Thermally Drawn-Based Microtubule Soft Continuum Robot for Cardiovascular Intervention.

Cardiovascular disease is becoming the leading cause of human mortality. In order to address this, flexible continuum robots have emerged as a promising solution for miniaturizing and automating vascular interventional equipment for diagnosing and treating cardiovascular diseases. However, existing continuum robots used for vascular intervention face challenges such as large cross-sectional sizes, inadequate driving force, and lack of navigation control, preventing them from accessing cerebral blood vessels or capillaries for medical procedures. Additionally, the complex manufacturing process and high cost of soft continuum robots hinder their widespread clinical application. In this study, we propose a thermally drawn-based microtubule soft continuum robot that overcomes these limitations. The proposed robot has cross-sectional dimensions several orders of magnitude smaller than the smallest commercially available conduits, and it can be manufactured without any length restrictions. By utilizing a driving strategy based on liquid kinetic energy advancement and external magnetic field for steering, the robot can easily navigate within blood vessels and accurately reach the site of the lesion. This innovation holds the potential to achieve controlled navigation of the robot throughout the entire blood vessel, enabling in situ diagnosis and treatment of cardiovascular diseases.

A Novel Intelligent Fault Diagnosis Method for Self-Priming Centrifugal Pumps.

The real-time diagnostic monitoring of self-priming centrifugal pumps is essential to ensure their safe operation. Nevertheless, owing to the intricate structure and complex operational conditions inherent in such pumps, existing fault diagnosis methods encounter challenges in effectively extracting crucial fault feature information and accurately identifying fault types. Consequently, this paper introduces an intelligent fault diagnosis method tailored for self-priming centrifugal pumps. The approach amalgamates refined time-shift multiscale fluctuation dispersion entropy, cosine pairwise-constrained supervised manifold mapping, and adaptive chaotic Aquila optimization support vector machine techniques. To begin with, refined time-shift multiscale fluctuation dispersion entropy is employed to extract fault-related features, adeptly mitigating concerns related to entropy domain deviations and instability. Subsequently, the application of cosine pairwise-constrained supervised manifold mapping serves to reduce the dimensionality of the extracted fault features, thereby bolstering the efficiency and precision of the ensuing identification process. Ultimately, the utilization of an adaptive chaotic Aquila optimization support vector machine facilitates intelligent fault classification, leading to enhanced accuracy in fault identification. The experimental findings unequivocally affirm the efficacy of the proposed method in accurately discerning among various fault types in self-priming centrifugal pumps, achieving an exceptional recognition rate of 100%. Moreover, it is noteworthy that the average correct recognition rate achieved by the proposed method surpasses that of five existing intelligent fault diagnosis techniques by a significant margin, registering a notable increase of 15.97%.

An ultra-broadband frequency-agile terahertz perfect absorber with perturbed MoS2 plasmon modes.

Multidomain dynamic manipulations for terahertz (THz) absorbers usually necessitate the orchestrated actions of several active elements, inevitably complicating the structural design and elongating the modulation time. Herein, we utilize the coupling between the total reflection prism and electrically-driven MoS2 to activate a tight field confinement in a deep-subwavelength interlayer, ultimately achieving frequency-agile absorption adjustments only with a gate voltage. Theoretical and simulation analysis results indicate that the redistributed electric field and susceptible dielectric response are attributed to the limited spatial near-field perturbation of surface plasmon resonances. We also demonstrate that perturbed MoS2 plasmon modes promote the formation of dual-phase singularities, significantly suppressing the attenuation of the absorption amplitude as large-scale frequency shifts, thereby extending the relative tuning range (WRTR) to 175.4%. These findings offer an efficient approach for expanding the horizon of THz absorption applications that require ultra-broadband and swift-response capabilities.

Intelligent fault detection scheme for constant-speed wind turbines based on improved multiscale fuzzy entropy and adaptive chaotic Aquila optimization-based support vector machine.

