Ensemble Simulation of Land Evapotranspiration in China Based on a Multi-Forcing and Multi-Model Approach

In order to reduce the uncertainty of offline land surface model （LSM） simulations of land evapotranspiration （ET）, we used ensemble simulations based on three meteorological forcing datasets [Princeton, ITPCAS （Institute of Tibetan Plateau Research, Chinese Academy of Sciences）, Qian] and four LSMs （BATS, VIC, CLM3.0 and CLM3.5）, to explore the trends and spatiotemporal characteristics of ET, as well as the spatiotemporal pattern of ET in response to climate factors over China's Mainland during 1982-2007. The results showed that various simulations of each member and their arithmetic mean （EnsAVlean） could capture the spatial distribution and seasonal pattern of ET sufficiently well, where they exhibited more significant spatial and seasonal variation in the ET compared with observation-based ET estimates （Obs_MTE）. For the mean annual ET, we found that the BATS forced by Princeton forcing overestimated the annual mean ET compared with Obs_MTE for most of the basins in China, whereas the VIC forced by Princeton forcing showed underestimations. By contrast, the Ens_Mean was closer to Obs_MTE, although the results were underestimated over Southeast China. Furthermore, both the Obs_MTE and Ens_Mean exhibited a significant increasing trend during 1982-98; whereas after 1998, when the last big EI Nifio event occurred, the Ens_Mean tended to decrease significantly between 1999 and 2007, although the change was not significant for Obs_MTE. Changes in air temperature and shortwave radiation played key roles in the long-term variation in ET over the humid area of China, but precipitation mainly controlled the long-term variation in ET in arid and semi-arid areas of China.

Nucleon Spin Content in a Relativistic Quark Potential Model Approach

Based on a relativistic quark model approach with an effective potential U(r) = (ac/2)(1 + γ0)r2, the spin content of the nucleon is investigated. Pseudo-scalar interaction between quarks and Goldstone bosons is employed to calculate the couplings between the Goldstone bosons and the nucleon. Different approaches to deal with the center of mass correction in the relativistic quark potential model approach are discussed.

Computer Assisted Alerts Using Mental Model Approach for Customer Service Improvement

Warning alerts are specially designed to protect user rights and safety to avoid serious damage caused by overlooking the essence of warning alerts. Today’s world of Information Communication Technology (ICT) needs improvement and to review the decisions of security experts in terms of improving warning designs and dialogues, and timely inform the authorities to take quick action at the right time and choice. Human behaviour is deeply involved in most of the security failures and its poor response. If we are able to check and monitor human behaviour in any organisation, we can achieve quality assurance and provide best services to our customers. We have arranged a study in the Center of Post Graduate Studies, International Islamic University, Malaysia (CPS-IIUM), department of Hajj Services-Makkah, and Hospital Management System-Makkah comprised of Observation, Interviews, Questionnaire and discussion based on organizational structure and job activities of people involved in different scenarios and positions under one umbrella of organizational objectives in order to trap the human error in order to take rapid action and response from the management team. Human behaviour is deeply observed and checked while performing different job activities in order to identify the serious errors at the right time during job performance at various levels. We have applied the concept of Brahm’s Language for the simulation of human behaviour which proves an opportunity to simulate human behaviour while performing job activities. Customer service can be improved easily if necessary measures and decisions are taken at the right time and place in any organisation.

Possible Modular Structure of Matter Based on the “YY Model” Approach —An Overview of the Construction Rules for Quarks and Atomic Nuclei with Configurative Examples for Neutron, Proton, Deuteron and Dineutron

The newly developed YY model contains a set of constitutive rules to describe the structures of atomic nuclei and subatomic particles, by using two elementary sub-quark particles, the Yin and Yang fermions of charge 1/3 forming all the particles of the Standard Model. This model suggests a modular structure of the universe, in which two elementary constituents recursively form all the matter. The advantage of this hypothesis is that it provides a total symmetry and a noticeably clear conceptual understanding. Moreover, it justifies the cosmological formation of a limited number of atoms, e.g., H and Li with their isotopes, considering that matter can be produced as a free agglomerate of semi-stable neutrons, which would lead to the feeding of baryonic matter in the universe. In this current article, some further theoretical aspects are proposed as an evolution of the YY model. They cover correlation paths between interacting quarks, the considerations of color forces between yin-yang elementary elements. Moreover, an agreement of the YY model with the Teplov approach based on harmonic quarks and oscillators is established, and the mass of Yin and Yang is considered. Two example nuclei are used for the analysis: a radioactively stable deuteron (containing a neutron and a proton) and a possible semi-stable dineutron (roughly “consisting of two neutrons”), which is purely theoretical, represent a very natural and legal nuclear state within YY model. Based on the results obtained here, some indications are given for a possible simple experimental verification providing proof for the stability or instability of the dineutron.

Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on （1） a screening method （Morris） for qualitative ranking of parameters, and （2） a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model （RSMSobol）. First, the Morris screening method was used to qualitatively identify the parameters＇ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model （RSM） were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

Thermoviscoelastic Modeling Approach for Predicting the Recovery Behaviors of Thermally Activated Amorphous Shape Memory Polymers

A thermoviscoelastic modeling approach is developed to predict the recovery behaviors of the thermally activated amorphous shape memory polymers(SMPs)based on the generalized finite deformation viscoelasticity theory.In this paper,a series of moduli and relaxation times of the generalized Maxwell model is estimated from the stress relaxation master curve by using the nonlinear regression(NLREG)method.Assuming that the amorphous SMPs are approximately incompressible isotropic elastomers in the rubbery state,the hyperelastic response of the materials is well modeled with a hyperelastic model in Ogden form.In addition,the Williams-Landel-Ferry(WLF)equation is used to describe the horizontal shift factor obtained with time-temperature superposition principle(TTSP).The finite element simulations show good agreement with the experimental thermomechanical behaviors.Moreover,the possibility of developing a temperature-responsive intravascular stent with the SMP studied here is investigated in terms of its thermomechanical property.Therefore,it can be concluded that the model has good prediction capabilities for the recovery behaviors of amorphous SMPs.

Modeling approaches to pressure balance dynamic system in shield tunneling

In order to deal with modeling problem of a pressure balance system with time-delay, nonlinear, time-varying and uncertain characteristics, an intelligent modeling procedure is proposed, which is based on artificial neural network(ANN) and input-output data of the system during shield tunneling and can overcome the precision problem in mechanistic modeling(MM) approach. The computational results show that the training algorithm with Gauss-Newton optimization has fast convergent speed. The experimental investigation indicates that, compared with mechanistic modeling approach, intelligent modeling procedure can obviously increase the precision in both soil pressure fitting and forecasting period. The effectiveness and accuracy of proposed intelligent modeling procedure are verified in laboratory tests.

Comparison of six statistical approaches in the selection of appropriate fish growth models

The performance of six statistical approaches,which can be used for selection of the best model to describe the growth of individual fish,was analyzed using simulated and real length-at-age data.The six approaches include coefficient of determination(R2),adjusted coefficient of determination(adj.-R2),root mean squared error(RMSE),Akaike's information criterion(AIC),bias correction of AIC(AICc) and Bayesian information criterion(BIC).The simulation data were generated by five growth models with different numbers of parameters.Four sets of real data were taken from the literature.The parameters in each of the five growth models were estimated using the maximum likelihood method under the assumption of the additive error structure for the data.The best supported model by the data was identified using each of the six approaches.The results show that R2 and RMSE have the same properties and perform worst.The sample size has an effect on the performance of adj.-R2,AIC,AICc and BIC.Adj.-R2 does better in small samples than in large samples.AIC is not suitable to use in small samples and tends to select more complex model when the sample size becomes large.AICc and BIC have best performance in small and large sample cases,respectively.Use of AICc or BIC is recommended for selection of fish growth model according to the size of the length-at-age data.

A STATISTICAL PREDICTION MODEL FOR SPRING RAINFALL OVER NORTHERN CHINA BASED ON THE INTERANNUAL INCREMENT APPROACH

The spring (March-April-May) rainfall over northern China (SPRNC) is predicted by using the interannual increment approach. DY denotes the difference between the current year and previous years. The seasonal forecast model for the DY of SPRNC is constructed based on the data that are taken from the 1965-2002 period (38 years), in which six predictors are available no later than the current month of February. This is favorable so that the seasonal forecasts can be made one month ahead. Then, SPRNC and the percentage anomaly of SPRNC are obtained by the predicted DY of SPRNC. The model performs well in the prediction of the inter-annual variation of the DY of SPRNC during 1965-2002, with a correlation coefficient between the predicted and observed DY of SPRNC of 0.87. This accounts for 76% of the total variance, with a low value for the average root mean square error (RMSE) of 20%. Both the results of the hindcast for the period of 2003-2010 (eight years) and the cross-validation test for the period of 1965-2009 (45 years) illustrate the good prediction capability of the model, with a small mean relative error of 10%, an RMSE of 17% and a high rate of coherence of 87.5% for the hindcasts of the percentage anomaly of SPRNC.

