Modeling the spread of an epidemic in presence of vaccination using cellular automata

The results of Kermack–McKendrick SIR model are planned to be reproduced by cellular automata (CA) lattice model. The CA algorithms are proposed to study the model of an epidemic, systematically. The basic goal is to capture the effects of spreading of infection over a scale of length. This CA model can provide the rate of growth of the infection over the space which was lacking in the mean-field like susceptible-infected-removed (SIR) model. The motion of the circular front of an infected cluster shows a linear behavior in time. The correlation of a particular site to be infected with respect to the central site is also studied. The outcomes of the CA model are in good agreement with those obtained from SIR model. The results of vaccination have been also incorporated in the CA algorithm with a satisfactory degree of success. The advantage of the present model is that it can shed a considerable amount of light on the physical properties of the spread of a typical epidemic in a simple, yet robust way.

Simulation of the Peritectic Phase Transition in Fe-C Alloys

In this work, a multi-phase cellular automaton (CA) model is extended for the quantitative simulation of peritectic phase transition. First, the effects of cooling rate/supersaturation and temperature on the peritectic transformation kinetics in Fe-C alloys are investigated by utilizing the present CA model. The CA simulations show that supersaturations in the parent phases (liquid and δ-ferrite) increase the L/γ interface growth velocity remarkably, but tinily for the δ/γ interface migration velocity. There exists a transition supersaturation for isothermal transformations, at which the growth rates of the two interfaces are equal. The transition supersaturation is found to increase with decreasing temperature. Microstructural evolution at different cooling rates during peritectic transformation is simulated using the experimental conditions. At low cooling rates, the δ/γ interface propagates at a higher velocity than the L/γ interface. At high cooling rates, however, the γ-phase grows more into the L-phase with a cellular morphology. Then, the proposed CA model is applied to simulate the microstructural evolution during peritectic reaction. It is observed that the γ-phase propagates along the L/δ interface and finally encircles the δ-phase. Meanwhile, the intervenient γ-phase grows in thickness through peritectic transformation. The CA simulations are compared reasonably well with the experimental data and analytical calculations.

Distributed Cooperative Jamming with Neighborhood Selection Strategy for Unmanned Aerial Vehicle Swarms

In system science, a swarm possesses certain characteristics which the isolated parts and the sum do not have. In order to explore emergence mechanism of a large–scale electromagnetic agents (EAs), a neighborhood selection (NS) strategy–based electromagnetic agent cellular automata (EA–CA) model is proposed in this paper. The model describes the process of agent state transition, in which a neighbor with the smallest state difference in each sector area is selected for state transition. Meanwhile, the evolution rules of the traditional CA are improved, and performance of different evolution strategies are compared. An application scenario in which the emergence of multi–jammers suppresses the radar radiation source is designed to demonstrate the effect of the EA–CA model. Experimental results show that the convergence speed of NS strategy is better than those of the traditional CA evolution rules, and the system achieves effective jamming with the target after emergence. It verifies the effectiveness and prospects of the proposed model in the application of electronic countermeasures (ECM).

Mixture toxicity of six pharmaceuticals towards Aliivibrio fischeri, Daphnia magna, and Lemna minor

As the knowledge on the joint effects of pharmaceuticals towards different non-target organisms is still limited, the aim of our study was to evaluate the toxicity of mixtures of pharmaceuticals, as well as their baseline toxicity towards three selected organisms, namely the bioluminescent bacteria Aliivibrio fischeri, the crustacean Daphnia magna, and the duckweed Lemna minor. Different mixtures composed of three up to five pharmaceuticals having the same or different mechanisms of action in terms of their therapeutic activity (non-steroidal anti-inflammatory drugs, opioid analgesic, antibacterial and anti-epileptic drugs) were investigated. The observed EC50s were compared with those predicted using the concentration addition (CA) and independent action (IA) models. In general, the EC50 values for mixtures predicted with the CA model were lower than those obtained with the IA model, although, in some cases, test predictions of these two models were almost identical. Most of the experimentally determined EC50 values for the specific mixtures were slightly higher than those predicted with the CA model; hence, a less than additive effect was noted. Based on the obtained results, it might be concluded that the CA model assumes the worst-case scenario and gives overall closer predictions; therefore, it should be recommended also for modeling the mixture toxicity of pharmaceuticals with different modes of action.

