Search

Abstract Objective To evaluate the safety and efficacy of the granisetron transdermal delivery system (GTDS) combined with Dexamethasone for preventing chemotherapy-induced nausea and vomiting (CINV) in patients receiving Capecitabine plus Oxaliplatin (CapeOX) therapy. Design Open-label, prospective, multi-center phase II trial. Setting Three institutions. Participants Fifty-four patients scheduled to receive CapeOX chemotherapy. Interventions Participants received GTDS (3.1 mg applied to the upper arm 48 h before chemotherapy, replaced on day 5, and discarded on day 12) and Dexamethasone. Main outcome measures The primary endpoint was the complete control rate of CINV. Secondary endpoints included the duration of delayed complete control, complete control rate in the acute phase, safety, and quality of life. Results The complete control rate for delayed CINV over the entire period (25–480 h) was 72.7% (95% CI 0.57–0.88). The duration of delayed complete control was 17.2 ± 4.5 days, with 51.5% of patients experiencing no nausea during the delayed phase. The complete control rate in the acute phase was 81.8% (95% CI 0.69–0.95). No serious adverse events related to the antiemetic regimen were reported. Conclusion Prolonged administration of GTDS is safe and effective for preventing CINV in patients with gastrointestinal malignancies treated with CapeOX. Trial Registration ClinicalTrials.gov registry (NCT05325190); registered on October 10, 2021.

Abstract Osteoarthritis (OA) is a chronic inflammatory disease characterized by cartilage destruction, synovitis, and osteophyte formation. Disease‐modifying treatments for OA are currently lacking. Because inflammation mediated by an imbalance of M1/M2 macrophages in the synovial cavities contributes to OA progression, regulating the M1 to M2 polarization of macrophages can be a potential therapeutic strategy. Basing on the inherent immune mechanism and pathological environment of OA, an immunoglobulin G‐conjugated bilirubin/JPH203 self‐assembled nanoparticle (IgG/BRJ) is developed, and its therapeutic potential for OA is evaluated. After intra‐articular administration, IgG conjugation facilitates the recognition and engulfment of nanoparticles by the M1 macrophages. The internalized nanoparticles disassemble in response to the increased oxidative stress, and the released bilirubin (BR) and JPH203 scavenge reactive oxygen species (ROS), inhibit the nuclear factor kappa‐B pathway, and suppress the activated mammalian target of rapamycin pathway, result in the repolarization of macrophages and enhance M2/M1 ratios. Suppression of the inflammatory environment by IgG/BRJ promotes cartilage protection and repair in an OA rat model, thereby improving therapeutic outcomes. This strategy of opsonization involving M1 macrophages to engulf carrier‐free BR/JPH203 nanoparticles to suppress inflammation for OA therapy holds great potential for OA intervention and treatment.

Underwater images often suffer from various degradations, such as color distortion, reduced visibility, and uneven illumination, caused by light absorption, scattering, and artificial lighting. However, most existing methods have focused on addressing singular or dual degradation aspects, lacking a comprehensive solution to underwater image degradation. This limitation hinders the application of vision technology in underwater scenarios. In this paper, we propose a framework for enhancing the quality of multi-degraded underwater images. This framework is distinctive in its ability to concurrently address color degradation, hazy blur, and non-uniform illumination by fusing RGB and Event signals. Specifically, an adaptive underwater color compensation algorithm is first proposed, informed by an analysis of the color degradation characteristics prevalent in underwater images. This compensation algorithm is subsequently integrated with a white balance algorithm to achieve color correction. Then, a dehazing method is developed, leveraging the fusion of sharpened images and gamma-corrected images to restore blurry details in RGB images and event reconstruction images. Finally, an illumination map is extracted from the RGB image, and a multi-scale fusion strategy is employed to merge the illumination map with the event reconstruction image, effectively enhancing the details in dark and bright areas. The proposed method successfully restores color fidelity, enhances image contrast and sharpness, and simultaneously preserves details of the original scene. Extensive experiments on the public dataset DAVIS-NUIUIED and our dataset DAVIS-MDUIED demonstrate that the proposed method outperforms state-of-the-art methods in enhancing multi-degraded underwater images.

Traditional deployable truss space structures previously had upper limits on their key indicators, such as the deployed area, folded ratio and total weight, and hence, the application of new extendable mechanisms with novel deployment types is desired. Foldable extendable tape spring booms made from FRP (fiber-reinforced polymer) laminate composites and their corresponding boom–membrane structures were invented in recent years to satisfy the needs of the large-scale requirements of spacecraft, especially for antennas, solar sails and solar arrays. This paper aimed to analyze the properties of the deployed states of extendable tape spring booms and their boom–membrane structures. By establishing an analytical model of the boom and the structure, the bending stiffness, critical buckling load of the boom and the fundamental frequency of the membrane structure were acquired. To provide more guidance on the boom–membrane structure design, a geometric and material parametric study was carried out. Meanwhile, an experimental study to investigate the deployed properties of the booms and membrane structures was introduced to afford some practical verification.

Traditional trajectory learning methods based on Imitation Learning (IL) only learn the existing trajectory knowledge from human demonstration. In this way, it can not adapt the trajectory knowledge to the task environment by interacting with the environment and fine-tuning the policy. To address this problem, a global trajectory learning method which combinines IL with Reinforcement Learning (RL) to adapt the knowledge policy to the environment is proposed. In this paper, IL is proposed to acquire basic trajectory skills, and then learns the agent will explore and exploit more policy which is applicable to the current environment by RL. The basic trajectory skills include the knowledge policy and the time stage information in the whole task space to help learn the time series of the trajectory, and are used to guide the subsequent RL process. Notably, neural networks are not used to model the action policy and the Q value of RL during the RL process. Instead, they are sampled and updated in the whole task space and then transferred to the networks after the RL process through Behavior Cloning (BC) to get continuous and smooth global trajectory policy. The feasibility and the effectiveness of the method was validated in a custom Gym environment of a flower drawing task. And then, we executed the learned policy in the real-world robot drawing experiment.

