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A candle filter consisting of a paper stack is an adequate device to clarify the grinding oil employed in the industrial post-processing of hard metal because of its cleaning efficiency and its regenerative capability by means of backwashing. The complex particle deposition mechanisms occurring in this device have not yet been investigated. Filtration experiments with a suitable particle-oil suspension were performed in a pilot filter and in a laboratory filter in order to investigate and model the mechanisms taking place in the process. The effect of operating parameters such as particle concentration and pressure difference on filter performance was evaluated. The results show that more than one filtration mechanism takes place simultaneously. While some depth filtration occurs at the beginning of the lifetime of a candle filter, blocking and cake filtration are the major mechanisms responsible for the filter clogging. Although blocking and cake filtration occur, to some extent, simultaneously, the cake filtration is the mechanism relevant for the modeling and scale-up of the long term filtration process. [ABSTRACT FROM AUTHOR]

Abstract: In the aforementioned paper, the optimum parameters of tuned mass dampers (TMD) are determined by the Harmony search (HS) algorithm in multiple degree of freedom (MDOF) structures under seismic excitations. The proposed approach is original; however, the mechanical model for the damped structure is not in agreement with previous results reported in the literature. Thus, the main focus of this discussion is to highlight the differences and analyze the impact on the obtained results. [Copyright &y& Elsevier]

This paper describes an accurate model of the Electrical Power Subsystem (EPS) of the Thermal Analysis Support and Environment Characterization Laboratory (TASEC-Lab), a university experiment developed for using in high-altitude balloon missions to measure and analyze the convection heat transfer. A Li-ion battery and two high efficient DC–DC converters have been characterized through laboratory tests and fitting to the experimental results. The developed mathematical models described in this paper are used to carry out accurate EPS simulations and are validated by comparison with the experimental data. • Design and modeling of the electrical power subsystem of a stratospheric balloon experiment. • New Li-lion battery discharging model that describes the three main parts: initial decay, linear, and exponential decay. • Formulation of a new DC/DC converter efficiency model. • Simulation and model validation of the electrical power subsystem. [ABSTRACT FROM AUTHOR]

In this paper we present a linear algorithm that estimates some physical parameters of a continuous-time system, described by an analytical mathematical model, when not all the state variables can be measured. The algorithm starts from the well-known subspace methods and applies some linear transformations to recover, at least partially, the estimated model in physical coordinates. Some analytical investigations and numerical experiments are shown for this method, which has general application within linear time-invariant (LTI) dynamical systems. [ABSTRACT FROM AUTHOR]

Abstract Sensitivity analysis provides information on the relative importance of model input parameters and assumptions. It is distinct from uncertainty analysis, which addresses the question 'How uncertain is the prediction?' Uncertainty analysis needs to map what a model does when selected input assumptions and parameters are left free to vary over their range of existence, and this is equally true of a sensitivity analysis. Despite this, many uncertainty and sensitivity analyses still explore the input space moving along one-dimensional corridors leaving space of the input factors mostly unexplored. Our extensive systematic literature review shows that many highly cited papers (42% in the present analysis) fail the elementary requirement to properly explore the space of the input factors. The results, while discipline-dependent, point to a worrying lack of standards and recognized good practices. We end by exploring possible reasons for this problem, and suggest some guidelines for proper use of the methods. Highlights • A systematic review of 280 scientific papers mentioning sensitivity analysis has been performed. • The analysis addresses the use of SA in the context of mathematical modelling, focusing on highly cited works. • Many highly-cited papers (42% in the present analysis) present a SA of poor quality. • The results, while discipline-dependent, point to a worrying lack of standards and good practices. • Some guidelines for proper use of the methods are suggested. [ABSTRACT FROM AUTHOR]

The spread of COVID-19 to more than 200 countries has shocked the public. Therefore, understanding the dynamics of transmission is very important. In this paper, the COVID-19 mathematical model has been formulated, analyzed, and validated using incident data from West Java Province, Indonesia. The model made considers the asymptomatic and symptomatic compartments and decreased immunity. The model is formulated in the form of a system of differential equations, where the population is divided into seven compartments, namely Susceptible Population (S 0) , Exposed Population (E) , Asymptomatic Infection Population (I A) , Symptomatic Infection Population (Y S) , Recovered Population (Z) , Susceptible Populations previously infected (Z 0) , and Quarantine population (Q). The results show that there has been an outbreak of COVID-19 in West Java Province, Indonesia. This can be seen from the basic reproduction number of this model, which is 3.180126127 (R 0 > 1). Also, the numerical simulation results show that waning immunity can increase the occurrence of outbreaks; and a period of isolation can slow down the process of spreading COVID-19. So if a strict social distancing policy is enforced like a quarantine, the outbreak will lessen. [ABSTRACT FROM AUTHOR]

In order to solve the problem of estimating the model parameters of hemodynamic model under low frequency interference, a new model parameter estimation algorithm (CSCKS) is proposed in this paper for the hemodynamic model with low frequency interference. The algorithm is based on SCKS algorithm to improve, its estimation effect is obviously better than the existing SCKS algorithm and DEM algorithm. We simulate the proposed algorithm and some other traditional algorithms to evaluate the estimation performance of CSCKS proposed in this paper under different Signal to Interference Ratio (SIR) and different integration steps, and through the simulation results, the estimated results of the algorithm were analyzed and compared. [ABSTRACT FROM AUTHOR]

