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.

Gate-Capacitance-Shift Approach and Compact Modeling for Quantum Mechanical Effects in Poly-Gates

A new approach,gate-capacitance-shift (GCS) approach,is described for compact modeling.This approach is piecewise for various physical effects and comprises the gate-bias-dependent nature of corrections in the nanoscale regime.Additionally,an approximate-analytical solution to the quantum mechanical (QM) effects in polysilicon (poly)-gates is obtained based on the density gradient model.It is then combined with the GCS approach to develop a compact model for these effects.The model results tally well with numerical simulation.Both the model results and simulation results indicate that the QM effects in poly-gates of nanoscale MOSFETs are non-negligible and have an opposite influence on the device characteristics as the poly-depletion (PD) effects do.

Combining descriptor-based analyses and mean-field modeling of the electrochemical interface to comprehend trends of catalytic processes at the solid/liquid interface

Electrocatalysis is undergoing a renaissance due to its central importance for a sustainable energy economy,relying on green(electro-)chemical processes to harvest,convert,and store energy.Theoretical considerations by electronic structure methods are key to identify potential material motifs for electrocatalytic processes at the solid/liquid interface.Most commonly,heuristic concepts in the realm of materials screening by the compilation of volcano plots are used,which rely on a plethora of simplifications and approximations of the complex electrochemical interface.While the investigation of the catalytic processes at the solid/liquid interface mainly relies on descriptor-based approaches,in the present future article it is discussed that the inclusion of the liquid part of the interface by mean-field models is crucial to elevate screening approaches to the next level.

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.

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.

Physical Network Approach Applied to Wind Turbine Modeling with Simscape Language

Model-Based Design is an efficient and cost-effective way to develop controls, signal processing, image processing, communications, mechatronics, and other embedded systems. Rather than re-lying on physical prototypes and textual specifications, Model-Based Design uses a system model as an executable specification throughout development. It supports system- and component-level design and simulation, automatic code generation, and continuous test and verification. This paper is focused firstly on the so-called model-based design and aims at presenting an up-to-date state of the art in this important field. Secondly, it develops a model based design for wind energy systems. Mathematical formulations and numerical implementations for different components of wind energy systems are highlighted with Simscape language. Finally, results are derived from simulations.

A Delivery Approach Modeling for Urban Freight Restocking

This paper proposes a modeling system developed in order to analyze the urban freight transport and logistics within urban and metropolitan areas. A review of models developed to simulate this segment of mobility is also reported. The review analysis highlights the limits of models for the ex-ante assessment of city logistics measures. For this reason this paper proposes a new modeling system approach for the assessment of city logistics measures. It is made of different steps approaching problems related to quantity OD （Origin-Destination） flows, restocking type OD flows, delivery OD flows, delivery OD flows for time slice and vehicle type, and vehicle OD flows. This modeling system has been specified and calibrated using some surveys carried out in the inner area of Rome where more than 500 truck drivers and more than 600 retailers have been interviewed.

Effect of the sloping seabed on 3D soil-spudcan interaction using a material point-finite element(MPM-FEM)model

The sloping seabed affects the bearing capacity and failure mechanism of soil,which may compromise the stability and safety of offshore structures such as jack-up platforms.This paper employs a coupled model combining the material point method and finite element method(MPM-FEM)to analyze the impact of sloping seabeds on the three-dimensional soil-spudcan interaction.The MPM-FEM model implements the B¯approach to solve the challenge of volumetric locking due to the incompressibility constraints imposed by yield criterion.It is validated against the centrifuge results.The effects of sloping seabeds on penetration resistance,soil flow pattern,lateral response,stress distribution,and failure mechanism are discussed.The soil mainly undergoes overall failure when the ratio of penetration depth to spudcan diameter(i.e.D P/D)is between 0 and 0.25.As the slope angle increases,the soil on the side of lower slope is expelled further,resulting in an asymmetric stress distribution and a larger horizontal sliding force of soil.When D P/D increases to 0.75,the soil transitions to localized plastic flow failure,and the range of soil flow affected by the spudcan penetration decreases.The results show that,when the slope angle increases,the lateral displacement and stress distribution on the lower slope of a sloping seabed is significantly larger than that of a horizontal seabed,impacting the spudcan and surrounding soil behavior.The study suggests that the seabed slope significantly affects the range of soil flow and failure at shallow penetration,indicating that the slope angle should be taken into account in the design and installation of offshore jack-up rigs,particularly in areas with sloping seabeds.