IntroductionReinforcement learning has been widely used in robot motion planning. However, for multi-step complex tasks of dual-arm robots, the trajectory planning method based on reinforcement learning still has some problems, such as ample exploration space, long training time, and uncontrollable training process. Based on the dual-agent depth deterministic strategy gradient (DADDPG) algorithm, this study proposes a motion planning framework constrained by the human joint angle, simultaneously realizing the humanization of learning content and learning style. It quickly plans the coordinated trajectory of dual-arm for complex multi-step tasks.MethodsThe proposed framework mainly includes two parts: one is the modeling of human joint angle constraints. The joint angle is calculated from the human arm motion data measured by the inertial measurement unit (IMU) by establishing a human-robot dual-arm kinematic mapping model. Then, the joint angle range constraints are extracted from multiple groups of demonstration data and expressed as inequalities. Second, the segmented reward function is designed. The human joint angle constraint guides the exploratory learning process of the reinforcement learning method in the form of step reward. Therefore, the exploration space is reduced, the training speed is accelerated, and the learning process is controllable to a certain extent.Results and discussionThe effectiveness of the framework was verified in the gym simulation environment of the Baxter robot's reach-grasp-align task. The results show that in this framework, human experience knowledge has a significant impact on the guidance of learning, and this method can more quickly plan the coordinated trajectory of dual-arm for multi-step tasks.

Abstract In recent years, various computational models have been proposed in neuroscience, but only a few have been utilized in robot visual navigation through engineering approaches. Besides, these engineering methods lack environmental adaptability, especially in the aspect of visual place recognition in weak or uncontrollable light fluctuations. To address this issue and enhance the performance of visual template matching and map loop closure detection in challenging lighting environments, this paper proposed a bionic visual navigation model that combines two neural network architectures, the Pulse Coupled Neural Network (PCNN) and Continuous Attractor Neural Network (CANN). In our navigation model, the visual features of the environments are encoded as temporal information by the spiking model and input into the local view cells for visual template matching. Additionally, we utilize the pose cells network, which incorporates the similarity between current and previous templates, along with odometry data, to encode spatial information and store it as an experience map. To validate the effectiveness of the proposed model, we conducted evaluations on datasets collected within our library, campus, and an open-source office dataset. The experimental results reveal that our algorithm increases the F1-score of template matching by approximately 10.5 $$ \% $$ % , 35.8 $$ \% $$ % , 61.7 $$ \% $$ % , and 1.9 $$ \% $$ % in each dataset, compared to the conventional RatSLAM method. Furthermore, our algorithm generates a more accurate map that closely correlates with the real-world trajectory without compromising on computation time. The results suggest that our bionic visual navigation model is reliable for both standard and extreme lighting conditions.

As a new method to fabricate TiC-reinforced Ti matrix composites, pre-melted electron beam freeform fabrication was proposed based on in situ metallurgy using a graphite diversion nozzle. The feasibility of the method was investigated. Initially, the liquid TC4, an α+β Ti alloy, flowed into the graphite diversion nozzle, corresponding to the reaction between TC4 and graphite. Then, the fabrication of TiC occurred, achieving Ti matrix composites. The liquid TC4 was subsequently deposited on the TC4 substrate, indicating consolidated metallurgical bonding and the optimized hardness and abrasive resistance presented by the surface of the TC4 substrate. The deposited layer's microstructure and the TiC strengthening phase morphology in different areas were varied, considering the difference in the cooling rate and the flow mode. The distribution and morphology of the TiC strengthening phase in different regions were analyzed to reveal the underlying reasons for the property optimization of the TiC-TC4 deposited layer.

In situ electroporation, a non-invasive technique for enhancing the permeability of cell membranes, has emerged as a powerful tool for intracellular delivery and manipulation. This method allows for the precise introduction of therapeutic agents, such as nucleic acids, drugs, and proteins, directly into target cells within their native tissue environment. Herein, we introduce an innovative electroporation strategy that employs a Janus particle (JP)-based microelectrode to generate a localized and controllable electric field within a microfluidic chip. The microfluidic device is engineered with an indium tin oxide (ITO)-sandwiched microchannel, where the electric field is applied, and suspended JP microelectrodes that induce a stronger localized electric field. The corresponding simulation model is developed to better understand the dynamic electroporation process. Numerical simulations for both single-cell and chain-assembled cell electroporation have been successfully conducted. The effects of various parameters, including pulse voltage, duration medium conductivity, and radius of Janus microelectrode, on cell membrane permeabilization are systematically investigated. Our findings indicate that the enhanced electric intensity near the poles of the JP microelectrode significantly contributes to the electroporation process. In addition, the distribution for both transmembrane voltage and the resultant nanopores can be altered by conveniently adjusting the relative position of the JP microelectrode, demonstrating a selective and in situ electroporation technique for spatial control over the delivery area. Moreover, the obtained differences in the distribution of electroporation between chain cells can offer insightful directives for the electroporation of tissues or cell populations, enabling the precise and targeted modulation of specific cell populations. As a proof of concept, this work can provide a robust alternative technique for the study of complex and personalized cellular processes.

Deployable and foldable tape-spring booms are widely used in aerospace technologies, especially for large-scale membrane structures. Semi-circular (STEM) and lenticular (CTM) boom cross-sections were invented for specific applications since these configurations have either a concise structure or a high twisting stiffness. Moreover, a triangular cross-section (TRAC) boom was proposed years ago, as its more scattered configuration could afford a higher bending stiffness after deployment. Meanwhile, blossoming is one of the most serious failure modes during boom deployment, and is commonly caused by a relatively high load acting on the boom tip. For the sake of avoiding blossoming failure, the highest load a boom can withstand should be found theoretically for a better design. This paper aims at acquiring the highest tip load (i.e., driving force) a TRAC boom can withstand through establishing an analytical model. Furthermore, a numerical analysis is carried out to provide some verification, whose modeling and analysis method has been verified by a comparison with the experimental data from previous investigations. The research in this paper gives more guidance for the design of deployable TRAC tape-spring booms.

Background: Robot-assisted fracture reduction systems can potentially reduce the risk of infection and improve outcomes, leading to significant health and economic benefits. However, these systems are still in the laboratory stage and not yet ready for commercialization due to unresolved difficulties. While previous reviews have focused on individual technologies, system composition, and surgical stages, a comprehensive review is necessary to assist future scholars in selecting appropriate research directions for clinical use.Methods: A literature review using Google Scholar identified articles on robot-assisted fracture reduction systems. A comprehensive search yielded 17,800, 18,100, and 16,700 results for “fracture reduction,” “computer-assisted orthopedic surgery,” and “robot-assisted fracture reduction,” respectively. Approximately 340 articles were selected, and 90 highly relevant articles were chosen for further reading after reviewing the abstracts.Results and Conclusion: Robot-assisted fracture reduction systems offer several benefits, including improved reduction accuracy, reduced physical work and radiation exposure, enhanced preoperative planning and intraoperative visualization, and shortened learning curve for skill acquisition. In the future, these systems will become integrated and practical, with automatic preoperative planning and high intraoperative safety.