The index of the peak magnitude of the fractional Fourier transform (FrFT) of linear frequency modulated (LFM) signals has been widely used as a powerful chirp-rate estimator. In this paper, we propose analytical approximations for the peak FrFT magnitude (PFM) of mono and multicomponent LFM signals, both for noiseless and noisy cases. In addition, we present a novel coarse-to-fine FrFT-based algorithm designed specifically for chirp-rate estimation of multi-component LFM signals. Our approach entails an initial coarse estimation of the chirp-rates for each component by utilizing our proposed mathematical models. By leveraging these models, we achieve improved performance and a reduced signal-to-noise breakdown threshold. Furthermore, we incorporate a unique and efficient estimate-and-subtract strategy to refine the estimated parameters using our proposed models. Rather than removing the components from the LFM signal, we utilize the derived model to identify and remove peaks in the PFM. This strategy enhances the algorithm's capability to handle challenging scenarios. Extensive simulation results demonstrate that our proposed algorithm performs very close to the Cramér–Rao lower bound. It effectively eliminates the leakage effect between signal components, avoids error propagation, and maintains an acceptable computational cost compared to other state-of-the-art methods. • Analytical approximations for the peak FrFT magnitude of LFM signals are presented. • We propose a fine-to-coarse estimation approach, which uses our models in parameter estimation of multi-component LFM signals. • Simulations show that the algorithm performs very close to the Cramer–Rao bound. • The algorithm has a very low computational cost, compared to the other methods. [ABSTRACT FROM AUTHOR]

The concept of geometrical multiscale modelling of non-Newtonian blood flow in patient-specific models is presented with the aim to provide a methodology for the assessment of hemodynamic significance of carotid artery stenoses. The content of the paper is divided into two consequent parts. In the first one, the principle of the fractional flow reserve (FFR) as an indicator of ischemia-inducing arterial stenoses is tested on three large arterial models containing the aortic arch and both left and right carotid arteries. Using the three-element Windkessel model as an outflow boundary condition, the blood flow simulations are carried out on the basis of data taken from the literature due to unavailable information on patient-specific flow and pressure waveforms. In the second part of the paper, the incorporation of real in-vivo measurements into the multiscale simulations is addressed by presenting a sequential algorithm for the estimation of Windkessel parameters. The ability of the described estimation method, which employs a non-linear state estimator (unscented Kalman filter) on zero-dimensional flow models, is demonstrated on two different patient-specific carotid bifurcation models. [ABSTRACT FROM AUTHOR]

This paper presents selection criteria based on inverse solutions for mathematical models that predict heat and mass transfer involved in contact baking of cake. For the models in focus, the evaporation-condensation effect occurring during baking is incorporated by non-equilibrium (model-I) and equilibrium approach (model-II). Effect of product parameters on baking process is studied. To select an accurate model, weighted least square objective function and complex step differentiation method are employed. A numerical study with simulated measurements is performed to check the feasibility of parameter estimation from a single experiment. This numerical study showed that model-II (i.e. with equilibrium approach) is more suitable for further inverse analysis. Significant weight given to temperature measurements resulted in better inverse solutions for both the models. Comparing final estimates from the models to the actual measurements, thermal properties approximated are close enough but mass transfer properties show deviations up to an order of magnitude. On extending the study for other baking conditions, it is found that inverse solution from model-II can adapt better than model-I for any change in the baking condition. Conclusively, a mechanistic model with equilibrium approach should be preferred than a model with explicit evaporation (model-I) for an inverse analysis. Eventually, this work aims to present an effective approach intended to resolve the challenges in accurate modeling of the baking process that arise due to limited knowledge of physical properties. [ABSTRACT FROM AUTHOR]

In this paper, the friction compensation method of SCARA robot is proposed. During the operation, the friction force in the system causes the heat to the robot and system. Since heat which occurred in the system is accumulated for a long time, the robot and system have been damaged. Moreover, performance and stability of the system have been reduced. Therefore, compensation of friction force is an important method for increase performance and stability of the system. In this paper, the disturbance observer is used with mathematic modelling of friction compensation for compensate the friction force in the system. From the simulation results, the system which has a friction force compensation can show the increasing of performance and stability. [ABSTRACT FROM AUTHOR]

This paper presents a simple membrane fouling model intended for use in conjunction with an activated sludge model to form an integrated model of a membrane bioreactor (MBR). It is based on an earlier development of Liang et al.[1] who divided fouling into short-term reversible fouling tantamount with cake deposition and long-term irreversible fouling representing th effects of irreversible particle deposition inside membrane pores. The original model was extended to account for cake detachment, back- flushing, and dependency of soluble microbial products (SMP) deposition on permeate flux. The model was identified on two sets of data from two experiments: a short-term flux stepping experiment under fluxes higher from the ‘threshold flux’ and a long-term filtration experiment under fluxes not exceeding the ‘threshold flux’. Additionally, the paper proposes a simple ‘pen and ruler’ identification method for unique estimation of model parameters based on the information obtained from flux stepping experiments. [ABSTRACT FROM AUTHOR]

Magnetic actuation methods for miniature robots are widely used thanks to their massive applications for precise micro-manipulations in inaccessible or hardly accessible environments, including medical and biological operations. The system studied is composed of miniature robots with permanent magnets inside the body and a coplanar coils actuator. This paper is aimed at the mathematical modeling of robots' movement, taking uncertainties and unmodeled dynamics into account. The crucial parameter is the friction coefficient between the planar actuator and the robot as it moves. An approach has been proposed to estimate it using the Kalman filter. Numerical simulations are compared with experiments to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The paper considers the impact of uncertainties associated with estimated parameters of the accelerometer model on the maximum values of absolute dynamic error. Assessment of such impact constitutes a novelty in determination of the maximum values of absolute dynamic error. The parameter estimation method of the accelerometer mathematical model in the frequency domain and the procedure for calculation of the associated uncertainties under the current regulations, are discussed in detail in the second section of this paper. The procedures for determining the maximum values of absolute dynamic error generated at the output of an accelerometer, when the input signals are constrained in magnitude and both in magnitude and rate of change, are discussed in the third section. This dynamic error has been determined in relation to the standard represented by the 15-th order Butterworth filter. The possibilities of application of both the continuous and the discrete procedures of error calculation have also been presented. For the execution of numerical integration, the Romberg method has been applied. Based on theoretical consideration, the results of both the parameter estimation of the Endevco87 accelerometer and calculation of the corresponding uncertainties are presented in the fourth section. The fifth section is devoted to the presentation of the most important assumptions, which allow the determination of the maximum values of absolute dynamic error. An assessment of the impact of the uncertainties associated with the estimated parameters on the value of absolute error is discussed in the last section of this paper. The numerical calculations were executed in the MathCad14 program. [ABSTRACT FROM AUTHOR]