Application of Bayesian Approach in the Parameter Estimation of Continuous Lumping Kinetic Model of Hydrocracking Process

Hydrocracking is a catalytic reaction process in the petroleum refineries for converting the higher boiling temperature residue of crude oil into a lighter fraction of hydrocarbons such as gasoline and diesel. In this study, a modified continuous lumping kinetic approach is applied to model the hydro-cracking of vacuum gas oil. The model is modified to take into consideration the reactor temperature on the reaction yield distribution. The model is calibrated by maximizing the likelihood function between the modeled and measured data at four different reactor temperatures. Bayesian approach parameter estimation is also applied to obtain the confidence interval of model parameters by considering the uncertainty associated with the measured errors and the model structural errors. Then Monte Carlo simulation is applied to the posterior range of the model parameters to obtain the 95% confidence interval of the model outputs for each individual fraction of the hydrocracking products. A good agreement is observed between the output of the calibrated model and the measured data points. The Bayesian approach based on the Markov Chain Monte Carlo simulation is shown to be efficient to quantify the uncertainty associated with the parameter values of the continuous lumping model.

An Agent-Oriented Modelling Approach for Agile Manufacturing

The dynamic variations in demand patterns and produ ct mix, driven by unpredictable changes in a global market, are placing manufactur ing systems under significant pressure. In order to remain competitive, manu facturing organisations must satisfy demands timely. This implies that companies must increase product varieties, reduce time-to-market, shorten product-life cycles and at the same time maintain good quality and reduce investment costs. Conventional methodologies for planning and control have been found to be inadeq uate in meeting these challenges. Agile manufacturing is the state-of-the-art concept that provides enterprises with the opportunity to react rapidly and cos t-effectively to changes that occur in their environment. Several paradigms suc h as Holonic Manufacturing Systems (HMS), Bionic Manufacturing Systems (BMS) and Fractal Factory have been developed to enable manufacturing systems achieve agi lity by integrating manufacturing activities into a coordinated framework. Despi te the differences in their origin (HMS from social organisation, BMS from biolo gy and Fractal Factory from Mathematics), these paradigms have overlapping conce pts and one of the most important is hierarchical organisational structure. This paper presents a conceptual hierarchically structured multi-agent architec ture for manufacturing systems’ modelling. Multi-Agent Systems (MAS) provide su itable techniques for implementing the above concepts and as a branch of Distrib uted Artificial Intelligence (DAI), have characteristics that have been explored in various applications. Such characteristics include self-organisation, flexi bility, scalability, and robustness. The proposed architecture provides a suit able decision-making framework where each agent represents a node in the hier archic tree of manufacturing systems such as the company as whole, each plant wi thin the company, each cell or line within the plant, each machine in a cell or line. Each agent has the ability to perceive and evaluate changes that occur in the manufacturing environment, interact with other agents in the system in order to reach an optimal decision, and act based on that decision. In other words, agents respond timely to unexpected changes by continuously co-ordinating t heir activities, and allocating manufacturing resources dynamically based on act ual shop-floor situation. The flexibility of this architecture also lies in its ability to accommodate both homogenous and heterogeneous agents, and its capabi lity for the dynamic addition and removal of agents using a conceptual intellige nt communication mechanism similar to the blackboard messaging system. A Bidding -Based Scheme (BBS) would be used to generate and evaluate alternative scenario at run-time. In addition, this architecture can be extended to meet the requir ements of enterprise integration.

Approach Choice and Innovative Model Design of Grass-root Agricultural Technology Promotion under the View of New Countryside

This paper introduces the approach types of agricultural technology promotion we can choose in the process of new countryside construction, and there are mainly 6 types, namely government-domination-oriented approach, market-development-oriented approach, university (scientific research institution)-education-oriented approach, farmers' participation-oriented approach, direct-training-oriented approach and individual-service-oriented approach, including 20 models. The problems existing in the grass-root agricultural technology promotion in the process of new village construction are analyzed as follows: the awareness of innovation is not strong; scientific research, education, and promotion are disjointed; the construction of specific work system lags behind; the structure of promotion team is irrational, and the quality of promotion personnel is yet to be promoted; the cultural quality of farmers is low, and the cooperative organizations are not sound; the working conditions are poor, and the basic security is inadequate; the investment channel is simple and the supply amount is badly short; the methods of agricultural technology promotion do not adapt to the requirements of new situation. According to the characteristics and internal requirements of grass-root agricultural technology promotion, we creatively design an overall working model, and put forward new measures from objective of grass-root agricultural technology promotion, service object, service content, organization work and scope, method, role of agricultural technology personnel and so on.