IntroductionBehavioral Cloning (BC) is a common imitation learning method which utilizes neural networks to approximate the demonstration action samples for task manipulation skill learning. However, in the real world, the demonstration trajectories from human are often sparse and imperfect, which makes it challenging to comprehensively learn directly from the demonstration action samples. Therefore, in this paper, we proposes a streamlined imitation learning method under the terse geometric representation to take good advantage of the demonstration data, and then realize the manipulation skill learning of assembly tasks.MethodsWe map the demonstration trajectories into the geometric feature space. Then we align the demonstration trajectories by Dynamic Time Warping (DTW) method to get the unified data sequence so we can segment them into several time stages. The Probability Movement Primitives (ProMPs) of the demonstration trajectories are then extracted, so we can generate a lot of task trajectories to be the global strategy action samples for training the neural networks. Notalby, we regard the current state of the assembly task as the via point of the ProMPs model to get the generated trajectories, while the time point of the via point is calculated according to the probability model of the different time stages. And we get the action of the current state according to the target position of the next time state. Finally, we train the neural network to obtain the global assembly strategy by Behavioral Cloning.ResultsWe applied the proposed method to the peg-in-hole assembly task in the simulation environment based on Pybullet + Gym to test its task skill learning performance. And the learned assembly strategy was also executed on a real robotic platform to verify the feasibility of the method further.DiscussionAccording to the result of the experiment, the proposed method achieves higher success rates compared to traditional imitation learning methods while exhibiting reasonable generalization capabilities. It shows that the ProMPs under geometric representation can help the BC method make better use of the demonstration trajectory and thus better learn the task skills.

While deep learning algorithms significantly improves the decoding performance of brain-computer interface (BCI) based on electroencephalogram (EEG) signals, the performance relies on a large number of high-resolution data for training. However, collecting sufficient usable EEG data is difficult due to the heavy burden on the subjects and the high experimental cost. To overcome this data insufficiency, a novel auxiliary synthesis framework is first introduced in this paper, which composes of a pre-trained auxiliary decoding model and a generative model. The framework learns the latent feature distributions of real data and uses Gaussian noise to synthesize artificial data. The experimental evaluation reveals that the proposed method effectively preserves the time-frequency-spatial features of the real data and enhances the classification performance of the model using limited training data and is easy to implement, which outperforms the common data augmentation methods. The average accuracy of the decoding model designed in this work is improved by (4.72±0.98)% on the BCI competition IV 2a dataset. Furthermore, the framework is applicable to other deep learning-based decoders. The finding provides a novel way to generate artificial signals for enhancing classification performance when there are insufficient data, thus reducing data acquisition consuming in the BCI field.

Tsunami monitoring and early warning systems are mainly established to deal with seismogenic tsunamis generated by sudden seafloor fault displacement. However, a global tsunami triggered by the 2022 Tonga volcanic eruption promoted the need for tsunami early warning and hazard mitigation of non-seismogenic tsunamis in coastal countries. This paper studied the spatiotemporal distribution characteristics of historical volcanic tsunamis and summarized high-risk areas of volcanic tsunamis. The circum southwestern Pacific volcanic zone, including the Sunda volcanic belt and the Indo-Australian plate, is a concentrated area of active volcanoes and major volcanic tsunamis. In addition, the challenges associated with adapting seismogenic tsunami techniques for use in the context of volcanic tsunamis were elucidated. At the same time, based on historical records and post-disaster surveys, typical historical volcanic tsunami events and involved mechanisms were summarized. The results show that a majority of volcanic tsunamis may involve multiple generation mechanisms, and some mechanisms show geographical distribution characteristics. The complexity of volcanic tsunami mechanisms poses challenges to tsunami early warning by measuring tsunami sources to evaluate the possible extent of impact, or using numerical modeling to simulate the process of a tsunami. Therefore, a concise overview of the lessons learned and the current status of early warning systems for volcanic tsunamis was provided. Finally, a conceptual scheme of monitoring systems for volcanic tsunamis based on historical volcanoes, real-time volcanic eruption information and sea level data, as well as remote sensing images, was presented.

In this paper, we propose a deep reinforcement learning-based framework that enables adaptive and continuous control of a robot to push unseen objects from random positions to the target position. Our approach takes into account contact information in the design of the reward function, resulting in improved success rates, generalization for unseen objects, and task efficiency compared to policies that do not consider contact information. Through reinforcement learning using only one object in simulation, we obtain a learned policy for manipulating a single object, which demonstrates good generalization when applied to the task of pushing unseen objects. Finally, we validate the effectiveness of our approach in real-world scenarios.

Accurate recognition of patients’ movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation.

The use of robots in various industries is evolving from mechanization to intelligence and precision. These systems often comprise parts made of different materials and thus require accurate and comprehensive target identification. While humans perceive the world through a highly diverse perceptual system and can rapidly identify deformable objects through vision and touch to prevent slipping or excessive deformation during grasping, robot recognition technology mainly relies on visual sensors, which lack critical information such as object material, leading to incomplete cognition. Therefore, multimodal information fusion is believed to be key to the development of robot recognition. Firstly, a method of converting tactile sequences to images is proposed to deal with the obstacles of information exchange between different modalities for vision and touch, which overcomes the problems of the noise and instability of tactile data. Subsequently, a visual-tactile fusion network framework based on an adaptive dropout algorithm is constructed, together with an optimal joint mechanism between visual information and tactile information established, to solve the problem of mutual exclusion or unbalanced fusion in traditional fusion methods. Finally, experiments show that the proposed method effectively improves robot recognition ability, and the classification accuracy is as high as 99.3%.