Timely and effective fault detection is essential to ensure the safe and reliable operation of wind turbines. However, due to the complex kinematic mechanisms and harsh working environments of wind turbine equipment, it is difficult to extract sensitive features and detect faults from acquired wind turbine signals. To address this challenge, a novel intelligent fault detection scheme for constant-speed wind turbines based on refined time-shifted multiscale fuzzy entropy (RTSMFE), supervised isometric mapping (SI), and adaptive chaotic Aquila optimization-based support vector machine (ACAOSVM) is proposed. In the first step, the RTSMFE method is used to fully extract features of the wind turbine system. The time-shifted coarse-grained construction technique and a refined computing technique are adopted in the RTSMFE method to enhance the capability of traditional multiscale fuzzy entropy for measuring the complexity of signals. Subsequently, an effective manifold learning approach, SI, is applied to obtain the important and low-dimensional feature set from the high-dimensional feature set. Finally, sensitive features are fed into the ACAOSVM classifier to identify faults. The proposed ACAO algorithm is used to optimize important parameters of the SVM, thereby improving its detection performance. Simulations and wind turbine experiments verified that the proposed RTSMFE outperforms existing entropy techniques in terms of complexity measurement and feature extraction. Furthermore, the proposed ACAOSVM classifier is superior to existing advanced classifiers for fault pattern recognition. Finally, the proposed intelligent fault detection scheme can more correctly and efficiently detect wind turbine single/hybrid faults than other recently published schemes.

Cu Nanowires and Nanoporous Ag Matrix Fabricated through Directional Solidification and Selective Dissolution of Ag-Cu Eutectic Alloys.

Cu nanowires and a nanoporous Ag matrix were fabricated through directional solidification and selective dissolution of Ag-Cu eutectic alloys. Ag-39.9at.%Cu eutectic alloys were directionally solidified at growth rates of 14, 25, and 34 µm/s at a temperature gradient of 10 K/cm. The Cu phase in the Ag matrix gradually changed from lamellar to fibrous with an increase in the growth rate. The Ag matrix phase was selectively dissolved, and Cu nanowires of 300-600 nm in diameter and tens of microns in length were prepared in 0.1 M borate buffer with a pH of 9.18 at a constant potential of 0.7 V (vs. SCE). The nanoporous Ag matrix was fabricated through selective dissolution of Cu fiber phase in 0.1 M acetate buffer with a pH of 6.0 at a constant potential of 0.5 V (vs. SCE). The diameter of Ag pores decreased with increasing growth rate. The diameter and depth of Ag pores increased when corrosion time was extended. The depth of the pores was 30 µm after 12 h.

Ensemble Dilated Convolutional Neural Network and Its Application in Rotating Machinery Fault Diagnosis.

Fault diagnosis of rotating machinery is an attractive yet challenging task. This paper presents a novel intelligent fault diagnosis scheme for rotating machinery based on ensemble dilated convolutional neural networks. The novel fault diagnosis framework employs a model training strategy based on early stopping optimization to ensemble several one-dimensional dilated convolutional neural networks (1D-DCNNs). By varying the dilation rate of the 1D-DCNN, different receptive fields can be obtained to extract different vibration signal features. The early stopping strategy is used as a model update threshold to prevent overfitting and save computational resources. Ensemble learning uses a weighted mechanism to combine the outputs of multiple 1D-DCNN subclassifiers with different dilation rates to obtain the final fault diagnosis. The proposed method outperforms existing state-of-the-art classical machine learning and deep learning methods in simulation studies and diagnostic experiments, demonstrating that it can thoroughly mine fault features in vibration signals. The classification results further show that the EDCNN model can effectively and accurately identify multiple faults and outperform existing fault detection techniques.

Wearable Real-Time Gesture Recognition Scheme Based on A-Mode Ultrasound.

A-mode ultrasound has the advantages of high resolution, easy calculation and low cost in predicting dexterous gestures. In order to accelerate the popularization of A-mode ultrasound gesture recognition technology, we designed a human-machine interface that can interact with the user in real-time. Data processing includes Gaussian filtering, feature extraction and PCA dimensionality reduction. The NB, LDA and SVM algorithms were selected to train machine learning models. The whole process was written in C++ to classify gestures in real-time. This paper conducts offline and real-time experiments based on HMI-A (Human-machine interface based on A-mode ultrasound), including ten subjects and ten common gestures. To demonstrate the effectiveness of HMI-A and avoid accidental interference, the offline experiment collected ten rounds of gestures for each subject for ten-fold cross-validation. The results show that the offline recognition accuracy is 96.92% ± 1.92%. The real-time experiment was evaluated by four online performance metrics: action selection time, action completion time, action completion rate and real-time recognition accuracy. The results show that the action completion rate is 96.0% ± 3.6%, and the real-time recognition accuracy is 83.8% ± 6.9%. This study verifies the great potential of wearable A-mode ultrasound technology, and provides a wider range of application scenarios for gesture recognition.