Groundings are among the most common and serious maritime accidents. The shape of the sea bottom is one of the important factors that determine the extent of grounding damage on ships, including loss of watertightness. This paper presents a four-step methodology for mathematically analyzing sea bottom shapes, where individual peaks are identified and isolated from larger datasets. To these individual peaks mathematical models are fitted and the goodness of the fit is evaluated. The aim is to develop mathematical rock models that can be used in grounding damage analysis. The method is applied to bottom topography data from the two busiest tanker harbors in Finland. As potential mathematical rock models common assumptions found in literature as well as new suggestions are used. It was observed that rocks vary in shape and size locally as well as between different geographical areas. This highlights the need to have rock models that are flexible enough to model a wide range of sizes and shapes so that the local conditions can be taken into account. This includes modeling asymmetrical shapes, which is not taken into account in the current models. The analysis results suggest using a binormal function to describe the bottom shape: This model gives overall better goodness-of-fit test results than the cone and polynomial models. In the data the binormal model can model well the also the bottom shapes where the polynomial and conical models had a good fit in statistical terms. Although the paper discusses data from the Gulf of Finland, the framework for analyzing bottom shapes is expected to be applicable to other sea areas as well. [ABSTRACT FROM AUTHOR]

Traditional Probabilistic Risk Assessments (PRAs) model dependencies through deterministic relationships in fault trees and event trees and parametric models of common cause failure (CCF) events. Popular CCF models do not recognize system specific defenses against dependencies and are restricted to identical components in redundant configurations. While this has allowed prediction of system reliability with little or no data, it is a limiting factor in many applications such as modeling the characteristics of a specific system design or incorporating the characteristics of failure when assessing a failure event׳s risk significance (known as an Event Assessment, or Significance Determination). This paper proposes the General Dependency Model (GDM), which uses Bayesian Network to model the probabilistic dependencies between components. This is done through the introduction of three parameters for each failure cause, which relate to physical attributes of the system being modeled, i.e., cause condition probability, component fragility, and coupling factor strength. Finally this paper demonstrates the development and use of the GDM in traditional applications of PRA, and for Event Assessments and Significance Determination. Examples of the quantification of the GDM model parameters for these applications are provided. [ABSTRACT FROM AUTHOR]

Buildings account for approximately 32% of the total energy consumption globally and up to 40% in the developed countries, which makes buildings a prime target for energy conservation. Various energy conservation measures (ECMs) have been proposed to improve the energy efficiency in buildings, and these ECMs are usually designed and assessed using calibrated building energy models. However, there is empirical evidence that reveals noticeable discrepancies between simulated performances of ECMs reported in building energy models and their actual performances measured in buildings. This paper examines two possible causes of such discrepancies. Specifically, this paper tests the following two hypotheses: (1) using assumed occupancy data as opposed to actual occupancy data in building energy simulation reduces the reliability of estimated performance of demand-driven ECMs; and (2) using an energy model built for one ECM to cross estimate energy consumption of another ECM is statistically inaccurate. An educational building was used as a test bed. The results proved both hypotheses true, showing that estimations were more accurate and consistent for models calibrated using actual occupancy compared with those using assumed occupancy, and that cross-ECM estimation resulted in statistical inaccuracy. The findings indicated that current building energy modeling methods have limited reliability in ECM performance assessment, and need to be improved to better support the design and implementation of ECMs in buildings. [ABSTRACT FROM AUTHOR]

In the last decades, gain-scheduling control techniques have consolidated as an efficient answer to analysis and synthesis problems for non-linear systems. Among the approaches proposed in the literature, the linear parameter varying (LPV) and Takagi–Sugeno (TS) paradigms have proved to be successful in dealing with the different trials that the analyzer, or the designer, of a gain-scheduled control system has to face. Despite the strong similarities between the two paradigms, research on LPV and TS systems has been performed in an independent way and some results that could be useful for both paradigms were obtained only for one of them. However, in recent works, some clues that there is a very close connection between LPV and TS worlds have been presented. The present paper openly addresses the presence of strong analogies between LPV and TS models, in an attempt to establish a bridge between these two worlds, so far considered different. In particular, this paper addresses the modeling problem, presenting two methods for the automated generation of LPV and TS systems, and introducing some measures in order to compare the obtained models. A mathematical example is used to illustrate the proposed methods. [ABSTRACT FROM AUTHOR]

