Implantable probe with integrated reference electrode for in situ neural signal and calcium ion monitoring

Monitoring the electrophysiology activity of neurons and blood calcium signals can enable a better understanding of disease-related neural system circuits.However,currently,in situ calcium ion monitoring tools are scarce and exhibit low integration and limited sensitivity.In this letter,we propose an implantable probe with an integrated in situ Ag/AgCl reference electrode(ISA/ARE)that can monitor action potential(AP)and Ca^(2+) concentrations.

聚⁃4⁃甲基⁃1⁃戊烯中空纤维膜式人工肺组件的氧气传质性能的研究

采用自制的聚-4-甲基-1-戊烯中空纤维膜组装成中空纤维膜式人工肺组件,以去离子水、生理盐水、甘油水溶液和Na2SO3水溶液来模拟血液,按照中空纤维膜内通氧气、中空纤维膜外走模拟液的方式进行体外性能测试,测定膜式人工肺组件在不同的液体流速、气体压力、中空纤维膜根数和模拟液种类下的氧气传输速率.实验表明:随着液体流速的加快、气体压力的变大和中空纤维膜根数的增多,膜式人工肺组件在四种模拟液下的氧气传质性能均对应不同幅度的强化.同时,依据质量守恒定律和双膜理论建立了一种膜式人工肺组件的氧气传质数学模型,为中空纤维膜传质性能研究和人工肺组件的结构优化提供了有益的参考.

Parameters Identification of Tunnel Jointed Surrounding Rock Based on Gaussian Process Regression Optimized by Difference Evolution Algorithm

Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.

Brainmask:an ultrasoft and moist microelectrocorticography electrode for accurate positioning and long-lasting recordings

Bacterial cellulose(BC),a natural biomaterial synthesized by bacteria,has a unique structure of a cellulose nanofiberweaved three-dimensional reticulated network.BC films can be ultrasoft with sufficient mechanical strength,strong water absorption and moisture retention and have been widely used in facial masks.These films have the potential to be applied to implantable neural interfaces due to their conformality and moisture,which are two critical issues for traditional polymer or silicone electrodes.In this work,we propose a micro-electrocorticography(micro-ECoG)electrode named“Brainmask”,which comprises a BC film as the substrate and separated multichannel parylene-C microelectrodes bonded on the top surface.Brainmask can not only guarantee the precise position of microelectrode sites attached to any nonplanar epidural surface but also improve the long-lasting signal quality during acute implantation with an exposed cranial window for at least one hour,as well as the in vivo recording validated for one week.This novel ultrasoft and moist device stands as a next-generation neural interface regardless of complex surface or time of duration.

Machine learning for adjoint vector in aerodynamic shape optimization

Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.