Turbulence modulation model for gas–particle flow based on probability density function approach

he paper focuses on the turbulence modulation problem in gas–particle flow with the use of probability density function（PDF） approach. By means of the PDF method, a general statistical moment turbulence modulation model without considering the trajectory difference between two phases is derived from the Navier–Stokes equations. A new turbulence production term induced by the dispersed-phase is analyzed and considered. Furthermore, the trajectory difference between two media is taken into account. Subsequently, a new k–ε turbulence modulation model in dilute particle-laden flow is successfully set up. Then, the changes to several terms, including the turbulence production, dissipation, and diffusion terms, are well described consequently. The promoted model provides a more probable explanation for the modification of particles on the turbulence. Finally, we applied the model to simulate a gas–particle turbulence flow case in a wall jet, and found that the simulation results agree well with the experimental data.

A Numerical Study on Effects of Land-Surface Heterogeneity from' Combined Approach' on Atmospheric ProcessPart II: Coupling-Model Simulations

Two land surface schemes, one the standard Biosphere / Atmosphere Transfer Scheme Version ie (BOZ) and the other B1Z based on B0Z and heterogeneously-treated by' combined approach' , were co 'pled to the meso-scale model MM4, respectively. Through the calculations of equations from the companion paper, parameters representing land surface heterogeneity and suitable for the coupling models were found out. Three cases were simulated for heavy rainfalls during 36 hours, and the sensitivity of short-term weather modeling to the land surface heterogeneity was tested. Through the analysis of the simulations of the three heavy rainfalls, it was demonstrated that BIZ, compared with BOZ, could more realistically reflect the features of the land surface heterogeneity, therefore could more realistically reproduce the circulation and precipitation amount in the heavy rainfall processes of the three cases. This shows that even short-term weather is sensitive to the land surface heterogeneity, which is more obvious with time passing, and whose influence is more pronounced in the lower layer and gradually extends to the middle and upper layer. Through the analysis of these simulations with BlZ, it is suggested that the bulk effect of smaller-scale fluxes (i.e., the momentum, water vapor and sensible heat fluxes) near the s ig nificantly-heterogeneous land surface is to change the larger-scale (i.e., meso-scale) circulation, and then to influence the development of the low-level jets and precipitation. And also, the complexity of the land-atmosphere interaction was shown in these simulations.

A New Approach to Intelligent Model Based Predictive Control Scheme

This paper describes a new approach to intelligent model based predictive control scheme for deriving a complex system. In the control scheme presented, the main problem of the linear model based predictive control theory in dealing with severe nonlinear and time variant systems is thoroughly solved. In fact, this theory could appropriately be improved to a perfect approach for handling all complex systems, provided that they are firstly taken into consideration in line with the outcomes presented. This control scheme is organized based on a multi-fuzzy-based predictive control approach as well as a multi-fuzzy-based predictive model approach, while an intelligent decision mechanism system (IDMS) is used to identify the best fuzzy-based predictive model approach and the corresponding fuzzy-based predictive control approach, at each instant of time. In order to demonstrate the validity of the proposed control scheme, the single linear model based generalized predictive control scheme is used as a benchmark approach. At last, the appropriate tracking performance of the proposed control scheme is easily outperformed in comparison with previous one.

A NEW MODELING AND FILTERING APPROACH FOR TRACKING MANEUVERING TARGETS

A new modeling and filtering approach for tracking maneuvering targets is presented in thispaper.The approach,which makes optimal estimate for the model With the random variable possible,depends on random step modeling of target maneuvers.In the new model,the unknown targetacceleration is treated as a random variable and then estimated directly.A detector is designed tofind out the target maneuvers and the estimation algorithm will be restarted when the maneuvers oc-cur.Combination of three-dimention Kalman filter with a detector forms a tracker for maneuveringtargets.The new tracking scheme is easy to implement and its capability is illustrated in two trackingexamples in which the new approach is compared with Mooses’on the performance.

A DFIM Sensor Faults Multi-Model Diagnosis Approach Based on an Adaptive PI Multiobserver—Experimental Validation

This paper studies the problem of diagnosis strategy for a doubly fed induction motor (DFIM) sensor faults. This strategy is based on unknown input proportional integral (PI) multiobserver. Thecontribution of this paper is on one hand the creation of a new DFIM model based on multi-model approach and, on the other hand, the synthesis of an adaptive PI multi-observer. The DFIM Volt per Hertz drive system behaves as a nonlinear complex system. It consists of a DFIM powered through a controlled PWM Voltage Source Inverter (VSI). The need of a sensorless drive requires soft sensors such as estimators or observers. In particular, an adaptive Proportional-Integral multi-observer is synthesized in order to estimate the DFIM’s outputs which are affected by different faults and to generate the different residual signals symptoms of sensor fault occurrence. The convergence of the estimation error is guaranteed by using the Lyapunov’s based theory. The proposed diagnosis approach is experimentally validated on a 1 kW Induction motor. Obtained simulation results confirm that the adaptive PI multiobserver consent to accomplish the detection, isolation and fault identification tasks with high dynamic performances.