• Mathematical models for solar cell parameters have been formulated. • Models are based only on information provided by manufacturer's datasheet. • Training data are generated using PV cell equivalent circuit equations. • Mathematical models are developed using Eureqa software. • Models' performance is very efficient based on different four assessments. This paper proposes a simple and efficient method to estimate the parameters of the photovoltaic (PV) cells at standard test conditions (STC). The proposed method is based on developing a mathematical model for each parameter of the single-diode equivalent circuit of the PV cell. The models are formulated using Eureqa software based on thousands of training data collected from simulating the equations that represent the equivalent circuit behavior of the PV cell. Only the data provided by the manufacturer's datasheet are needed to execute the models and evaluate the parameters. The performance of the developed models is then assessed by comparison with simulation data, Simulink library modules, experimental data, and various recent reported algorithms. The assessments have shown an overall efficient performance of the models to evaluate the equivalent circuit parameters for any poly and monocrystalline PV cell/module at STC. In addition, the models are in good agreement and, in some cases, have outperformed various algorithms presented in the literature in terms of the accuracy of predicting the I-V curve characteristics. [ABSTRACT FROM AUTHOR]

We present and discuss a variety of mathematical models that have been proposed to capture the dynamic behavior of epidemic processes. We first present traditional group models for which no underlying graph structures are assumed, thus implying that instantaneous mixing between all members of a population occurs. Then we consider models driven by similar principles, but involving non-trivial networks where spreading occurs between connected nodes. We present stability analysis results for selected models from both classes, as well as simple least squares approaches for estimating the spreading parameters of the virus from data for each basic networked model structure. We also provide some simulation models. The paper should serve as a succinct, accessible guide for systems and control research efforts toward understanding and combating COVID-19 and future pandemics. [ABSTRACT FROM AUTHOR]

Problems in existing methods of production tracking in off-site construction result in schedule delays and increased costs. To eliminate these deficiencies, an autonomous production tracking that analyzes real-time production data is proposed. A specific implementation of the proposed production tracking mechanisms has been developed for a large off-site construction plant in Australia, and is in the process of installation. The paper shows that: (i) The production model in off-site construction is always nonlinear in the outcome due to the presence of variability (ii) in systems with a periodic production target, deviation from the schedule converges to zero at the end of production period and the same downward trend should be followed in designing plan buffers and (iii) long-term production performance in off-site construction can autonomously be monitored and controlled by observing critical variables of production. The paper provides those who manage off-site construction with recommendations on effective production tracking and management. The models and propositions in this research are of practical value and can be used to detect impending production shortfalls against periodic targets in the short-term, and adjust capacity parameters and production targets in long-term planning. [ABSTRACT FROM AUTHOR]

In the present paper, we consider the p -adic λ-model on the Cayley tree of order two. It is considered generalized p -adic quasi Gibbs measure depending on a parameter ρ ∈ ℚ p , for the λ-model. In the p -adic setting there are several kinds of phase transitions such as storing phase transition, phase transition in terms of the generalized p -adic quasi Gibbs measures. In the paper, we consider two regimes with respect to the parameter ρ. The existence of generalized p -adic Gibbs measures in both regimes is proved. We prove the existence of the phase transition for the p -adic λ model on the Cayley tree of order two in the first regime. It turns out that in the second regime, we are able to establish the strong phase transition for a class of λ-models on the same tree. To prove the main results, we employ the methods of p -adic analysis, and therefore, our results are not valid in the real setting. [ABSTRACT FROM AUTHOR]

With the attractive advantages of high efficiency and environmental friendliness, the ground-coupled heat pump system (GCHPs) has been widely applied in various buildings around the world in recent years, and the knowledge of underground thermal properties is a prerequisite for correct design of GCHPs. As an effective way to obtain thermal properties, thermal response test (TRT) has become a routine tool for the design of larger plants with ground heat exchangers (GHEs). This paper summarizes the specifications for in situ TRT, including test setup, minimum duration, heat input rate etc., analyzes the mathematical models currently available for GHE in TRT, and compares the parameter identification methods which are necessary to obtain reasonable properties according to in situ TRT data. In addition, this paper discusses and summarizes the shortages and imperfects of the current research on TRT and gives some recommendations for future work. [ABSTRACT FROM AUTHOR]

Abstract: Lithium-ion battery systems employed in high power demanding systems such as electric vehicles require a sophisticated monitoring system to ensure safe and reliable operation. Three major states of the battery are of special interest and need to be constantly monitored, these include: battery state of charge (SoC), battery state of health (capcity fade determination, SoH), and state of function (power fade determination, SoF). In a series of two papers, we propose a system of algorithms based on a weighted recursive least quadratic squares parameter estimator, that is able to determine the battery impedance and diffusion parameters for accurate state estimation. The functionality was proven on different battery chemistries with different aging conditions. The first paper investigates the general requirements on BMS for HEV/EV applications. In parallel, the commonly used methods for battery monitoring are reviewed to elaborate their strength and weaknesses in terms of the identified requirements for on-line applications. Special emphasis will be placed on real-time capability and memory optimized code for cost-sensitive industrial or automotive applications in which low-cost microcontrollers must be used. Therefore, a battery model is presented which includes the influence of the Butler–Volmer kinetics on the charge-transfer process. Lastly, the mass transport process inside the battery is modeled in a novel state-space representation. [Copyright &y& Elsevier]

Abstract: Based on previous experimental findings and theoretical developments, this paper presents the formulation and numerical algorithms of a novel constitutive model for sand with special considerations for cyclic behaviour and accumulation of large post-liquefaction shear deformation. Appropriate formulation for three volumetric strain components enables the model to accurately predict loading and load reversal behaviour of sand, fully capturing the features of cyclic mobility. Compliance with the volumetric compatibility condition, along with reversible and irreversible dilatancy, allows for physically based simulation of the generation and accumulation of shear strain at zero effective stress after initial liquefaction. A state parameter was incorporated for compatibility with critical state soil mechanics, enabling the unified simulation of sand at various densities and confining pressures with a same set of parameters. The determination methods for the 14 model parameters are outlined in the paper. The model was implemented into the open source finite-element framework OpenSees using a cutting-plane stress integration scheme with substepping. The potentials of the model and its numerical implementation were explored via simulations of classical drained and undrained triaxial experiments, undrained cyclic torsional experiments, and a dynamic centrifuge experiment on a single pile in liquefiable soil. The results showed the model’s great capabilities in simulating small to large deformation in the pre- to post-liquefaction regime of sand. [Copyright &y& Elsevier]