Deformation Modeling and Simulation of a Novel Bionic Software Robotics Gripping Terminal Driven by Negative Pressure Based on Classical Differential Algorithm.

A general pneumatic soft gripper is proposed in this paper. Combined with the torque balance theory, the mathematical theoretical model of bending deformation of soft gripper is established based on Yeoh constitutive model and classical differential geometry. Assuming that the pressure in each inner cavity is evenly distributed, the input gas is in an ideal state, which is approximately treated as an isothermal condition, and all orifices experience blocked flow. In addition, compared with the mechanical work of gas, the energy related to gas flow and heat transfer is negligible. The nonlinear mechanical properties of silicone rubber are studied. It is regarded as isotropic and incompressible material, which is characterized by strain energy per unit volume. The material constant coefficients C 10 and C 20 are determined through the uniaxial tensile test, and the software gripper is simulated on the ABAQUS platform. The bending deformation models of grippers with three different force-bearing cavity structures are analyzed and compared, and the software clamping structure with the bending deformation most in line with the application conditions is selected. The limit input air pressure of the gripper and the situation of enveloping the clamping target object are analyzed. Through the bending deformation experiment, the maximum deformation angle is 72.4°. The relative error between the simulation analysis data and the prediction results of the mathematical model is no more than 3.5%, which verifies the effectiveness of the simulation and the correctness of the mathematical theoretical model of bending deformation. The soft manipulator proposed in this paper has good adaptability to grasping objects of different shapes and sizes. The minimum diameter of the target object that can be clamped is 0.1 mm. It can clamp the object weighing up to 1 kg. It has compact size, light weight, high ductility, and flexibility.

Bioinspired soft microrobots actuated by magnetic field.

In contrast to traditional large-scale robots, which require complicated mechanical joints and material rigidity, microrobots made of soft materials have exhibited amazing features and great potential for extensive applications, such as minimally invasive surgery. However, microrobots are faced with energy supply and control issues due to the miniaturization. Magnetic field actuation emerges as an appropriate approach to tackle with these issues. This review summarizes the latest progress of biomimetic soft microrobots actuated by magnetic field. Starting with an overview of the soft material and magnetic material adopted in the magnetic field actuated soft microrobots, the various fabrication methods and design structures of soft microrobots are summarized. Subsequently, practical and potential applications, such as targeted therapy, surgical operation, and the transportation of microscopic objects, in the fields of biomedicine and environmental remediation are presented. In the end, some current challenges, and the future development trends of magnetic soft microrobots are briefly discussed. This review is expected to offer a helpful guidance for the new researchers of biomimetic soft microrobots actuated by magnetic field.

Visible Light-Driven Jellyfish-like Miniature Swimming Soft Robot.

Soft actuators that exhibit large deformation and can move at a fast speed in response to external stimuli have been in high demand for biomimetic applications. In this paper, we propose a convenient approach to fabricate a reversible and thermal-responsive composite hydrogel. Under the irradiation of visible light, the striped hydrogel can bend at a speed of up to 65.72°/s with carbon nanotubes loaded at a concentration of 3 mg/mL. A jellyfish-like miniature soft robot is made using this hydrogel. When driven by visible light, the robot can move at a maximum speed of 3.37 mm/s. Besides swimming, other motion modes, including walking and jumping, are also achieved by the robot. In addition, the robot can perform directional transportation of tiny objects. As a new actuation approach for the research of jellyfish-like miniature soft robots, this work is of great significance to the development of flexible bionic robots. Moreover, this work also offers some important insights into the research of biomimetic robots driven by visible light.

Biomimetic soft micro-swimmers: from actuation mechanisms to applications.

Underwater robot designs inspired by the behavior and morphological characteristics of aquatic animals can provide reinforced mobility and energy efficiency. In the past two decades, the emerging materials science and integrated circuit technology have been combined and applied to various types of bionic soft underwater miniaturized robots by researchers around the world. Further, the potential applications of biomimetic soft micro-swimmers in the biological and medical fields have been explored. Here, this paper reviews the development of biomimetic soft tiny swimmers, which are designed based on a variety of intelligent materials and control strategies. This review focuses on the various actuation mechanisms of soft tiny swimmers reported in the past two decades and classifies these robots into four categories: fish-like, snake-like, jellyfish-like and microbial-inspired ones. Besides, this review considers the practical challenges faced by actuation mechanisms of each type of robot, and summarizes and prospects how these challenges affect the potential applications of robots in real environments.