Optimal Cooperative Secondary Control for Islanded DC Microgrids via a Fully Actuated Approach

DC-DC converter-based multi-bus DC microgrids(MGs) in series have received much attention, where the conflict between voltage recovery and current balancing has been a hot topic. The lack of models that accurately portray the electrical characteristics of actual MGs while is controller design-friendly has kept the issue active. To this end, this paper establishes a large-signal model containing the comprehensive dynamical behavior of the DC MGs based on the theory of high-order fully actuated systems, and proposes distributed optimal control based on this. The proposed secondary control method can achieve the two goals of voltage recovery and current sharing for multi-bus DC MGs. Additionally, the simple structure of the proposed approach is similar to one based on droop control, which allows this control technique to be easily implemented in a variety of modern microgrids with different configurations. In contrast to existing studies, the process of controller design in this paper is closely tied to the actual dynamics of the MGs. It is a prominent feature that enables engineers to customize the performance metrics of the system. In addition, the analysis of the stability of the closed-loop DC microgrid system, as well as the optimality and consensus of current sharing are given. Finally, a scaled-down solar and battery-based microgrid prototype with maximum power point tracking controller is developed in the laboratory to experimentally test the efficacy of the proposed control method.

Development of a DFN-based probabilistic block theory approach for bench face angle design in open pit mining

In open pit mining,uncontrolled block instabilities have serious social,economic and regulatory consequences,such as casualties,disruption of operation and increased regulation difficulties.For this reason,bench face angle,as one of the controlling parameters associated with block instabilities,should be carefully designed for sustainable mining.This study introduces a discrete fracture network(DFN)-based probabilistic block theory approach for the fast design of the bench face angle.A major advantage is the explicit incorporation of discontinuity size and spatial distribution in the procedure of key blocks testing.The proposed approach was applied to a granite mine in China.First,DFN models were generated from a multi-step modeling procedure to simulate the complex structural characteristics of pit slopes.Then,a modified key blocks searching method was applied to the slope faces modeled,and a cumulative probability of failure was obtained for each sector.Finally,a bench face angle was determined commensurate with an acceptable risk level of stability.The simulation results have shown that the number of hazardous traces exposed on the slope face can be significantly reduced when the suggested bench face angle is adopted,indicating an extremely low risk of uncontrolled block instabilities.

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.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

On the Genesis of the Marshall-Olkin Family of Distributions via the T-X Family Approach: Statistical Modeling

In the last couple of years,there Has been an increased interest among the statisticians to dene new families of distributions by adding one or more additional parameter(s)to the baseline distribution.In this regard,a number of families have been introduced and studied.One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions.Whenever,we see a new method,the natural questions come in to mind are(i)what are the genesis of the newly proposed method and(ii)how did the proposed method is obtained.No doubt,the Marshall-Olkin family is a very useful method and has attracted the researchers.But,unfortunately,the authors failed to provide the explanation about the genesis of the method that how this family of distributions is obtained.To address this issue,in this article,an attempt Has been made to provide a straight forward computation about the genesis of the Marshall-Olkin family that somehow completes its derivation.The genesis of the Marshall-Olkin family is based on the T-X family approach.Furthermore,we have showed that other extensions of the Marshall-Olkin family can also be obtained via the T-X family method.Finally,a real-life application form insurance science is presented to illustrate the newly proposed extension of the Marshall-Olkin family.

Disharmonious Ventricular Relationship and Topology for the Given Atrioventricular Connections.Contemporary Diagnostic Approach Using 3D Modeling and Printing

In the last issue,two case reports separately present examples of the extremely rare and complex congenital heart diseases that show concordant atrioventricular connections to the L-looped ventricles in the presence of situs solitus.Both cases highlight that the relationship between the two ventricles within the ventricular mass is not always harmonious with the given atrioventricular connection.Such disharmony between the connections and relationships requires careful assessment of the three basic facets of cardiac building blocks,namely their morphology,the relationship of their component parts,and their connections with the adjacent segments.3D imaging and printing can now facilitate an otherwise difficult diagnosis in such complex situations.Rotation of either the 3D images or the models permit accurate assessment of the ventricular topologic pattern by creating the right ventricular en-face septal view,thus facilitating placement of the observer’s hands.As we now emphasize,an alternative approach,which might prove more attractive to imagers,is to rotate the ventricular mass to provide the ventricular apical view,thus permitting determination of the ventricular relationship without using the hands.