Lanthanide-doped heterostructured nanocomposites were developed by using cation exchange method and successfully applied toward advanced optical anti-counterfeiting and information storage

Psoriasis is a chronic inflammatory skin disease characterized by erythema, scaling, and skin thickening. Topical drug application is recommended as the first-line treatment. Many formulation strategies have been developed and explored for enhanced topical psoriasis treatment. However, these preparations usually have low viscosity and limited retention on the skin surface, resulting in low drug delivery efficiency and poor patient satisfaction. In this study, we developed the first water-responsive gel (WRG), which has a distinct water-triggered liquid-to-gel phase transition property. Specifically, WRG was kept in a solution state in the absence of water, and the addition of water induced an immediate phase transition and resulted in a high viscosity gel. Curcumin was used as a model drug to investigate the potential of WRG in topical drug delivery against psoriasis. In vitro and in vivo data showed that WRG formulation could not only extend skin retention but also facilitate the drug permeating across the skin. In a mouse model of psoriasis, curcumin loaded WRG (CUR-WRG) effectively ameliorated the symptoms of psoriasis and exerted a potent anti-psoriasis effect by extending drug retention and facilitating drug penetration. Further mechanism study demonstrated that the anti-hyperplasia, anti-inflammation, anti-angiogenesis, anti-oxidation, and immunomodulation properties of curcumin were amplified by enhanced topical drug delivery efficiency. Notably, neglectable local or systemic toxicity was observed for CUR-WRG application. This study suggests that WRG is a promising formulation for topically psoriasis treatment.

Psoriasis is a common chronic immune-mediated skin disease characterized by hyperproliferation and aberrant differentiation of keratinocytes and massive infiltration of inflammatory immune cells. Recent studies showed that Signal Transducer and Activator of Transcription 3 (STAT3), which plays an important role in cell survival, proliferation, differentiation, angiogenesis, and immune responses, is constitutively activated in epidermal keratinocytes of human psoriatic skin lesions. In addition, STAT3 promotes the differentiation and expansion of T cells and facilitates cytokine production, thereby exacerbating the condition of psoriasis. Alantolactone (ALT) is a sesquiterpene lactone compound that could selectively suppress STAT3 activation, but its effectiveness and application in psoriasis treatment have not been determined. In this study, we developed ALT loaded chitosan/hyaluronic acid nanoparticles (CHALT), and investigated its therapeutic potential for psoriasis therapy. CHALT effectively abrogated the hyperproliferation by inducing ROS-mediated apoptosis with loss of mitochondrial membrane potential, and also inhibited IL-6-induced STAT3 signaling activation and inflammatory reaction in HaCaT cell line. In an Imiquimod (IMQ)-induced psoriasis model, the topical treatment of psoriasis lesions with CHALT effectively attenuated the STAT3 hyperactivation within keratinocytes and ameliorated the symptoms of psoriasis. In addition, it was found that CHALT restricted the recruitment of immune cells. These results indicated that ALT-based nanoformulation CHALT holds great potential for psoriasis therapy.

Abstract Pulse diagnosis is an irreplaceable part of traditional Chinese medical science. However, application of the traditional pulse monitoring method was restricted in the modernization of Chinese medical science since it was difficult to capture real signals and integrate obscure feelings with a modern data platform. Herein, a novel multichannel pulse monitoring platform based on traditional Chinese medical science pulse theory and wearable electronics was proposed. The pulse sensing platform simultaneously detected pulse conditions at three pulse positions (Chi, Cun, and Guan). These signals were fitted to smooth surfaces to enable 3-dimensional pulse mapping, which vividly revealed the shape of the pulse length and width and compensated for the shortcomings of traditional single-point pulse sensors. Moreover, the pulse sensing system could measure the pulse signals from different individuals with different conditions and distinguish the differences in pulse signals. In addition, this system could provide full information on the temporal and spatial dimensions of a person’s pulse waveform, which is similar to the true feelings of doctors’ fingertips. This innovative, cost-effective, easily designed pulse monitoring platform based on flexible pressure sensor arrays may provide novel applications in modernization of Chinese medical science or intelligent health care.

Task and Motion Planning for a 7-DOF serial robot containing only revolute joints needs a fast and numerically stable inverse kinematics solution. The analytical methods are real-time, but they are robot dependent. Although the numerical methods based on inverse differential kinematics can solve the inverse kinematics problem (IKP) of 7R robots with arbitrary geometric parameters, their numerical stability is insufficient. Moreover, it is time-consuming for them to converge to a single solution that depends on the initial guess. The main innovation of this article is the development of a practical method for solving the IKP of general 7R robots. The proposed method can find multiple solutions and is also faster than the numerical method. It combines symbolic preprocessing, Sylvester dialytic elimination, and eigendecomposition. First, according to the Denavit-Hartenberg convention of geometric representation, a general kinematics model of a 7R manipulator is established. Secondly, the joint variables are separated by symbolic preprocessing, and then two symbolic matrices are obtained. After numerical substitution, a square matrix of polynomial coefficients is computed through Gaussian and Sylvester dialytic elimination. Finally, utilizing the eigendecomposition of the matrix restructured from the square matrix, the values of some variables are obtained, and then the remaining variables are solved by back-substitution. The simulation and experimental data are analyzed. The comparison results verify that the proposed method applies to solving the IKP of general 7R robots and is almost real-time, effective, and numerically stable.

This paper proposes a method to realize the robot’s self-learning of environment by using an episode cognition model inspired by the hippocampus. The episode cognition map is suitable for robot navigation in an unknown environment, which solves the problem of robustness of the robot’s perception in complex and dynamic environments. The model called the hippocampus episode cognitive network (HEC), is based on the hippocampus CA1, CA3, and the dentate gyrus, and combined with the adaptive resonance theory (ART) network. It extract new events through the incremental generation of cognitive neurons, and encode the events into episodes through space-time connections. The episode nodes can be connected to generate an episode cognition map. This method can learn, store, and update information for autonomous mobile robots in an unknown environment. Based on the episode cognitive map, the path trajectory can be predicted through the playback of the episode neuron. The experimental results on the mobile robot show that this method can effectively improve the robot’s adaptability for location and mapping in a complex and dynamic environment.

During the ultraprecise cutting of micro-structure surface with fast tool servo (FTS), the hysteresis of piezoelectric actuators (PEAs) are affected by dynamic voltage excitations and real-time cutting force, which declines the servo accuracy and cutting performance. In this paper, for a multi-input-single-output (MISO) cutting system, a cross-coupling rate-dependent Prandtl-Ishlinskii (CRPI) model is proposed and identified for the dynamic hysteresis of PEAs under dynamic voltage excitation and external loads. A model reference adaptive control method is then presented to eliminate the positioning nonlinearity of PEAs. The hysteresis modeling accuracy is discussed and the adaptive controller is validated through experiments.

