Simulation of Discrete-Continuous Process via E-Network

A novel model order reduction method for the construction of parametric dynamic compact thermal models of nonlinear heat diffusion problems is proposed. The method extends a previous method, recently proposed by the Authors, for the construction of parametric dynamic compact thermal models of linear heat diffusion problems. The method has been validated through the analysis of a simple die in which high levels of non-linearities and large variations of parameters occur.

Storm tide (combination of storm surge and the astronomical tide) flooding is a natural hazard with significant global social and economic consequences. For this reason, government agencies and stakeholders need storm tide flood maps to determine population and infrastructure at risk to present and future levels of inundation. Computer models of varying complexity are able to produce regional-scale storm tide flood maps and current model types are either static or dynamic in their implementation. Static models of storm tide utilize storm tide heights to inundate locations hydrologically connected to the coast, whilst dynamic models simulate physical processes that cause flooding. Static models have been used in regional-scale storm tide flood impact assessments, but model limitations and coarse spatial resolutions contribute to uncertain impact estimates. Dynamic models are better at estimating flooding and impact but are computationally expensive. In this study we have developed a dynamic reduced-complexity model of storm tide flooding that is computationally efficient and is applied at hyper-resolutions (<100 m cell size) over regional scales. We test the performance of this dynamic reduced-complexity model and a separate static model at three test sites where storm tide observational data are available. Additionally, we perform a flood impact assessment at each site using the dynamic reduced-complexity and static model outputs. Our results show that static models can overestimate observed flood areas up to 204 % and estimate more than twice the number of people, infrastructure, and agricultural land affected by flooding. Overall we find that that a reduced-complexity dynamic model of storm tide provides more conservative estimates of coastal flooding and impact.

Maximizing the Effectiveness of Integrated Reservoir Studies: Practical Approaches to Improving the Process and Results

Time Series: State Space Methods

Detailed kinetic models at the network reaction level are usually constructed using enzymatic mechanistic rate equations and the associated kinetic parameters. However, during the cellular life cycle thousands of different reactions occur, which makes it very difficult to build a detailed large-scale ldnetic model. In this work, we provide a critical overview of specific limitations found during the reconstruction of the central carbon metabolism dynamic model from E. coli (based on kinetic data available). In addition, we provide clues that will hopefully allow the systems biology community to more accurately construct metabolic dynamic models in the future. The difficulties faced during the construction of dynamic models are due not only to the lack of kinetic information but also to the fact that some data are still not curated. We hope that in the future, with the standardization of the in vitro enzyme protocols the approximation of in vitro conditions to the in vivo ones, it will be possible to integrate the available kinetic data into a complete large scale model. We also expect that collaborative projects between modellers and biologists will provide valuable kinetic data and permit the exchange of important information to solve most of these issues.

In this paper, we discuss the relation between liquidity and the business cycle.There are some reasons that we should link liquidity with the business cycle. The“flight to liquidity” or “flight to quality” hypothesis might be the most important one. It is more likely that an investor shifts his portfolio from those stocks with lower liquidity or higher risk when the economy is in a recession.Instead of business cycle measures, we use the NBER recession signal as the proxy of real economy. We apply dynamic and static probit model to monthly liquidity proxy, because the NBER recession is not always available in current period. Besides we use Amihud (2002) ILR as our liquidity proxy.The dynamic probit model with liquidity added does not result in a significance improvement on prediction. No matter in sample or out of sample, the pure dynamic probit model performs well with itself history only. However, as we mentioned earlier, NBER recession signal is usually announced well until the recession has happened for months, leading to an infeasibility to implement dynamic model. On the other hand, liquidity plays an important role in static probit model. We find a robust positive relation between liquidity and recession. Adding liquidity as a predictor in the static model would significantly improve the forecast accuracy in th short horizon. This observation remains robust with comparison to other financial variables ( e.g. term spread, credit spread, market volatility, and excess return). We also find the existence of “size effect” that small firm is more sensitive to future recession. Our finding above is consistent with our claims of “flight to liquidity; that is, it is more likely for market participants to shift his portfolio from those stock with lower liquidity or higher risk in advance when the economy would be in a recession. The primary contribution of this paper is that liquidity proxy, Amihud’s liquidity proxy here, does provide useful information when predicting the future recession. We find that the liquidity proxy performs well especially in a short prediction horizon. Besides, the liquidity of small firms is more informative in prediction.