Abstract: A number of techniques have been recently proposed to implement molecular communication, a novel method which aims to implement communication networks at the nanoscale, known as nanonetworks. A common characteristic of these techniques is that their main resource consists of molecules, which are inherently discrete. This paper presents DIRECT, a novel networking model which differs from conventional models by the way of treating resources as discrete entities; therefore, it is particularly aimed to the analysis of molecular communication techniques. Resources can be involved in different tasks in a network, such as message encoding, they do not attenuate in physical terms and they are considered 100% reusable. The essential properties of DIRECT are explored and the key parameters are investigated throughout this paper. [Copyright &y& Elsevier]

This paper deals with the Bayesian estimation of flow parameters in a porous material based on the measurements of local temperature at selected points inside the packed-bed. The unknown probability distribution function of estimated parameters was sampled using a Markov Chain Monte Carlo (MCMC) method. The MCMC method is efficient in sampling unknown distributions, but it is extremely time-consuming. The Proper Orthogonal Decomposition was used to construct off-line, low order approximation of the porous media flow problem. This model was further incorporated into the Metropolis-Hastings algorithm to retrieve the posterior distribution of the unknown parameters. This approach allowed us to decrease the computational time necessary to generate an ergodic Markov Chain. The efficiency of the proposed approach was tested on the simulated data generated with commercial CFD tools, and finally, it was applied to the real data acquired on the dedicated experimental stand. • Research highlight are summarized as follows: • Development of the mathematical model of biomass drying process. • Development of the coupled POD-MCMC inverse methodology estimate unknown parameters of the model. • Analysis of the accuracy of the developed approach. • Application of the inverse method to estimate model parameters for a real measurement data. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we study multiscale finite element methods for Richards’ equation, a mathematical model to describe fluid flow in unsaturated and highly heterogeneous porous media. In order to compute solutions of Richard’s equation, one can use numerical homogenization or multiscale methods that use two-grid procedures: a fine-grid that resolves the heterogeneities and a coarse grid where computations are done. The idea is that the coarse solution procedure captures the fine-grid variations of the solution. Since the media has complicated variations inside of coarse-grid blocks, a large error can be generated during the computation of coarse-scale solutions. In this paper, we consider the case of highly varying coefficients where variations can occur within coarse regions we develop accurate multiscale methods. In order to obtain accurate coarse-scale numerical solutions for Richards’ equation, we design an effective multiscale method that is able to capture the multiscale features of the solution without discarding the small scale details. With a careful choice of the coarse basis functions for multiscale finite element methods, we can significantly reduce errors. We use coarse basis functions construction that combines local spectral problems and a Reduced Basis (RB) approach. This is an extension to the nonlinear case of the method proposed by Efendiev et al. (2012) that combines spectral constructions of coarse spaces with RB procedures to efficiently solve linear parameter dependent flow problems. The construction of coarse spaces begins with an initial choice of multiscale basis functions supported in coarse regions. These basis functions are complemented using a local, parameter dependent, weighted eigenvalue problem. The obtained basis functions can capture the small scale features of the solution within a coarse-grid block and give us an accurate coarse-scale solution. The RB procedures are used to efficiently solve for all possible flow scenarios encountered in every single iteration of a fixed point iterative method. We present representative numerical experiments. [Copyright &y& Elsevier]

Abstract: This paper studies the impact of inaccurate demand beliefs on dynamic quantity-setting market games. The conventional assumption that players share a uniform and accurate demand model in game is relaxed by a more realistic model—players individually make their subjective demand estimation possibly with errors. A dynamic game with such subjective demand belief for two heterogeneous players is built. Based on this model, the impact of demand belief errors on the game’s equilibriums and their stability is investigated. The results shows that the stability region is not only determined by the parameters of the system configuration and the bidding adjustment-as pointed out in conventional model, but also by the player’s imperfect knowledge about the market. Thus new dynamic behaviors and instability may arise given an original error-free system (where all players are assumed to have perfect and uniform knowledge about the market). It is suggested that in order to avoid possible system instability, the design of players’s adjustment speed, and the possible demand belief errors from different players must be taken into account. Moreover, this paper points out that the various behavior patterns of players have different influence on the stability regions and corresponding dynamic characteristics of system equilibriums. The changes of players’s profits and customer surplus induced by inaccurate beliefs are also studied. The stability analyses, dynamics behaviors, and system performance in steady state are validated with a set of numerical experiments. [Copyright &y& Elsevier]

Abstract: Irrigation canals are open-flow water hydraulic systems, whose objective is mainly to convey water from its source down to its final users. They are large distributed systems characterized by non-linearity and dynamic behavior that depends on the operating point. Moreover, in canals with multiple reaches dynamic behavior is highly affected by the coupling among them. The physical model for those systems leads to a distributed-parameter model whose description usually requires partial differential equations (PDEs). However, the solution and parameter estimation of those PDE equations can only be obtained numerically and imply quite time-consuming computations that make them not suitable for real-time control purposes. Alternatively, in this paper, it will be shown that open-flow canal systems can be suitably represented for control purposes by using linear parameter-varying (LPV) models. The advantage of this approach compared to the use of PDE equation is that allows simpler models which are suitable for control design and whose parameters can be easily identified from input–output data by means of classical identification techniques. In this paper, the well-known control-oriented, model named integral delay zero (IDZ), that is able to represent the canal dynamics around a given operating point by means of a linear time-invariant (LTI) model is extended to multiple operating points by means of an LPV model. The derivation of this LPV model for single-reach open-flow canal systems as well as its extension to multiple-reach open-flow canals is proposed. In particular, the proposed methodology allows deriving the model structure and estimating model parameters using data by means of identification techniques. Thus, a gray-box control model is obtained whose validation is carried out using single-pool and two-pool test canals obtaining satisfactory results. [Copyright &y& Elsevier]

