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The current paper examines how a commitment to a principle, adhered to by an individual agent, becomes an accepted standard of an epistemic community. Addressing this question requires three steps: first, to define the terms used throughout the paper, and especially the characteristics of commitments to a principle. The second step is to find a mechanism through which such epistemic commitments are introduced to an epistemic community and in certain cases are adopted as the standard by the community. While there could be several such mechanisms, the current paper focuses on the practice of model formulation. The third step is to demonstrate the analytical framework developed in the first two steps in a case study. The case study chosen for this paper is the unique approach to feedback analysis adopted by the ecologist and population geneticist Richard Levins. In what follows I will show that part of the features that made Levins’ approach unique was his Marxist commitments, and his attempt to embody those commitments in feedback analysis by formally representing them as modeling assumptions. [ABSTRACT FROM AUTHOR]

In light of the pressing issue of climate change and escalating global carbon emissions, this study investigates the efficacy of blue carbon ecosystems — namely, mangroves, sea-grasses, and tidal marshes — in carbon sequestration and climate mitigation. Focused on formulating actionable strategies, we examine these ecosystems’ capacity to serve as natural carbon sinks under nutrient stress conditions. Utilizing a sophisticated mathematical model that incorporates both deterministic and stochastic elements, we simulate the carbon and nitrogen cycling within these ecosystems to capture the complexity and variability of natural processes. Our equilibrium analysis identifies critical thresholds at which these ecosystems optimally function as carbon sinks. Furthermore, our sensitivity analysis highlights key parameters such as nutrient availability and hydrodynamic conditions that significantly influence these thresholds. Results indicate that management practices focusing on nutrient regulation could enhance the carbon sequestration potential of these ecosystems. The insights derived from this research not only deepen our understanding of the role of blue carbon in climate mitigation but also offer concrete recommendations for policymakers and conservationists to optimize these natural resources in combating global warming. [ABSTRACT FROM AUTHOR]

In this research work, we developed a fractional-order model for the transmission dynamics of malaria, incorporating two control strategies: health education campaigns and the use of insecticides. The theoretical analysis of the model is presented, including the computation of disease-free equilibrium and basic reproduction number. We analyzed the stability of the proposed model using a well-formulated Lyapunov function. Furthermore, model parameter estimation was carried out using real data from malaria cases reported in Zimbabwe. We found that the fractional-order model provided a better fit to the real data compared to the classical integer-order model. Sensitivity analysis of the basic reproduction number was performed using computed partial rank correlation coefficients to assess the effect of each parameter on malaria transmission. Additionally, we conducted numerical simulations to evaluate the impact of memory effects on the spread of malaria. The simulation results indicated that the order of derivatives significantly influences the dynamics of malaria transmission. Moreover, we simulated the model to assess the effectiveness of the proposed control strategies. Overall, the interventions were found to have the potential to significantly reduce the spread of malaria within the population. [ABSTRACT FROM AUTHOR]

The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter but that may differ appreciably in other respects. We study the general structure of models under which the maximum likelihood estimator of the parameter of interest is consistent under arbitrary misspecification of the nuisance part of the model. A specialization of the general results to matched-comparison and two-groups problems gives a more explicit and easily checkable condition in terms of a notion of symmetric parameterization, leading to a broadening and unification of existing results in those problems. The role of a generalized definition of parameter orthogonality is highlighted, as well as connections to Neyman orthogonality. The issues involved in obtaining inferential guarantees beyond consistency are briefly discussed. [ABSTRACT FROM AUTHOR]

Banyumas is one of the famous tourist destinations in Central Java with its natural attractions. Tourism growth in Banyumas requires capable and sustainable transportation infrastructure. However, current policies and investments in Banyumas are inappropriate for developing transportation and tourism infrastructure that creates non-significant impact. Therefore, the development of tourism projection should consider environmental, social, economics, and technology interrelated in developing transportation and tourism infrastructure issues. The tourism supply chain has a unique structure that is different from typical supply chains with high complexity because many stakeholders are involved. This research aims to project Banyumas tourism development with the impact of transportation system improvement. This conceptual model was developed from literature reviews and other necessary sources, and then validated by experts. The conceptual model examines the relationship between variables involved in the tourism supply chain and transportation system in Banyumas that can be developed into a quantitative model using Causal Loop Diagram, an initial stage of system dynamics. This model used to analyze the behavior of visitor projection of the Banyumas tourism supply chain system in Indonesia. [ABSTRACT FROM AUTHOR]

