Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Identification of Hammerstein systems with asymmetric dead-zone nonlinearities using canonical parameterized model

A non-iterative identification method with parameterization of the unknown dead-zone is proposed for Hammerstein systems in presence of asymmetric dead-zone nonlinearities. The canonical parameterized model which is a single expression without segmentation is utilized to describe the dead-zone, based on which a universal-type parametric model can be established to approximate the entire system. This model can be established without separating the nonlinear part from the linear part. The dead-zone parameters and the coefficients in the linear transfer function can be estimated simultaneously according to the proposed algorithm. Numerical experiments are presented to illustrate the effectiveness of the proposed scheme.

Parameterized thermal model of a mixed mantle convection

Simple parameterized models, either whole mantle convection or layered mantleconvection, cannot explain the tectonic characteristics of the Earth's evolution history, therefore a mixed mantle convection model has been carried out in this paper. We introduce a time-dependent parameter F, which denotes the ratio betWeen the mantle material involved in whole mantle convection and the material of the entire mantle, and introduce a local Rayleigh number Raloc as well as two critical numbers Ra1 and Ra2. These parameters are used to describe the stability of the phase boundary between the upper and lower mantle. The result shows that the mixed mantle convection model is able to simulate the episodic tectonic evolution of the Earth.

The cooling models of Earth’s early mantle

The thermal state of the early Earth’s interior and its way of cooling are crucial for its subsequent evo-lution.Earth is initially hot as it acquired enormous heat in response to violent processes during its formation,e.g.,the Moon-forming giant impact,the segregation and formation of its metallic core,the tidal interaction with the early Moon,and the decay of radioactive elements,etc.In the meantime,the cooling mechanisms of early Earth’s mantle remain elusive despite their importance,and the previously proposed cooling models of the mantle are controversial.In this paper,we first reviewed several prevalent parameter-ized thermal evolution models of the early mantle.The models give unrealistic predictions since they were estab-lished solely based on a single tectonic regime,such as the stagnant-lid regime,or relied on the disputable existence of the plate tectonics prior to-3.5 Ga.Then we argue that the mantle should have started to cool down from a very hot state after the solidification of the ferocious magma ocean.Instead of using one single scaling law to describe a single-stage model,we suggest that an episodic multi-stage cooling model(EMCM)of the early mantle could be more plausible to account for the mantle’s early cooling process.The model reconciles with the fact that the mantle cools down from a hot state prior to*3.5 Ga and can also explain the well-constrained post-3.5 Ga thermal history of the mantle.

Introduction to Mesh Based Generated Lumped Parameter Models for Electromagnetic Problems using Triangular Elements

This paper is an introduction to mesh based generated reluctance network modeling using triangular elements.Many contributions on mesh based generated reluctance networks using rectangular shaped elements have been published,but very few on those generated from a mesh using triangular elements.The use of triangular elements is aimed at extending the application of the approach to any shape of modeled devices.Basic concepts of the approach are presented in the case of electromagnetic devices.The procedure for coding the approach in the case of a flat linear permanent magnet machine is presented.Codes developed under MATLAB environment are also included.

A 3D attention U-Net network and its application in geological model parameterization

To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.

Fourth-Order Predictive Modelling: I. General-Purpose Closed-Form Fourth-Order Moments-Constrained MaxEnt Distribution

This work (in two parts) will present a novel predictive modeling methodology aimed at obtaining “best-estimate results with reduced uncertainties” for the first four moments (mean values, covariance, skewness and kurtosis) of the optimally predicted distribution of model results and calibrated model parameters, by combining fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters. Underlying the construction of this fourth-order predictive modeling methodology is the “maximum entropy principle” which is initially used to obtain a novel closed-form expression of the (moments-constrained) fourth-order Maximum Entropy (MaxEnt) probability distribution constructed from the first four moments (means, covariances, skewness, kurtosis), which are assumed to be known, of an otherwise unknown distribution of a high-dimensional multivariate uncertain quantity of interest. This fourth-order MaxEnt distribution provides optimal compatibility of the available information while simultaneously ensuring minimal spurious information content, yielding an estimate of a probability density with the highest uncertainty among all densities satisfying the known moment constraints. Since this novel generic fourth-order MaxEnt distribution is of interest in its own right for applications in addition to predictive modeling, its construction is presented separately, in this first part of a two-part work. The fourth-order predictive modeling methodology that will be constructed by particularizing this generic fourth-order MaxEnt distribution will be presented in the accompanying work (Part-2).