Cycloid drives are widely used in various mechanical systems due to their high reduction ratio, compact size, and high efficiency. Tooth surface wear is a major problem that affects the reliability and durability of cycloid drives. However, compared to the research on the wear of involute gears, the prediction of tooth surface wear in cycloid drives is relatively limited and less extensive. To fill this gap, the theoretical wear model of the tooth surface of cycloid-pin gear pairs is proposed based on the Hertz contact theory and Archard’s formula, with consideration of tooth profile modifications. Firstly, the loaded tooth contact analysis model is established to determine the relative sliding velocity and tooth contact stress. Secondly, the calculation steps of single tooth surface wear are presented within one gear mesh cycle. With this, the effects of the tooth profile modifications, the operating conditions such as output torque, input speed, and the assembly eccentricity on the wear depth within one gear mesh cycle are investigated. This study gives a deeper understanding of the tooth surface wear mechanisms of cycloid drives and could be employed to assist gear design and to improve the wear resistance.

The pelvis and its surrounding soft tissues create a complicated mechanical environment that greatly affects the success of fixing broken pelvic bones with surgical navigation systems and/or surgical robots. However, the modeling of the pelvic structure with the more complex surrounding soft tissues has not been considered in the current literature. The study developed an integrated finite element model of the pelvis, which includes bone and surrounding soft tissues, and verified it through experiments. Results from the experiments showed that including soft tissue in the model reduced stress and strain on the pelvis compared to when it was not included. The stress and strain distribution during pelvic loading was similar to what is typically seen in research studies and more accurate in modeling the pelvis. Additionally, the correlation with the experimental results from the predecessor’s study was strong (R2 = 0.9627). The results suggest that the integrated model established in this study, which includes surrounding soft tissues, can enhance the comprehension of the complex biomechanics of the pelvis and potentially advance clinical interventions and treatments for pelvic injuries.

Deployable extendable booms are widely used in aerospace technology due to many advantages they have, such as high folded-ratio, lightweight and self-deployable properties. A bistable FRP composite boom can not only extend its tip outwards with a corresponding rotation speed on the hub, but can also drive the hub rolling outwards with a fixed boom tip, which is commonly called roll-out deployment. In a bistable boom’s roll-out deployment process, the second stability can keep the coiled section from chaos without introducing a controlling mechanism. Because of this, the boom’s roll-out deployment velocity is not under control, and a high moving speed at the end will give the structure a big impact. Therefore, predicting the velocity in this whole deployment process is necessary to be researched. This paper aims to analyze the roll-out deployment process of a bistable FRP composite tape-spring boom. First, based on the Classical Laminate Theory, a dynamic analytical model of a bistable boom is established through the energy method. Afterwards, an experiment is introduced to produce some practical verification for comparison with the analytical results. According to the comparison with the experiment, the analytical model is verified for predicting the deployment velocity when the boom is relatively short, which can cover most booms using CubeSats. Finally, a parametric study reveals the relationship between the boom properties and the deployment behaviors. The research of this paper will give some guidance to the design of a composite roll-out deployable boom.

Robots with openness and flexibility have attracted a large number of researchers to conduct in-depth studies in the field of surface machining. However, there is a redundant degree of freedom (DOF) in 6-DOF robot machining: when a ball end milling cutter is used to process curved parts, the tool point needs to strictly follow the planned milling trajectory, but the tool axis vector only needs to be within a certain range. During the machining process, the rotation of the tool around its axis is not constrained. Therefore, it is necessary to optimize the redundant DOF. Aiming at the redundant DOF of the tool axis vector in ball end milling for surface parts, a Redundancy Optimization strategy for Minimum Joint trajectory (ROMJ) is proposed. It takes the shortest trajectory of robot joints as the optimization objective, and the numerical optimization method is adopted to carry out the optimal design of tool axis vector trajectory in the milling process. Before optimization, to decrease the data volume, the number of track points is sampled and adjusted based on curve characterization errors. In the optimization process, considering the obvious difference in the load quality characteristics of the robot joints, a Redundancy Optimization strategy for Minimum Joint trajectory considering the different Load of joints (ROMJ-L) is proposed. The load difference coefficients of each joint are introduced into the optimization objective of the trajectory of robot joints. By using this method, the optimal design of each joint trajectory of the robot is realized. In order to verify the methods proposed in this paper, a comparison experiment is carried out. The results show that under the same tool point trajectory, the proposed methods can significantly reduce the robot joint trajectory, and the joint trajectory is influenced by the load difference of each joint. Finally, an Eflin-10 robot is used to process the butterfly trajectory tool path by the trajectory planned by the ROMJ-L method, and the results show that the method is practical.

The inverse kinematics problem involves the study that the inverse kinematics solver needs to calculate the values of the joint variables given the desired pose of the end-effector of a robot. However, to apply to seven-degree-of-freedom robots with arbitrary configuration, analytical methods need to fix one joint and set an increment when the current value fails to solve the inverse kinematics problem. Although numerical methods based on inverse differential kinematics are efficient in solving the inverse kinematics problem of seven-degree-of-freedom robots with arbitrary geometric parameters, they are deficient in numerical stability and time-consuming for convergence to one solution governed by the initial guess. In order to reduce the execution time of an inverse kinematics solver, this article introduces a speedup method for analytical and numerical methods, which can improve their performance.

Summary: Cell mechanics is closely related to and interacts with cellular functions, which has the potential to be an effective biomarker to indicate disease onset and progression. Although several techniques have been developed for measuring cell mechanical properties, the issues of limited measurement data and biological significance because of complex and labor-intensive manipulation remain to be addressed, especially for the dielectrophoresis-based approach that is difficult to utilize flow measurement techniques. In this work, a dielectrophoresis-based solution is proposed to automatically obtain mass cellular mechanical data by combining a designed microfluidic device integrated the functions of cell capture, dielectrophoretic stretching, and cell release and an automatic control scheme. Experiments using human umbilical vein endothelial cells and breast cells revealed the automation capability of this device. The proposed method provides an effective way to address the low-throughput problem of dielectrophoresis-based cell mechanical property measurements, which enhance the biostatistical significance for cellular mechanism studies.