The harsh operating environment of automotive and aerospace applications causes printed circuit board (PCB) connectors to be susceptible to intermittent high contact resistance, which eventually leads to a failure resulting in the loss of signal integrity. Pin fretting within the mating connector is often the cause of these failures. Over the past several years, there has been a significant increase in the laboratory-based testing of sample connectors for pin fretting. These laboratory-based results have shown the primary cause of pin fretting to be due to the relative motion within the mating connector. However, quantification of the relative motion in different vibration environments by considering the dynamics of PCBs has not been studied yet. This paper develops a new methodology for studying pin fretting within the mating connector. The developed methodology is based on the quantification of relative motion as a measure of pin fretting by considering the dynamics of PCBs, printed circuit board assemblies (PCBAs), and mounting brackets. To do this, a continuous, lightly damped, multi-degree of freedom (MDOF) model is developed for the assembly consisting of PCBs, PCBAs, and mounting brackets. The behavior of the system is investigated by exciting the system using harmonic and random vibration signals. In the harmonic signal analysis, a frequency domain-based approach is presented to compute the relative motion of the mating connector, while a pseudo-random time series signal derived from the power spectrum density (PSD) of the signal is used to excite the system for the random vibration excitation analysis. The relative response vector is then computed based on the system's response. The results of the relative motion of the mating connectors are presented in terms of the maximum amplitude of relative motion and the cumulative relative movement. The significance of the cumulative relative movement is that the complex phenomenon of pin fretting in the mating connectors can be represented by a simple time series model that can be used to correlate the material degradation. Finally, two numerical examples using the analytical and finite element-based (FE) technique are shown to demonstrate the proposed methodology. The examples show that the method proposed here is systematic and constructive in quantifying the pin fretting behavior. [ABSTRACT FROM AUTHOR]

• The Coastal Generalized Ecosystem Model (CGEM) is an open-source model. • CGEM offers different formulations for rate processes, providing a flexible model structure. • Model formulations have significant effects on simulation outcomes. • Flexible model structure is important for adapting models to different ecosystems. • Formulations impact simulation outcomes for climate change and HABs. The Coastal Generalized Ecosystem Model (CGEM) is a biogeochemical model developed to study regulating processes of water-column optical properties, water-column and benthic carbon, oxygen, and nutrient cycles, and phytoplankton and zooplankton dynamics. CGEM offers numerous formulations for important rate processes, providing users flexibility in altering model structure. This flexibility also provides a means for evaluating model structural uncertainty and impacts on simulations, which are rarely evaluated with numerical ecosystem models. As an open-source model, CGEM also offers users the option to implement new formulations or modify existing routines. We also provide a full description of the model formulations, state variables, and model parameters in CGEM. Using two published case studies, we explore how different formulations for light attenuation, phytoplankton temperature growth response, and sediment processes impact simulations. We discuss CGEM's role as a new ecosystem model within the modeling community and opportunities to address current and future water quality issues. [ABSTRACT FROM AUTHOR]

The growth and treatment of tumors is an important problem to society that involves the manifestation of cellular phenomena at length scales on the order of centimeters. Continuum mechanical approaches are being increasingly used to model tumors at the largest length scales of concern. The issue of how to best connect such descriptions to smaller-scale descriptions remains open. We formulate a framework to derive macroscale models of tumor behavior using the thermodynamically constrained averaging theory (TCAT), which provides a firm connection with the microscale and constraints on permissible forms of closure relations. We build on developments in the porous medium mechanics literature to formulate fundamental entropy inequality expressions for a general class of three-phase, compositional models at the macroscale. We use the general framework derived to formulate two classes of models, a two-phase model and a three-phase model. The general TCAT framework derived forms the basis for a wide range of potential models of varying sophistication, which can be derived, approximated, and applied to understand not only tumor growth but also the effectiveness of various treatment modalities. [ABSTRACT FROM AUTHOR]