Abstract: Thermal properties (i.e. thermal conductivity and heat capacity) are important parameters that influence the temperature of building materials and thermal performance of built environment. These properties are required as fundamental inputs for modeling and simulating thermal behavior of built environment. Most existing methods for measuring thermal properties of building materials are based on 1-D steady-state heat transfer theory. The critical challenge for these methods has been the difficulty in achieving a 1-D heat flow condition for the testing specimen. A multi-dimensional transient method is needed to reduce the challenge and requirement on the testing specimen size and shape, and make it possible to accurately measure all the thermal properties from one single test. This paper first developed a multi-dimensional transient model and a practical tool to simulate the transient temperature at any location on a beam or cylinder specimen of any size subject to convection heat transfer. Case studies verified that this model can be used to, if the thermal properties are known, simulate the transient temperature at any location for a specimen of various shapes and sizes, and predict the time to reach a specified target temperature for mechanical and other testing. Secondly, this paper developed and validated partly by case studies on both asphalt and concrete materials a procedure for back-calculating the thermal properties of a specimen of various shapes and sizes. Thermal properties of novel building materials (various initiative cool materials such as porous concrete and high thermal resistance materials) can be easily measured with that procedure. [Copyright &y& Elsevier]

Abstract: Pareto distributions, and power laws in general, have demonstrated to be very useful models to describe very different phenomena, from physics to finance. In recent years, the econophysical literature has proposed a large amount of papers and models justifying the presence of power laws in economic data. Most of the times, this Paretianity is inferred from the observation of some plots, such as the Zipf plot and the mean excess plot. If the Zipf plot looks almost linear, then everything is ok and the parameters of the Pareto distribution are estimated. Often with OLS. Unfortunately, as we show in this paper, these heuristic graphical tools are not reliable. To be more exact, we show that only a combination of plots can give some degree of confidence about the real presence of Paretianity in the data. We start by reviewing some of the most important plots, discussing their points of strength and weakness, and then we propose some additional tools that can be used to refine the analysis. [Copyright &y& Elsevier]

Abstract: In this paper, the Carman–Kozeny (C–K) model is proposed as a means of predicting flux decline for every stage of membrane cleaning, which can be strongly affected by pore blockage and by the growth of a cake layer. A combination of a kinetic model and the C–K model was utilized to determine specific parameters that can be used to estimate the decline in flux. The model was shown to be in excellent agreement with the experimental data obtained during the crossflow microfiltration of microalgal biomass over several cleaning cycles. The mean flux, given suitable transmembrane pressure (TMP), was also predicted by the model. The cleaning kinetics of the process suggested that a higher energy barrier E c and a higher rate constant k 0 were present for the final cycle of cleaning process, resulting from the extra energy required to break the bonds between the deposited cells and the membrane. This paper also focused on the electrostatic interaction between the microalgal cells and the membrane surface. The concentration polarization (CP) thickness of microalgae on a cellulose acetate (CA) membrane was calculated by the CDE model based on the balance of three transport mechanisms, namely, convection, diffusion and electrophoretic migration, with consideration being given to the negative charge of the microalgae and the membrane surface. The CP thickness was found to be strongly dependent on the membrane surface charge and the number of membrane cleaning cycles. The full interaction between the microalgae and the membrane was characterized according to a DLVO and an extended DLVO approach based on the strength of three separate interactions, namely, the van-der-Waals interaction (vdW), electrostatic (ES) repulsion and acid–base (AB) interactions. Both DLVO and XDLVO analysis provide useful methods for predicting the potential role of these interactions in the adhesion of microalgae to membranes. The XDLVO analysis determined a higher repulsion than did the DLVO analysis as a result of the high AB interaction energy in aqueous systems. [Copyright &y& Elsevier]

Abstract: Detection of anomalies is a broad field of study, which is applied in different areas such as data monitoring, navigation, and pattern recognition. In this paper we propose two measures to detect anomalous behaviors in an ensemble of classifiers by monitoring their decisions; one based on Mahalanobis distance and another based on information theory. These approaches are useful when an ensemble of classifiers is used and a decision is made by ordinary classifier fusion methods, while each classifier is devoted to monitor part of the environment. Upon detection of anomalous classifiers we propose a strategy that attempts to minimize adverse effects of faulty classifiers by excluding them from the ensemble. We applied this method to an artificial dataset and sensor-based human activity datasets, with different sensor configurations and two types of noise (additive and rotational on inertial sensors). We compared our method with two other well-known approaches, generalized likelihood ratio (GLR) and One-Class Support Vector Machine (OCSVM), which detect anomalies at data/feature level. We found that our method is comparable with GLR and OCSVM. The advantages of our method compared to them is that it avoids monitoring raw data or features and only takes into account the decisions that are made by their classifiers, therefore it is independent of sensor modality and nature of anomaly. On the other hand, we found that OCSVM is very sensitive to the chosen parameters and furthermore in different types of anomalies it may react differently. In this paper we discuss the application domains which benefit from our method. [Copyright &y& Elsevier]