Dynamic software testing and evaluation are important software engineering subjects and can be used to improve reliability. In this paper, we propose a dynamic software testing and evaluation model based on the state space method. In this model, the run time features of software are quantified into one state vector; the program scale, difficulties, structures, and development expenses are quantified into one input vector; the total software complexity is quantified into one output vector; and the software testing and evaluation process is regarded as a feedback control process. Dynamic software testing and evaluation with the state space method can adaptively regulate the critical factors that drastically affect software reliability. Experimental results show that the proposed method is useful for reducing costs, shortening developing time, and improving reliability.

Modular modeling of air-conditioning system with state-space method and graph theory

In digitally controlled DC-DC converters with a single voltage feedback loop, the two quantizers, namely the A/D converter and the digital pulse-width modulator (DPWM), can cause undesirable limit-cycle oscillations. In this paper, static and dynamic models that include the quantization effects are derived and used to explain the origins of limit-cycle oscillations. In the static model, existence of DC solution, which is a necessary no-limit-cycle condition, is examined using a graphical method. A concept of amplitude and offset dependent gain is introduced to extend the describing function method and derive the dynamic system model. From the static and dynamic models, no-limit-cycle conditions associated with A/D, DPWM and compensator design criteria are derived. The conclusions are illustrated by simulation and experimental examples.

In digitally controlled dc-dc converters with a single voltage feedback loop, the two quantizers, namely the analog-to-digital (A/D) converter and the digital pulse-width modulator (DPWM), can cause undesirable limit-cycle oscillations. In this paper, static and dynamic models that include the quantization effects are derived and used to explain the origins of limit-cycle oscillations. In the static model, existence of dc solution, which is a necessary no-limit-cycle condition, is examined using a graphical method. Based on the generalized describing function method, the amplitude and offset-dependent gain model of a quantizer is applied to derive the dynamic system model. From the static and dynamic models, no-limit-cycle conditions associated with A/D, DPWM and compensator design criteria are derived. The conclusions are illustrated by simulation and experimental examples

Integration of dynamic and static modeling on guided inquiry learning can help students to understand chemical concepts at macroscopic, symbolic and microscopic levels. This research aimed to examine and to explain the differences of students’ multiple representation ability that was taught by guided inquiry with dynamic and static modeling in redox reaction concept. This research used descriptive and quasi-experimental design. Data were analyzed using descriptive and manova statistics analysis. The results showed that the ability of students’ macroscopic level that was taught by guided inquiry with dynamic and static modeling is the same. While the student’s symbolic and microscopic level ability that was taught by guided inquiry with dynamic modeling higher than student’s ability that was taught by guided inquiry with static modeling. The implication of this research is chemistry learning with dynamic modeling can help students to construct chemical concept more easily and gain the complete understanding.

As the main cause of large blackouts, understanding the complex interactions involved in cascading failure propagation is a critical area of ongoing research. The static model of cascading failures is a well-accepted method to investigate the cascading effects in power systems. However, as the power system evolves, dynamic behaviours become more variable, it is necessary to evaluate the validity of the assumptions in the static model. This paper compares and validates a conventional static model with a novel dynamic model capturing frequency dynamics. N-2 contingency analysis is used to trigger cascading events and the unserved demand and the number of branch outages are used as metrics. Analysis using the 39-bus, 200-bus and 2000-bus benchmark systems reveal similarities and differences between the static and dynamic models and reveal, for the first time, how these differences vary as network size grows. Sensitivity analysis on system dispatch capabilities provides some initial guidance to help reduce the differences between models and helps with understanding of the relative importance of cascading mechanisms.