Temporally Preserving Latent Variable Models:Offline and Online Training for Reconstruction and Interpretation of Fault Data for Gearbox Condition Monitoring

Latent variable models can effectively determine the condition of essential rotating machinery without needing labeled data.These models analyze vibration data via an unsupervised learning strategy.Temporal preservation is necessary to obtain an informative latent manifold for the fault diagnosis task.In a temporalpreserving context,two approaches exist to develop a condition-monitoring methodology:offline and online.For latent variable models,the available training modes are not different.While many traditional methods use offline training,online training can dynamically adjust the latent manifold,possibly leading to better fault signature extraction from the vibration data.This study explores online training using temporal-preserving latent variable models.Within online training,there are two main methods:one focuses on reconstructing data and the other on interpreting the data components.Both are considered to evaluate how they diagnose faults over time.Using two experimental datasets,the study confirms that models from both training modes can detect changes in machinery health and identify faults even under varying conditions.Importantly,the complementarity of offline and online models is emphasized,reassuring their versatility in fault diagnostics.Understanding the implications of the training approach and the available model formulations is crucial for further research in latent variable modelbased fault diagnostics.

Dynamic vaccine distribution model based on epidemic diffusion rule and clustering approach

Due to the fact that the emergency medicine distribution is vital to the quick response to urgent demand when an epidemic occurs, the optimal vaccine distribution approach is explored according to the epidemic diffusion rule and different urgency degrees of affected areas with the background of the epidemic outbreak in a given region. First, the SIQR （susceptible, infected, quarantined,recovered） epidemic model with pulse vaccination is introduced to describe the epidemic diffusion rule and obtain the demanded vaccine in each pulse. Based on the SIQR model, the affected areas are clustered by using the self-organizing map （SOM） neutral network to qualify the results. Then, a dynamic vaccine distribution model is formulated, incorporating the results of clustering the affected areas with the goals of both reducing the transportation cost and decreasing the unsatisfied demand for the emergency logistics network. Numerical study with twenty affected areas and four distribution centers is carried out. The corresponding numerical results indicate that the proposed approach can make an outstanding contribution to controlling the affected areas with a relatively high degree of urgency, and the comparison results prove that the performance of the clustering method is superior to that of the non-clustering method on controlling epidemic diffusion.

Similarity measure of sedimentary successions and its application in inverse stratigraphic modeling

This paper presents a unique and formal method of quantifying the similarity or distance between sedimentary facies successions from measured sections in outcrop or drilled wells and demonstrates its first application in inverse stratigraphic modeling. A sedimentary facies succession is represented with a string of symbols, or facies codes in its natural vertical order, in which each symbol brings with it one attribute such as thickness for the facies. These strings are called attributed strings. A similarity measure is defined between the attributed strings based on a syntactic pattern-recognition technique. A dynamic programming algorithm is used to calculate the similarity. Inverse stratigraphic modeling aims to generate quantitative 3D facies models based on forward stratigraphic modeling that honors observed datasets. One of the key techniques in inverse stratigraphic modeling is how to quantify the similarity or distance between simulated and observed sedimentary facies successions at data locations in order for the forward model to condition the simulation results to the observed dataset such as measured sections or drilled wells. This quantification technique comparing sedimentary successions is demonstrated in the form of a cost function based on the defined distance in our inverse stratigraphic modeling implemented with forward modeling optimization.

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.

Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

To represent model uncertainties more comprehensively,a stochastically perturbed parameterization(SPP)scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics,convection,boundary layer,and surface layer parameterization schemes,as well as the stochastically perturbed parameterization tendencies(SPPT)scheme,and the stochastic kinetic energy backscatter(SKEB)scheme,is applied in the Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System(GRAPES-REPS)to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes.Six experiments are performed for a summer month(1-30 June 2015)over China and multiple verification metrics are used.The results show that:(1)All stochastic experiments outperform the control(CTL)experiment,and all combinations of stochastic parameterization schemes perform better than the single SPP scheme,indicating that stochastic methods can effectively improve the forecast skill,and combinations of multiple stochastic parameterization schemes can better represent model uncertainties;(2)The combination of all three stochastic physics schemes(SPP,SPPT,and SKEB)outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill;(3)Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed.SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields,and it contributes most to the improvement in spread and outliers for wind;(4)The introduction of SPP has a positive added value,and does not lead to large changes in the evolution of the kinetic energy(KE)spectrum at any wavelength;(5)The introduction of SPPT and SKEB would cause a 5%-10%and 30%-80%change in the KE of mesoscale systems,and all three stochastic schemes(SPP,SPPT,and SKEB)mainly affect the KE of mesoscale systems.This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.