Abstract: The current paper deals with the mathematical models of predator–prey system where a transmissible disease spreads among the predator species only. Four mathematical models are proposed and analysed with several popular predator functional responses in order to show the influence of functional response on eco-epidemic models. The existence, boundedness, uniqueness of solutions of all the models are established. Mathematical analysis including stability and bifurcation are observed. Comparison among the results of these models allows the general conclusion that relevant behaviour of the eco-epidemic predator–prey system, including switching of stability, extinction, persistence and oscillations for any species depends on four important parameters viz. the rate of infection, predator interspecies competition and the attack rate on susceptible predator. The paper ends with a discussion of the biological implications of the analytical and numerical results. [Copyright &y& Elsevier]

Abstract: This paper studies the effects of learning and forgetting on a two-stage production system and the position of a potential bottleneck in the system. We start with developing a model for a two-stage serial production system where semi-finished items are fed by the first stage to the second stage, which, in turn, processes the items to their final state. The finished items are transferred either to a subsequent stage or to customers. The paper assumes that both stages of the production system considered are subject to learning and forgetting effects. Learning quickens the production rate as more experience is gained (i.e., when the number of repetitions increases), while forgetting has the opposite effect when production is intermittent (i.e., experience is lost over production breaks). The paper studies how different values of the learning and forgetting parameters influence the ratio of the production rates of both stages and the flow of material in the system. The results of the paper indicate that learning may cause a bottleneck to shift its position in a production system. This happens when an initially slower stage overtakes a previously faster stage over time due to a higher learning rate. The paper thus contributes to the literature on moving bottlenecks and provides practitioners with a model that helps predicting where bottlenecks may arise in the production system, and which enables the system to flexibly react to moving bottlenecks. [Copyright &y& Elsevier]

Abstract: Motion is a common phenomenon in the real world so the consistence of virtual and real motion is one of hot focuses in augmented reality system. This paper mainly analyzes virtual and real motion blur consistence in augmented reality system, and mainly do improvement in two aspects, one is how to get the motion blur parameters, the other is how to render motion blur. From the available literature it can be seen that, most achieved methods to get motion blur parameters are based on machine vision. But this paper first acquires the motion trajectory by magnetic tracking, and then gets motion blur parameters. Considering the available literature on motion blur rendering methods, they mainly contain convolution and accumulator caching and so on. Due to rotation translation compound motion blur, this paper does rendering in advance based on improved mathematical model, and then render virtual object through looking up table in augmented reality system. Experimental results show that the new virtual and real motion blur consistence render algorithm has low time complexity and achieve the desired effect in simulation environment and real scenes. [Copyright &y& Elsevier]

Abstract: Problem: This paper aims to address two related issues when applying hierarchical Bayesian models for road safety analysis, namely: (a) how to incorporate available information from previous studies or past experiences in the (hyper) prior distributions for model parameters and (b) what are the potential benefits of incorporating past evidence on the results of a road safety analysis when working with scarce accident data (i.e., when calibrating models with crash datasets characterized by a very low average number of accidents and a small number of sites). Method: A simulation framework was developed to evaluate the performance of alternative hyper-priors including informative and non-informative Gamma, Pareto, as well as Uniform distributions. Based on this simulation framework, different data scenarios (i.e., number of observations and years of data) were defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. Results: This study shows how the accuracy of model parameter estimates (inverse dispersion parameter) is considerably improved when incorporating past evidence, in particular when working with the small number of observations and crash data with low mean. The results also illustrates that when the sample size (more than 100 sites) and the number of years of crash data is relatively large, neither the incorporation of past experience nor the choice of the hyper-prior distribution may affect the final results of a traffic safety analysis. Conclusions: As a potential solution to the problem of low sample mean and small sample size, this paper suggests some practical guidance on how to incorporate past evidence into informative hyper-priors. By combining evidence from past studies and data available, the model parameter estimates can significantly be improved. The effect of prior choice seems to be less important on the hotspot identification. Impact on Industry: The results show the benefits of incorporating prior information when working with limited crash data in road safety studies. [Copyright &y& Elsevier]

Abstract: The effect of difference in susceptibility on the dynamics of tuberculosis is an ongoing study. Several genes in TB co-receptors (e.g. HLA and non-HLA) have been correlated with susceptibility and resistance to tuberculosis and rate of progression to active TB. In this paper, we present a novel mathematical model that distinguishes between susceptibility amongst the population. The model classifies the susceptibles as having no, partial or full natural resistance to tuberculosis and the latently infecteds as rapid, normal or very slow (or no) progressors to active TB depending on the genes. The goal of this paper is to investigate the impact of such heterogeneity on the spread of tuberculosis and to identify key parameters that could be used in understanding these heterogeneities more fully. We derived the reproduction number for the model and examine the relative contributions to from the three latently infected classes as well as the infectious classes. The effect of treatment under the heterogeneity is also examined. [Copyright &y& Elsevier]

Abstract: In this paper, performance-based parameter tuning method of model-driven Two-Degree-of-Freedom PID (MD TDOF PID) control system has been proposed to enhance the control performances of a process. Known for its ability of stabilizing the unstable processes, fast tracking to the change of set points and rejecting disturbance, the MD TDOF PID has gained research interest recently. The tuning methods for the reported MD TDOF PID are based on internal model control (IMC) method instead of optimizing the performance indices. In this paper, an Integral of Time Absolute Error (ITAE) zero-position-error optimal tuning and noise effect minimizing method is proposed for tuning two parameters in MD TDOF PID control system to achieve the desired regulating and disturbance rejection performance. The comparison with Two-Degree-of-Freedom control scheme by modified smith predictor (TDOF CS MSP) and the designed MD TDOF PID tuned by the IMC tuning method demonstrates the effectiveness of the proposed tuning method. [Copyright &y& Elsevier]