Bus bridging evacuation refers to transporting affected passengers to their destination stations during metro disruption by buses dispatched from depots. Planning bus bridging plays an important role in maintaining the level of service of metro rail and serving affected passenger demands. This paper presents an integrated planning and operational model for bus bridging evacuation, in which the varying bridging passenger demand at disrupted metro stations, the arrival and departure of bus fleets, the dwelling time of a bus fleet, and the number of alighting and boarding passengers at disrupted metro stations are captured in detail. Based on the dynamics of a bus bridging system, bus bridging routes are planned to stop at intermediate disrupted metro stations between their dispatched disrupted metro station and turnover stations. Results of a case study with Shanghai metro line 16 demonstrate the advantage of the proposed planning model in real-world application. The proposed model and its simulation-based solving algorithm yield economical bridging plans with fewer dispatched buses and lower operation cost compared with our previous model. Results of the case study show that total operation cost and computation time are contradictory with each other. The combination of larger population size, larger maximum number of generations, and larger crossover rate probably contributes to finding the globally optimal bus dispatch plan. [ABSTRACT FROM AUTHOR]

This paper introduces a new large‐eddy simulation model, FastEddy®, purpose built for leveraging the accelerated and more power‐efficient computing capacity of graphics processing units (GPUs) toward adopting microscale turbulence‐resolving atmospheric boundary layer simulations into future numerical weather prediction activities. Here a basis for future endeavors with the FastEddy® model is provided by describing the model dry dynamics formulation and investigating several validation scenarios that establish a baseline of model predictive skill for canonical neutral, convective, and stable boundary layer regimes, along with boundary layer flow over heterogeneous terrain. The current FastEddy® GPU performance and efficiency gains versus similarly formulated, state‐of‐the‐art CPU‐based models is determined through scaling tests as 1 GPU to 256 CPU cores. At this ratio of GPUs to CPU cores, FastEddy® achieves 6 times faster prediction rate than commensurate CPU models under equivalent power consumption. Alternatively, FastEddy® uses 8 times less power at this ratio under equivalent CPU/GPU prediction rate. The accelerated performance and efficiency gains of the FastEddy® model permit more broad application of large‐eddy simulation to emerging atmospheric boundary layer research topics through substantial reduction of computational resource requirements and increase in model prediction rate. Plain Language Summary: This paper introduces a new model for atmospheric flows, FastEddy®, engineered to permit faster, more power‐efficient, and more broad engagement in simulation of atmospheric flows at high levels of spatial and temporal detail by using graphics cards for accelerating computations. A model description and set of pertinent validation efforts are provided along with performance intercomparison versus two state‐of‐the‐art and widely used models of a similar vein. The documentation of formulation and validity along with the accelerated performance and more power‐efficient capability of FastEddy® provides a comprehensive and robust basis for future adoption and extension as an enabling technology for high‐impact atmospheric boundary layer research and applications. Key Points: We introduce a new GPU accelerated LES modelMultiple validation cases demonstrate commensurate skill to LES models in literatureComparison to exemplar CPU models shows improved prediction rate and reduced power consumption [ABSTRACT FROM AUTHOR]

The analysis of dielectric response function obtained from the power transformer is a well‐accepted technique in insulation diagnosis. A convenient way of analysing time‐domain dielectric response φ (t) is the formulation of simple resistance−capacitance‐based circuits like the conventional Debye model (CDM) or the modified Debye model (MDM) that are capable of modelling φ (t). However, available techniques do not guarantee unique branch parameters of such circuits for a given φ (t). In fact, the branch parameters are known to depend on the curve‐fitting procedure opted during model formulation. This makes accuracy of CDM and MDM parameter‐based diagnosis techniques less reliable. Furthermore, it seems logical to analyse the time‐domain dielectric response of oil‐paper insulation using a model containing time‐varying parameters. In this study, a methodology is proposed using which such an insulation model (containing time‐varying parameters) is formulated using time‐domain insulation response. Related analysis presented suggests that the proposed model is immune to problems associated with available insulation models (containing time‐invariant parameters). The performance of the proposed model in indicating insulation condition is tested on data obtained from several real‐life power transformers. [ABSTRACT FROM AUTHOR]

