The COVID-19 pandemic: model-based evaluation of non-pharmaceutical interventions and prognoses

For the electrically actuated microbeam subjected to a combination of DC and AC voltage loadings, we studied the nonlinear dynamical responses and internal resonance analytically. The flexible boundary condition is considered. The modal interaction due to three-to-one internal resonance between the first and second modes is highlighted. The method of multiple scales is employed to get the modulation equation, which describes the amplitude and phases of the involving modes. Then, the equilibrium solutions of the modulation equation are calculated and their stability is examined. The frequency and force response curves reflecting the primary resonance (the first mode) are presented. We find that the first and second modes in the proposed model are coupled, and hence the energy transfer can occur between the involving modes. Moreover, it is found that the response of the system may encounter Hopf bifurcation for some specific parameters. As for simulation, the Galerkin scheme is applied to verify the analytical results in terms of time history, phase-plane portrait, Fourier spectrum, and Poincare section. The simulation results are in good agreement with the analytical ones.     

The effects of learning in production and group size on the lot-sizing problem

One of the lot-sizing problem extensions that received noticeable attention in the literature is the one that investigated the effects of learning in production. The studies along this line of research assumed learning to improve with the number of repetitions following a power form. There is evidence also that the group size, i.e., the number of workers learning in a group affects performance (time per unit). This note revisits the problem and modifies it by incorporating the group size, along with cumulative production, as a proxy for measuring performance. Numerical examples are provided to illustrate the behavior of the modified model. The results of the two models are also compared to draw some meaningful insights and conclusions. Although the results favor using a simple univariate learning curve, considering group size when modeling lot-sizing problems can significantly affect the unit production cost.     

A modified SPH method to model the coalescence of colliding non-Newtonian liquid droplets

This paper develops a modified smoothed particle hydrodynamics (SPH) method to model the coalescence of colliding non-Newtonian liquid droplets. In the present SPH, a van der Waals (vdW) equation of state is particularly used to represent the gas-to-liquid phase transition similar to that of a real fluid. To remove the unphysical behavior of the particle clustering, also known as tensile instability, an optimized particle shifting technique is implemented in the simulations. To validate the numerical method, the formation of a Newtonian vdW droplet is first tested, and it clearly demonstrates that the tensile instability can be effectively removed. The method is then extended to simulate the head-on binary collision of vdW liquid droplets. Both Newtonian and non-Newtonian fluid flows are considered. The effect of Reynolds number on the coalescence process of droplets is analyzed. It is observed that the time up to the completion of the first oscillation period does not always increase as the Reynolds number increases. Results for the off-center binary collision of non-Newtonian vdW liquid droplets are lastly presented. All the results enrich the simulations of the droplet dynamics and deepen understandings of flow physics. Also, the present SPH is able to model the coalescence of colliding non-Newtonian liquid droplets without tensile instability.     

Multi-stable quantum droplets in optical lattices

Nonlinear Dynamics - We address the nonlinear dynamics of binary Bose-Einstein condensates with mutually symmetric spinor components trapped in an optical lattice. The interaction between the...     

Unravelling the Enigma of Nonoxidative Conversion of Methane on Iron Single-Atom Catalysts

The direct, nonoxidative conversion of methane on a silica-confined single-atom iron catalyst is a landmark discovery in catalysis, but the proposed gas-phase reaction mechanism is still open to discussion. Here, we report a surface reaction mechanism by computational modeling and simulations. The activation of methane occurs at the single iron site, whereas the dissociated methyl disfavors desorption into gas phase under the reactive conditions. In contrast, the dissociated methyl prefers transferring to adjacent carbon sites of the active center (Fe₁©SiC₂), followed by C−C coupling and hydrogen transfer to produce the main product (ethylene) via a key −CH−CH₂ intermediate. We find a quasi Mars–van Krevelen (quasi-MvK) surface reaction mechanism involving extracting and refilling the surface carbon atoms for the nonoxidative conversion of methane on Fe₁©SiO₂ and this surface process is identified to be more plausible than the alternative gas-phase reaction mechanism.     

