Facile autoreduction synthesis of core-shell Bi-Bi2O3/CNT with 3-dimensional neural network structure for high-rate performance supercapacitor

Core-shell Bi-Bi2 O3/CNT(carbon nanotube) with 3-dimensional neural network structure where Bi-Bi2O3 nanospheres act as cell bodies supported by a 3-dimensional network of CNTs acting as synapses is designed and prepared by simple solvothermal method and subsequent annealing autoreduction treatment,and this structure facilitates the efficient transport of electrons.It can provide two electron transfer paths due to the double contact of Bi2O3 shell with CNT and metal Bi core which enhances the efficiency of the electrochemical reaction.The Bi-Bi2 O3/CNT electrode shows a high gravimetric capacitance of 850 F g-1(1 A g-1),and the specific capacitance of Bi-Bi2O3/CNT can be still 714 F g-1 at 30 A g-1 indicating excellent rate performance.The asymmetric supercapacitor is assembled with Bi-Bi2 O3/CNT as the negative electrode and Ni(OH)2/CNT as the positive electrode,delivering a high energy density of 36.7 Wh kg-1 and a maximum power density of 8000 W kg-1.Therefore,the core-shell Bi-Bi2O3/CNT with 3-dimensional neural network structure as the negative electrode of supercapacitor shows great potential in the field of energy storage in the future.

Prediction of Superconductivity for Oxides Based on Structural Parameters and Artificial Neural Network Method

Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.

VARIABLE STRUCTURE CONTROL APPLIED IN ELECTRO-HYDRAULIC SERVO SYSTEM WITH ANN

The variable structure control （VSC） theory is applied to the electro-hydraulic servo system here. The VSC control law is achieved using Lyapunov method and pole placement. To eliminate the chattering phenomena, a saturation function is adopted. The proposed VSC approach is fairly robust to load disturbance and system parameter variation. Since the distortion. including phase lag and amplitude attenuation occurs in the system sinusoid response, the amplitude and phase control （APC） algorithm, based on Adaline neural network and using LMS algorithm, is developed for distortion cancellation. The APC controller is simple and can on-line adjust, thus it gives accurate tracking.

Physical Mechanism Enabled Neural Network for Power System Dynamic Security Assessment

Data-driven artificial intelligence technologies have emerged as increasingly fascinating tools for assessing power system security.However,their inherent mechanism of inexplicability and unreliability now limits their scalability in power systems.To address this problem,this paper proposes a neural network design method empowered by physical mechanisms for power system security assessment.It incorporates geometric characteristics of dynamic security regions into the network training process and constructs connections between network structure and system's unstable mode,which can perform security assessment with a neural network efficiently while ensuring physical plausibility.Furthermore,a credibility evaluation mechanism is established to ensure credibility of neural network predictions and reduce misclassifications.Finally,effectiveness of the proposed method is verified on test systems.Methods and considerations in building a neural network with interpretable structures and credible predictions can provide a reference for machine intelligence applied in other industrial systems.

Relationship between atomic structure and crystal type studied by artificial neural network

We have used chemical bond parameters and pattern recognition method to investigatethe regularities of the crystal type of alloy phase,and achieved good results.Theparameters used,however,are semi-empirical paramters,which are not very strict fromtheoretical viewpoint.In this letter,we use the numbers describing atomic structure(thenumbers of valence electrons Z1,Z2,the principal quantum numbers of valence electrons n1,

Multi-Feature Fusion Based Structural Deep Neural Network for Predicting Answer Time on Stack Overflow

Stack Overflow provides a platform for developers to seek suitable solutions by asking questions and receiving answers on various topics.However,many questions are usually not answered quickly enough.Since the questioners are eager to know the specific time interval at which a question can be answered,it becomes an important task for Stack Overflow to feedback the answer time to the question.To address this issue,we propose a model for predicting the answer time of questions,named Predicting Answer Time(i.e.,PAT model),which consists of two parts:a feature acquisition and fusion model,and a deep neural network model.The framework uses a variety of features mined from questions in Stack Overflow,including the question description,question title,question tags,the creation time of the question,and other temporal features.These features are fused and fed into the deep neural network to predict the answer time of the question.As a case study,post data from Stack Overflow are used to assess the model.We use traditional regression algorithms as the baselines,such as Linear Regression,K-Nearest Neighbors Regression,Support Vector Regression,Multilayer Perceptron Regression,and Random Forest Regression.Experimental results show that the PAT model can predict the answer time of questions more accurately than traditional regression algorithms,and shorten the error of the predicted answer time by nearly 10 hours.

In Silico Investigation of Agonist Activity of a Structurally Diverse Set of Drugs to hPXR Using HM-BSM and HM-PNN

The human pregnane X receptor（hPXR） plays a critical role in the metabolism, transport and clearance of xenobiotics in the liver and intestine. The hPXR can be activated by a structurally diverse of drugs to initiate clinically relevant drug-drug interactions. In this article, in silico investigation was performed on a structurally diverse set of drugs to identify critical structural features greatly related to their agonist activity towards h PXR. Heuristic method（HM）-Best Subset Modeling（BSM） and HM-Polynomial Neural Networks（PNN） were utilized to develop the linear and non-linear quantitative structure-activity relationship models. The applicability domain（AD） of the models was assessed by Williams plot. Statistically reliable models with good predictive power and explain were achieved（for HM-BSM, r^2=0.881, q^2_（LOO）=0.797, q^2_（EXT）=0.674; for HM-PNN, r^2=0.882, q^2_（LOO）=0.856, q^2_（EXT）=0.655）. The developed models indicated that molecular aromatic and electric property, molecular weight and complexity may govern agonist activity of a structurally diverse set of drugs to h PXR.

TRACKING ERROR REDUCTION IN CNC MACHINING BY RESHAPING THE KINEMATIC TRAJECTORY

In this paper,a method of reducing the tracking error in CNC machining is proposed.The structured neural network is used to approximate the discontinuous friction in CNC machining,which has jump points and uncertainties.With the estimated nonlinear friction function,the reshaped trajectory can be computed from the desired one by solving a second order ODE such that when the reshaped trajectory is fed into the CNC controller,the output is the desired trajectory and the tracking error is eliminated in certain sense.The proposed reshape method is also shown to be robust with respect to certain parameters of the dynamic system.