It is common to encounter polytomous and nominal responses with latent variables in social or behavior research, and a variety of polytomous and nominal item response theory (IRT) models are available for applied researchers across diverse settings. With its flexibility and scalability, the Bayesian approach using the Markov chain Monte Carlo (MCMC) method demonstrates its great advantages for polytomous and nominal IRT models. However, the potential of the Bayesian approach would not be fully realized without model formulations that can cover various models and effective fit measures for model assessment or criticism. This research first provided formulations for typical models that are representative of different modeling groups. Then, a series of discrepancy measures that can offer diagnostic information for model-data misfit were introduced. Simulation studies showed that the formulation worked as expected, and some of the fit measures were more useful than the others or across different situations. [ABSTRACT FROM AUTHOR]

This paper deals with intermodal transportation of goods from Europe to the United States. The main objective is the development of a mathematical model called Transatlantic multi-mode container routing problem. It provides a tool for network optimization in transportation science. The developed model enables users to determine the optimal collaborative routing in a network system that achieves the minimization of costs for a company that ships a single commodity from multiple origins to multiple destinations. The model consists of network of maritime shipping lines between the ports and then networks of road and railway transportation on both sides of the Atlantic Ocean. In order to develop the model it was necessary to make assumptions, define sets, parameters, decision variables, constraints and objective function. Branch and cut algorithm used by GAMS modelling software provides the solution of the model. Short analysis of trends and statistics of road and rail transportation as complements to maritime transportation on both continents is also presented. [ABSTRACT FROM AUTHOR]

Summary The thermodynamically constrained averaging theory (TCAT) is used to formulate a modeling framework for mechanistic models of species transport in multiphase porous medium systems containing two fluid phases and a solid phase. Primary restrictions guide the selection of entities and the set of conservation and balance equations needed to formulate an augmented entropy inequality (EI). Classical irreversible thermodynamics is upscaled from the microscale to the macroscale and used to provide a connection among material derivatives that arise from the conservation and balance equations under near-equilibrium conditions. An essentially exact constrained EI (CEI) is derived to approach the force-flux form of the EI that is desired. A set of approximations is applied to the CEI to produce a non-unique simplified EI (SEI), which is in the strict force-flux form needed to guide the formulation of closure relations. Sets of secondary restrictions are applied to the general SEI to form simpler subsets of the general SEI that apply for the local thermal equilibrium and isothermal cases. The SEI is then used to constrain the permissible form of the closure relations and to formulate a set of low-order closure relations. A specific model instance is formulated in closed form by specifying a complete set of conservation equations and closure relations. Many other model instances can be derived from the general modeling framework presented, and these potential extensions are discussed. [ABSTRACT FROM AUTHOR]

Species distribution models have been used extensively in many fields, including climate change biology, landscape ecology and conservation biology. In the past 3 decades, a number of new models have been proposed, yet researchers still find it difficult to select appropriate models for data and objectives. In this review, we aim to provide insight into the prevailing species distribution models for newcomers in the field of modelling. We compared 11 popular models, including regression models (the generalized linear model, the generalized additive model, the multivariate adaptive regression splines model and hierarchical modelling), classification models (mixture discriminant analysis, the generalized boosting model, and classification and regression tree analysis) and complex models (artificial neural network, random forest, genetic algorithm for rule set production and maximum entropy approaches). Our objectives are: (i) to compare the strengths and weaknesses of the models, their characteristics and identify suitable situations for their use (in terms of data type and species-environment relationships) and (ii) to provide guidelines for model application, including 3 steps: model selection, model formulation and parameter estimation. [ABSTRACT FROM AUTHOR]

