Magnetic dilution of a honeycomb lattice XY magnet CoTiO3

We report our study of cobalt (II) titanate, CoTiO3, in which magnetic Co ions are replaced by non-magnetic ions. The antiferromagnetic ordering transition of CoTiO3 around 37 K is described with ferromagnetic honeycomb layers coupled antiferromagnetically along the crystallographic c direction. The effect of magnetic dilution on the Néel temperature of this material is investigated through the doping of Zn2+ and Mg2+ in place of Co2+ for various dilution levels up to x + y = 0.46 in Co1-x-yZnxMgyTiO3. Single phase polycrystalline samples have been synthesized and their structural and magnetic properties have been examined. A linear relation between dilution and the Néel temperature is observed over a wide doping range. A linear extrapolation would suggest that the required dilution level to suppress magnetic order is around x + y ∽ 0.74, well beyond the classical percolation threshold. The implication of this observation for microscopic models for describing CoTiO3 is discussed.

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

Digital Training Platform for Comprehensive Traffic Simulation

The paper deals with the development of software for traffic flow simulation combining the widest spectrum of mathematical approaches used in this field. Macro- and microscopic models, models of cellular automata as well as different numerical methods of their computer implementation are incorporated into a digital platform. Original developments of the authors of the paper such as quasi-gas dynamic traffic model and multilane cellular automata model take the main place. Potential users of the software are students and researchers. The platform possesses an intuitive graphical interface ensuring interactivity. Microsoft Visual Studio with C# is chosen as the development environment, the Unity 3D engine is employed for visualization and collaboration with WinForm projects. In the future, the platform can be transformed into a network computer laboratory providing access to information resources via Internet.

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

Forecasting the Transmission Trends of Respiratory Infectious Diseases with an Exposure-Risk-Based Model at the Microscopic Level

Respiratory infectious diseases (e.g., COVID- 19) have brought huge damages to human society, and the accurate prediction of their transmission trends is essential for both the health system and policymakers. Most related studies concentrate on epidemic trend forecasting at the macroscopic level, which ignores the microscopic social interactions among individuals. Meanwhile, current microscopic models are still not able to sufficiently decipher the individual-based spreading process and lack valid quantitative tests. To tackle these problems, we propose an exposure-risk-based model at the microscopic level, including 4 modules: individual movement, virion-laden droplet movement, individual exposure risk estimation, and prediction of new cases. First, the front two modules reproduce the movements of individuals and the droplets of infectors’ expiratory activities. Then, the outputs are fed to the third module for estimating the personal exposure risk. Accordingly, the number of new cases is predicted in the final module. Our model outperforms 4 existing macroscopic or microscopic models through the forecast of new cases of COVID-19 in the United States. Specifically, mean absolute error, root mean square error and mean absolute percentage error by our model are 2454.70, 3170.51, and 3.38% smaller than the minimum results of comparison models, respectively. In sum, the proposed model successfully describes the scenarios from a microscopic perspective and shows great potential for predicting the transmission trends with different scenarios and management policies.

Analysis and comparison of traffic flow models: a new hybrid traffic flow model vs benchmark models

Background This paper compares a hybrid traffic flow model with benchmark macroscopic and microscopic models. The proposed hybrid traffic flow model may be applied considering a mixed traffic flow and is based on the combination of the macroscopic cell transmission model and the microscopic cellular automata. Modelled variables The hybrid model is compared against three microscopic models, namely the Krauß model, the intelligent driver model and the cellular automata, and against two macroscopic models, the Cell Transmission Model and the Cell Transmission Model with dispersion, respectively. To this end, three main applications were considered: (i) a link with a signalised junction at the end, (ii) a signalised artery, and (iii) a grid network with signalised junctions. Results The numerical simulations show that the model provides acceptable results. Especially in terms of travel times, it has similar behaviour to the microscopic model. By contrast, it produces lower values of queue propagation than microscopic models (intrinsically dominated by stochastic phenomena), which are closer to the values shown by the enhanced macroscopic cell transmission model and the cell transmission model with dispersion. The validation of the model regards the analysis of the wave propagation at the boundary region.

Properties of 4ΛH Hypernucleus in Three-Cluster Microscopic Models

Within the framework of microscopic three-cluster algebraic models with possible consideration of clustering types (D + n) + Λ, (D + Λ) + n, and (n + Λ) + D, the properties of discrete spectrum states of hypernucleus 4ΛH and continuous spectrum states in the 3H + Λ channel are studied. It is shown that the cluster structure is almost completely determined by the clustering (D + n) + Λ with a rather appreciable effect from the polarization of the binary subsystem (D + n) due to its interaction with the Λ particle.