Abstract 11269: Recombinant Soluble Thrombomodulin in Prolonged Porcine Cardiac Arrest

Introduction: Sudden Cardiac Arrest (CA) affects more than 400,000 people per year in the United States. Although a third of these patients survive to hospital admission, another 60-70% go on to die due to failed recovery of vital organ function. Microvascular thrombosis has been suggested as a potential contributor to prolonged organ dysfunction, but no antithrombotic therapies have been shown to be beneficial and coagulofibrinolytic abnormalities in prolonged CA remain poorly understood. Objectives: To establish key biomarkers of porcine coagulation and fibrinolysis in the setting of prolonged CA and cardiopulmonary resuscitation (CPR) and test the ability of ART-123 (recombinant human thrombomodulin alpha) to reverse these abnormalities. Methods: 15 pigs (n=5 per group) underwent 8 minutes of no-flow CA followed by 50 minutes of mechanical CPR. Animals were randomized to receive saline or ART-123 (~1mg/kg) pre-arrest (5 minutes prior to ventricular fibrillation) or post-arrest (2 minutes after initiation of CPR). Results: Robust and ongoing activation of coagulation and fibrinolysis were detected throughout the resuscitation. After 50 minutes of CPR, plasma tests suggested consumptive coagulopathy, while whole blood testing (thromboelastography) indicated a persistent hypercoagulable state. ART-123 had a clear anticoagulant effect irrespective of timing (TAT complexes 381±25 vs. 238±18 vs. 226±12, p<0.01, and d-dimer 4.86±0.54 vs. 2.39±0.2 vs. 2.46±0.21 for vehicle, pre-arrest, post-arrest, p = 0.05). A pro-fibrinolytic effect was also observed, but only when the drug was given before no-flow, with a significant increase in levels of free endogenous tPA (1.2±0.12 vs. 3.29±0.29 vs. 1.72±0.3, p < 0.001) and corresponding suppression of free PAI-1 (0.59±0.15 vs. 0.14±0.01 vs. 0.41±0.09, p < 0.001). Conclusion: Our porcine CA model provides an excellent platform for evaluating antithrombotic interventions. Plasma testing after prolonged CA/CPR suggests consumptive coagulopathy, although TEG indicates a persistent hypercoagulable state. ART-123 given before no-flow or just after CPR demonstrates antithrombotic effects, although the specific modes of action depending on the timing of administration.

A Continuous Model for Designing Corridor Systems with Modular Autonomous Vehicles Enabling Station-wise Docking

The “asymmetry” between spatiotemporally varying passenger demand and fixed-capacity transportation supply has been a long-standing problem in urban mass transportation (UMT) systems around the world. The emerging modular autonomous vehicle (MAV) technology offers us an opportunity to close the substantial gap between passenger demand and vehicle capacity through station-wise docking and undocking operations. However, there still lacks an appropriate approach that can solve the operational design problem for UMT corridor systems with MAVs efficiently. To bridge this methodological gap, this paper proposes a continuum approximation (CA) model that can offer near-optimal solutions to the operational design for MAV-based transit corridors very efficiently. We investigate the theoretical properties of the optimal solutions to the investigated problem in a certain (yet not uncommon) case. These theoretical properties allow us to estimate the seat demand of each time neighborhood with the arrival demand curves, which recover the “local impact” property of the investigated problem. With the property, a CA model is properly formulated to decompose the original problem into a finite number of subproblems that can be analytically solved. A discretization heuristic is then proposed to convert the analytical solution from the CA model to feasible solutions to the original problem. With two sets of numerical experiments, we show that the proposed CA model can achieve near-optimal solutions (with gaps less than 4% for most cases) to the investigated problem in almost no time (less than 10 ms) for large-scale instances with a wide range of parameter settings (a commercial solver may even not obtain a feasible solution in several hours). The theoretical properties are verified, and managerial insights regarding how input parameters affect system performance are provided through these numerical results. Additionally, results also reveal that, although the CA model does not incorporate vehicle repositioning decisions, the timetabling decisions obtained by solving the CA model can be easily applied to obtain near-optimal repositioning decisions (with gaps less than 5% in most instances) very efficiently (within 10 ms). Thus, the proposed CA model provides a foundation for developing solution approaches for other problems (e.g., MAV repositioning) with more complex system operation constraints whose exact optimal solution can hardly be found with discrete modeling methods.