Abstract: This work is the ninth in a series of papers on the thermodynamically constrained averaging theory (TCAT) approach for modeling flow and transport phenomena in multiscale porous medium systems. A fundamental approach is developed to model the transition region between a two-fluid-phase porous medium system and a single-fluid-phase system, including species transport. A general model formulation is developed along with an entropy inequality to guide the specification of closure relations. The general model formulation and entropy inequality are then used to specify a closed system. The transition region model developed in this work is a generalization and extension of coupling conditions commonly used in sharp interface models. The theoretical framework has multiple areas of potential applicability including terrestrial-atmospheric contact zones, surface water-sediment interface zones, and industrial drying processes. [Copyright &y& Elsevier]

Abstract: Models formulated to associate odors and odorants in many industrial and agricultural fields ignore the potential effect of relative humidity on odor perception, and are not validated. This study addresses literature limitations by formulating a model that includes relative humidity and by validating the model. The model employs measured paired values, n = 102, of indoor odors and odorants from freshly dewatered biosolids in a post-digestion dewatering building of a Water Reclamation Plant (WRP). A random sub-sample of n = 32 is used to validate the model by associating predicted vs. measured values (R 2 = 0.90). The model is validated again with a smaller independent database from a second WRP (R 2 = 0.85). Moreover this study asserts that reduction of hydrogen sulfide concentrations, conventionally used as a surrogate of sewage odors, to acceptable levels does not assure acceptable odor levels. It is concluded that: (1) The addition of relative humidity results in a stronger association between odors and odorants than the use of H2S alone; (2) the two step model validation indicates that the model is not simply site-specific but can be applied to similar facilities; and (3) the model is a promising tool for designing odor and odorant control strategies, the ultimate goal of engineering studies. [Copyright &y& Elsevier]

The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models. [Copyright &y& Elsevier]

Abstract: Transfer learning is the ability to apply previously learned knowledge to new problems or domains. In qualitative reasoning, model formulation is the process of moving from the unruly, broad set of concepts used in everyday life to a concise, formal vocabulary of abstractions, assumptions, causal relationships, and models that support problem-solving. Approaching transfer learning from a model formulation perspective, we found that analogy with examples can be used to learn how to solve AP Physics style problems. We call this process analogical model formulation and implement it in the Companion cognitive architecture. A Companion begins with some basic mathematical skills, a broad common sense ontology, and some qualitative mechanics, but no equations. The Companion uses worked solutions, explanations of example problems at the level of detail appearing in textbooks, to learn what equations are relevant, how to use them, and the assumptions necessary to solve physics problems. We present an experiment, conducted by the Educational Testing Service, demonstrating that analogical model formulation enables a Companion to learn to solve AP Physics style problems. Across six different variations of relationships between base and target problems, or transfer levels, a Companion exhibited a 63% improvement in initial performance. While already a significant result, we describe an in-depth analysis of this experiment to pinpoint the causes of failures. Interestingly, the sources of failures were primarily due to errors in the externally generated problem and worked solution representations as well as some domain-specific problem-solving strategies, not analogical model formulation. To verify this, we describe a second experiment which was performed after fixing these problems. In this second experiment, a Companion achieved a 95.8% improvement in initial performance due to transfer, which is nearly perfect. We know of no other problem-solving experiments which demonstrate performance of analogical learning over systematic variations of relationships between problems at this scale. [Copyright &y& Elsevier]

Abstract: This work is the seventh in a series that introduces and employs the thermodynamically constrained averaging theory (TCAT) for modeling flow and transport in multiscale porous medium systems. This paper expands the previous analyses in the series by developing models at a scale where spatial variations within the system are not considered. Thus the time variation of variables averaged over the entire system is modeled in relation to fluxes at the boundary of the system. This implementation of TCAT makes use of conservation equations for mass, momentum, and energy as well as an entropy balance. Additionally, classical irreversible thermodynamics is assumed to hold at the microscale and is averaged to the megascale, or system scale. The fact that the local equilibrium assumption does not apply at the megascale points to the importance of obtaining closure relations that account for the large-scale manifestation of small-scale variations. Example applications built on this foundation are suggested to stimulate future work. [Copyright &y& Elsevier]

