Engineering a dual-responsive, exosome-surface anchored DNA nanosensor for microenvironment monitoring in vivo.

We propose an azoreductase and pH dual-responsive amphiphilic pyramidal tetrahedral DNA probe to construct a circulating exosome-surface anchored nanosensor for microenvironment monitoring in vivo. Based on the shuttle behavior, biocompatibility of the exosomes, and easy synthesis and modification of nucleic acids, both in vitro and in vivo results demonstrate that this nanosensor has excellent performance for activatable O2 and pH imaging, and thus can distinguish disease areas from normal tissue areas. We expect it to be a useful tool for obtaining early abnormal fluctuations in the microenvironment, which is of great significance for the early detection of diseases.

A temporal-spatial attention-based action recognition method for intelligent fault diagnosis.

The intelligent fault diagnosis of video data has become a demanding task in industrial applications. However, existing models require expensive computational cost and memory demand, which makes this technology applied in factories impossible. To address this problem, a temporal-spatial attention-based action recognition method (TARM) integrating TAB (temporal-attention-based frame splitting model), SAB (spatial-attention-based agent focusing mode) and LSB (long-short term feature learning mode) is proposed. TAB first extracts important frames from raw videos. Then, SAB refines video data by reinforcing their essential features and weakening unnecessary features. Furthermore, LSB monitors action type of video data by establishing recurrent convolutional architectures. Finally, the performance of TARM in terms of training time and fault diagnosis accuracy are validated by comparing with six state-of-the-art video diagnosis methods.

Visualization of O2/ATP cross-talk in living cells with a smart fluorescent nanoprobe.

Herein, we propose a dual-responsive fluorescent nanoprobe to visualize the cross-talk between O2 and adenosine triphosphate (ATP) in living cells. We hope it will be a helpful tool for the further understanding of cellular metabolism and further facilitating risk warning in the process of adaptation to consistent environmental pressures in premalignant lesions.

Investigations on a mathematical model for optimum impedance compensation of a giant magnetostrictive ultrasonic transducer and its resonance characteristics.

Giant magnetostrictive materials (GMMs) have been widely used to fabricate transducers with high-energy output because of their excellent properties. However, there are few reports on mathematical models to optimize the impedance compensation and resonance characteristics of giant magnetostrictive transducers. In this study, a giant magnetostrictive ultrasonic transducer (GMUT) suitable for rotary ultrasonic machining systems is proposed. A mathematical model for optimum impedance compensation that considers the loss in energy conversion is established to maximize the use of ultrasonic energy. The frequency characteristics of the electrical feedback signal in the resonance state are investigated, and the resonance zone found is used for frequency tracking. An impedance analyzer is used to determine the parameters of the mathematical model, and the validity of the optimum compensation capacitance is verified by experiments. The frequency characteristics of the minimum current, active power, and amplitude are obtained to obtain the resonance zone in the GMUT with the lowest energy consumption. The results of this study provide a reference for frequency tracking.

An amplitude prediction model for a giant magnetostrictive ultrasonic transducer.

Rotary ultrasonic machining (RUM) is widely used in the processing of brittle and hard materials. The application of giant magnetostrictive ultrasonic transducers (GMUT) with effective vibration performance is an increasingly popular field of research within RUM. A generalized amplitude prediction model for GMUT is obtained in this paper by first providing an equivalent kinetics model of the GMUT. Considering the influence on interaction force between Terfenol-D and the external mechanical mechanism, the prestress mechanism of Terfenol-D and the joint face of the horn are determined as equivalent to two spring-damping systems in series, and a general GMUT vibration equation is established. The equivalent stiffness of the prestress mechanism is then identified, and the mechanical quality factor of the vibration system is calculated by impedance analysis. The influence of the joint face of the horn and the prestress mechanism on the amplitude is then studied by nonlinear least square fitting. Based on a magnetostriction and magnetization model, an odd power amplitude prediction model with mechanical quality factor, excitation current amplitude, and excitation frequency is proposed. The experimental results demonstrate that the proposed model can effectively predict the output amplitude of the GMUT with different mechanical quality factors for different excitation signals, providing a method for system design and optimization of the GMUT.

Fault tolerant control of multivariable processes using auto-tuning PID controller.

Fault tolerant control of dynamic processes is investigated in this paper using an auto-tuning PID controller. A fault tolerant control scheme is proposed composing an auto-tuning PID controller based on an adaptive neural network model. The model is trained online using the extended Kalman filter (EKF) algorithm to learn system post-fault dynamics. Based on this model, the PID controller adjusts its parameters to compensate the effects of the faults, so that the control performance is recovered from degradation. The auto-tuning algorithm for the PID controller is derived with the Lyapunov method and therefore, the model predicted tracking error is guaranteed to converge asymptotically. The method is applied to a simulated two-input two-output continuous stirred tank reactor (CSTR) with various faults, which demonstrate the applicability of the developed scheme to industrial processes.