Modified multiscale weighted permutation entropy and optimized support vector machine method for rolling bearing fault diagnosis with complex signals.

The rolling bearing vibration signals are complex, non-linear, and non-stationary, it is difficult to extract the sensitive features and diagnose faults by conventional signal processing methods. This paper focuses on the sensitive features extraction and pattern recognition for rolling bearing fault diagnosis and proposes a novel intelligent fault-diagnosis method based on generalized composite multiscale weighted permutation entropy (GCMWPE), supervised Isomap (S-Iso), and marine predators algorithm-based support vector machine (MPA-SVM). Firstly, a novel non-linear technology named GCMWPE was presented, allowing the extraction of bearing features from multiple scales and enabling the construction of a high-dimensional feature set. The GCMWPE uses the generalized composite coarse-grained structure to overcome the shortcomings of the original structure in multiscale weighted permutation entropy and obtain more stable entropy values. Subsequently, the S-Iso algorithm was introduced to obtain the main features and reduce the GCMWPE set dimensionality. Finally, a combination of GCMWPE and S-Iso set was input to the MPA-SVM for diagnosis and identification. The marine predators algorithm (MPA) was used to obtain the optimal SVM parameters. The effectiveness of the proposed fault diagnosis method was confirmed through two bearing fault diagnosis experiments. The results have shown that the proposed method can be used to correctly diagnose bearing states with high diagnostic accuracy.

Thin thermally grown oxide thickness detection in thermal barrier coatings based on SWT-BP neural network algorithm and terahertz technology.

Terahertz time-domain spectroscopy is a contactless and nondestructive testing technique that is often used to measure the thickness of layered materials. However, the technique presents limited thickness detection resolution, especially in the thin thermally grown oxide (TGO) of thermal barrier coatings whose thickness is below 30 µm. In this study, an SWT-BP algorithm combining a stationary wavelet transform (SWT) and a backpropagation (BP) neural network was proposed, and the regression coefficient of SWT-detailed results was 0.92. The prediction results were in good agreement with the real-time results; it demonstrated that the proposed algorithm was able to achieve a thickness prediction of up to 1-29 µm of the TGO. The proposed algorithm is suitable for thin thickness detection of the TGO.

Rise of cyborg microrobot: different story for different configuration.

By integrating organic parts achieved through evolution and inorganic parts developed by human civilisation, the cyborg microrobot is rising by taking advantage of the high flexibility, outstanding energy efficiency, extremely exquisite structure in the natural components and the fine upgradability, nice controllability in the artefact parts. Compared to the purely synthetic microrobots, the cyborg microrobots, due to the exceptional biocompatibility and biodegradability, have already been utilised in in situ diagnosis, precise therapy and other biomedical applications. In this review, through a thorough summary of recent advances of cyborg microrobots, the authors categorise the cyborg microrobots into four major classes according to the configuration between biomaterials and artefact materials, i.e. microrobots integrated inside living cell, microrobots modified with biological debris, microrobots integrated with single cell and microrobots incorporated with multiple cells. Cyborg microrobots with the four types of configurations are introduced and summarised with the combination approaches, actuation mechanisms, applications and challenges one by one. Moreover, they conduct a comparison among the four different cyborg microrobots to guide the actuation force promotion, locomotion control refinement and future applications. Finally, conclusions and future outlook of the development and potential applications of the cyborg microrobots are discussed.

Untethered microgripper-the dexterous hand at microscale.

Untethered microgrippers that can navigate in hard-to-reach and unpredictable environments are significantly important for biomedical applications such as targeted drug delivery, micromanipulation, minimally invasive surgery and in vivo biopsy. Compared with the traditional tethered microgrippers, the wireless microgrippers, due to the exceptional characteristics such as miniaturized size, untethered actuation, dexterous and autonomous motion, are projected to be promising microtools in various future applications. In this review, we categorize the untethered microgrippers into five major classes, i.e. microgrippers responsive to thermal, microgrippers actuated by magnetic fields, microgrippers responsive to chemicals, light-driven microgrippers and hybrid actuated microgrippers. Firstly, the actuation mechanisms of these microgrippers are introduced. The challenges faced by these microgrippers are also covered in this part. With that, the fabrication methods of these microgrippers are summarized. Subsequently, the applications of microgrippers are presented. Additionally, we conduct a comparison among different actuation mechanisms to explore the advantages and potential challenges of various types of microgrippers. In the end of this review, conclusions and outlook of the development and potential applications of the microgrippers are discussed.

