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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]

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]

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.

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.

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]

This paper aims to explore the optimal control of the novel pandemic COVID-19 using non-clinical approach. We formulate a mathematical model to analyze the transmission of the infection through different human compartments. By applying a sensitivity test, we obtain the sensitivity indexes of the parameters involved in the transmission of the disease. We demonstrate the most active/sensitive parameters to analyze the spread of the coronavirus COVID-19. The most active transmission parameters are interposed by introducing control variables. The control intervention is in the form of smart lockdown, frequent handwash, control of the disease’s side effects, face mask, and sanitizer. We Formulate Hamilton and Lagrangian to investigate the existence of the optimal control. Pontryagin’s Maximum Principle describes the control variables in the optimal control model. The objective function is designed to reduce both the infection and the cost of interventions. We use numerical simulation to verify the results of the control variables by Matlab 2019.

• 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]

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.

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.

This paper aims to explore the optimal control of the novel pandemic COVID-19 using non-clinical approach. We formulate a mathematical model to analyze the transmission of the infection through different human compartments. By applying a sensitivity test, we obtain the sensitivity indexes of the parameters involved in the transmission of the disease. We demonstrate the most active/sensitive parameters to analyze the spread of the coronavirus COVID-19. The most active transmission parameters are interposed by introducing control variables. The control intervention is in the form of smart lockdown, frequent handwash, control of the disease's side effects, face mask, and sanitizer. We Formulate Hamilton and Lagrangian to investigate the existence of the optimal control. Pontryagin's Maximum Principle describes the control variables in the optimal control model. The objective function is designed to reduce both the infection and the cost of interventions. We use numerical simulation to verify the results of the control variables by Matlab 2019. [ABSTRACT FROM AUTHOR]

Abstract 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.

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]

Numerous models have been put forth to help with the growing demand for the establishment of biodiversity reserves. One site selection model that has been used in several recent studies is SITES [S.J. Andelman, I. Ball, F.W. Davis and D.M. Stoms, SITES V 1.0: an analytical toolbox for designing ecoregional conservation portfolios, Unpublished manual prepared for the nature conservancy, 1999, 1-43. (available at http://www.biogeog.ucsb.edu /projects/tnc/toolbox.html)]. SITES includes two heuristic solvers: based on Greedy and Simulated Annealing. We discuss the formulation of the SITES model, present a new formulation for that problem, and solve a number of test problems optimally using off-the-shelf software. We compared our optimal results with the SITES Simulated Annealing heuristic and found that SITES frequently returns significantly suboptimal solutions. Our results add further support to the argument, started by Underhill [L.G. Underhill, Optimal and suboptimal reserve selection algorithms, Biol. Conserv. 70 (1994) 85-87], continuing through Rodrigues and Gaston [A.S.L. Rodrigues and K.J. Gaston, Optimization in reserve selection procedures - why not?, Biol. Conserv. 107 (2002) 123-129], for greater integration of optimal methods in the reserve design/selection literature.

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]

Building models for problem solving is a common practice for many disciplines. Model formulation is a complex process. Researchers have in the past approached this process from a variety of angles including planning, model structuring, model integration, and model representation, among others. Here we use observations of expert modelers in an attempt to understand the process. The observations, made in the form of protocol analysis, identified three important facets to this process: formulation tasks, control considerations, and the opportunism in the process of formulation. Using these observations, the AEROBA system was constructed, based on the blackboard paradigm. The system design and implementation details are presented. A consultation trace with AEROBA is also provided. [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]

Organizations today face intense competitive pressure to maximize their performance over time as customer expectations increase. The literature suggests that there is a need to develop a tool or techniques that practitioners in the industry can apply to help in support system design for operating assets as a long-term service solution in order to maintain optimized performance and obtain the best return on investments. This technique should integrate the industry domain knowledge to create and deliver support solutions for in service assets. There is no generic architecture for system support engineering available for practitioners to use. This will lead to the following question "Can industrial practitioners have a generic architecture to simplify the development of such a system"? If the answer is yes; then how possible is it? Therefore, this paper will present an empirical approach to model the formulation for a system support engineering (SSE) generic framework. The development of the SSE Framework combines both literature analysis and empirical work. Also, it will provide a possible answer to the research question and suggest further recommendations and opportunities for future research. [ABSTRACT FROM AUTHOR]