Abstract: This work is the sixth in a series of papers on the thermodynamically constrained averaging theory (TCAT) approach for modeling flow and transport phenomena in multiscale porous medium systems. Building upon the general TCAT framework and the mathematical foundation presented in previous works, the limiting case of connected two-fluid-phase flow is considered. A constrained entropy inequality is developed based upon a set of primary restrictions. Formal approximations are introduced to deduce a general simplified entropy inequality (SEI). The SEI is used along with secondary restrictions and closure approximations consistent with the SEI to produce a general functional form of a two-phase-flow model. The general model is in turn simplified to yield a hierarchy of models by neglecting common curves and by neglecting both common curves and interfaces. The simplest case considered corresponds to a traditional two-phase-flow model. The more sophisticated models including interfaces and common curves are more physically realistic than traditional models. All models in the hierarchy are posed in terms of precisely defined variables that allow for a rigorous connection with the microscale. The explicit nature of the restrictions and approximations used in developing this hierarchy of models provides a clear means to both understand the limitations of traditional models and to build upon this work to produce more realistic models. [Copyright &y& Elsevier]

Abstract: This work is the fifth in a series of papers on the thermodynamically constrained averaging theory (TCAT) approach for modeling flow and transport phenomena in multiscale porous medium systems. The general TCAT framework and the mathematical foundation presented in previous works are used to develop models that describe species transport and single-fluid-phase flow through a porous medium system in varying physical regimes. Classical irreversible thermodynamics formulations for species in fluids, solids, and interfaces are developed. Two different approaches are presented, one that makes use of a momentum equation for each entity along with constitutive relations for species diffusion and dispersion, and a second approach that makes use of a momentum equation for each species in an entity. The alternative models are developed by relying upon different approaches to constrain an entropy inequality using mass, momentum, and energy conservation equations. The resultant constrained entropy inequality is simplified and used to guide the development of closed models. Specific instances of dilute and non-dilute systems are examined and compared to alternative formulation approaches. [Copyright &y& Elsevier]

Abstract: This work is the fourth in a series of papers on the thermodynamically constrained averaging theory (TCAT) approach for modeling flow and transport phenomena in multiscale porous medium systems. The general TCAT framework and the mathematical foundation presented in previous works are built upon by formulating macroscale models for conservation of mass, momentum, and energy, and the balance of entropy for a species in a phase volume, interface, and common curve. In addition, classical irreversible thermodynamic relations for species in entities are averaged from the microscale to the macroscale. Finally, we comment on alternative approaches that can be used to connect species and entity conservation equations to a constrained system entropy inequality, which is a key component of the TCAT approach. The formulations detailed in this work can be built upon to develop models for species transport and reactions in a variety of multiphase systems. [Copyright &y& Elsevier]

The premise of this study is that the presence of bromide has a substantial effect on both the speciation and total formation of trihalomethane (THM). Consequently, models of water containing substantial bromide concentrations require refinement because they are only calibrated with raw water with high humic acid content. This study investigates and reports efforts on such refinement. The objectives of work reported in this paper are to formulate and validate a new correlative model that is based on physical principles and incorporates high levels of bromide that affect THM formation using raw water rich in both humic and fulvic acid. Two types of THM precursors are considered in the model discussed in this paper: (1) activated aromatic groups, which are more reactive with chlorine than with bromine, and (2) aliphatic groups, which are more reactive with bromine than with chlorine. Aliphatic and aromatic carbons are incorporated in the model by the inclusion of pertinent variables such as C/N and Br/Cl2 in the algorithm. The model also includes NH4+. This variable affects chlorine consumption that, in turn, potentially affects THM formation. For the first time ever, organic carbon to organic nitrogen and bromide to chlorine ratios is also used as variables potentially affecting THM formation when treating drinking water. The THM model formulated in this paper is an empirical model based on scientific principles and not simply a regression equation. The formulated model is pronounced valid based on a validation effort that employs a portion of an EPA database that was not used for model formulation. [ABSTRACT FROM AUTHOR]