Opti mization and Model of Laminar Cooling Control Systemfor Hot Strip Mills

The structure of laminar cooling control system for hot rolling was introduced and the control mode, cooling strategy, segment tracking and model recalculation were analyzed. The parameters of air/water cooling models were optimized by regressing the data gathering in situ, and satisfactory effect was obtained. The coiling temperature can be controlled within ±15℃.

Modeling mechanism and extension of GM (1,1)

Firstly, the research progress of grey model GM （1,1） is summarized, which is divided into three development stages： assimilation, alienation and melting stages. Then, the matrix analysis theory is used to study the modeling mechanism of GM （1,1）, which decomposes the modeling data matrix into raw data transformation matrix, accumulated generating operation matrix and background value selection matrix. The changes of these three matrices are the essential reasons affecting the modeling and the accuracy of GM （1,1）. Finally, the paper proposes a generalization grey model GGM （1,1）, which is a extended form of GM （1,1） and also a unified form of model GM （1,1）, model GM （1,1,α）, stage grey model, hopping grey model, generalized accumulated model, strengthening operator model, weakening operator model and unequal interval model. And the theory and practical significance of the extended model is analyzed.

Extraction of temperature dependences of small-signal model parameters in SiGe HBT HICUM model

In this work, temperature dependences of small-signal model parameters in the SiGe HBT HICUM model are presented. Electrical elements in the small-signal equivalent circuit are first extracted at each temperature, then the temperature dependences are determined by the series of extracted temperature coefficients, based on the established temperature for- mulas for corresponding model parameters. The proposed method is validated by a 1x 0.2 x 16 μm2 SiGe HBT over a wide temperature range （from 218 K to 473 K）, and good matching is obtained between the extracted and modeled resuits. Therefore, we believe that the proposed extraction flow of model parameter temperature dependence is reliable for characterizing the transistor performance and guiding the circuit design over a wide temperature range.

A modified lumped parameter model of distribution transformer winding

The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately,but it requires complex calculations.Lumped parameter model requires less calculations,but its applicable frequency range is not wide.This paper studies the amplitude-frequency characteristics of the lightning wave,compares the transformer modelling methods and finally proposes a modified lumped parameter model,based on the above comparison.The proposed model minimizes the errors provoked by the lumped parameter approximation,and the hyperbolic functions of the distributed parameter model.By this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage response.The proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

i4OilS pill, an Operational Marine Oil Spill Forecasting Model for Bohai Sea

Oil spill models can effectively simulate the trajectories and fate of oil slicks, which is an essential element in contingency planning and effective response strategies prepared for oil spill accidents. However, when applied to offshore areas such as the Bohai Sea, the trajectories and fate of oil slicks would be affected by time-varying factors in a regional scale, which are assumed to be constant in most of the present models. In fact, these factors in offshore regions show much more variation over time than in the deep sea, due to offshore bathymetric and climatic characteristics. In this paper, the challenge of parameterizing these offshore factors is tackled. The remote sensing data of the region are used to analyze the modification of wind-induced drift factors, and a well-suited solution is established in parameter correction mechanism for oil spill models. The novelty of the algorithm is the self-adaptive modification mechanism of the drift factors derived from the remote sensing data for the targeted sea region, in respect to empirical constants in the present models. Considering this situation, a new regional oil spill model(i4Oil Spill) for the Bohai Sea is developed, which can simulate oil transformation and fate processes by Eulerian-Lagrangian methodology. The forecasting accuracy of the proposed model is proven by the validation results in the comparison between model simulation and subsequent satellite observations on the Penglai 19-3 oil spill accident. The performance of the model parameter correction mechanism is evaluated by comparing with the real spilled oil position extracted from ASAR images.

MODELING AND EXPERIMENTAL STUDY OF A PASSIVE HYDRAULIC ENGINE MOUNT WITH INERTIA AND DIRECT-DECOUPLER

To investigate the dynamic characteristics and damping theory of the passive hydraulic engine mount （PHEM）, numerical prediction is performed through lumped parameter model. System parameters, including volume compliance of the decoupler chamber, effective piston area, fluid inertia and resistance of inertia track and direct-decoupler, are identified by means of experiments and finite element method （FEM）. Dynamic behaviors are tested with elastomer test system for purpose of validating PHEM. With incorporation of inertia track and direct-decoupler, PHEM behaves effective and efficient vibration isolation in range of both low and high frequencies. The comparison of the numerical results with the experimental observations shows that the present PHEM achieves fairly good performance for the engine vibration isolation.