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 paper introduces a new model for disease outbreaks. This model describes the disease evolution through a system of nonlinear differential equations with distributed-delay. The main difference between classical SIR-model resides in the fact that the recovery rate of the population is expressed as a distributed-delay term modeling the time spent being sick by infected people. This model is identified to fit realistic measurements which shows the effectiveness of the model. Finally, we develop an optimal campaign vaccination strategy based on recent results on the impulsive control of time-delay systems. [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]

Understanding how cells grow and adapt under various nutrient conditions is pivotal in the study of biological stoichiometry. Recent studies provide empirical evidence that cells use multiple strategies to maintain an optimal protein production rate under different nutrient conditions. Mathematical models can provide a solid theoretical foundation that can explain experimental observations and generate testable hypotheses to further our understanding of the growth process. In this study, we generalize a modeling framework that centers on the translation process and study its asymptotic behaviors to validate algebraic manipulations involving the steady states. Using experimental results on the growth of E. coli under C-, N-, and P-limited environments, we simulate the expected quantitative measurements to show the feasibility of using the model to explain empirical evidence. Our results support the findings that cells employ multiple strategies to maintain a similar protein production rate across different nutrient limitations. Moreover, we find that the previous study underestimates the significance of certain biological rates, such as the binding rate of ribosomes to mRNA and the transition rate between different ribosomal stages. Furthermore, our simulation shows that the strategies used by cells under C- and P-limitations result in a faster overall growth dynamics than under N-limitation. In conclusion, the general modeling framework provides a valuable platform to study cell growth under different nutrient supply conditions, which also allows straightforward extensions to the coupling of transcription, translation, and energetics to deepen our understanding of the growth process., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

This paper provides a survey of model management literature within the mathematical modeling domain. The first part of the survey is a review and a summary of the literature. After giving some basic definitions of modeling, modeling life cycle, and model management, two representative algebraic modeling languages followed by three approaches to modeling are introduced. These approaches are database, graph-based, and knowledge-based. The discussion is followed by a review of two specialized model management systems. The second part of the survey is a categorization of various modeling systems based on the modeling functions they provide and some of their features. These functions include life cycle support and model base administration. The degree of model independence provided by model management systems and the implemented environment systems is also summarized. The last part of the paper provides directions for future research. [ABSTRACT FROM AUTHOR]

Formulation of a model involves organization of certain elements of reality into an abstract structure that exhibits features and characteristics of the reality The formulation process is complex, and this paper explores that complexity by looking at five previously analyzed problem situations in the managerial domain. This is done in several steps. First, the existing model formulation support (MFS) architectures are analyzed using structural-mechanisms-policies (SMP) model to extract the characteristic attributes of the model formulation process. It is then argued that an integration of these attributes into an architecture, called integrated modeling architecture (IMA), would be useful in supporting the complexities of the model formulation process. The validity of such an architecture is also examined using the verbalization transcripts for the five problems mentioned above. Our protocol analysis shows that the IMA attributes can explain the complexities of the model formulation process. [ABSTRACT FROM AUTHOR]

This article presents an algebraic approach to formulating and solving large models for sequential decisions under uncertainty. With this approach, decision analysis optimization methods can be applied to complex decision problems which are generally analyzed in management science practice using heuristics. Using the approach, a decision problem is formulated in terms of decision variables, random variables, and functions relating these variables. This leads to a compact representation, and a simple algorithm can be used to quickly solve algebraic models that would have decision trees with several hundred thousand endpoints. An application to research and development planning illustrates the usefulness of such large sequential decision models. [ABSTRACT FROM AUTHOR]

We introduce a general framework that guides the management scientist's formulation of deterministic models of production processes. Using the framework, we reformulate the constraints of familiar linear programming-based planning models to specifically treat components of production lead time, thereby realizing a more accurate representation of the production process. In addition, the reformulation accommodates noninteger values for lead times as well as unequal-length planning periods. Manufacturing Resources Planning (MRP) and the Critical Path Method (CPM) are recast in terms of the framework, revealing opportunities for model generalization and extension, and their relationship to linear programming models. [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]