Low-complexity model predictive control of a four-level active neutral point clamped inverter without weighting factors

The four-level active neutral point clamped(ANPC)inverter has become increasingly widely used in the renewable energy indus-try since it offers one more voltage level without increasing the total number of active switches compared to the three-level ANPC inverter.The model predictive current control(MPCC)is a promising control method for multi-level inverters.However,the conven-tional MPCC suffers from high computational complexity and tedious weighting factor tuning in multi-level inverter applications.A low-complexity MPCC without weighting factors for a four-level ANPC inverter is proposed in this paper.The computational burden and voltage vector candidate set are reduced according to the relationship between voltage vector and neutral point voltage balance.The proposed MPCC shows excellent steady-state and dynamics performances while ensuring the neutral point voltage balancing.The efficacy of the proposed MPCC is verified by simulation and experimental results.

DTFLOW:Inference and Visualization of Single-cell Pseudotime Trajectory Using Diffusion Propagation

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data.Although substantial studies have been conducted in recent years,more effective methods are still strongly needed to infer the developmental processes accurately.This work devises a new method,named DTFLOW,for determining the pseudotemporal trajectories with multiple branches.DTFLOW consists of two major steps:a new method called Bhattacharyya kernel feature decomposition(BKFD)to reduce the data dimensions,and a novel approach named Reverse Searching on k-nearest neighbor graph(RSKG)to identify the multi-branching processes of cellular differentiation.In BKFD,we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm,and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix.The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets.We compare the efficiency of DTFLOW with the published state-of-the-art methods.Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories.The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.

Mathematical modeling of female breast cancer in Japan

Cancer incidence rates are significantly diferent all over the world.Breast cancer is affected by many factors,the most important being genetics and lifestyle.The aim of this paper is to study the mutation mechanisms of breast cancer for Japanese women by fitting the incidence data of three high-quality population areas in Japan from 1985 to 2010.To achieve this goal,we have set up multi-stage models within the mathematical model of Moolgavkar,Venzon,and Knudson.Such models take both mutation rates and clonal expansion rates as parameters in each compartment into consideration.Based on our simulation outcomes,two to twelve driver mutations are sufficient in the pathway to female breast cancer in Japan.On the other hand,a previous study demonstrated that breast cancer in American women requires two to fourteen gene mutations to get a cancerous cell.Moreover,the 3-stage mat hematical model is the optimal model as it fits clinical data very nicely for all affected cases of females in Japan and the US.The genetic instability has a promincnt effect on the tumorigenesis of Japanese females caused by the first four driver mutations.The calculated results for Japanese women are compared with previous works for American women.

QUALITATIVE ANALYSIS OF A STOCHASTIC PREDATOR-PREY SYSTEM WITH DISEASE IN THE PREDATOR

A stochastic predator prey system with disease in the predator population is proposed, the existence of global positive solution is derived. When the white noise is small, there is a stationary distribution. In addition, conditions of global stability for the determin- istic system are also established from the above result. By Lyapunov function, the long time behavior of solution around the disease-free equilibrium of deterministic system is derived. These results mean that stochastic system has the similar property with the corresponding deterministic system. When the white noise is small, however, large envi- ronmental noise makes the result different. Finally, numerical simulations are carried out to support our findings.

Global dynamics and optimal control of an influenza model with vaccination, media coverage and treatment

In this work, in order to identify the most effective measure and combinations of several measures to control influenza spread, we propose an SVEIAR influenza model with imperfect vaccination, media coverage and antiviral treatment. The global dynamics of the model is explored. Sensitivity analysis of the basic reproduction number and the endemic equilibrium is conducted to evaluate the effectiveness of influenza control measures. Purthermore, an optimal control problem incorporating the three measures is formulated to design optimal control strategies for influenza. The cost-effectiveness analysis reveals that combining the three measures is the most cost-effective among the strategies considered. Numerical simulations show that media propaganda can play a dominant role in curbing influenza transmission.