Parameter identifications for a rotor system based on its finite element model and with varying speeds

In order to achieve prediction for vibration of rotating machinery, an accurate finite element （FE） model and an efficient parameter identification method of the rotor system are required. In this research, a test rig is used as a prototype of a rotor system to validate a novel parameter identification technique based on an FE model. Rotor shaft vibration at varying operating speeds is measured and correlated with the FE results. Firstly, the theories of the FE modelling and identification technique are introduced. Then disk unbalance parameter, stiffness and damping coefficients of the bearing supports on the test rig are identified. The calculated responses of the FE model with identified parameters are studied in comparison with the experimental results.

Introduction to Mesh Based Generated Lumped Parameter Models for Electromagnetic Problems

This paper is an introduction to mesh based generated reluctance network modeling.An overview of scientific works which led to the development of this approach is first presented.Basic concepts of the approach are then presented in the case of electromagnetic devices.A step-by-step procedure for coding the approach in the case of a flat linear permanent magnet machine is presented.Codes developed under MATLAB and Scilab environments are also included.

Parameter sensitivity study of the biogeochemical model in the China coastal seas

In order to develop a coupled basin scale model of ocean circulation and biogeochemical cycling,we present a biogeochemical model including 12 components to study the ecosystem in the China coastal seas（CCS）.The formulation of phytoplankton mortality and zooplankton growth are modified according to biological characteristics of CCS.The four sensitivity biological parameters,zooplankton assimilation efficiency rate（ZooAE_N）,zooplankton basal metabolism rate（ZooBM）,maximum specific growth rate of zooplankton（μ_（20）） and maximum chlorophyll to carbon ratio（Chl2C_m） are obtained in sensitivity experiments for the phytoplankton,and experiments about the parameter μ_（20＇）,half-saturation for phytoplankton NO_3 uptake（K_（NO_3）） and remineralization rate of small detritusN（SDeRRN） are conducted.The results demonstrate that the biogeochemical model is quite sensitive to the zooplankton grazing parameter when it ranges from 0.1 to 1.2 d^（-1）.The K_（NO_3） and SDeRRN also play an important role in determining the nitrogen cycle within certain ranges.The sensitive interval of KNO_3 is from 0.1 to 1.5（mmol/m^3）^（-1）,and interval of SEdRRN is from 0.01 and 0.1 d^（-1）.The observational data from September 1998 to July 2000 obtained at SEATS station are used to validate the performance of biological model after parameters optimization.The results show that the modified model has a good capacity to reveal the biological process features,and the sensitivity analysis can save computational resources greatly during the model simulation.

Implementing a multispecies size-spectrum model in a datapoor ecosystem

Multispecies ecological models have been used for predicting the effects of fishing activity and evaluating the performance of management strategies. Size-spectrum models are one type of physiologically-structured ecological model that provide a feasible approach to describing fish communities in terms of individual dietary variation and ontogenetic niche shift. Despite the potential of ecological models in improving our understanding of ecosystems, their application is usually limited for data-poor fisheries. As a first step in implementing ecosystem-based fisheries management（EBFM）, this study built a size-spectrum model for the fish community in the Haizhou Bay, China. We describe data collection procedures and model parameterization to facilitate the implementation of such size-spectrum models for future studies of data-poor ecosystems. The effects of fishing on the ecosystem were exemplified with a range of fishing effort and were monitored with a set of ecological indicators. Total community biomass, biodiversity index, W-statistic, LFI（Large fish index）, Mean W（mean body weight） and Slope（slope of community size spectra） showed a strong non-linear pattern in response to fishing pressure, and largest fishing effort did not generate the most drastic responses in certain scenarios. We emphasize the value and feasibility of developing size-spectrum models to capture ecological dynamics and suggest limitations as well as potential for model improvement. This study aims to promote a wide use of this type of model in support of EBFM.

The improved model of estimating global whitecap coverage based on satellite data

The pro and con of whitecap parameterizations and a statistical wave breaking model are discussed. An improved model is derived by combining satellite-based parameterization and the wave breaking model. The appropriate constants for the general wave state are obtained by considering the breaking condition of the wave slope and fitting with the satellite-based parameterization. The result is close to the constants based on the whitecap data from Monahan. Comparing with satellite-based data and the original model＇s results, the improved model＇s results are consistent with satellite-based data and previous studies. The global seasonal distributions of the whitecap coverage averaged from 1998 to 2008 are presented. Spatial and seasonal features of the whitecap coverage are analyzed.

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications.However,the various working conditions would degrade the diagnostic performance and make gear fault diagnosis(GFD)more and more challenging.In this paper,a novel model parameter transfer(NMPT)is proposed to boost the performance of GFD under varying working conditions.Based on the previous transfer strategy that controls empirical risk of source domain,this method further integrates the superiorities of multi-task learning with the idea of transfer learning(TL)to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition(target domain)and another(source domain),and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable.For NMPT implementation,insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task.Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions.The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.