Anti-Markovnikov Hydroamination of Racemic Allylic Alcohols to Access Chiral γ-Amino Alcohols

A ruthenium-catalyzed formal anti-Markovnikov hydroamination of allylic alcohols for the synthesis of chiral γ-amino alcohols is presented. Proceeding via an asymmetric hydrogen-borrowing process, the catalysis allows racemic secondary allylic alcohols to react with various amines, affording enantiomerically enriched chiral γ-amino alcohols with broad substrate scope and excellent enantioselectivities (68 examples, up to >99 % ee).     

Video deraining via nonlocal low-rank regularization

Outdoor videos captured in rainy weather may be significantly corrupted by the undesired rain streaks, which severely affect the video processing tasks in outdoor computer vision systems. In this paper, we propose a tensor-based video rain streaks removal method using the nonlocal low-rank regularization. Specifically, we first divide videos into overlapped spatial–temporal patches. Then for each patch, we group its nonlocal similar spatial–temporal patches to form a third-order tensor. To model the clean videos, we characterize the wealth redundancy by adopting the tensor nuclear norm to regularize the low-rankness of the third-order tensors formed by similar spatial–temporal patches of clean videos. We also consider the piecewise smoothness and the temporal continuity of clean videos and utilize the unidirectional total variation to enhance the smoothness and continuity. Moreover, as rain streaks are sparse and smooth along the rain direction, we model the rain streaks by employing an ℓ₁ norm and the unidirectional total variation penalty to boost the sparsity and directional smoothness, respectively. We develop an efficient alternating direction method of multipliers to solve the proposed model. Experimental results on both synthetic and real rainy videos show that our method outperforms the state-of-the-art methods quantitatively and qualitatively.     

Novel biogeography-based optimization algorithm with hybrid migration and global-best Gaussian mutation

The Biogeography-Based Optimization algorithm and its variants have been used widely for optimization problems. To get better performance, a novel Biogeography-Based Optimization algorithm with Hybrid migration and global-best Gaussian mutation is proposed in this paper. Firstly, a linearly dynamic random heuristic crossover strategy and an exponentially dynamic random differential mutation one are presented to form a hybrid migration operator, and the former is used to get stronger local search ability and the latter strengthen the global search ability. Secondly, a new global-best Gaussian mutation operator is put forward to balance exploration and exploitation better. Finally, a random opposition learning strategy is merged to avoid getting stuck in local optima. The experiments on the classical benchmark functions and the complexity functions from CEC-2013 and CEC-2017 test sets, and the Wilcoxon, Bonferroni-Holm and Friedman statistical tests are used to evaluate our algorithm. The results show that our algorithm obtains better performance and faster running speed compared with quite a few state-of-the-art competitive algorithms. In addition, experimental results on Minimum Spanning Tree and K-means clustering optimization show that our algorithm can cope with these two problems better than the comparison algorithms.     

Pattern formation in a variable diffusion predator–prey model with additive Allee effect

This paper deals with a variable diffusion predator–prey model with additive Allee effect. A good understanding of the existence of steady states is gained for the case  $\sigma =0$. The result shows that the reduce problem has multiple solutions. Moreover, by applying the singular perturbation method, we give a proof of existence of large amplitude solutions when  $\sigma$ is sufficiently small.     

An Intuitive Electric-field Contribution Decomposition Model for Chemical Processes and Its Applications on Diels-Alder Reactions

External electric field(EEF) has shown its advantages in tuning chemical reaction as an efficient and feasible-to-control tool. In this paper, we explored the mechanisms of three EEF-regulated Diels-Alder reactions including two traditional-DA reactions to form two C-C single bonds and a hetero-DA reaction to form both a C-C and a C-O bond, respectively, and introduced an EEF contribution decomposition(ECD) model to understand how the EEF coupled with the intrinsic nuclear and electronic redistributions so as to affect chemical reaction. The ECD model, by decomposing the overall EEF effects into geometry re-equilibrium and static induction parts, can give a clear and quantitative picture of a physical quantity change upon EEF, as demonstrated on relative energies, activation barriers, charge distribution and dipole moments. The ECD analyses will shed light on the effective tuning of chemical reactions by the electric field.