Abstract The ability to control interfacial tension electrochemically is uniquely available for liquid metals (LMs), in particular gallium‐based LM alloys. This imparts them with excellent locomotion and deformation capabilities and enables diverse applications. However, electrochemical oxidation of LM is a highly dynamic process, which often induces Marangoni instabilities that make it almost impossible to elongate LM and manipulate its morphology directly and precisely on a 2D plane without the assistance of other patterning methods. To overcome these limitations, this study investigates the use of an LM–iron (Fe) particle mixture that is capable of suppressing instabilities during the electrochemical oxidation process, thereby allowing for superelongation of the LM core of the mixture to form a thin wire that is tens of times of its original length. More importantly, the elongated LM core can be manipulated freely on a 2D plane to form complex patterns. Eliminating Marangoni instabilities also allows for the effective spreading and filling of the LM–Fe mixture into molds with complex structures and small features. Harnessing these excellent abilities, a channel‐less patterning method for fabricating elastomeric wearable sensors is demonstrated to detect motions. This study shows the potential for developing functional and flexible structures of LM with superior performance.

Sensors: measuring torque A microelectromechanical resonant sensor is proposed for small-scale torsion tests and demonstrated for the testing of silicon microbeams. It is important to understand the mechanical properties of small-scale materials used in MEMS and NEMS. Various systems have been reported for this under a range of loading conditions, but the application of torque to small-scale samples is particularly hard to measure. Here, a joint team led by Xueyong Wei from Xi’an Jiaotong University and Yajing Shen from City University of Hong Kong reports a MEMS-based resonator sensor that can measure bending and torsion over a wide dynamic range with a high force resolution. They demonstrate their system for the testing of silicon microbeams, which are found to be five times stronger than bulk silicon under pure torsion.

Object detection plays an important role in computer vision. It has a variety of applications, including security detection, vehicle recognition, and service robots. With the continuous improvement of public databases and the development of deep learning, object detection has witnessed significant breakthroughs. However, the object detection of sweeping robots during operations should consider various factors, including the camera angle, indoor scenery, and identification of object category. To the best of our knowledge, no corresponding database on these conditions has been developed. In this study, we review the development of object detection based on deep learning in computer vision. Then, we propose a large-scale publicly available benchmark dataset called object detection for sweeping robots in home scenes (ODSR-IHS). The dataset has 6,000 images and 16,409 instances of 14 object categories. Finally, we evaluate several state-of-the-art methods on the ODSR-IHS dataset and transplant them to the hardware to establish a benchmark dataset for object recognition research on sweeping robots.

In view of the problem that the doctor cannot quickly obtain the three-dimensional information of the liver surface during the traditional open resection of liver tumor without radiation, a new three-dimensional reconstruction system of liver surface based on binocular structured light is proposed in this paper to obtain three-dimensional information of liver surface rapidly. The hardware construction and software module design of the three-dimensional reconstruction system of the liver surface are carried out. The multi-frequency heterodyne method is proposed to solve the problem of the lack of three-dimensional reconstruction of the liver surface. A GPU-CPU collaborative acceleration method is proposed to speed up the three-dimensional reconstruction of intraoperative liver surface. On this basis, a human respiratory platform is built to simulate the effect of human respiration on three-dimensional reconstruction of the liver surface in a real surgical environment. The accuracy and speed of the system are verified by the liver experiments: The errors of three-dimensional reconstruction of liver surface at expiratory peak and inspiratory peak are 0.78 ± 0.18mm and 0.92 ± 0.21 mm, respectively. The average time required for three-dimensional reconstruction of liver surface based on GPU-CPU collaborative calculation is 3.93s. The speed of three-dimensional reconstruction of liver surface based on GPU-CPU collaborative calculation is 52% faster than that of three-dimensional reconstruction of liver surface based on CPU. It can be seen that the system has high accuracy and fast speed, which can meet the basic needs of the doctor in surgery.

Piezoelectric actuators are characterized by high positioning accuracy, high stiffness and a fast response and are widely used in ultra-precision machining technologies such as fast tool servo technology and ultrasonic machining. The rapid response characteristics of piezoelectric actuators often determine the overall quality of machining. However, there has been little research on the fast response characteristics of piezoelectric actuators, and this knowledge gap will lead to low precision and poor quality of the final machining. The fast response characteristics of a piezoelectric actuator were studied in this work. Firstly, the piezoelectric actuator was divided into a no-load state and a load state according to the working state. A fast response analysis and output characteristic analysis were carried out, the corresponding dynamic model was established, and then the model was simulated. Finally, an experimental system was established to verify the dynamic model of the piezoelectric actuator’s fast response by conducting an experiment in which the piezoelectric actuator bounces a steel ball. The experimental results verify the correctness of the model and show that the greater the cross-sectional area and height of the piezoelectric actuator, the higher the bouncing height of the ball, and the better the dynamic performance of the piezoelectric actuator. It is believed that this study has guiding significance for the application of the dynamic characteristics of piezoelectric actuators in the machining field.

The piezoelectric actuator has been widely used in modern precision cutting technology due to its fast response speed and high positioning accuracy. In recent years, with the development of precision technology, modern cutting requires higher and higher cutting accuracy and efficiency. Therefore, this paper proposes a feedforward control method based on the modified Bouc–Wen (MBW) model. Firstly, a novel asymmetrical modified Bouc–Wen model with an innovative form of shape control function is developed to model the hysteresis nonlinearity property of piezoelectric actuators. Then, a self-adaptive cooperative particle swarm optimization (PSO) algorithm is developed to identify the parameters of MBW model. The comparative evaluation reveals that the MBW model outperforms the classical Bouc–Wen (CBW) model by 66.4% in modeling accuracy. Compared with traditional PSO algorithm, the self-adaptive cooperative PSO algorithm can obtain minimum fitness in parameter identification. Furthermore, the feedforward control strategy is realized to improve the position tracking accuracy. A position tracking experiment verifies that the feedforward control strategy improves the tracking accuracy of piezoelectric actuators significantly compared with the open-loop control strategy.

Inspired by rodents’ ability to navigate freely in a given space, bionavigation systems provide alternatives to traditional probabilistic solutions. This paper proposed a bionic path planning method based on RatSLAM to provide a novel viewpoint for robots to make a more flexible and intelligent navigation scheme. A neural network fusing historic episodic memory was proposed to improve the connectivity of the episodic cognitive map. It is biomimetically important to generate an episodic cognitive map and establish a one-to-one correspondence between the events generated by episodic memory and the visual template of RatSLAM. The episodic cognitive map can be improved by imitating the rodents’ behavior of memory fusion to produce better path planning results. The experimental results of different scenarios illustrate that the proposed method identified the connectivity between way points, optimized the result of path planning, and improved the flexibility of the system.

