this study was to incorporate expert experience(using the Markov-chain process)and data from field experiments to propose a model for SMA performance using the Bayesian approach.The implementation of these sources resulted in a well-organized method to develop a performance model for SMA pavements,which did not have a long-term data.Finally,a linear performance model was established to calculate the SMA service life.The service life of SMA can be predicted explicitly according to the developed performance model which has been validated using a new set of data.

Dynamic model and performance analysis of landing buffer for bionic locust mechanism

The landing buffer is an important problem in the research on bionic locust jumping robots, and the different modes of landing and buffering can affect the dynamic performance of the buffering process significantly. Based on an experimental observation, the different modes of landing and buffering are determined, which include the different numbers of landing legs and different motion modes of legs in the buffering process. Then a bionic locust mechanism is established, and the springs are used to replace the leg muscles to achieve a buffering effect. To reveal the dynamic performance in the buffering process of the bionic locust mechanism, a dynamic model is established with different modes of landing and buffering. In particular, to analyze the buffering process conveniently, an equivalent vibration dynamic model of the bionic locust mechanism is proposed.Given the support forces of the ground to the leg links, which can be obtained from the dynamic model, the spring forces of the legs and the impact resistance of each leg are the important parameters affecting buffering performance, and evaluation principles for buffering performance are proposed according to the aforementioned parameters. Based on the dynamic model and these evaluation principles, the buffering performances are analyzed and compared in different modes of landing and buffering on a horizontal plane and an inclined plane. The results show that the mechanism with the ends of the legs sliding can obtain a better dynamic performance. This study offers primary theories for buffering dynamics and an evaluation of landing buffer performance,and it establishes a theoretical basis for studies and engineering applications.

Performance analysis model for real-time Ethernet-based computer numerical control system

In order to optimize the embedded system implementation for Ethernet-based computer numerical control （CNC） system, it is very necessary to establish the performance analysis model and further adopt the codesign method from the control, communication and computing perspectives. On the basis of analyzing real-time Ethemet, system architecture, time characteristic parameters of control-loop ere, a performance analysis model for real-time Ethemet-based CNC system was proposed, which is able to include the timing effects caused by the implementation platform in the simulation. The key for establishing the model is accomplished by designing the error analysis module and the controller nodes. Under the restraint of CPU resource and communication bandwidth, the experiment with a case study was conducted, and the results show that if the deadline miss ratio of data packets is 0.2%, then the percentage error is 1.105%. The proposed model can be used at several stages of CNC system development.

Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

Performance Analytical Model of IEEE 802.11 Distributed Coordination Function

IEEE 802.11 distributed coordination function （DCF） is a distributed medium access scheme based on carrier sense multiple access with collision avoidance （CSMA/CA） protocol. Many literatures have analyzed the performance of IEEE802.11 DCF. However, such literatures either used simulation methods or built the analytical models under the assumption that the saturation condition was satisfied. To overcome such a problem, in this paper, a hi-dimensional Markovian model has been introduced to depict the DCF mechanism. The proposed model introduced an idle stage and a discrete time M/G/1 queue to deduce the channel throughput under finite load traffic. Simulation results proved the accuracy of the proposed model.

PRF: a process-RAM-feedback performance model to reveal bottlenecks and propose optimizations

Performance models provide insightful perspectives to predict performance and to propose optimization guidance.Although there has been much researches,pinpointing bottlenecks of various memory access patterns and reaching high accurate prediction of both regular and irregular programs on various hardware configurations are still not trivial.This work proposes a novel model called process-RAM-feedback(PRF)to quantify the overhead of computation and data transmission time on general-purpose multi-core processors.The PRF model predicts the cost of instruction for singlecore by a directed acyclic graph(DAG)and the transmission time of memory access between each memory hierarchy through a newly designed cache simulator.By using performance modeling and feedback optimization method,this paper uses PRF model to analyze and optimize convolution,sparse matrix-vector multiplication and sn-sweep as case study for covering with typical regular kernel to irregular and data dependence.Through the PRF model,it obtains optimization guidance with various sparsity structures,algorithm designs,and instruction sets support on different data sizes.