Abstract: By the use of modelling software, this paper set up the three-dimensional physical model of single-stage turbine and guided it into the pre-process software. After meshing the turbine flow channel, simulating the mud flow characteristics in Turbine with CFD, and calculating the pressure drawdown at the inlet/outlet and the pressure distributions in single-stage turbine. Such results indicate that the pressure drawdown of single-stage turbine is influenced by the mud discharge capacity, drilling fluid density and viscosity. The pressure drop increases with the augment of mud discharge, which is approximately linear. There are significantly positive correlations between the pressure drawdown and the increase of mud density. The research in the paper provides reliable guidance in turbine flow channel design and the drilling technological parameters optimizing. [Copyright &y& Elsevier]

Abstract: In-situ stress field is very important in the numerical simulation and stability analysis as well as in the engineering design and construction. So it requests an effective analysis method. The typical in-situ stress analysis method is multivariate regression, which is based on 3-D FEM direct modeling, but it may be not so accurate to obtain the distribution of in-situ stress field of the project, because when the multivariate regression is carried out, the cross influence of the main factors of in-situ stress field is ignored, and the relationship of in-situ stress field and the main factors is too complicated to express by subharmonic multivariate function. This paper presents an improved method back propagation (BP) neural network back analysis of in-situ stress field, based on the calculated results of multivariate linear regression analysis. For in the ANN (Artificial Neural Networks) method, the training samples are difficult to generate, while combining the linear regression method with ANN method this problem can be solved perfectly. Linear elastic multivariate regression is carried out to determine the general bounds of optimized paraments, and uniform design method is carried out to settle different combinations of factor levels. Training samples are gained by FEM analysis. And the visual neural network by using ActiveX technology is realized in this paper, by taking full advantages of strong capability in computing of Matlab and features of friendly interface of VB perfectly. In fact, one needs to know the status of the in-situ stress field at some important location in the engineering, by using neural network method, intelligent expression method of the in-situ stress field is presented, and the developed man-computer interface has the property of easy use in the actual engineering. [Copyright &y& Elsevier]

Abstract: Robot is widely used in industrial domain and pose accuracy of end-effector is an important indicator for judging robot performance. Calibration is a necessary for operating industrial robot and an effective and efficient means of accuracy improvement. This paper describes the development of a calibration procedure for a five degree of freedom serial robot using the laser tracker. The main goal of this paper is to utilize measurements relative to the end-effector of the robot to compensate errors. The robot kinematic model is computed to help identify the deviations. Robot parameter deviations can be identified so that the nominal parameters can be corrected. The experimental results are validated effectively. [Copyright &y& Elsevier]

Abstract: Bayesian method is good to improve precision and earn cost, but it would be constrain to use in situation of field data be different with prior data in overall distribution, as generally change model or give up Bayesian method in this situation. In this paper, with consideration of physical connection of prior data and field data, thoughts of using Bayesian fusion method in different overall distribution between prior data and field data was developed, which was from 3 conditions of function of prior data and field data: distribution form and parameters were known, parameter had prior distribution and only distribution form were know. At last, an example was given to explain the usage method promoted by this paper. [Copyright &y& Elsevier]

Abstract: Combining the theory of Copula with statistics of extremes, this paper studied the dependence between drainage area and drainage height difference of the six relief factors of debris flow. The correlation coefficient Kendall''s τ, Spearman''s ρ, and the tail association measure χ have been calculated. Moreover, in order to describe the extreme dependent structure about the two relief factors comprehensively, this paper makes a bivariate exceedance model and gives the tail distribution and the parameter estimation methods. Finally, as examples, this paper analyses the dependence of relief factors of the debris flow. [Copyright &y& Elsevier]

Abstract: Currently, variable speed wind turbine generators (VSWTs) are the type of wind turbines most widely installed. For wind energy studies, they are usually modelled by means the approximation of the manufacturer power curve using a generic equation. In literature, several expressions to do this approximation can be found; nevertheless, there is not much information about which is the most appropriate to represent the energy produced by a VSWT. For this reason, in this paper, it is carried out a review of the equations commonly used to represent the power curves of VSWTs: polynomial power curve, exponential power curve, cubic power curve and approximate cubic power curve. They have been compared to manufacturer power curves by using the coefficients of determination, as fitness indicators, and by using the estimation of energy production. Data gathered from nearly 200 commercial VSWTs, ranging from 225 to 7500kW, has been used for this analysis. Results of the analysis presented in the paper show that exponential and cubic approximations give the higher R 2 values and the lower error in energy estimation. With the approximate cubic power curve quite high values of R 2 and low errors in energy estimation are achieved, which makes this kind of approximation very interesting due to its simplicity. Finally, the polynomial power curve shows the worst results mainly due to its sensitivity to the data given by the manufacturer. [Copyright &y& Elsevier]

Abstract: The cost and performance of PV plants goes with the module under consideration. However the electrical parameters of the modules vary from those provided by the manufacturer with the ageing of the module. Therefore, the behavior of the mathematical model of a PV module cannot match the real operating conditions. There are papers having proposed an improved model of a PV module that makes use only parameters provided by manufacturer''s datasheets without requiring the use of any numerical methods. This paper aims to interpret by using one of the available models represented as Norton''s equivalent circuit which is a simple and approximate model. Norton''s circuit model representing the module helps knowing the behavior of solar module. The study and performance of this model is compared with that of the existing model using Matlab and the results are matching to the greater extent. The results obtained for both the models are validated by conducting series of experiments on a physical module. Thus, the work signifies yet another model for a PV module to evaluate the performance parameters [Copyright &y& Elsevier]

Abstract: The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China–Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced. [Copyright &y& Elsevier]