Abstract Hollow upconversion nanoparticles with tunable central cavity size can be used as self-referenced luminescent thermometers over a wide temperature range.

ObjectivesThe objective of this study was to investigate common functional near-infrared spectroscopy (fNIRS) features of mental fatigue induced by different tasks. In addition to distinguishing fatigue from non-fatigue state, the early signs of fatigue were also studied so as to give an early warning of fatigue.MethodsfNIRS data from 36 participants were used to investigate the common character of functional connectivity network corresponding to mental fatigue, which was induced by psychomotor vigilance test (PVT), cognitive work, or simulated driving. To analyze the network reorganizations quantitatively, clustering coefficient, characteristic path length, and small worldness were calculated in five sub-bands (0.6–2.0, 0.145–0.600, 0.052–0.145, 0.021–0.052, and 0.005–0.021 Hz). Moreover, we applied a random forest method to classify three fatigue states.ResultsIn a moderate fatigue state: the functional connectivity strength between brain regions increased overall in 0.021–0.052 Hz, and an asymmetrical pattern of connectivity (right hemisphere > left hemisphere) was presented. In 0.052–0.145 Hz, the connectivity strength decreased overall, the clustering coefficient decreased, and the characteristic path length increased significantly. In severe fatigue state: in 0.021–0.052 Hz, the brain network began to deviate from a small-world pattern. The classification accuracy of fatigue and non-fatigue was 85.4%. The classification accuracy of moderate fatigue and severe fatigue was 82.8%.ConclusionThe preliminary research demonstrates the feasibility of detecting mental fatigue induced by different tasks, by applying the functional network features of cerebral hemoglobin signal. This universal and robust method has the potential to detect early signs of mental fatigue and prevent relative human error in various working environments.

The hippocampus and its accessory are the main areas for spatial cognition. It can integrate paths and form environmental cognition based on motion information and then realize positioning and navigation. Learning from the hippocampus mechanism is a crucial way forward for research in robot perception, so it is crucial to building a calculation method that conforms to the biological principle. In addition, it should be easy to implement on a robot. This paper proposes a bionic cognition model and method for mobile robots, which can realize precise path integration and cognition of space. Our research can provide the basis for the cognition of the environment and autonomous navigation for bionic robots.

Ni foils with various thickness were used as interlayer to achieve the laser tailored welding of 22MnB5 press-hardened steel (PHS) with Al-Si coating. The effect of Ni content on the microstructure, hardness and strength were studied. The results showed that Ni reduced the content of solidified primary δ phase in the fusion zone, and promoted the transformation from δ phase into γ phase. δ ferrite was absent with the Ni content of 4.7–19.5 wt.%. In addition, the fusion zone was in γ phase region during post-weld heat treatment at 950℃ because Ni can reduce the Ac3 temperature (the temperature at all ferrite was transformed into austenite). The microstructure of fusion zone after post-weld heat treatment was lath martensite with the Ni content of 4.7–11.2 wt.%. The average hardness was similar to base metal, and the tensile strength of welded joint was increased by ∼22% of as-received joint. Ni was effective to suppress the formation of δ ferrite in Al-rich fusion zone, which is potential to achieve the laser welding of PHS with Al-Si coating.

Abstract Background The traditional navigation interface was intended only for two-dimensional observation by doctors; thus, this interface does not display the total spatial information for the lesion area. Surgical navigation systems have become essential tools that enable for doctors to accurately and safely perform complex operations. The image navigation interface is separated from the operating area, and the doctor needs to switch the field of vision between the screen and the patient’s lesion area. In this paper, augmented reality (AR) technology was applied to spinal surgery to provide more intuitive information to surgeons. The accuracy of virtual and real registration was improved via research on AR technology. During the operation, the doctor could observe the AR image and the true shape of the internal spine through the skin. Methods To improve the accuracy of virtual and real registration, a virtual and real registration technique based on an improved identification method and robot-assisted method was proposed. The experimental method was optimized by using the improved identification method. X-ray images were used to verify the effectiveness of the puncture performed by the robot. Results The final experimental results show that the average accuracy of the virtual and real registration based on the general identification method was 9.73 ± 0.46 mm (range 8.90–10.23 mm). The average accuracy of the virtual and real registration based on the improved identification method was 3.54 ± 0.13 mm (range 3.36–3.73 mm). Compared with the virtual and real registration based on the general identification method, the accuracy was improved by approximately 65%. The highest accuracy of the virtual and real registration based on the robot-assisted method was 2.39 mm. The accuracy was improved by approximately 28.5% based on the improved identification method. Conclusion The experimental results show that the two optimized methods are highly very effective. The proposed AR navigation system has high accuracy and stability. This system may have value in future spinal surgeries.

Abstract Background Radiation resistance is the main cause of recurrence after radiotherapy, and increased autophagy after radiotherapy is related to radiotherapy resistance. This study aims to investigate the reversal effect of baicalin on radioresistance and its related mechanism. Methods CCK-8 and flow cytometry were used to detect the effect of proliferation and apoptosis by baicalin. Clone formation test was used to verify the effect of baicalin radiosensitization. Western blot analysis and electron microscopy were employed to observe the effect of baicalin on autophagy. Results Compared with the radiation therapy (RT) group, the RT combined baicalin (RT + BA) group showed a significantly low 2 Gy survival fraction of radiation therapy (P

In chest and abdomen robotic radiosurgery, due to the motion delay of the robotic manipulator, the tumor position tracking process has a period of delay. This delay ultimately affects the accuracy of radiosurgery treatment. To address the influence of the delay in robotic radiosurgery, a Long-and-Short-Term Memory (LSTM) network as a deep Recurrent Neural Network (RNN) has been applied in a prediction network model for respiratory motion tracking in recent years. However, patients' respiratory state may change in the process of treatment, which may influence the accuracy of prediction. Therefore, it is necessary to update the prediction network through additional data, such as the actual position of the tumor obtained by X-ray imaging. However, the LSTM network has a long update time, and it may not be able to complete the prediction model update in a cycle of X-ray acquisition. To solve this problem, a fast prediction model based on Bidirectional Gated Recurrent Unit (Bi-GRU), is proposed in this paper. This method can reduce the average updating time of the network model by 30%.