Screw theory based mathematical modeling and kinematic analysis of a novel ankle rehabilitation robot with a constrained 3-PSP mechanism topology.

As a common athletics injury in orthopedics clinic, ankle injury may affect a person's daily life and ankle injury rehabilitation has gained increasing interests from the medical and robotic societies. A novel hybrid ankle rehabilitation robot is proposed, which composing of a serial and a parallel part. In order to analyze its kinematic performances, the parallel part of the robot is simplified as a constrained 3-PSP parallel mechanism. A mathematical model for the parallel part of the robot is established based on the screw theory. Then the inverse kinematics is obtained, and the reciprocal twists, Jacobian matrices and the singularity of the robot are analyzed. Finally the workspace of the central point on the moving platform is predicted. The kinematic analyses manifest that the proposed hybrid rehabilitation robot not only can realize three kinds of ankle rehabilitation motions, but also can eliminate singularity with enhanced workspace. The workspace of the central point reveals that the hybrid robot can fully meet the demanded rehabilitation space by comparing with the clinic demands. Our results reveals the characteristic structure of the hybrid rehabilitation robot and its superiority, it offers some basis data for the future enhancement of the device.

Tandem polymer solar cells: simulation and optimization through a multiscale scheme.

In this paper, polymer solar cells with a tandem structure were investigated and optimized using a multiscale simulation scheme. In the proposed multiscale simulation, multiple aspects - optical calculation, mesoscale simulation, device scale simulation and optimal power conversion efficiency searching modules - were studied together to give an optimal result. Through the simulation work, dependencies of device performance on the tandem structures were clarified by tuning the thickness, donor/acceptor weight ratio as well as the donor-acceptor distribution in both active layers of the two sub-cells. Finally, employing searching algorithms, we optimized the power conversion efficiency of the tandem polymer solar cells and located the optimal device structure parameters. With the proposed multiscale simulation strategy, poly(3-hexylthiophene)/phenyl-C61-butyric acid methyl ester and (poly[2,6-(4,4-bis-(2-ethylhexyl)-4H-cyclopenta[2,1-b;3,4-b]dithiophene)-alt-4,7-(2,1,3-benzothiadiazole)])/phenyl-C61-butyric acid methyl ester based tandem solar cells were simulated and optimized as an example. Two configurations with different sub-cell sequences in the tandem photovoltaic device were tested and compared. The comparison of the simulation results between the two configurations demonstrated that the balance between the two sub-cells is of critical importance for tandem organic photovoltaics to achieve high performance. Consistency between the optimization results and the reported experimental results proved the effectiveness of the proposed simulation scheme.

FDTD-based quantitative analysis of terahertz wave detection for multilayered structures.

Experimental investigations have shown that terahertz pulsed imaging (TPI) is able to quantitatively characterize a range of multilayered media (e.g., biological issues, pharmaceutical tablet coatings, layered polymer composites, etc.). Advanced modeling of the interaction of terahertz radiation with a multilayered medium is required to enable the wide application of terahertz technology in a number of emerging fields, including nondestructive testing. Indeed, there have already been many theoretical analyses performed on the propagation of terahertz radiation in various multilayered media. However, to date, most of these studies used 1D or 2D models, and the dispersive nature of the dielectric layers was not considered or was simplified. In the present work, the theoretical framework of using terahertz waves for the quantitative characterization of multilayered media was established. A 3D model based on the finite difference time domain (FDTD) method is proposed. A batch of pharmaceutical tablets with a single coating layer of different coating thicknesses and different refractive indices was modeled. The reflected terahertz wave from such a sample was computed using the FDTD method, assuming that the incident terahertz wave is broadband, covering a frequency range up to 3.5 THz. The simulated results for all of the pharmaceutical-coated tablets considered were found to be in good agreement with the experimental results obtained using a commercial TPI system. In addition, we studied a three-layered medium to mimic the occurrence of defects in the sample.