Performance modeling of positive degraded task-pair with helper-thread in CMP

Helper-thread of a task can hide the memory access time of irregular data on the chip muhi-core processor （CMP）. For constructing a compiler that effectively supports the helper-thread of a task in the multi-core scenario based on the last level shared cache, this paper studies its performance stable condi- tions. Unfortunately, there is no existing model that allows extensive investigation of the impact of stable conditions, we present the base of pre-computation that is formalized by our degraded task-pair 〈 T, T＇ 〉 with the helper-thread, and its stable conditions are analyzed. Finally, a novel performance model and a constructing method of pre-computation based on our positive degraded task-pair are proposed. The efficient results are shown by our experiments. If we further exploit memory level parallelism （MLP） for our task-pair, the task-pair 〈 T, T＇ 〉 can reach better performance.

Applying Analytical Hierarchy Process to a WiMAX Performance Evaluation Model

Evaluating performance of individual features of WiMAX technology is a topic of widespread discussion. Currently, there is no quantitative way of measuring WiMAX technology so that wireless operators can meet their design objectives. This paper outlines a set of design criteria for WiMAX and provides a decision-making aid that ranks the importance of criteria using Analytic Hierarchy Process （AHP）. This ranking should sufficiently reflect market expectations of the relative importance of various design criteria. A model integrating AHP priorities with enhanced Data Envelopment Analysis （DEA） is the basis for formulating a technological value in simple, comparable format. A case study is provided to show how this technological value is used to evaluate a three year network deployment plan. In the future, this model could be extended to WiMAX equipment suppliers for the purpose of validating performance targets of individual criteria, and enhancing supplier roadmaps for future network development.

On the E-Valuation of Certain E-Business Strategies on Firm Performance by Adaptive Algorithmic Modeling： An Alternative Strategic Managerial Approach

This paper describes an innovative adaptive algorithmic modeling approach, for solving a wide class of e-business and strategic management problems under uncertainty conditions. The proposed methodology is based on basic ideas and concepts of four key-field interrelated sciences, i.e., computing science, applied mathematics, management sciences and economic sciences. Furthermore, the fundamental scientific concepts of adaptability and uncertainty are shown to play a critical role of major importance for a （near） optimum solution of a class of complex e-business/services and strategic management problems. Two characteristic case studies, namely measuring e-business performance under certain environmental pressures and organizational constraints and describing the relationships between technology, innovation and firm performance, are considered as effective applications of the proposed adaptive algorithmic modeling approach. A theoretical time-dependent model for the evaluation of firm e-business performances is also proposed.

New Thoughts on the Construction and Transition of the Balanced Score Card Model of the Performance Value to Chinese Leaders of the Party and Government

Applying the strategic performance method of the balanced score card and the new thinking, the text structures the balanced score card theoretical model Party and government, further investigates and sets up of performance evaluation to the Chinese leaders of the the applied function model and appraisal model of the balanced score card theoretical model of performance evaluation to the Chinese leaders of the Party and government. The foundation and implementation of these kinds of models can improve its competence and reign power; will promote the whole performance development of the Chinese Party and government organs, it will create the enormous economic benefits and social benefits.

Differences between CMIP6 and CMIP5 Models in Simulating Climate over China and the East Asian Monsoon

We compare the ability of coupled global climate models from the phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6,respectively)in simulating the temperature and precipitation climatology and interannual variability over China for the period 1961–2005 and the climatological East Asian monsoon for the period 1979–2005.All 92 models are able to simulate the geographical distribution of the above variables reasonably well.Compared with earlier CMIP5 models,current CMIP6 models have nationally weaker cold biases,a similar nationwide overestimation of precipitation and a weaker underestimation of the southeast–northwest precipitation gradient,a comparable overestimation of the spatial variability of the interannual variability,and a similar underestimation of the strength of winter monsoon over northern Asia.Pairwise comparison indicates that models have improved from CMIP5 to CMIP6 for climatological temperature and precipitation and winter monsoon but display little improvement for the interannual temperature and precipitation variability and summer monsoon.The ability of models relates to their horizontal resolutions in certain aspects.Both the multi-model arithmetic mean and median display similar skills and outperform most of the individual models in all considered aspects.