The traditional navigation system used in spinal puncture is not able to monitor the surgical process in real time and the accuracy of navigation is unsatisfactory. In this study, an augmented reality surgical navigation system based on multi-view virtual and real registration is proposed to solve these problems. The theory of virtual and real registration in augmented reality technology is analyzed, and the methods of single-view and multi-view virtual and real registration are compared. The principle of coordinate transformation in the surgical navigation module is analyzed. The platform of augmented reality surgical navigation system based on multi-view virtual and real registration technology is designed. The experiments of the spinal model are used to verify the accuracy of virtual and real registration. The accuracy of the proposed navigation system is verified by the experiment of simulating puncture operation with robotic control of surgical tool. The experimental results show that the accuracy of single-view and multi-view virtual and real registration is 9.85±0.80mm and 1.62±0.22mm respectively. The accuracy of the augmented reality surgical navigation system added with multi-view virtual and real registration technology is 1.70±0.25mm, which is 35% higher than that of the augmented reality surgical navigation system not previously used. The augmented reality surgical navigation system for spinal puncture based on multi-view virtual and real registration proposed in this study can meet the requirements of physician on the accuracy of surgery. It can also help physician to monitor the surgical process in real time and improve the success rate of surgery.

The Back Propagation (BP) neural network has the problems of low accuracy and poor convergence in the process of binocular camera calibration. A method based on BP neural network optimized by improved genetic simulated annealing algorithm (IGSAA-BP) is proposed to solve these problems to complete the binocular camera calibration. The method of combining Gaussian scale space and Harris corner detection operator is used for corner detection. A matched algorithm of homonymous corner is proposed by combining point-to-point spatial mapping and grid motion statistics. The pixel values of the homonymous corner and three-dimensional coordinate values are taken as the input and output of BP neural network respectively. The crossover and mutation probability of genetic simulated annealing algorithm and the annealing criterion are improved, the IGSAA-BP neural network is used to calibrate the binocular camera. The average calibration accuracy of BP neural network and IGSAA-BP neural network is 0.71mm and 0.03mm, respectively. The average calibration accuracy of binocular camera is improved by 96%. The iteration speed is increased by 20 times and global optimization ability is improved. It can be seen that the IGSAA-BP neural network can improve the calibration accuracy of binocular camera and accelerate convergence speed.

The traditional Cubature Kalman Filter (CKF) and its derived algorithms cannot work without the two hypotheses of Kalman Filter (KF), one is that the system model is accurate and the other is the system is only influenced by independent white noise with known statistical characteristics. However, it is difficult to fully guarantee the above hypotheses in actual operation. The presence of multiplicative noise undermines the first hypothesis, while the additive noise correlation undermines the second hypothesis. Given invalid hypotheses, the estimated performances of CKF and its derivative algorithms are degraded drastically. To solve this issue, this article proposes Multiplicative Noises and Additive Correlated Noises Cubature Kalman Filter (MACNCKF), which solves state estimation problems involving multiplicative noise and additive noise while maintaining CKF advantages. Moreover, this algorithm has been testified to be correct and easy to be transplanted to CKF and its derivative algorithm after strict mathematical derivation. When the system lacks multiplicative noise and additive noise, the algorithm is degraded to corresponding algorithm in a general form, which helps extend the application environment of CKF and its derivative algorithms, thus improving robustness. Numerical simulation and experiments on quadruped robot system indicate that MACNCKF can effectively solve state estimation problem involving both multiplicative noise and additive noise. Given time consumption in MACNCKF basically comparable to that in CKF, MACNCKF has improved estimation accuracy, robustness and reliability.

Past research indicates that childhood emotional maltreatment (CEM) undermines the quality of adult romantic relationships by fostering negative characteristics in survivors. Two longitudinal studies investigated the hypothesis that decreased compassionate goals toward partners over time explain the association between CEM and declining relationship quality. In Study 1, CEM predicted decreased compassionate goals over time, which in turn predicted decreased relationship quality in individuals in romantic relationships. Study 2 replicated this effect in romantically involved couples and showed that partners’ high compassionate goals attenuated the decline in compassionate goals associated with reported CEM. These results point to the importance of examining how CEM may affect positive relationship processes and the protective roles of partners’ compassionate goals.

Due to their fascinating solitary and collective behavior, photochemical microrobots have attracted extensive attention from researchers and have obtained a series of outstanding research progress in recent years. However, due to the limitation of using a single light source, the realization of reconfigurable and controllable motion behaviors of the photochemical microrobot is still facing a series of challenges. To release these restrictions, we reported a multi-light-field-coupling-based method for driving the photochemical microrobot or its swarm in a regulatable manner. Here, we first designed a control system for coupling multiple light sources to realize the programmable application of four light sources in different directions. Then a TiO2-based photochemical microrobot was prepared, with its surface electric field distribution under different lighting conditions estimated by modeling-based simulation, where the feasibility of regulating the microrobot’s motion behavior via the proposed setup was verified. Furthermore, our experimental results show that under the action of the compound light fields, we can not only robustly control the motion behavior of a single TiO2 microrobot but also reconfigure its collective behaviors. For example, we realized the free switching of the single TiO2 microrobots’ movement direction, and the controllable diffusion, aggregation, the locomotion and merging of TiO2 microrobot swarms. Our discovery would provide potential means to realize the leap-forward control and application of photochemical microrobots from individuals to swarms, as well as the creation of active materials and intelligent synthetic systems.

Microassembly is one of the key techniques in various advanced industrial applications. Meanwhile, high success rates for axial hole assembly of thin-walled deep-cavity-type items remain a challenging issue. Hence, the flexible assembly approach of thin-walled deep-cavity parts is investigated in this study using the assembly of the key components, the microtarget component TMP (thermomechanical package) and the hohlraum in ICF (inertial confinement fusion) research, as examples. A clamping force-assembly force mapping model based on multisource microforce sensors was developed to overcome the incapacity of microscopic vision to properly identify the condition of components after contact. The ICF microtarget flexible assembly system, which integrates multisource microforce sensing and a six degrees of freedom micromotion sliding table, is presented to address the constraint that the standard microassembly approach is difficult to operate once the parts contact. This method can detect contact force down to the mN level, modify deviation of the component posture efficiently, and achieve nondestructive ICF microtarget assembly at the end.