A Native SPICE Implementation of Memristor Models for Simulation of Neuromorphic Analog Signal Processing Circuits

Since the memristor emerged as a programmable analog storage device, it has stimulated research on the design of analog/mixed-signal circuits with the memristor as the enabler of in-memory computation. Due to the difficulty in evaluating the circuit-level nonidealities of both memristors and CMOS devices, SPICE-accuracy simulation tools are necessary for perfecting the art of neuromorphic analog/mixed-signal circuit design. This article is dedicated to a native SPICE implementation of the memristor device models published in the open literature and develops case studies of applying such a circuit simulation with MOSFET models to study how device-level imperfections can make adversarial effects on the analog circuits that implement neuromorphic analog signal processing. Methods on memristor stamping in the framework of modified nodal analysis formulation are presented, and implementation results are reported. Furthermore, functional simulations on neuromorphic signal processing circuits including memristors and CMOS devices are carried out to validate the effectiveness of the native SPICE implementation of memristor models from the perspectives of simulation accuracy, efficiency, and convergence for large-scale simulation tasks.

Real-Time Numerical Simulation for Accurate Soft Tissues Modeling during Haptic Interaction

The simulation of fabrics physics and its interaction with the human body has been largely studied in recent years to provide realistic-looking garments and wears specifically in the entertainment business. When the purpose of the simulation is to obtain scientific measures and detailed mechanical properties of the interaction, the underlying physical models should be enhanced to obtain better simulation accuracy increasing the modeling complexity and relaxing the simulation timing constraints to properly solve the set of equations under analysis. However, in the specific field of haptic interaction, the desiderata are to have both physical consistency and high frame rate to display stable and coherent stimuli as feedback to the user requiring a tradeoff between accuracy and real-time interaction. This work introduces a haptic system for the evaluation of the fabric hand of specific garments either existing or yet to be produced in a virtual reality simulation. The modeling is based on the co-rotational Finite Element approach that allows for large displacements but the small deformation of the elements. The proposed system can be beneficial for the fabrics industry both in the design phase or in the presentation phase, where a virtual fabric portfolio can be shown to customers around the world. Results exhibit the feasibility of high-frequency real-time simulation for haptic interaction with virtual garments employing realistic mechanical properties of the fabric materials.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.

Preliminary Study: Learning the Impact of Simulation Time on Reentry Location and Morphology Induced by Personalized Cardiac Modeling

Personalized cardiac modeling is widely used for studying the mechanisms of cardiac arrythmias. Due to the high demanding of computational resource of modeling, the arrhythmias induced in the models are usually simulated for just a few seconds. In clinic, it is common that arrhythmias last for more than several minutes and the morphologies of reentries are not always stable, so it is not clear that whether the simulation of arrythmias for just a few seconds is long enough to match the arrhythmias detected in patients. This study aimed to observe how long simulation of the induced arrhythmias in the personalized cardiac models is sufficient to match the arrhythmias detected in patients. A total of 5 contrast enhanced MRI datasets of patient hearts with myocardial infarction were used in this study. Then, a classification method based on Gaussian mixture model was used to detect the infarct tissue. For each reentry, 3 s and 10 s were simulated. The characteristics of each reentry simulated for different duration were studied. Reentries were induced in all 5 ventricular models and sustained reentries were induced at 39 stimulation sites in the model. By analyzing the simulation results, we found that 41% of the sustained reentries in the 3 s simulation group terminated in the longer simulation groups (10 s). The second finding in our simulation was that only 23.1% of the sustained reentries in the 3 s simulation did not change location and morphology in the extended 10 s simulation. The third finding was that 35.9% reentries were stable in the 3 s simulation and should be extended for the simulation time. The fourth finding was that the simulation results in 10 s simulation matched better with the clinical measurements than the 3 s simulation. It was shown that 10 s simulation was sufficient to make simulation results stable. The findings of this study not only improve the simulation accuracy, but also reduce the unnecessary simulation time to achieve the optimal use of computer resources to improve the simulation efficiency and shorten the simulation time to meet the time node requirements of clinical operation on patients.

Integrated and Control-Oriented Simulation Tool for Optimizing Urban Drainage System Operation

With the management and operation of urban drainage systems (UDS) becoming more complicated and difficult, integrated models aiming to control and manage the entire drainage system are under enormous demand. Ideally, integrated models, as a potential tool for meeting the increasing demands, should combine both conceptual and mechanistic models that merge all UDS components and balance simulation accuracy with time constraints. Within this context, our study introduces an innovative modeling software, Simuwater, which couples multiple principles, simulates multiple components, and combines optimized control functions, playing a role in the integrated simulation and overflow control application of UDS. The software has been utilized in a real-time case-control study in one city of China, and it obtained significant optimized operation results to reduce combined sewer overflow (CSO) by making full use of the storage facilities and actuators. As the Simuwater model continues to improve in depth and breadth, it will play an increasingly important role in more application scenarios of UDS.