Abstract: Analysis of longitudinal, spatial and epidemiological data often requires modelling dispersions and dependence among the measurements. Moreover, data involving counts or proportions usually exhibit greater variation than would be predicted by the Poisson and binomial models. We propose a strategy for the joint modelling of mean, dispersion and correlation matrix of nonnormal multivariate correlated data. The parameter estimation for dispersions and correlations is based on the Whittle’s [P. Whittle, Gaussian estimation in stationary time series, Bull Inst. Statist. Inst. 39 (1962) 105-129.] Gaussian likelihood of the partially standardized data which eliminates the mean parameters. The model formulation for the dispersions and correlations relies on a recent unconstrained parameterization of covariance matrices and a graphical method [M. Pourahmadi, Joint mean–covariance models with applications to longitudinal data: unconstrained parameterization, Biometrika 86 (1999) 677–690] similar to the correlogram in time series analysis. We show that the estimating equations for the regression and dependence parameters derived from a modified Gaussian likelihood (involving two distinct covariance matrices) are broad enough to include generalized estimating equations and its many recent extensions and improvements. The results are illustrated using two datasets. [Copyright &y& Elsevier]

Chemical engineers are turning to multiscale modelling to extend traditional modelling approaches into new application areas and to achieve higher levels of detail and accuracy. There is, however, little advice available on the best strategy to use in constructing a multiscale model. This paper presents a starting point for the systematic analysis of multiscale models by defining several integrating frameworks for linking models at different scales. It briefly explores how the nature of the information flow between the models at the different scales is influenced by the choice of framework, and presents some restrictions on model—framework compatibility. The concepts are illustrated with reference to the modelling of a catalytic packed bed reactor. [Copyright &y& Elsevier]

In the context of controlling the attitude of a rigid body, this communique uses recent results on representations of torques (moments) to establish cost functions. The resulting cost functions are both properly invariant under whatever choice of Euler angles is used to parameterize the rotation of the rigid body and have transparent physical interpretations. The function is related to existing works in geometric control theory and applications of optimal control theory to biomechanical systems. [ABSTRACT FROM AUTHOR]

Proposed is a max‐margin discriminative random fields model for multimodal human action recognition. To incorporate multiple modalities for joint modelling, a specific graphical structure with parallel sequential observations and related hidden‐state layers is designed. Moreover, the corresponding potential functions for model formulation are designed. For model learning, the max‐margin learning method is proposed to discover both latent correlation among multimodal data and temporal context within individual modality. A comparison experiment shows that the proposed model can boost the performance of human action recognition by taking advantage of complementary characteristics from multiple modalities. [ABSTRACT FROM AUTHOR]

Abstract: This short note proposes two model reformulations to improve the Hougen–Watson mathematical form of kinetic models and validate them on a typical heterogeneous kinetic problem. [Copyright &y& Elsevier]

• Proposed a rigorous formulation for gas hydrate formation and growth. • Addressed several practically relevant issues of porous media and saltwater. • Validated with data generated in reservoir mimicking environment at south China sea. • Observed a promising performance at the wide range of conditions. A potential source of energy is encapsulated in the form of natural gas hydrates found in the diverse permafrost and marine geological settings. To understand the hydrate dynamics, we articulate a rigorous mathematical formulation by contemplating the pervasive role of indigenous elements, including porous material and saline water of the natural gas hydrate reservoirs. This formulation takes into account the characteristic behavior of the porous media in the aspects of their porosity and particle size distribution, and the dynamic contribution of the saline water towards the hydrate formation and growth in reservoir mimicking environment. All relevant model parameters are tuned by using the generalized reduced gradient nonlinear optimization technique. The model is employed to investigate the dynamics of methane hydrate at diverse field conditions (e.g. porous medium with varying sizes of clay, sand and silt particles, amount of saline water, and operating pressure and temperature). Validating the model with experimental data for three different setups, its performance improvement is quantified in terms of the absolute average relative deviation (AARD). This proposed model shows a promising performance at the wide range of conditions. [ABSTRACT FROM AUTHOR]