Intracellular sorting pathways of the amyloid precursor protein provide novel neuroprotective strategies

Alzheimer＇s disease（AD）is the most common cause of senile dementia.It is characterized by the formation of plaques mainly composed of the amyloid-beta peptide（Aβ）.Diverse lines of evidence support the notion that accumulation of Aβis a primary cause of AD pathogenesis（Huang and Mucke,2012）.Amyloid precusor protein（APP）processing is dependent on its subcelluar trafficking pathway：Aβis derived from APP by proteolyric processing.

Multi-objective Function Optimization for Environmental Control of a Greenhouse Based on a RBF and NSGA-Ⅱ

To better meet the needs of crop growth and achieve energy savings and efficiency enhancements,constructing a reliable environmental model to optimize greenhouse decision parameters is an important problem to be solved.In this work,a radial-basis function(RBF)neural network was used to mine the potential changes of a greenhouse environment,a temperature error model was established,a multi-objective optimization function of energy consumption was constructed and the corresponding decision parameters were optimized by using a non-dominated sorting genetic algorithm with an elite strategy(NSGA-Ⅱ).The simulation results showed that RBF could clarify the nonlinear relationship among the greenhouse environment variables and decision parameters and the greenhouse temperature.The NSGA-Ⅱ could well search for the Pareto solution for the objective functions.The experimental results showed that after 40 min of combined control of sunshades and sprays,the temperature was reduced from 31℃to 25℃,and the power consumption was 0.5 MJ.Compared with tire three days of July 24,July 25 and July 26,2017,the energy consumption of the controlled production greenhouse was reduced by 37.5%,9.1%and 28.5%,respectively.

Parameter influence law analysis and optimal design of a dual mass flywheel

The influence of the dynamic parameters of a dual mass flywheel(DMF)on its vibration reduction performance is analyzed,and several optimization algorithms are used to carry out multiobjective DMF optimization design.First,the vehicle powertrain system is modeled according to the parameter configuration of the test vehicle.The accuracy of the model is verified by comparing the simulation data with the test results.Then,the model is used to analyze the influence of the moment of inertia ratio,torsional stiffness,and damping in reducing DMF vibration.The speed fluctuation amplitude at the transmission input shaft and the natural frequency of the vehicle are taken as the optimization objectives.The passive selection method,multiobjective particle swarm optimization,and the nondominated sorting genetic algorithm based on an elite strategy are used to carry out DMF multiobjective optimization design.The advantages and disadvantages of these algorithms are evaluated,and the best optimization algorithm is selected.