Competition Equilibrium Analysis of China’s Luxury Car Market Based on Three-Dimensional Grey Lotka–Volterra Model

An enterprise must be able to conduct in-depth analysis of the existing data as the information has certain grey characteristics, if it wants to occupy a dominant position in the fierce market competition. In this paper, a compound three-dimensional grey Lotka–Volterra model is developed to carry out the grey transformation of the original data, so that the data can have better simulation accuracy, and the observation noise of the original data can be reduced. The competitive situation analysis based on the three-dimensional grey Lotka–Volterra model can help enterprises better understand the market situation. This paper takes the luxury brand automobile market in mainland China as an example to conduct a competitive analysis and a balanced development simulation. It can be found that, based on Three Species System Analysis, there is a symbiotic relationship among automobile enterprises and that the three species model can be adopted in analyzing the competition and cooperation among enterprises. Through balanced development of a Symbiotic System Analysis, the results of symbiotic optimization under the achievable equilibrium state of three populations are obtained and they show that the proposed method can be used effectively to conduct the market competition analysis. It is thus of great importance to study the relationships among enterprises as it is helpful for enterprises to make strategic policies.

A Rapid Prediction Model of Urban Flood Inundation in a High-Risk Area Coupling Machine Learning and Numerical Simulation Approaches

Climate change has led to increasing frequency of sudden extreme heavy rainfall events in cities, resulting in great disaster losses. Therefore, in emergency management, we need to be timely in predicting urban floods. Although the existing machine learning models can quickly predict the depth of stagnant water, these models only target single points and require large amounts of measured data, which are currently lacking. Although numerical models can accurately simulate and predict such events, it takes a long time to perform the associated calculations, especially two-dimensional large-scale calculations, which cannot meet the needs of emergency management. Therefore, this article proposes a method of coupling neural networks and numerical models that can simulate and identify areas at high risk from urban floods and quickly predict the depth of water accumulation in these areas. Taking a drainage area in Tianjin Municipality, China, as an example, the results show that the simulation accuracy of this method is high, the Nash coefficient is 0.876, and the calculation time is 20 seconds. This method can quickly and accurately simulate the depth of water accumulation in high-risk areas in cities and provide technical support for urban flood emergency management.

A VIRTUAL PISTON-TYPE WAVE MAKER BASED ON OPENFOAM

OpenFOAM is an open source CFD (Computational Fluid Dynamics) toolbox and recently attracts many researchers to develop codes based on it for their own applications. In order to numerically generate waves based on the wave-maker theory for a piston motion, numerical improvements have been done on the base of OpenFOAM by the author. In gen- eral, the present new tool can be employed to simulate wave generation as long as the piston motion is given. This paper presents the related computational procedure and simulations for generating relatively long finite-amplitude waves ac- cording to Madsen’s second-order wave-maker theory. The sensitivities of the computed incident wave profile to grid density and time step are investigated for the case of generating a wave with permanent form. The simulation accuracy is validated by comparison with the analytical solution and available experimental data.

A Multigrid Dynamic Bidirectional Coupled Surface Flow Routing Model for Flood Simulation

Surface flow routing is an important component in hydrologic and hydrodynamic research. Based on a literature review and comparing the different coupling models (the hydrologic model and hydrodynamic model), a multigrid dynamic bidirectional coupled surface flow routing model (M-DBCM), consisting of diffusion wave equations (DWEs) and shallow water equations (SWEs), is proposed herein based on grids with different resolutions. DWEs were applied to obtain runoff routing in coarse grid regions to improve the computational efficiency, while the DWEs and SWEs were bidirectionally coupled to detail the flood dynamics in fine grid regions to obtain good accuracy. In fine grid zones, the DWEs and SWEs were connected by an internal moving boundary, which ensured the conservation of mass and momentum through the internal moving boundary. The DWEs and SWEs were solved by using the time explicit scheme, and different time steps were adopted in regions with different grid sizes. The proposed M-DBCM was validated via three cases, and the results showed that the M-DBCM can effectively simulate the process of surface flow routing, which had reliable computational efficiency while maintaining satisfactory simulation accuracy. The rainfall runoff in the Goodwin Creek Watershed was simulated based on the proposed M-DBCM. The results showed that the discharge hydrographs simulated by the M-DBCM were closer to the measured data, and the simulation results were more realistic and reliable, which will be useful in assisting flood mitigation and management.

ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Genome-scale metabolic models (GEMs) have been widely used for phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space inaccessible. Inspired by previous studies that take allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviors under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.