We recently developed a dynamic force matching technique for converting a coarse-grained (CG) model of a molecular system, with a CG potential energy function, into a dynamic CG model with realistic dynamics [A. Davtyan et al., J. Chem. Phys. 142, 154104 (2015)]. This is done by supplementing the model with additional degrees of freedom, called "fictitious particles." In that paper, we tested the method on CG models in which each molecule is coarse-grained into one CG point particle, with very satisfactory results. When the method was applied to a CG model of methanol that has two CG point particles per molecule, the results were encouraging but clearly required improvement. In this paper, we introduce a new type (called type-3) of fictitious particle that exerts forces on the center of mass of two CG sites. A CG model constructed using type-3 fictitious particles (as well as type-2 particles previously used) gives a much more satisfactory dynamic model for liquid methanol. In particular, we were able to construct a CG model that has the same self-diffusion coefficient and the same rotational relaxation time as an all-atom model of liquid methanol. Type-3 particles and generalizations of it are likely to be useful in converting more complicated CG models into dynamic CG models.

Mathematical models are efficient instrument of engines and their automatic control systems designing. The main areas of the models application are simulation modeling of the controlled object at analysis, synthesis and semi-natural simulation, and also model-based engine controlling algorithms designing. In this case, a set of mathematical models is used that is derived from the initial (base) thermo-gas-dynamic model of working process, which is usually designed and supported by the engine designer. However, it not satisfies the requirement of real-time calculations when the model simulates the engine dynamics at operation with the real electronic hardware. Lacking of the above-listed shortcomings dynamic model of the engine is formed as a combination of simplified static and dynamic models. In this case, the dynamic model has a linear structure and characterizes dynamic relations in a local area close to the engine static characteristics that is represented as the static model. This dynamic model can be determined by linearization of the base thermo-gas-dynamic model. The base model is grounded on characteristics of the engine components, which are built using experimental results and a peace-linear interpolation. Due to the peace-linear interpolation of the characteristics, relations between the engine parameters have breaks, that causes errors in calculations, which are done using the model, and not corresponds to real processes in the engine. The drastic method to overcome this problem is a perfection of thermo-gas-dynamic model by smoothing the characteristics of components. However, this will mismatch the model, which is used by the ASC designer, and the base model of the engine designer. This paper considers approximation of the dynamic model coefficients, which are determined using the component-based thermo-gas-dynamic model with the peace-linear interpolation of the components’ characteristics. The research is aimed in improvement of the used linear dynamic models in a state space and automation of their forming for the engine automatic control systems quality increasing and synthesis acceleration.

BackgroundThis paper compares cost-effectiveness results from two models of maternal immunization to prevent pertussis in infants in Brazil, one static, one dynamic, to explore when static models are adequate for public health decisions and when the extra effort required by dynamic models is worthwhile. MethodsWe defined two scenarios to explore key differences between static and dynamic models, herd immunity and time horizon. Scenario 1 evaluates the incremental cost/DALY of maternal acellular pertussis (aP) immunization as routine infant vaccination coverage ranges from low/moderate up to, and above, the threshold at which herd immunity begins to eliminate pertussis. Scenario 2 compares cost-effectiveness estimates over the models’ different time horizons. Maternal vaccine prices of $9.55/dose (base case) and $1/dose were evaluated. ResultsThe dynamic model shows that maternal immunization could be cost-saving as well as life-saving at low levels of infant vaccination coverage. When infant coverage reaches the threshold range (90–95%), it is expensive: the dynamic model estimates that maternal immunization costs $2 million/DALY at infant coverage > 95% and maternal vaccine price of $9.55/dose; at $1/dose, cost/DALY is $200,000. By contrast, the static model estimates costs/DALY only modestly higher at high than at low infant coverage. When the models’ estimates over their different time horizons are compared at infant coverage < 90–95%, their projections fall in the same range. ConclusionsStatic models may serve to explore an intervention’s cost-effectiveness against infectious disease: the direction and principal drivers of change were the same in both models. When, however, an intervention too small to have significant herd immunity effects itself, such as maternal aP immunization, takes place against a background of vaccination in the rest of the population, a dynamic model is crucial to accurate estimates of cost-effectiveness. This finding is particularly important in the context of widely varying routine infant vaccination rates globally. Clinical Trial registryClinical Trial registry name and registration number: Not applicable.