Examining oxidation in β-NiAl and β-NiAl+Hf alloys by stochastic cellular automata simulations

We present results from a stochastic cellular automata (CA) model developed and employed for examining the oxidation kinetics of NiAl and NiAl+Hf alloys. The rules of the CA model are grounded in diffusion probabilities and basic principles of alloy oxidation. Using this approach, we can model the oxide scale thickness and morphology, specific mass change and oxidation kinetics as well as an approximate estimate of the stress and strains in the oxide scale. Furthermore, we also incorporate Hf in the grain boundaries and observe the “reactive element effect”, where doping with Hf results in a drastic reduction in the oxidation kinetics concomitant with the formation of thin, planar oxide scales. Interestingly, although we find that grain boundaries result in rapid oxidation of the undoped NiAl, they result in a slower-growing oxide and a planar oxide/metal interface when doped with Hf.

Urban Dynamic Expansion Computer Simulation of RF-NH-CA Model Considering Neighborhood Heterogeneity

“Neighborhood” as the principle of “the closer the distance, the more relevant the attributes”, is often used as a key driving factor for the urban dynamic modeling of cellular automata; however, the current implementation of the “neighborhood” idea is mostly adopted Mean probability method. This method affects the accuracy of urban dynamic simulation to a certain extent because it ignores the spatial heterogeneity of neighboring cells. Based on the random forest method to evaluate the suitability probability of land use, this study uses the intensity gradient change characteristics of the luminous data to endow the traditional neighborhood cell heterogeneity characteristics, and builds a random forest neighborhood heterogeneity CA model (Random forest Neighborhood Heterogeneity Cellular Automata, RF-NH-CA), and verified the effectiveness of the model by simulating the changes in urban land use in the 21 districts of Chongqing’s main city from 2010 to 2017 through a multi-scheme comparative experiment. The results showed that the overall simulation accuracy of the RF-NH-CA model reached 97.59%, and the Kappa coefficient reached 0.7434; compared with the traditional models RF-CA, ANN-CA and Logistic-CA, FoM increased by 0.0274,0.0383,0.0579, respectively. The Kappa coefficient increased by 0.0162,0.0229,0.0351 respectively. Studies have shown that giving the neighborhood cell heterogeneity through luminous data has played a role in improving the accuracy of land use simulation, which is more in line with the real urban expansion.

Multi-Scenario Simulation of Urban Growth Under Integrated Urban Spatial Planning: A Case Study of Wuhan, China

Although many publications have noted the impact of urban planning on urban development and land-use change, the incorporation of planning constraints into urban growth simulation has not been adequately addressed so far. This study aims to develop a planning-constrained cellular automata (CA) model by combining cell-based trade-off between urban growth and natural conservation with a zoning-based planning implementation mechanism. By adjusting the preference parameters of different planning zones, multiple planning-constrained scenarios can be generated. Taking the Wuhan Urban Development Area (WUDA), China as a case study, the planning-constrained CA model was applied to simulate current and future urban scenarios. The results show a higher simulation accuracy compared to the model without planning constraints. With the weakening of planning constraints, urban growth tends to occupy more ecological and agricultural land with high conservation priority. With the increase in preference on urban growth or natural conservation, the future urban land pattern will become more fragmented. Furthermore, new urban land beyond the planned urban development area can be captured in future urban scenarios, which will provide certain early warning. The simulation of the current urban spatial pattern should help planners and decisionmakers to evaluate the past implementation of urban planning, and scenarios simulation can provide effective support for future urban planning by evaluating the consequences.